Measurement application device control unit, measurement application device, and method

Abstract

The present disclosure provides a measurement application device control unit comprising a text-based input interface configured to receive text-based user requests regarding a measurement application device, a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate response data regarding the measurement application device based on the text-based user requests, and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the response data. Further, the present disclosure provides a measurement application device, and a respective method.

Description

MEASUREMENT APPLICATION DEVICE CONTROL UNIT, MEASUREMENT APPLICATION DEVICE, AND METHODTECHNICAL FIELD

[0001] A first aspect of the present disclosure relates to a measurement application device control unit, a measurement application device, and a respective method.BACKGROUND

[0002] Although applicable to any type of user-controller measurement application device, the present disclosure will mainly be described in conjunction with laboratory-destined measurement application devices, like oscilloscopes.

[0003] Modern measurement application devices comprise a plurality of functions that support specific tasks in measurement applications. To this end, the measurement application devices may comprise a plurality of options and configuration parameters that a user may setup for a specific measurement application.

[0004] Accordingly, there is a need for simplifying measurement application device setup.SUMMARYFirst aspect:

[0005] The above stated problem is solved by the features of the independent claims according to the first aspect of the present disclosure. It is understood, that independent claims of a claim category may be formed in analogy to the dependent claims of another claim category.

[0006] Accordingly, it is provided:

[0007] A measurement application device control unit comprising a text-based input interface configured to receive text-based user requests regarding a measurement application device, a pretrained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate response data regarding the measurement application device based on the text-based user requests, and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the response data.

[0008] Further, it is provided:

[0009] A measurement application device comprising a measurement application device control unit according to any one of the embodiments disclosed herein, and a measurement application device controller coupled to the measurement application device control unit.

[0010] Further, it is provided:

[0011] A method for controlling a measurement application device, the method comprising receiving text-based user requests regarding a measurement application device, generating response data regarding the measurement application device based on the text-based user requests with a pre-trained artificial-intelligence algorithm, and outputting the response data.

[0012] The present disclosure is based on the finding that modern measurement application devices may comprise a large number of user-configurable parameters that may be difficult to memorize and understand for users.

[0013] The most important options may be presented prominently on a main screen of the respective measurement application device. However, other options that may be required only for very specific measurement tasks may e.g., be hidden in sub-menus of the user interface of the measurement application device.

[0014] Especially, inexperienced users may face difficulties when setting-up a measurement application and configuring the measurement application devices. Inexperienced users may even lack the knowledge about the specific functions of prominently presented configuration options, and especially the existence of such configuration options that are e.g., hidden in sub-menus of the user interface of the measurement application device.

[0015] The present disclosure, therefore, provides the measurement application device control unit, the method for controlling a measurement application device, and a respective measurement application device.

[0016] The measurement application device control unit comprises a text-based input interface. The text-based input interface may receive text-based user requests from a user. Such textbased user requests may comprise natural language text that refers to the measurement application device.

[0017] The text-based input interface may be implemented as a data interface that receives the text in a binary form e.g., as ASCII encoded text, or as text encoded in any other character encoding scheme. Such a data interface may be provided as hardware interface e.g., as a network interface, and a program interface, like an API or a callback function, or a combination of both.

[0018] The text-based input interface may comprise any other type of interface, like an API, callback functions, shared memory, a web-based III, a REST-API, a class or interface, like a Python class or Python interface, without being limited to these examples.

[0019] The text-based user requests are then provided to the pre-trained artificial-intelligence algorithm that generates respective response data based on the provided text-based user requests.

[0020] The pre-trained artificial-intelligence algorithm may, especially, be trained to receive text-based user requests that are formulated in a natural language style e.g., as a user would speak the text-based user requests to other users, or as a user would formulate the text-based user requests e.g., in an online forum.

[0021] The response data may be provided in different forms, as will be explained in more detail below. Generally, in embodiments the response data may be provided in textual form. The term “textual form” in the context of the present disclosure may refer to any type of text that may include, but is not limited to, natural language text, and text in a programming or scripting language.

[0022] The measurement application device control unit further comprises an output interface that outputs the response data. The explanations provided above for the input interface may apply mutatis mutandis to the output interface. In embodiments, the input interface and the output interface may be implemented as a single data interface.

[0023] In a measurement application device, the response data may e.g., be provided to a measurement application device controller that may further process the response data. The measurement application device controller may e.g., output the response data to a user via a display.

[0024] In embodiments, the measurement application device may comprise an audio output interface e.g., a speaker, and may output the response data as audio output to a user. To this end, a text-to-speech engine may be provided in the measurement application device for receiving the response data from the pre-trained artificial-intelligence algorithm, and converting the response data into audio.

[0025] It is understood, that the method for controlling a measurement application device according to the present disclosure may not only be performed locally in a single measurement application device. Instead, the method according to the present disclosure may also be performed remotely or in a distributed fashion.

[0026] In embodiments, the measurement application device control unit may e.g., be provided remotely to a measurement application device. The measurement application device controlunit may e.g., be provided as a server, or server-based or cloud-based application, that may be accessed by a user via a web interface. Any control data that the measurement application device control unit may generate for the measurement application device may be provided to the measurement application device via a respective network or data connection. To this end, the measurement application device may comprise a respective communication interface.

[0027] In embodiments, the communication interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0028] Further the server or cloud server and the measurement application device may communicate via an intermediary network with each other, and such a network may comprise any type of network devices, like switches, hubs, routers, firewalls, and different types of network technologies.

[0029] Generally, such a server may be a dedicated server that may be implemented as a single hardware device. The server may also be implemented as a distributed system comprising a plurality of servers, optionally with a load balancer, that distributes the load over the servers. The server may also be provided as a so-called cloud or cloud-server system that implements the server via virtualization methods independently of the underlying hardware.

[0030] In embodiments, the measurement application device control unit may be operated independently of the measurement application device, and may not even be communicatively coupled to the measurement application device. In such embodiments the user may be the sole recipient of the generated response data, or the generated response data may be stored on a data carriers, like a USB memory, that may be connected to the measurement application device by a user.

[0031] The measurement application device control unit may, in embodiments, comprise or may be provided in or as part of at least one of a dedicated processing element e.g., a processing unit, a microcontroller, a field programmable gate array, FPGA, a complex programmable logic device, CPLD, an application specific integrated circuit, ASIC, or the like. A respective program or configuration may be provided to implement the required functionality. The measurement application device control unit may at least in part also be provided as a computer program product comprising computer readable instructions that may be executed by a processing element. In a further embodiment, the measurement application device control unit may be provided as addition or additional function or method to the firmware or operating system of a processing element that is already present in the respective application as respective computer readable instructions e.g., in the measurement application device. Such computer readable instructions may be stored in a memorythat is coupled to or integrated into the processing element, like the measurement application device controller of the measurement application device. The processing element may load the computer readable instructions from the memory and execute them. The same applies to any other element, unit or function disclosed herein as part of the measurement application device control unit, the measurement application device, and the method for controlling a measurement application device.

[0032] In addition, it is understood, that any required supporting or additional hardware may be provided like e.g., a power supply circuitry and clock generation circuitry.

[0033] In the context of the present disclosure, a measurement application device may comprise any device that may be used in a measurement application to acquire an input signal or to generate an output signal, or to perform additional or supporting functions in a measurement application. A measurement application device may also comprise or be implemented as program application or program applications, also called measurement program application or measurement program applications, that may be executed on a computer device and that may communicate with other measurement application devices in order to perform a measurement task. A measurement application, also called measurement setup, may e.g., comprise at least one or multiple different measurement application devices for performing electric, magnetic, or electromagnetic measurements, especially on single devices under test. Such electric, magnetic, or electromagnetic measurements may e.g., be performed in a measurement laboratory or in a production facility in the respective production line. A measurement application or measurement setup may serve to qualify the single devices under test i.e., to determine the proper electrical operation of the respective devices under test.

[0034] Measurement application devices to this end may comprise at least one signal acquisition section for acquiring electric, magnetic, or electromagnetic signals to be measured from a device under test, or at least one signal generation section for generating electric, magnetic, or electromagnetic signals that may be provided to the device under test. Such a signal acquisition section may comprise, but is not limited to, a front-end for acquiring, filtering, and attenuating or amplifying electrical signals. The signal generation section may comprise, but is not limited to, respective signal generators, amplifiers, and filters.

[0035] Further, when acquiring signals, measurement application devices may comprise a signal processing section that may process the acquired signals. Processing may comprise converting the acquired signals from analog to digital signals, and any other type of digital signal processing, for example, converting signals from the time-domain into the frequency-domain.

[0036] The measurement application devices may also comprise a user interface to display the acquired signals to a user and allow a user to control the measurement application devices. Of course, a housing may be provided that comprises the elements of the measurement application device. It is understood, that further elements, like power supply circuitry, and communication interfaces may be provided.

[0037] A measurement application device may be a stand-alone device that may be operated without any further element in a measurement application to perform tests on a device under test. Of course, communication capabilities may also be provided for the measurement application device to interact with other measurement application devices.

[0038] A measurement application device may comprise, for example, a signal acquisition device e.g., an oscilloscope, especially a digital oscilloscope, a spectrum analyzer, or a vector network analyzer. Such a measurement application device may also comprise a signal generation device e.g., a signal generator, especially an arbitrary signal generator, also called arbitrary waveform generator, or a vector signal generator. Further possible measurement application devices comprise devices like calibration standards, or measurement probe tips.

[0039] Of course, at least some of the possible functions, like signal acquisition and signal generation, may be combined in a single measurement application device.

[0040] In embodiments, the measurement application device may comprise pure data acquisition devices that are capable of acquiring an input signal and of providing the acquired input signal as digital input signal to a respective data storage or application server. Such pure data acquisition devices not necessarily comprise a user interface or display. Instead, such pure data acquisition devices may be controlled remotely e.g., via a respective data interface, like a network interface or a USB interface. The same applies to pure signal generation devices that may generate an output signal without comprising any user interface or configuration input elements. Instead, such signal generation devices may be operated remotely via a data connection.

[0041] With the measurement application device control unit, the measurement application device, and the method according to the present disclosure, a user may interact with measurement application devices, like the ones described above, naturally, like speaking with another user.

[0042] Further embodiments of the present disclosure are subject of the further dependent claims and of the following description, referring to the drawings.

[0043] In the following, the dependent claims referring directly or indirectly to claim 1 according to the first aspect of the present disclosure are described in more detail. For the avoidance of doubt, the features of the dependent claims relating to the measurement application device controlunit can be combined in all variations with each other and the disclosure of the description is not limited to the claim dependencies as specified in the claim set. Further, the features of the other independent claims may be combined with any of the features of the dependent claims relating to the measurement application device control unit in all variations, wherein in the method respective method steps perform the function of the respective elements.

[0044] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise an audio input interface configured to receive spoken language requests, and a speech recognition unit coupled to the audio input interface and the textbased input interface. The speech recognition unit may be configured to convert the spoken language requests into text-based requests, and to provide the text-based requests to the text-based input interface as text-based user requests.

[0045] The audio input interface may be provided as local or hardware audio input interface in the measurement application device controller, or in a measurement application device that implements or comprises the measurement application device controller.

[0046] In other embodiments, the audio input interface may be provided as data interface that receives audio data from another device. Such a data interface may be provided as a hardware interface, or a software interface, or a combination of both. Generally, the hardware interface of the audio interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0047] The audio input interface is coupled to a speech recognition unit that converts the spoken language requests i.e. , audio data, received via the audio input interface into text-based user requests. Such text-based user requests may then be provided to the text-based input interface, as any other text-based user requests for further processing by the pre-trained artificial-intelligence algorithm.

[0048] The speech recognition unit may be provided as a hardware-based unit, a softwarebased unit, or a combination of both, as already indicated above for the measurement application device control unit.

[0049] With the audio input interface, and the speech recognition unit a user may communicate naturally with the measurement application device control unit, as if he was speaking with another user.

[0050] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may be pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0051] The pre-trained artificial-intelligence algorithm needs to be trained on data regarding the measurement application device that the text-based user requests refer to. Of course, in embodiments, the pre-trained artificial-intelligence algorithm may be trained on information regarding multiple different measurement application devices.

[0052] Usually, a wide selection of training data is already available from the manufacturers of the respective measurement application device(s). Such training data may be provided e.g., in the form of manuals, datasheets, and application notes that a manufacturer of a measurement application device may publish.

[0053] An exemplary extract from a manual of an oscilloscope regarding the setting of the voltage range on an input that may be applied to all aspects of the present disclosure may read as follows:

[0054] “You can select the sensitivity of the analog inputs by using the knob in the VERTICAL section (VOLTS / DIV) in 1-2-5 steps of 1m / div to 10V / div. The knob is associated with the active channel (push the respective channel key to activate the desired channel). Pushing the knob once will switch to a continuous sensitivity setting. You can use the smaller knob in the VERTICAL section (POSITION) to determine vertical settings for the active channel. Press the MENU key to access advanced options. On page 2|2 of this menu, you can add a DESKEW. To activate this offset push the corresponding soft menu key. You can set the offset value using the universal knob or the KEYPAD key in the CURSOR / MENU section. Each analog channel may be shifted in time by ±32 ns. This deskew setting is used to compensate different signal delays when using different cable lengths or probes.”

[0055] Such an explanation may be accompanied by an image of the oscilloscope, or the section comprising the buttons, switches and knobs for the vertical section.

[0056] In embodiments, the pre-trained artificial-intelligence algorithm may be pre-trained based on general data that is not specific to any measurement application device.

[0057] The training data relating to one or more measurement application devices may then be used to perform a fine-tuning of the pre-trained artificial-intelligence algorithm. Such a fine-tuning will then allow the pre-trained artificial-intelligence algorithm to respond more adequately to the text-based user requests regarding the measurement application device.

[0058] In an embodiment, a further trained algorithm may be provided that is trained to provide a textual description of images and diagrams. Such a trained algorithm may be used to explain the content of images or diagrams provided in the training data, especially in manuals, datasheets, and application notes, to the pre-trained artificial-intelligence algorithm in textual form for performing the training of the pre-trained artificial-intelligence algorithm. In embodiments, any other type of adequate pre-processing may be applied, like an optical character recognition.

[0059] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may comprise a large language model based on at least one of a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0060] While above, two specific types of large language models are explicitly disclosed, it is understood, that the pre-trained artificial-intelligence algorithm may comprise any algorithm that may be trained on a set of training data such that it may generate the response data based on textbased user requests.

[0061] Other possible types of algorithms or models include, but are not limited to, any type of language models, like statistical models like N-grams, recurrent neuronal network, like long short term, LSTM, models, and transformer models.

[0062] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a user input interface coupled to the pre-trained artificial-intelligence algorithm, and configured to receive user input. The pre-trained artificial-intelligence algorithm may be configured to generate the response data regarding the measurement application device based on the text-based user requests and the user input.

[0063] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the user input interface may comprise at least one of a button, a switch, a knob, a touchscreen, a keyboard, a mouse, a camera, and a gesture sensor.

[0064] The user input interface is a second interface that allows a user to interact with the measurement application device control unit, or a measurement application device that implements or comprises the measurement application device control unit.

[0065] The expression “user input interface” refers to a user interface that provides any other interfacing means to a user that is not text-based or natural-language-based.

[0066] The user input interface may, therefore, comprise any other type of physical interface that allows a user to interact physically with the measurement application device control unit, or the measurement application device.

[0067] Apart from the above-mentioned physical interaction elements, like buttons, knobs, touchscreens and so on, the interaction may also be gesture based. To this end, a camera, or any other sensor for acquiring gestures, like a LIDAR or ultrasound sensor, may be provided.

[0068] With this user input interface as second input means for a user, the user may formulate text-based user requests that refer to details of the user input or that are further specified by the user input.

[0069] The user may in exemplary embodiments actuate any element of the user input interface, and provide a text-based user requests regarding that element.

[0070] In an example, the user may e.g., actuate a knob, switch, or button, or indicate a section on a display of the measurement application device control unit, or the measurement application device, and provide a rather general text-based user request, like “what is this good for”.

[0071] The pre-trained artificial-intelligence algorithm may then fuse information regarding the user input, and the text-based user requests for generating the response data.

[0072] The capability of performing such a fusion may be provided to the pre-trained artificialintelligence algorithm in the fine-tuning part of the training phase mentioned above. Especially, the information provided in manuals of measurement application devices may identify input elements or sections of a display of the respective measurement application device and explain the respective functions. When trained with that information, the pre-trained artificial-intelligence algorithm may provide response data that explains the respective functions to the user.

[0073] The fusion of the information regarding the user input, and the text-based user requests may also be performed by the pre-conditioner mentioned below.

[0074] In such embodiments, the pre-conditioner may provide preceding request components, that may be provided before the text-based user requests. Such preceding request components may e.g., indicate which input element or display section the user refers to.

[0075] An exemplary preceding request component may be formulated as: “The user touches the rotary knob referred to as ‘voltage level’, and asks:”. The actual text-based user request may then be provided after this preceding request component to the pre-trained artificial-intelligence algorithm.

[0076] The full request provided to the pre-trained artificial-intelligence algorithm may then read e.g., “The user touches the rotary knob referred to as ‘voltage level’, and asks: ‘what is this good for?’ ”.

[0077] Other examples may refer to the user indicating a section on the screen e.g., “The user moves the mouse over the left-most button in the lower display section, and asks: ‘what is this good for?’ ”, or “The user gestures toward the vertical axis of a diagram shown in the display, and asks: ‘what is this good for?’ ”

[0078] Of course, the present disclosure is not limited to the above-presented examples, and may be applied to any other input element, or type of user input.

[0079] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a translator coupled to the text-based input interface, and the pre-trained artificial-intelligence algorithm, wherein the translator may be configured to translate text-based user requests in a language that the pre-trained artificial-intelligence algorithm is not trained to operate on into translated text-based user requests in a language that the pretrained artificial-intelligence algorithm is trained to operate on, and to provide the translated textbased user requests to the pre-trained artificial-intelligence algorithm.

[0080] The pre-trained artificial-intelligence algorithm may be trained to operate on text-based user requests that are provided in specific languages. The pre-trained artificial-intelligence algorithm may, consequently, not be trained to operate on text-based user requests that are provided in languages that the pre-trained artificial-intelligence algorithm is not trained to operate on.

[0081] In order to support the cooperation of multiple users with different language speaking capabilities, the translator may be provided in the measurement application device control unit.

[0082] The translator may receive the text-based user requests in languages that the pretrained artificial-intelligence algorithm is not trained to operate on. The translator may then translate the text-based user requests into a language the pre-trained artificial-intelligence algorithm is trained to operate on.

[0083] The translator enables multi-lingual teams that have team members with different levels of languages that the pre-trained artificial-intelligence algorithm is trained to operate on, to cooperate easily even when each team member speaks the respective mother tongue instead of a language that the pre-trained artificial-intelligence algorithm is trained to operate on.

[0084] In embodiments, the translator may comprise a trained artificial intelligence algorithm that is trained to translate natural language text from one language into another language. In embodiments, the translator comprises a respective algorithm that is trained on general text data. In other embodiments, such an algorithm trained on general text data may be fined tuned with text data that is specific to the technical field of measurement application devices.

[0085] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a pre-conditioner coupled to the pre-trained artificial-intelligence algorithm. The pre-conditioner may be configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0086] The pre-conditioner may serve to indicate general information to the pre-trained artificial-intelligence algorithm that a user may e.g., skip or forget to include in the text-based user requests.

[0087] The pre-conditioning text-based request may be provided to the pre-trained artificialintelligence algorithm preceding the actual text-based user requests, as exemplarily explained above for the user input interface.

[0088] In embodiments, the pre-conditioner may provide the pre-trained artificial-intelligence algorithm with information about at least one of, without being limited to, the measurement application device the user may refer to, a measurement application setup a user is using, the measurement application devices present in the measurement application setup, specifications of the measurement application device(s), and a required type or format for the response data.

[0089] Exemplary pre-conditioning text-based requests formulated by the pre-conditioner may comprise, but are not limited to, requests like:

[0090] “Two measurement inputs of an oscilloscope of model OSCI1 are coupled via measurement probes of type PROBE to a device under test. The device under test is a microcontroller, and the measurement probes are coupled to the I2C signal lines of the microcontroller.”

[0091] “In the measurement setup a signal generator is coupled to the input of a device under test that is an amplifier. Further, an oscilloscope is coupled to the output of the amplifier.”

[0092] The pre-conditioner may in embodiments comprise a further trained artificial-intelligence algorithm that is trained to generated pre-conditioning text-based requests as exemplarily indicated above. Such a trained artificial-intelligence algorithm may e.g., be trained based on sets of exemplary descriptions of measurement application setups and the respective pre-conditioning text-based requests. This further trained artificial-intelligence algorithm may comprise a large language model, or any other type of algorithm, as explained herein for the pre-trained artificial-intelligence algorithm.

[0093] In other embodiments, the pre-conditioner may be implemented without a trained artificial intelligence algorithm. Such embodiments of the pre-conditioner may comprise e.g., a state machine, and may be configured to concatenate respective information about a measurement application setup into respective pre-conditioning text-based requests. The information about the measurement application setup may e.g., be provided in tabular form, wherein each line comprises information about a component of the measurement application setup. Generally, the information may be provided in any adequate, especially structured, format.

[0094] Such information may e.g., indicate the type of element, like signal generation device, signal acquisition device, device under test, probe, adapter, or the like. This information may also indicate the number of outputs or inputs of the respective element, and how the inputs and outputs are coupled to other elements.

[0095] In such embodiments, the pre-conditioner may, for example, simply insert the information in template strings, and concatenate the template strings for all elements in the measurement application setup to generate the respective pre-conditioning text-based request.

[0096] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to receive text-based user requests regarding the usage of the measurement application device, and to generate usage response data that comprises a respective explanation regarding the usage of the measurement application device.

[0097] As already indicated above, a user may provide questions regarding the usage of the measurement application device. Such requests may be answered by the pre-trained artificial-intelligence algorithm with response data that comprises a textual description that is an answer to the user’s question.

[0098] The measurement application device control unit, consequently, allows inexperienced users to instantly learn how to use a certain feature of the measurement application device, without the need to consult a training manual or another user that is more knowledgeable.

[0099] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to receive text-based user requests regarding the control of the measurement application device, and to generate control response data that comprises respective control commands regarding the usage of the measurement application device.

[0100] The control response may, instead of explaining different features of the measurement application device to a user, directly provide control commands that a user may implement in the measurement application device or in multiple measurement application devices of a measurement setup.

[0101] Such control commands may be provided as natural text commands to a user. Exemplary control commands may comprise, but are not limited to, “set the voltage range to 0V - 100V”, “set the amplification factor to 10x”, “set the scaling of the diagram X-axis to 0.5”, “start the measurement”, “stop the measurement”, “start the signal generation”, “stop the signal generation”, and the like.

[0102] Generally, the control commands may comprise a command regarding any option, or parameter, or user input that a measurement application device may comprise or allow.

[0103] In embodiments, the text-based user requests may also refer to former measurements or measurement protocols or measurement results of former measurements. A user may e.g., indicate “repeat the last measurement”, or “repeat the measurement performed yesterday”.

[0104] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a code generator that is coupled to or integrated into the pretrained artificial-intelligence algorithm. The code generator may be configured to generate configuration and control commands for the measurement application device in a predetermined control or programming language, and to provide the generated configuration and control commands to a controller of the measurement application device.

[0105] While the above-explained types of control commands may be easily understood by a user, a measurement application device may not directly be controlled with such commands.

[0106] However, modern measurement application devices not only comprise a user interface for controlling the respective measurement application device. Instead, such measurement application devices may usually also be controlled programmatically.

[0107] Such control programs may be provided e.g., in a domain specific scripting or programming language, like the SCPI language (Standard Commands for Programmable Instruments). In other embodiments, the control programs may be provided in more general programming languages, like Python, or JavaScript, that may be interpreted in the measurement application device, or as compiled programs that are executed in the measurement application device.

[0108] The code generator, or the pre-trained artificial-intelligence algorithm with the integrated code generator may, therefore, be trained or configured to generate control commands in the response data. The control commands may be provided in any adequate format, like the above-mentioned control programs, without being limited to such programs.

[0109] In such an embodiment, the pre-trained artificial-intelligence algorithm may receive a text-based user requests that requests the measurement application device or multiple measurement application devices of a measurement application setup to be configured in a specific manner.

[0110] The code generator, or the pre-trained artificial-intelligence algorithm may then generate the respective control commands in the form of direct control commands or control program commands that may be provided to a measurement application device for execution. In embodiments, the generated control commands may be presented to a user prior to providing the control commands to a measurement application device for execution.

[0111] The measurement application device control unit may further comprise a code verification unit or code verifier. The code verification unit may be configured to analyze the generated code and detect inadequate commands or configurations in the generated code. Such a code verification unit may comprise any one of, but is not limited to, an artificial-intelligence based code analyzer, and a rule-based code analyzer. If the code verification unit detects an inadequate command or configuration, the code verification unit may present its finding to a user e.g., via a user interface, and ask the user, if the generated code is acceptable or not.

[0112] The term “inadequate” with regard to the generated code may refer to commands or configurations that may damage a device under test or a measurement application device, or thatcause costs for a user e.g., because they refer to installing additional applications on a measurement device.

[0113] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may comprise an algorithm that is locally executed on the measurement application device.

[0114] Depending on the measurement application that the measurement application device control unit is used in, the measurement data, and also the specific configuration of the measurement setup, may be confidential.

[0115] In such embodiments, the details of the measurement setup, or the measurement application should not be transmitted to a public service that may host the pre-trained artificial-intelligence algorithm.

[0116] In such embodiments, the pre-trained artificial-intelligence algorithm may be locally executed in the measurement application device.

[0117] In embodiments, the pre-trained artificial-intelligence algorithm that is executed locally in the measurement application device may comprise an algorithm that is adapted to the processing and memory resources that are available in the measurement application device locally.

[0118] In embodiments, the locally executed pre-trained artificial-intelligence algorithm may be executed on a server that is communicatively coupled to the measurement application device via a secured connection. A secured connection may be provided by providing both devices on the same network that is within the premises of the user of the measurement application device, or by establishing a VPN or any other encrypted communication between the measurement application device, and the respective server.

[0119] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a complexity estimator coupled to the text-based input interface. The complexity estimator may be configured to estimate the complexity of the text-based user requests, and to forward the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.

[0120] As indicated above, a locally executed pre-trained artificial-intelligence algorithm may be adapted to the processing and memory resources that are locally available in the respective measurement application device.

[0121] Consequently, such a pre-trained artificial-intelligence algorithm may not comprise the capacity to answer all possible text-based user requests, and may be limited to answer only simple text-based user requests.

[0122] In order to allow a user to use complex text-based user requests, the measurement application device control unit may be provided with the complexity estimator.

[0123] The complexity estimator may analyze the received text-based user requests, and may determine or estimate the complexity of the text-based user requests. If the determined or estimated complexity is higher than a predetermined threshold, the respective text-based user request may be provided to an external, more powerful pre-trained artificial-intelligence algorithm. The external pre-trained artificial-intelligence algorithm may e.g., be provided as network attached server or cloud server, as already indicated above.

[0124] The predetermined threshold may, of course, be adapted to the capabilities of a locally executed pre-trained artificial-intelligence algorithm. An exemplary complexity measure may e.g., comprise the number of letters, or the number of words, or the number of sentences, or a combination of any of these.

[0125] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the complexity estimator may be configured to request a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm.

[0126] As explained above, data regarding a measurement setup, or a measurement application may be confidential. Therefore, the complexity estimator may ask a user for consent or permission prior to providing or transmitting a text-based user request to an external pre-trained artificialintelligence algorithm.

[0127] If the user declines, the complexity estimator may inform the user that the text-based user requests may eventually not be answered correctly with the locally executed pre-trained artificial-intelligence algorithm, and provide the text-based user requests to the locally executed pretrained artificial-intelligence algorithm.

[0128] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the complexity estimator may be configured to provide the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

[0129] The request anonymizer may be an intermediary device that may be contacted by the complexity estimator instead of directly providing the text-based user requests to an external pretrained artificial-intelligence algorithm.

[0130] Although not explicitly claimed, the request anonymizer is disclosed herein as separate entity that may be used independently from any measurement application device or measurement application device control unit. The external pre-trained artificial-intelligence algorithm, and the request anonymizer may both be combined with, and used with any aspect of the present disclosure.

[0131] The request anonymizer may e.g., comprise a server that bundles text-based user requests from multiple measurement application device control units, and forwards the text-based user requests to the externally operated pre-trained artificial-intelligence algorithm without including any information that might identify a measurement application device control unit or measurement application device. The request anonymizer may e.g., remove such information from the text-based user requests, or replace such information with other predetermined information.

[0132] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a text-based user request generator configured to receive at least one of an image, a video, or a non-textual description of at least one of measurement application device, a device under test, and a measurement application setup, wherein the textbased user request generator may further be configured to generate text-based user requests based on the at least one of the image or the video, and to provide the generated text-based user requests to the pre-trained artificial-intelligence algorithm.

[0133] With the text-based user request generator the user is provided with alternative means for providing his request to the measurement application processing device.

[0134] Instead of providing the pre-trained artificial-intelligence algorithm with a specific textbased user request, the user may simply provide one or more images or videos of a respective measurement application. Other non-textual descriptions may e.g., comprise log files from a measurement application device, like a log file from a cellular tester.

[0135] The text-based user request generator may than analyze the respective images or videos and generate a respective text-based user request that resembles the measurement application setup shown in the images or videos.Second aspect:

[0136] A second aspect of the present disclosure relates to a measurement application processing device, a respective measurement application, and a respective computer-implemented method.

[0137] Although applicable to any type of measurement application, the present disclosure according to the second aspect will mainly be described in conjunction with laboratory-destined measurement applications that use user-operated measurement application devices, like oscilloscopes.

[0138] Modern measurement application devices comprise a plurality of functions that support specific tasks in measurement applications. To this end, the measurement application devices may comprise a plurality of options and configuration parameters that a user may set-up for a specific measurement application, and may comprise a plurality of input and output interfaces for acquiring and outputting measurement signals.

[0139] Accordingly, according to the second aspect there is a need for simplifying measurement application set-up.

[0140] The above stated problem is solved by the features of the independent claims according to the second aspect. It is understood, that independent claims of a claim category may be formed in analogy to the dependent claims of another claim category.

[0141] Accordingly, it is provided:

[0142] A measurement application processing device comprising a text-based input interface configured to receive text-based user requests regarding a measurement application, a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate response data regarding the measurement application based on the text-based user requests, and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the response data.

[0143] Further, it is provided:

[0144] A measurement application comprising a measurement application processing device according to the second aspect of the present disclosure, and at least one measurement application device.

[0145] Further, it is provided:

[0146] A computer-implemented method comprising receiving text-based user requests regarding a measurement application, generating with a pre-trained artificial-intelligence algorithm response data regarding the measurement application based on the text-based user requests, and outputting the response data.

[0147] The present disclosure is based on the finding that modern measurement application devices may comprise a large number of user-configurable parameters that may be difficult to memorize and understand for users.

[0148] The most important options may be presented prominently on a main screen of the respective measurement application device. However, other options that may be required only for very specific measurement tasks may e.g., be hidden in sub-menus of the user interface of the measurement application device.

[0149] With such measurement application devices, a large number of different measurement applications may be set-up and performed.

[0150] Especially, inexperienced users may face difficulties when setting-up a measurement applications and configuring the measurement application devices in such a measurement application. Inexperienced users may even lack the knowledge about the specific function of prominently presented configuration options, and especially the existence of such configuration options that are e.g., hidden in sub-menus of the user interface of the measurement application device. Further, inexperienced users may lack the knowledge of how to connect the different measurement application devices and devices under test to each other.

[0151] The present disclosure, therefore, provides the measurement application processing device, a respective measurement application device, and the computer-implemented method.

[0152] The measurement application processing device comprises a text-based input interface. The text-based input interface may receive text-based user requests from a user. Such textbased user requests may comprise natural language text that refers to the measurement application.

[0153] The text-based input interface may be implemented as a data interface that receives the text in a binary form e.g., as ASCII encoded text, or as text encoded in any other character encoding scheme. Such a data interface may be provided as hardware interface e.g., as a network interface, and a program interface, like an API or a callback function, or a combination of both.

[0154] The text-based input interface may comprise any other type of interface, like an API, callback functions, shared memory, a web-based III, a REST-API, a class or interface, like a Python class or interface, without being limited to these examples.

[0155] The text-based user requests are then provided to the pre-trained artificial-intelligence algorithm that generates respective response data based on the provided text-based user requests.

[0156] The pre-trained artificial-intelligence algorithm may, especially, be trained to receive text-based user requests that are formulated in a natural language style e.g., as a user would speak the text-based user requests to other users, or as a user would formulate the text-based user requests e.g., in an online forum.

[0157] The response data may be provided in different forms, as will be explained in more detail below. Generally, in embodiments the response data may be provided in textual form. The term “textual form” in the context of the present disclosure may refer to any type of text that may include, but is not limited to, natural language text, and text in a programming or scripting language, or a combination of both.

[0158] The measurement application processing device further comprises an output interface that outputs the response data. The explanations provided above for the input interface may apply mutatis mutandis to the output interface. In embodiments, the input interface and the output interface may be implemented as a single data interface.

[0159] In the measurement application, the response data may e.g., be provided to a measurement application device controller of a measurement application device that may further process the response data. The measurement application device controller may e.g., output the response data to a user via a display, or perform internal configurations based on the response data.

[0160] In embodiments, such measurement application device may comprise an audio output interface e.g., a speaker, and may output the response data as audio output to a user. To this end, a text-to-speech engine may be provided in the measurement application device for receiving the response data from the pre-trained artificial-intelligence algorithm, and converting the response data into audio.

[0161] In other embodiments, the method according to the present disclosure may also be performed remotely or in a distributed fashion. In embodiments, the measurement application processing device may e.g., be provided remotely to one or multiple measurement application devices. The measurement application processing device may e.g., be provided as a server, or server-based or cloud-based application, that may be accessed by a user via a web interface. Any control data that the measurement application processing device may generate for the one or more measurement application devices may be provided to the one or more measurement application devices via a respective network or data connection. To this end, the measurement application device may comprise a respective communication interface.

[0162] In embodiments, the communication interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0163] Further, the server or cloud server and the one or more measurement application devices may communicate via an intermediary network with each other, and such a network may comprise any type of network devices, like switches, hubs, routers, firewalls, and different types of network technologies.

[0164] Generally, such a server may be a dedicated server that may be implemented as a single hardware device. The server may also be implemented as a distributed system comprising a plurality of servers, optionally with a load balancer, that distributes the load over the servers. The server may also be provided as a so-called cloud or cloud-server system that implements the server via virtualization methods independently of the underlying hardware.

[0165] In embodiments, the measurement application processing device may be operated independently of the one or more measurement application devices, and may not even be communicatively coupled to the one or more measurement application devices. In such embodiments the user may be the sole recipient of the generated response data, or response data that may be directly interpreted by the one or more measurement application devices may be stored on a respective data carrier.

[0166] The measurement application processing device may, in embodiments, comprise or may be provided in or as part of at least one of a dedicated processing element e.g., a processing unit, a microcontroller, a field programmable gate array, FPGA, a complex programmable logic device, CPLD, an application specific integrated circuit, ASIC, or the like. A respective program or configuration may be provided to implement the required functionality. The measurement application processing device may at least in part also be provided as a computer program product comprising computer readable instructions that may be executed by a processing element. In a further embodiment, the measurement application processing device may be provided as addition or additional function or method to the firmware or operating system of a processing element that is already present in the respective application as respective computer readable instructions e.g., in the measurement application device. Such computer readable instructions may be stored in a memory that is coupled to or integrated into the processing element, like the measurement application device controller of the measurement application device. The processing element may load the computer readable instructions from the memory and execute them. The same applies to any other element, unit or function disclosed herein as part of the measurement application processing device,the measurement application device, and the method for controlling a measurement application device.

[0167] In addition, it is understood, that any required supporting or additional hardware may be provided like e.g., a power supply circuitry and clock generation circuitry.

[0168] In the context of the present disclosure, a measurement application device may comprise any device that may be used in a measurement application to acquire an input signal or to generate an output signal, or to perform additional or supporting functions in a measurement application. A measurement application device may also comprise or be implemented as application or applications, also called measurement application or measurement applications, that may be executed on a computer device and that may communicate with other measurement application devices in order to perform a measurement task. A measurement application, also called measurement setup, may e.g., comprise at least one or multiple different measurement application devices for performing electric, magnetic, or electromagnetic measurements, especially on single devices under test. Such electric, magnetic, or electromagnetic measurements may be performed in a measurement laboratory or in a production facility in the respective production line. A measurement application or measurement setup may serve to qualify the single devices under test i.e. , to determine the proper electrical operation of the respective devices under test.

[0169] Measurement application devices to this end may comprise at least one signal acquisition section for acquiring electric, magnetic, or electromagnetic signals to be measured from a device under test, or at least one signal generation section for generating electric, magnetic, or electromagnetic signals that may be provided to the device under test. Such a signal acquisition section may comprise, but is not limited to, a front-end for acquiring, filtering, and attenuating or amplifying electrical signals. The signal generation section may comprise, but is not limited to, respective signal generators, amplifiers, and filters.

[0170] Further, when acquiring signals, measurement application devices may comprise a signal processing section that may process the acquired signals. Processing may comprise converting the acquired signals from analog to digital signals, and any other type of digital signal processing, for example, converting signals from the time-domain into the frequency-domain.

[0171] The measurement application devices may also comprise a user interface to display the acquired signals to a user and allow a user to control the measurement application devices. Of course, a housing may be provided that comprises the elements of the measurement application device. It is understood, that further elements, like power supply circuitry, and communication interfaces may be provided.

[0172] A measurement application device may be a stand-alone device that may be operated without any further element in a measurement application to perform tests on a device under test. Of course, communication capabilities may also be provided for the measurement application device to interact with other measurement application devices.

[0173] A measurement application device may comprise, for example, a signal acquisition device e.g., an oscilloscope, especially a digital oscilloscope, a spectrum analyzer, or a vector network analyzer. Such a measurement application device may also comprise a signal generation device e.g., a signal generator, especially an arbitrary signal generator, also called arbitrary waveform generator, or a vector signal generator. Further possible measurement application devices comprise devices like calibration standards, or measurement probe tips.

[0174] Of course, at least some of the possible functions, like signal acquisition and signal generation, may be combined in a single measurement application device.

[0175] In embodiments, the measurement application device may comprise pure data acquisition devices that are capable of acquiring an input signal and of providing the acquired input signal as digital input signal to a respective data storage or application server. Such pure data acquisition devices not necessarily comprise a user interface or display. Instead, such pure data acquisition devices may be controlled remotely e.g., via a respective data interface, like a network interface or a USB interface. The same applies to pure signal generation devices that may generate an output signal without comprising any user interface or configuration input elements. Instead, such signal generation devices may be operated remotely via a data connection.

[0176] With the measurement application processing device, the measurement application, and the method according to the present disclosure, a user may describe a measurement application and receive recommendations regarding the respective measurement applications as if he was communicating with another user.

[0177] For example, the text-based user requests may refer to a general description of a measurement application. The response data provided by the pre-trained artificial-intelligence algorithm may then comprise information on how to implement such a measurement application, and what measurement application devices to use for the measurement application.

[0178] In embodiments, the measurement application processing device may adapt the style of the response data to the level of knowledge of the user. This may e.g., be performed by using the below-mentioned pre-conditioner. The pre-conditioner may e.g., generate a respective pre-conditioning text-based request that indicates that the response should comprise a specific level of detail.

[0179] Further embodiments of the present disclosure are subject of the further dependent claims and of the following description, referring to the drawings.

[0180] In the following, the dependent claims referring directly or indirectly to claim 35 according to the second aspect of the present disclosure are described in more detail. For the avoidance of doubt, the features of the dependent claims relating to the measurement application processing device can be combined in all variations with each other and the disclosure of the description is not limited to the claim dependencies as specified in the claim set. Further, the features of the other independent claims may be combined with any of the features of the dependent claims relating to the measurement application processing device in all variations, wherein in the method respective method steps perform the function of the respective measurement application processing device elements.

[0181] In an embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise an audio input interface configured to receive spoken language requests, and a speech recognition unit coupled to the audio input interface and the textbased input interface. The speech recognition unit may be configured to convert the spoken language requests into text-based requests, and to provide the text-based requests to the text-based input interface as text-based user requests.

[0182] The audio input interface may be provided as local or hardware audio input interface in the measurement application processing device, or a measurement application device that implements or comprises the measurement application processing device.

[0183] In other embodiments, the audio input interface may be provided as data interface that receives audio data from another device. Such a data interface may be provided as a hardware interface, or a software interface, or a combination of both. Generally, the hardware interface of such a data interface as audio interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non- visible light-based interface, especially an infrared interface.

[0184] The audio input interface is coupled to a speech recognition unit that converts the spoken language requests i.e. , audio data, received via the audio input interface into text-based user requests. Such text-based user requests may then be provided to the text-based input interface, as any other text-based user requests for further processing by the pre-trained artificial-intelligence algorithm.

[0185] The speech recognition unit may be provided as a hardware-based unit, a softwarebased unit, or a combination of both, as already indicated above for the measurement application processing device.

[0186] With the audio input interface, and the speech recognition unit a user may communicate naturally with the measurement application processing device, as if he was speaking with another user.

[0187] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may be pre-trained using at least one of manuals regarding one or more measurement application devices or measurement applications, datasheets regarding one or more measurement application devices or measurement applications, application notes regarding one or more measurement application devices or measurement applications, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0188] The pre-trained artificial-intelligence algorithm needs to be trained on data regarding one or more measurement application devices and measurement applications that may be performed with the one or multiple measurement application devices, that the text-based user requests may refer to.

[0189] Usually, a wide selection of training data is already available from the manufacturers of the respective measurement application device(s). Such training data may be provided e.g., in the form of manuals, datasheets, and application notes that a manufacturer of a measurement application device may publish.

[0190] In embodiments, the pre-trained artificial-intelligence algorithm may be pre-trained based on general data, like freely available texts, that are not specific to any measurement application device.

[0191] The training data relating to the one or more measurement application devices and possible measurement applications may then be used to perform a fine-tuning of the pre-trained artificial-intelligence algorithm. Such a fine-tuning will then allow the pre-trained artificial-intelligence algorithm to respond adequately to the text-based user requests regarding the measurement application.

[0192] In an embodiment, a further trained algorithm may be provided that is trained to provide a textual description of images and diagrams. Such a trained algorithm may be used to explain the content of images or diagrams provided in the training data, especially in manuals, datasheets, andapplication notes, to the pre-trained artificial-intelligence algorithm in textual form for performing the training of the pre-trained artificial-intelligence algorithm.

[0193] In a further embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may comprise a large language model based on at least one of a statistical model, like an N-gram, a recurrent neuronal network, like a long short term, LSTM, model, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0194] While above, two specific types of large language models are explicitly disclosed, it is understood, that the pre-trained artificial-intelligence algorithm may comprise any algorithm that may be trained on a set of training data such that it may generate the response data based on textbased user requests.

[0195] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to receive text-based user requests regarding the setup of the measurement application, and to generate set-up response data that comprises a respective explanation regarding the setup of the measurement application.

[0196] As already indicated above, a user may provide questions regarding a measurement application, especially the setup of a specific measurement application.

[0197] A user may e.g., want to measure a specific type of device, like an amplifier, or signal, like a specific bus signal, for example, an SPI or I2C signal. In embodiments, the user may be more specific and may want to measure a voltage level of an output pin of a specific device under test, like an amplifier.

[0198] Possible text-based user requests may sound like, but are not limited to, “How do I measure an SPI bus?”, “How to measure an I2C bus?”, “What set-up do I need to measure the voltage level at the output of an amplifier of type XXX”, XXX being the type of amplifier.

[0199] The pre-trained artificial-intelligence algorithm may be trained to generate response data that comprises specific explanations regarding such a setup i.e. , information that explains to the user how to create or set-up the respective measurement application.

[0200] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to generate set-up response data that indicates whichmeasurement application devices to use, and how to connect the measurement application devices to set-up the measurement application.

[0201] When implementing a specific measurement application, a user may require information about which measurement application devices to use for the measurement application.

[0202] The pre-trained artificial-intelligence algorithm may, consequently, be trained to indicate which measurement application devices a user requires to implement the respective measurement application.

[0203] In embodiments, the pre-trained artificial-intelligence algorithm may be configured to provide multiple alternative set-ups each using different measurement application devices.

[0204] The pre-trained artificial-intelligence algorithm may be configured to provide information regarding a single set-up without further request by the user. The user may then actively request an alternative set-up, and the pre-trained artificial-intelligence algorithm may provide such an alternative set-up.

[0205] The pre-trained artificial-intelligence algorithm may also be configured to correct a measurement application set-up, if the user indicates errors or inaccuracies in a provided measurement application set-up. A user may e.g., indicate that a certain measurement application device is not available, that is part of an indicated measurement application set-up. In such cases, the pretrained artificial-intelligence algorithm may provide the user with an alternative measurement application set-up, that does no include the respective measurement application device, but instead includes one or multiple alternative measurement application devices that replace the not-available measurement application device.

[0206] In a further embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to generate a block diagram of the set-up of the measurement application.

[0207] As indicated above, the responses provided by the pre-trained artificial-intelligence algorithm may be text-based. However, a text-based explanation of a measurement application setup may be difficult to understand for a user, especially, if the measurement application set-up is complex and comprises multiple different measurement application devices.

[0208] The pre-trained artificial-intelligence algorithm may, therefore, also be trained to or configured to output a block diagram of the measurement application set-up.

[0209] Such a block diagram may e.g., be provided by the pre-trained artificial-intelligence algorithm in the form of an ASCII based diagram, also called ASCII art diagram. In other embodiments, the pre-trained artificial-intelligence algorithm may also output the ASCII code for an SVG file that comprises the respective block diagram. In embodiments, a combination is possible, and the pre-trained artificial-intelligence algorithm may provide an ASCII art diagram and the SVG file.

[0210] In further embodiments, that may be combined with the above embodiments, the pretrained artificial-intelligence algorithm may forward a textual description of the block diagram to an image generation algorithm, for example, an artificial intelligence-based image generation algorithm. Such pre-trained artificial-intelligence algorithm may comprise at least one of a neuronal network, a transformer, and a generative adversarial network.

[0211] With the block diagram a user will be provided with an easy-to-understand representation of the measurement application set-up that will allow him to easily implement the suggested measurement application set-up.

[0212] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificialintelligence algorithm may further be configured to generate control response data that comprises respective control commands for at least one measurement application device.

[0213] The control response data may, instead of explaining different features of the measurement application to a user, directly provide control commands that a user may implement in the measurement application i.e. , in one or multiple measurement application devices of a measurement application.

[0214] Such control commands may be provided as natural text commands to a user. Exemplary control commands may comprise, but are not limited to, “set the voltage range to 0V - 100V”, “set the amplification factor to 10x”, “set the scaling of the diagram X-axis to 0.5”, “start the measurement”, “stop the measurement”, “start the signal generation”, “stop the signal generation”, and the like.

[0215] Generally, the control commands may comprise a command regarding any option, or parameter, or user input that a measurement application device of the measurement application set-up may comprise or allow.

[0216] In a further embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise a code generator that is coupled to or integrated into the pre-trained artificial-intelligence algorithm. The code generator may be configured to generateconfiguration and control commands for the measurement application device in a predetermined control or programming language.

[0217] While the above-explained types of control commands may be easily understood by a user, a measurement application device may not directly be controlled with such text-based commands.

[0218] However, modern measurement application devices not only comprise a user interface for controlling the respective measurement application device. Instead, such measurement application devices may usually also be controlled programmatically.

[0219] Such control programs may be provided e.g., in a domain specific scripting or programming language, like the SCPI language (Standard Commands for Programmable Instruments). In other embodiments, the control programs may be provided in more general programming languages, like Python, or JavaScript, that may be interpreted in the measurement application device, or as compiled programs that are executed in the measurement application device.

[0220] The code generator, or the pre-trained artificial-intelligence algorithm with the integrated code generator may, therefore, be trained or configured to generate control commands in the response data. The control commands may be provided in any adequate format, like the above-mentioned control programs, without being limited to such programs.

[0221] In such an embodiment, the pre-trained artificial-intelligence algorithm may receive a text-based user request regarding the measurement application set-up, and may output any embodiment of a description of the measurement application set-up described herein to the user.

[0222] In an embodiment, the pre-trained artificial-intelligence algorithm may in addition output the configuration and control commands for one or multiple measurement application devices of the measurement application set-up in the predetermined control or programming language.

[0223] If an external code generator is used, the pre-trained artificial-intelligence algorithm may provide the text-based control commands indicated above to the code generator. The code generator may then generate the respective configuration and control commands for one or multiple measurement application devices of the measurement application set-up in the predetermined control or programming language.

[0224] In a setting, in which the measurement application processing device is communicatively coupled to the measurement application devices, the measurement application processing device may provide the configuration and control commands directly to the respective measurement application devices.

[0225] The pre-trained artificial-intelligence algorithm, or the code generator may also be configured to generate code that implements functions required in a measurement application set-up, that are not available in the respective measurement application devices.

[0226] Such code may be provided e.g., in the form of interpretable code e.g., Python or JavaScript code, that may be interpreted in a measurement application device, or in the form of executable code, like compiled C, or C++ code, that may be executed in a measurement application device. In embodiments, the code may be provided as VHDL code or configuration code for a configurable logic element, like an FPAG, in the measurement application device.

[0227] The code may implement data processing functions that may be requested by the user in the text-based user request, but are not available in the respective measurement application devices.

[0228] To this end, the measurement application devices may provide a respective API that allows the code to access measurement data and output processed data for further processing in the measurement application device e.g., for displaying the processed data to a user.

[0229] With this option of generating code, a user that has no programming knowledge may still request functions for a measurement application set-up that would require such programming knowledge to implement.

[0230] In embodiments, the pre-trained artificial-intelligence algorithm may provide an explanation of the generated code. Such an explanation may e.g., be requested via the pre-conditioner mentioned below.

[0231] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise a pre-conditioner coupled to the pre-trained artificialintelligence algorithm. The pre-conditioner may be configured to provide a pre-conditioning textbased request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0232] The pre-conditioner may serve to indicate general information to the pre-trained artificial-intelligence algorithm that a user may e.g., skip or forget to include in the text-based user requests. Other information that may be provided by the pre-conditioner includes general information about the equipment available to a user, the knowledge level of the user, or any other information that may be relevant, also for human users, for planning a measurement application set-up.

[0233] The pre-conditioning text-based request may be provided to the pre-trained artificialintelligence algorithm preceding the actual text-based user requests.

[0234] In embodiments, the pre-conditioner may provide the pre-trained artificial-intelligence algorithm with information about at least one of, without being limited to, the measurement application devices that are available to the user, specifications of the measurement application device^), a required type or format for the response data.

[0235] Exemplary pre-conditioning text-based requests formulated by the pre-conditioner may comprise, but are not limited to, requests like:

[0236] “Available measurement application devices comprise MEAS1 , MEAS2, ... , MEASx. For your response select only from the available measurement application devices.”, wherein MEAS1 to MEASx are the available measurement application devices.

[0237] “The measurement application device MEAS1 is provided with the optional modules MODI , MOD2, MOD3. If necessary, include these modules in the recommended measurement application set-up.”, wherein MODI , MOD2, MOD3 may be optional modules that a user may install in a measurement application device.

[0238] The pre-conditioner may in embodiment comprise a further trained artificial-intelligence algorithm that is trained to generated pre-conditioning text-based requests as exemplarily indicated above. Such a trained artificial-intelligence algorithm may e.g., be trained based on sets of exemplary descriptions of measurement application setups and the respective pre-conditioning textbased requests.

[0239] In other embodiments, the pre-conditioner may be implemented without a trained artificial intelligence algorithm. Such embodiments of the pre-conditioner may comprise e.g., a state machine, and may be configured to concatenate respective information about a measurement application setup into respective pre-conditioning text-based requests. The information about the measurement application setup may e.g., be provided in tabular form, wherein each line comprises information about a component measurement application setup. Generally, the information may be provided in any adequate, especially structured, format.

[0240] Such information may e.g., indicate the type of element, like signal generation device, signal acquisition device, device under test, probe, adapter, or the like. This information may also indicate the number of outputs or inputs of the respective element, and what value ranges the inputs and outputs support. Generally, such data may include any data that is usually provided in manuals or datasheets regarding the respective measurement application devices.

[0241] In such embodiments, the pre-conditioner may, for example, simply insert the information in template strings, and concatenate the template strings for all elements in the measurement application setup to generate the respective pre-conditioning text-based request. A combination with the artificial-intelligence based pre-conditioner is possible.

[0242] In an embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificial-intelligence algorithm may be configured to augment the text-based user requests and output the augmented text-based user requests via the output interface. The pre-trained artificial-intelligence algorithm may be configured to generate the response data after confirmation of the augmented text-based user requests by the user.

[0243] Augmenting in this contest refers to including additional information in the text-based user requests, that a user may not know or may have forgotten to include in his text-based user requests.

[0244] The augmented text-based user requests may be seen as a kind of technically more precisely formulated text-based user requests. The pre-trained artificial-intelligence algorithm may also be configured to suggest improvements to the original text-based user requests.

[0245] In embodiments, such functions of the pre-trained artificial-intelligence algorithm may be implemented using the pre-conditioner. The pre-conditioner may e.g., provide a pre-conditioning text-based request that may exemplarily indicate at least one of “Provide a more detailed version of the following request for generating a measurement application set-up, instead of generating the actual measurement application set-up.”, or “Correct the following request for generating a measurement application set-up, instead of generating the actual measurement application set-up.”

[0246] Providing an augmented text-based user requests may also comprise indicating to a user in the augmented text-based user requests that a measurement application device that he referenced in the original text-based user requests lacks a specific installable option required for performing a specific measurement application. The user may then also be provided with the option to install the specific option.

[0247] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise a text-based user request generator configured to receive at least one of a non-textual description, like an image or a video of a measurement application device, a device under test, or a measurement application set-up. The text-based user request generator may further be configured to generate text-based user requests based on the at least one of the image or the video.

[0248] With the text-based user request generator the user is provided with alternative means for providing his request to the measurement application processing device.

[0249] Instead of providing the pre-trained artificial-intelligence algorithm with a specific textbased user request, the user may simply provide one or more images or videos of a respective measurement application.

[0250] The text-based user request generator may than analyze the respective images or videos and generate a respective text-based user request that resembles the measurement application set-up shown in the images or videos.

[0251] In an embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the pre-trained artificial-intelligence algorithm may be configured to receive text-based user requests regarding the maintenance of a measurement application device in a measurement application, and to generate maintenance response data that comprises a respective explanation regarding the maintenance of the respective measurement application device.

[0252] Maintenance of a measurement application device is an important task in every measurement application. Requests referring to the maintenance of a measurement application device, therefore, comprise a request regarding a measurement application according to the present disclosure.

[0253] In order to allow the pre-trained artificial-intelligence algorithm to support the user in correctly servicing and maintaining a respective measurement application device, the pre-trained artificial-intelligence algorithm may be trained with respective maintenance and service manuals that a manufacturer of the measurement application device may provide. As additional training material, the documentation of the development process for the respective measurement application device may be used as training data for the pre-trained artificial-intelligence algorithm. Generally, the training material may also comprise at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes regarding manuals of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits

[0254] In embodiments, the pre-trained artificial-intelligence algorithm may provide the response data that comprises the explanation regarding the maintenance of the respective measurement application device on a specific user request.

[0255] In other embodiments, the pre-trained artificial-intelligence algorithm may provide such explanations without a specific request from the user. Instead, the pre-trained artificial-intelligence algorithm may provide such explanations, if performing the respective service or repair may improve a measurement result in a respective measurement application set-up. The features regarding the maintenance of the measurement application device may, therefore, be combined with all other features of all other embodiments of the measurement application processing device.Third aspect:

[0256] A third aspect of the disclosure relates to a measurement application device control unit, a measurement application device, and a respective method.

[0257] Although applicable to any type of measurement application device, the present disclosure according to the third aspect will mainly be described in conjunction with laboratory-destined measurement application devices, like oscilloscopes.

[0258] Modern measurement application devices comprise a plurality of functions that support specific tasks in measurement applications. To this end, the measurement application devices may comprise a plurality of options and configuration parameters that a user may set-up for a specific measurement application.

[0259] Accordingly, according to the third aspect there is a need for simplifying measurement application device set-up.

[0260] The above stated problem is solved by the features of the independent claims according to the third aspect. It is understood, that independent claims of a claim category may be formed in analogy to the dependent claims of another claim category.

[0261] Accordingly, it is provided:

[0262] A measurement application device control unit comprising a text-based input interface configured to receive text-based user requests regarding a measurement application device, a pretrained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate configuration data regarding the measurement application device based on the textbased user requests, and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the configuration data.

[0263] Further, it is provided:

[0264] A measurement application device comprising a measurement application device control unit according to the present disclosure, and a measurement application device controller coupled to the measurement application device control unit.

[0265] Further, it is provided:

[0266] A computer-implemented method comprising receiving text-based user requests regarding a measurement application device, generating configuration data regarding the measurement application device based on the text-based user requests with a pre-trained artificial-intelligence algorithm, and outputting the configuration data.

[0267] The present disclosure is based on the finding that modern measurement application devices may comprise a large number of user-configurable parameters that may be difficult to memorize and understand for users.

[0268] The most important options may be presented prominently on a main screen of the respective measurement application device. However, other options that may be required only for very specific measurement tasks may e.g., be hidden in sub-menus of the user interface of the measurement application device.

[0269] Especially, inexperienced users may face difficulties when setting-up a measurement application and configuring the measurement application devices. Inexperienced users may even lack the knowledge about the specific function of prominently presented configuration options, and especially the existence of such configuration options that are e.g., hidden in sub-menus of the user interface of the measurement application device.

[0270] The present disclosure, therefore, provides the measurement application device control unit, a respective measurement application device, and the method for controlling a measurement application device.

[0271] The measurement application device control unit comprises a text-based input interface. The text-based input interface may receive text-based user requests from a user. Such textbased user requests may comprise natural language text that refers to the measurement application device.

[0272] The text-based input interface may be implemented as a data interface that receives the text in a binary form e.g., as ASCII encoded text, or as text encoded in any other character encoding scheme. Such a data interface may be provided as hardware interface e.g., as a network interface, and a program interface, like an API or a callback function, or a combination of both.

[0273] The text-based input interface may comprise any other type of interface, like an API, callback functions, shared memory, a web-based III, a REST-API, a class or interface, like a Python class or interface, without being limited to these examples.

[0274] The text-based user requests are then provided to the pre-trained artificial-intelligence algorithm that generates respective configuration data based on the provided text-based user requests.

[0275] The pre-trained artificial-intelligence algorithm may, especially, be trained to receive text-based user requests that are formulated in a natural language style e.g., as a user would speak the text-based user requests to other users, or as a user would formulate the text-based user requests e.g., in an online forum.

[0276] The configuration data may be provided in different forms, as will be explained in more detail below. Generally, in embodiments the configuration data may be provided in textual form. The term “textual form” in the context of the present disclosure may refer to any type of text that may include, but is not limited to, natural language text, and text in a programming or scripting language.

[0277] The measurement application device control unit further comprises an output interface that outputs the configuration data. The explanations provided above for the input interface may apply mutatis mutandis to the output interface. In embodiments, the input interface and the output interface may be implemented as a single data interface.

[0278] In a measurement application device, the configuration data may e.g., be provided to a measurement application device controller that may further process the configuration data. The measurement application device controller may e.g., perform internal configurations based on the configuration data, or output the configuration data to a user via a display.

[0279] In embodiments, the measurement application device may comprise an audio output interface e.g., a speaker, and may output the configuration data as audio output to a user. To this end, a text-to-speech engine may be provided in the measurement application device for receiving the configuration data from the pre-trained artificial-intelligence algorithm, and converting the configuration data into audio. The text-to-speech engine may also be provided external to the measurement application device, and provide audio data to the measurement application device for reproduction to a user.

[0280] It is understood, that the method for controlling a measurement application device according to the present disclosure may not only be performed locally in a single measurement application device. Instead, the method according to the present disclosure may also be performed remotely or in a distributed fashion.

[0281] In embodiments, the measurement application device control unit may e.g., be provided remotely to a measurement application device. The measurement application device control unit may e.g., be provided as a server, or server-based or cloud-based application, that may be accessed by a user via a web interface. Any control data that the measurement application device control unit may generate for the measurement application device may be provided to the measurement application device via a respective network or data connection. To this end, the measurement application device may comprise a respective communication interface.

[0282] In embodiments, the communication interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especiallyan Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0283] Further the server or cloud server and the measurement application device may communicate via an intermediary network with each other, and that such a network may comprise any type of network devices, like switches, hubs, routers, firewalls, and different types of network technologies.

[0284] Generally, such a server may be a dedicated server that may be implemented as a single hardware device. The server may also be implemented as a distributed system comprising a plurality of servers, optionally with a load balancer, that distributes the load over the servers. The server may also be provided as a so-called cloud or cloud-server system that implements the server via virtualization methods independently of the underlying hardware.

[0285] In embodiments, the measurement application device control unit may be operated independently of the measurement application device, and may not even be communicatively coupled to the measurement application device. In such embodiments the user may be the sole recipient of the generated configuration data.

[0286] The measurement application device control unit may, in embodiments, comprise or may be provided in or as part of at least one of a dedicated processing element e.g., a processing unit, a microcontroller, a field programmable gate array, FPGA, a complex programmable logic device, CPLD, an application specific integrated circuit, ASIC, or the like. A respective program or configuration may be provided to implement the required functionality. The measurement application device control unit may at least in part also be provided as a computer program product comprising computer readable instructions that may be executed by a processing element. In a further embodiment, the measurement application device control unit may be provided as addition or additional function or method to the firmware or operating system of a processing element that is already present in the respective application as respective computer readable instructions e.g., in the measurement application device. Such computer readable instructions may be stored in a memory that is coupled to or integrated into the processing element, like the measurement application device controller of the measurement application device. The processing element may load the computer readable instructions from the memory and execute them. The same applies to any other element, unit or function disclosed herein as part of the measurement application device control unit, the measurement application device, and the method for controlling a measurement application device, like the speech recognition unit, the feedback data analyzer, and the pre-conditioner.

[0287] In addition, it is understood, that any required supporting or additional hardware may be provided like e.g., a power supply circuitry and clock generation circuitry.

[0288] In the context of the present disclosure, a measurement application device may comprise any device that may be used in a measurement application to acquire an input signal or to generate an output signal, or to perform additional or supporting functions in a measurement application. A measurement application device may also comprise or be implemented as application or applications, also called measurement application or measurement applications, that may be executed on a computer device and that may communicate with other measurement application devices in order to perform a measurement task. A measurement application, also called measurement setup, may e.g., comprise at least one or multiple different measurement application devices for performing electric, magnetic, or electromagnetic measurements, especially on single devices under test. Such electric, magnetic, or electromagnetic measurements may be performed in a measurement laboratory or in a production facility in the respective production line. A measurement application or measurement setup may serve to qualify the single devices under test i.e. , to determine the proper electrical operation of the respective devices under test.

[0289] Measurement application devices to this end may comprise at least one signal acquisition section for acquiring electric, magnetic, or electromagnetic signals to be measured from a device under test, or at least one signal generation section for generating electric, magnetic, or electromagnetic signals that may be provided to the device under test. Such a signal acquisition section may comprise, but is not limited to, a front-end for acquiring, filtering, and attenuating or amplifying electrical signals. The signal generation section may comprise, but is not limited to, respective signal generators, amplifiers, and filters.

[0290] Further, when acquiring signals, measurement application devices may comprise a signal processing section that may process the acquired signals. Processing may comprise converting the acquired signals from analog to digital signals, and any other type of digital signal processing, for example, converting signals from the time-domain into the frequency-domain.

[0291] The measurement application devices may also comprise a user interface to display the acquired signals to a user and allow a user to control the measurement application devices. Of course, a housing may be provided that comprises the elements of the measurement application device. It is understood, that further elements, like power supply circuitry, and communication interfaces may be provided.

[0292] A measurement application device may be a stand-alone device that may be operated without any further element in a measurement application to perform tests on a device under test. Of course, communication capabilities may also be provided for the measurement application device to interact with other measurement application devices.

[0293] A measurement application device may comprise, for example, a signal acquisition device e.g., an oscilloscope, especially a digital oscilloscope, a spectrum analyzer, or a vector network analyzer. Such a measurement application device may also comprise a signal generation device e.g., a signal generator, especially an arbitrary signal generator, also called arbitrary waveform generator, or a vector signal generator. Further possible measurement application devices comprise devices like calibration standards, or measurement probe tips.

[0294] Of course, at least some of the possible functions, like signal acquisition and signal generation, may be combined in a single measurement application device.

[0295] In embodiments, the measurement application device may comprise pure data acquisition devices that are capable of acquiring an input signal and of providing the acquired input signal as digital input signal to a respective data storage or application server. Such pure data acquisition devices not necessarily comprise a user interface or display. Instead, such pure data acquisition devices may be controlled remotely e.g., via a respective data interface, like a network interface or a USB interface. The same applies to pure signal generation devices that may generate an output signal without comprising any user interface or configuration input elements. Instead, such signal generation devices may be operated remotely via a data connection.

[0296] With the measurement application device control unit, the measurement application device, and the method according to the present disclosure, a user may easily configure a measurement application device without specific knowledge of the respective measurement application device.

[0297] The pre-trained artificial-intelligence algorithm will provide the respective configuration data based on the text-based user request, and the user may perform a measurement in a measurement application using the configuration data.

[0298] Further embodiments of the present disclosure are subject of the further dependent claims and of the following description, referring to the drawings.

[0299] In the following, the dependent claims referring directly or indirectly to claim 64 according to the third aspect of the present disclosure are described in more detail. For the avoidance of doubt, the features of the dependent claims relating to the measurement application device control unit can be combined in all variations with each other and the disclosure of the description is not limited to the claim dependencies as specified in the claim set. Further, the features of the other independent claims may be combined with any of the features of the dependent claims relating to the measurement application device control unit in all variations, wherein in the method respective method steps perform the function of the respective measurement application device control unit elements.

[0300] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a feedback interface configured to receive measurement feedback data, wherein the pre-trained artificial-intelligence algorithm is configured to generate a further set of configuration data based on the original text-based user request, and the received measurement feedback data.

[0301] A user-provided text-based user request may be very general, like: “Setup an I2C measurement over a 0.5s period with 100kBaud data rate, and limit the power supply to 20mA”. With such a general indication, a general configuration of a measurement application device may be generated by the pre-trained artificial-intelligence algorithm.

[0302] However, such a general measurement configuration may not in all cases provide the user with the relevant information. For example, if the measurement application task is to identify errors on the I2C bus, a user may not find such errors with the general configuration.

[0303] Therefore, the feedback interface is provided. With the feedback interface, the measurement application device control unit may be provided with the measurement feedback data. The measurement feedback data will then allow the pre-trained artificial-intelligence algorithm to generate a refined set of configuration data that may be provided to the measurement application device for performing another measurement that better fulfills the specific measurement application task.

[0304] In the example of the I2C bus, in the measurement feedback data an indication of voltage glitches at every raising signal flank may be found. The pre-trained artificial-intelligence algorithm may, therefore, generate the further set of configuration data to trigger on the raising flanks of the signal, and to show the signal for a certain amount of time preceding the raising flank, and to provide an adequate time scale on the X axis of a display of the measurement application device.

[0305] The feedback interface may be a data interface that may receive measurement feedback data from the respective measurement application device. The measurement feedback data may comprise any data acquired during a measurement or derived from the measurement.

[0306] The measurement feedback data may exemplarily comprise, but is not limited to, the raw measurement data, and screenshots of a display of the measurement application device showing a waveform of the acquired signal.

[0307] In embodiments, the pre-trained artificial-intelligence algorithm may be capable of operating directly on the measurement feedback data.

[0308] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device may further comprise a feedback data analyzer arranged between the feedback interface, and the pre-trained artificial-intelligence algorithm. The feedback data analyzer may be configured to generate a text-based feedback description of the measurement feedback data, and to provide the text-based feedback description to the pre-trained artificial-intelligence algorithm.

[0309] The feedback data analyzer may comprise another trained artificial-intelligence algorithm that may be trained to convert the measurement feedback data into the text-based feedback description.

[0310] Such an algorithm may be trained to provide a text-based description of image or waveform data. The algorithm may, especially, be trained to describe details of the measurement data that a user may easily miss based on the general configuration data. The algorithm may comprise or be based on a respective visual foundation model.

[0311] Training data for the feedback data analyzer may be taken e.g., from manuals, datasheets, and available measurement reports, that comprise descriptions of measurement results, and e.g., images of measured waveforms. In embodiments, the feedback data analyzer may comprise an image-to-text conversion algorithm that is trained on general image and text data, and fine-tuned based on data from manuals, datasheets, and available measurement reports, as indicated above.

[0312] In other embodiments, the measurement application device may comprise the feedback data analyzer, and the feedback data analyzer may provide the text-based feedback description to the measurement application device control unit.

[0313] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the feedback data analyzer may be configured to identify at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data, and to provide a respective text-based feedback description.

[0314] A user may lack the experience to identify relevant data in the measurement results, and especially to identify anomalies.

[0315] Therefore, with the feedback data analyzer being configured to identify such anomalies, relevant measurement values, or relevant measurement waveform sections, the configuration data may be re-generated to configure the measurement application device to better acquire the relevant measurement data.

[0316] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a feedback output interface that is configured to output the measurement feedback data to a user. The pre-trained artificial-intelligence algorithm may be configured to generate a further set of configuration data based on the original text-based user request, and a further text-based user request received after the measurement feedback data is output to the user.

[0317] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the feedback output interface may output the text-based feedback description to the user.

[0318] With the feedback output interface, the measurement feedback data, especially the text-based feedback description, may be provided to the user. To this end, the feedback output interface may comprise a display or an audio interface. The text-based feedback description may be shown to a user on the display. The text-based feedback description may also be converted into audio with a text-to-speech converter and may be played to the user via an audio interface.

[0319] The user, after receiving the measurement feedback data, may then decide to provide instructions to the pre-trained artificial-intelligence algorithm that result in corrected or amended configuration data that may be provided to the respective measurement application device.

[0320] This feedback loop may be repeated as many times as required, and may also be combined with the automatic regeneration of the configuration data described above.

[0321] The user may, therefore, enter into a kind of dialogue with the measurement application device control unit, and iteratively improve the measurement results.

[0322] In embodiments, the pre-trained artificial-intelligence algorithm may comprise a fixed pre-trained model.

[0323] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may be configured to perform reinforced learning based on the further textbased user request received after the measurement feedback data is output to the user.

[0324] Using a flexible model that may be further trained e.g., using reinforced learning, the quality of the configuration data may continuously be improved.

[0325] Further, it is possible to train the pre-trained artificial-intelligence algorithm for application specific measurement tasks. It is for example possible, to train the pre-trained artificial-intelligence algorithm specifically for automotive measurement applications. In such embodiments, the further text-based user request may be automatically generated for the reinforced learning.

[0326] For performing the reinforced learning, multiple measurement application device control units may provide respective learning data to a centrally trained pre-trained artificial-intelligence algorithm. Such a centrally trained pre-trained artificial-intelligence algorithm may e.g., be hosted and trained by a manufacturer of the measurement application device control unit. Alternatively, locally trained pre-trained artificial-intelligence algorithms may be provided to a central model management unit that may then merge the locally trained pre-trained artificial-intelligence algorithms.

[0327] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise an audio input interface configured to receive spoken language requests. The measurement application device control unit may further comprise a speech recognition unit coupled to the audio input interface and the text-based input interface. The speech recognition unit may be configured to convert the spoken language requests into textbased requests, and to provide the text-based requests to the text-based input interface as textbased user requests.

[0328] The audio input interface may be provided as local or hardware audio input interface in the measurement application device control unit, or a measurement application device that implements or comprises the measurement application device control unit.

[0329] In other embodiments, the audio input interface may be provided as data interface that receives audio data from another device. Such a data interface may be provided as a hardware interface, or a software interface, or a combination of both. Generally, the hardware interface of the audio interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0330] The audio input interface is coupled to a speech recognition unit that converts the spoken language requests i.e. , audio data, received via the audio input interface into text-based user requests. Such text-based user requests may then be provided to the text-based input interface, as any other text-based user requests for further processing by the pre-trained artificial-intelligence algorithm.

[0331] The speech recognition unit may be provided as a hardware-based unit, a softwarebased unit, or a combination of both, as already indicated above.

[0332] With the audio input interface, and the speech recognition unit a user may communicate naturally with the measurement application device control unit, as if he was speaking with another user.

[0333] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may be pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0334] The pre-trained artificial-intelligence algorithm needs to be trained on data regarding the measurement application device that the text-based user requests refer to. Of course, in embodiments, the pre-trained artificial-intelligence algorithm may be trained on information regarding multiple different measurement application devices.

[0335] Usually, a wide selection of training data is already available from the manufacturers of the respective measurement application device(s). Such training data may be provided e.g., in the form of manuals, datasheets, and application notes that a manufacturer of a measurement application device may publish.

[0336] In embodiments, the pre-trained artificial-intelligence algorithm may be pre-trained based on general data that is not specific to any measurement application device.

[0337] The training data relating to one or more measurement application devices may then be used to perform a fine-tuning of the pre-trained artificial-intelligence algorithm. Such a fine-tuning will then allow the pre-trained artificial-intelligence algorithm to respond adequately to the textbased user requests regarding the measurement application device.

[0338] In an embodiment, a further trained algorithm may be provided that is trained to provide a textual description of images and diagrams. Such a trained algorithm may be used to explain the content of images or diagrams provided in the training data, especially in manuals, datasheets, and application notes, to the pre-trained artificial-intelligence algorithm in textual form for performing the training of the pre-trained artificial-intelligence algorithm.

[0339] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificial-intelligence algorithm may comprise a large language model based on at least one of a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0340] While above, two specific types of large language models are explicitly disclosed, it is understood, that the pre-trained artificial-intelligence algorithm may comprise any algorithm that may be trained on a set of training data such that it may generate the configuration data based on text-based user requests.

[0341] Other possible types of algorithms or models include, but are not limited to, any type of language models, like statistical models like N-grams, recurrent neuronal network, like long short term, LSTM, models, and transformer models.

[0342] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a pre-conditioner coupled to the pre-trained artificialintelligence algorithm. The pre-conditioner may be configured to provide a pre-conditioning textbased request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0343] The pre-conditioner may serve to indicate general information to the pre-trained artificial-intelligence algorithm that a user may e.g., skip or forget to include in the text-based user requests.

[0344] The pre-conditioning text-based request may be provided to the pre-trained artificialintelligence algorithm preceding the actual text-based user requests, as exemplarily explained above for the user input interface.

[0345] In embodiments, the pre-conditioner may provide the pre-trained artificial-intelligence algorithm with information about at least one of, without being limited to, the measurement application devices present in the measurement application setup, specifications of the measurement application device(s), a required type or format for the configuration data.

[0346] Exemplary pre-conditioning text-based requests formulated by the pre-conditioner may comprise, but are not limited to, requests like:

[0347] “The available measurement application devices are a signal generator, an oscilloscope, and a network analyzer.”

[0348] ‘The oscilloscope is equipped with the options OPT1 , OPT2, ...”.

[0349] In the oscilloscope applications APT1 , APT2, ... are installed”.

[0350] “In the oscilloscope applications APT3, APT4, ... may be installed”.

[0351] The pre-conditioner may in embodiment comprise a further trained artificial-intelligence algorithm that is trained to generated pre-conditioning text-based requests as exemplarily indicated above. Such a trained artificial-intelligence algorithm may e.g., be trained based on sets of exemplary input data and the respective pre-conditioning text-based requests.

[0352] In other embodiments, the pre-conditioner may be implemented without a trained artificial intelligence algorithm. Such embodiments of the pre-conditioner may comprise e.g., a state machine. The pre-conditioner may be configured to concatenate respective information about a measurement application setup into respective pre-conditioning text-based requests. The information about the measurement application setup may e.g., be provided in tabular form, wherein each line comprises information about a component measurement application setup. Generally, the information may be provided in any adequate, especially structured, format.

[0353] Such information may e.g., indicate the type of element, like signal generation device, signal acquisition device, device under test, probe, adapter, or the like. This information may also indicate the number of outputs or inputs of the respective element, and how the inputs and outputs may be coupled to other elements.

[0354] In such embodiments, the pre-conditioner may, for example, simply insert the information in template strings, and concatenate the template strings for all elements in the measurement application setup to generate the respective pre-conditioning text-based request.

[0355] The configuration data may be generated by the pre-trained artificial-intelligence algorithm as text-based commands that a user may understand and may manually implement at a respective measurement application device.

[0356] Such control commands may be provided as natural text commands to a user. Exemplary control commands may comprise, but are not limited to, “set the voltage range to 0V - 100V”, “set the amplification factor to 10x”, “set the scaling of the diagram X-axis to 0.5”, “start the measurement”, “stop the measurement”, “start the signal generation”, “stop the signal generation”, and the like.

[0357] In an embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the measurement application device control unit may further comprise a code generator that is coupled to or integrated into the pretrained artificial-intelligence algorithm. The code generator may be configured to generate control commands in a predetermined control or programming language, and to provide the generatedcontrol commands in the respective control or programming language to a controller of a measurement application device.

[0358] The control commands may form part of the configuration data. The control commands may also implement the full set of configuration data, if it is not provided to the user prior to controlling the measurement application device.

[0359] Modern measurement application devices not only comprise a user interface for controlling the respective measurement application device. Instead, such measurement application devices may usually also be controlled programmatically.

[0360] Such control programs may be provided e.g., in a domain specific scripting or programming language, like the SCPI language (Standard Commands for Programmable Instruments). In other embodiments, the control programs may be provided in more general programming languages, like Python, or JavaScript, that may be interpreted in the measurement application device, or as compiled programs that are executed in the measurement application device.

[0361] The code generator, or the pre-trained artificial-intelligence algorithm with the integrated code generator may, therefore, be trained or configured to generate control commands in the configuration data. The control commands may be provided in any adequate format, like the above-mentioned control programs, without being limited to such programs.

[0362] In such an embodiment, the pre-trained artificial-intelligence algorithm may receive a text-based user request that requests executing a specific measurement application e.g., performing specific measurement tasks.

[0363] The code generator, or the pre-trained artificial-intelligence algorithm may then generate the respective control commands in the form of direct control commands or control program commands that may be provided to a measurement application device for execution. In embodiments, the generated control commands may be presented to a user prior to providing the control commands to a measurement application device for execution.

[0364] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the pre-trained artificialintelligence algorithm may comprise an algorithm that is locally executed on the measurement application device.

[0365] Depending on the measurement application that the measurement application device control unit is used in, the measurement data, and also the specific configuration of the measurement setup, may be confidential.

[0366] In such embodiments, the details of the measurement setup, or the measurement application should not be transmitted to a public service that may host the pre-trained artificial-intelligence algorithm.

[0367] In such embodiments, the pre-trained artificial-intelligence algorithm may be locally executed in the measurement application device.

[0368] In embodiments, the pre-trained artificial-intelligence algorithm that is executed locally in the measurement application device may comprise an algorithm that is adapted to the processing and memory resources that are available in the measurement application device locally.

[0369] In embodiments, the pre-trained artificial-intelligence algorithm may be executed on a server that is communicatively coupled to the measurement application device via a secured connection. A secured connection may be provided by providing both devices on the same network that is within the premises of the user of the measurement application device, or by establishing a VPN or any other encrypted communication between the measurement application device, and the respective server.

[0370] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, measurement application device control unit may further comprise a complexity estimator coupled to the text-based input interface. The complexity estimator may be configured to estimate the complexity of the text-based user requests, and forward the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.

[0371] As indicated above, a locally executed pre-trained artificial-intelligence algorithm may be adapted to the processing and memory resources that are locally available in the respective measurement application device.

[0372] Consequently, such a pre-trained artificial-intelligence algorithm may not comprise the capacity to answer all possible text-based user requests, and may be limited to answer only simple text-based user requests.

[0373] In order to allow a user to use complex text-based user requests, the measurement application device control unit may be provided with the complexity estimator.

[0374] The complexity estimator may analyze the received text-based user requests, and may determine or estimate the complexity of the text-based user requests. If the determined or estimated complexity is higher than a predetermined threshold, the respective text-based user requestmay be provided to an external, more powerful pre-trained artificial-intelligence algorithm. The external pre-trained artificial-intelligence algorithm may e.g., be provided as network attached server or cloud server, as already indicated above.

[0375] The predetermined threshold may, of course, be adapted to the capabilities of a locally executed pre-trained artificial-intelligence algorithm. An exemplary complexity measure may e.g., comprise the number of letters, or the number of words, or the number of sentences, or a combination of any of these.

[0376] In another embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the complexity estimator may be configured to request a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificialintelligence algorithm.

[0377] As explained above, data regarding a measurement setup, or a measurement application may be confidential. Therefore, the complexity estimator may ask a user for consent or permission prior to providing or transmitting a text-based user request to an external pre-trained artificialintelligence algorithm.

[0378] If the user declines, the complexity estimator may inform the user that the text-based user requests may eventually not be answered correctly with the locally executed pre-trained artificial-intelligence algorithm, and provide the text-based user requests to the locally executed pretrained artificial-intelligence algorithm.

[0379] In a further embodiment, which can be combined with all other embodiments of the measurement application device control unit mentioned above or below, the complexity estimator may be configured to provide the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

[0380] The anonymizer may be an intermediary device that may be contacted by the complexity estimator instead of directly providing the text-based user requests to an external pre-trained artificial-intelligence algorithm.

[0381] Although not explicitly claimed, the anonymizer is disclosed herein as separate entity that may be used independently from any measurement application device or measurement application device control unit.

[0382] The anonymizer may e.g., comprise a server that bundles text-based user requests from multiple measurement application device control units, and forwards the text-based user requests to the externally operated pre-trained artificial-intelligence algorithm without including anyinformation that might identify a measurement application device control unit or measurement application device. The anonymizer may e.g., remove such information from the text-based user requests, or replace such information with other predetermined information.Fourth aspect:

[0383] A fourth aspect of the disclosure relates to a measurement application processing device, a measurement application device, and a computer-implemented method.

[0384] Although applicable to any type of measurement application device, the present disclosure according to the fourth aspect will mainly be described in conjunction with laboratory-destined measurement application devices, like oscilloscopes.

[0385] Modern measurement application devices comprise a plurality of functions that support specific tasks in measurement applications. To this end, the measurement application devices may comprise a plurality of options and configuration parameters that a user may set-up for a specific measurement application. Many of these options or parameters will reflect on the display of the measurement application device during a measurement. Especially an inexperienced user may have difficulties interpreting the measurement results on the display of the measurement application device.

[0386] Accordingly, according to the fourth aspect there is a need for supporting users in interpreting measurement results.

[0387] The above stated problem is solved by the features of the independent claims according to the fourth aspect. It is understood, that independent claims of a claim category may be formed in analogy to the dependent claims of another claim category.

[0388] Accordingly, it is provided:

[0389] A measurement application processing device comprising a feedback interface configured to receive measurement feedback data from at least one measurement application device, a feedback data analyzer coupled to the feedback interface, and configured to generate a text-based feedback description of the measurement feedback data, and an output interface coupled to the feedback data analyzer and configured to output the text-based feedback description to a user.

[0390] Further, it is provided:

[0391] A measurement application device comprising a measurement interface, and a measurement application processing device according to the fourth aspect of the present disclosure coupled to the measurement interface, wherein the measurement interface is configured to provide measurement data to the measurement application processing device as measurement feedback data.

[0392] Further, it is provided:

[0393] A computer-implemented method comprising receiving measurement feedback data from at least one measurement application device, generating a text-based feedback description of the measurement feedback data, and outputting the text-based feedback description to a user.

[0394] The present disclosure is based on the finding that modern measurement application devices may provide users with a very complex and detailed measurement result.

[0395] For inexperienced users it may be difficult to correctly interpret or fully understand the content of a display of a measurement application device.

[0396] Users may, therefore, face difficulties when performing a measurement at the time of correctly interpreting the measurement results, and a respective expert may not always be present,

[0397] The present disclosure, therefore, provides the measurement application processing device, a respective measurement application device, and the computer-implemented method.

[0398] The measurement application processing device comprises the feedback interface. With the feedback interface, the measurement application processing device may be provided with the measurement feedback data.

[0399] The feedback interface may be a data interface that may receive measurement feedback data from the respective measurement application device. The measurement feedback data may comprise any data acquired during a measurement or derived from the measurement.

[0400] The measurement feedback data may exemplarily comprise, but is not limited to, the raw measurement data, display information, like screenshots of a display of the measurement application device showing a waveform of the acquired signal, measurement values, waveforms, marker information, and trigger information.

[0401] The feedback data analyzer may then generate the text-based feedback description for a user, and output that text-based feedback description via the output interface to the user.

[0402] The text-based feedback description may explain the content of the measurement feedback data to a user. The user may e.g., be provided with an explanation of a waveform that is measured with a measurement application device.

[0403] If, for example, the measurement context is known, the text-based feedback description may comprise detailed information about the measurement feedback data in the respective context. Such detailed in formation may e.g., include explanations regarding the used measurement application devices, the device under test, and the interpretation of measurement results tak-ing into account the types of measurement application devices and devices under test in the measurement application. The text-based feedback description may e.g., in the case of the measurement of an amplifier refer to a -3dB point, and explain details about this point. In case of an eye diagram being shown in the measurement feedback data, the text-based feedback description may refer to the size of the eyes.

[0404] In embodiments, the feedback data analyzer may refer to standards, like communication standards. In the above-example of the eye diagram, the feedback data analyzer may e.g., indicate if the eye diagram conforms to a respective standard or not.

[0405] If the context is not known, the text-based feedback description may comprise different possible interpretations of the measurement feedback data. A user may also be presented with a short list of the possible interpretations, and for one option selected by the user the text-based feedback description may be generated in detail.

[0406] It is understood, that the method according to the present disclosure may not only be performed locally in a single measurement application device. In such a single measurement application device embodiment, the method may still be performed taking into account a plurality of different measurement application devices of the respective measurement application.

[0407] In other embodiments, the method according to the present disclosure may also be performed remotely or in a distributed fashion. In embodiments, the measurement application processing device may e.g., be provided remotely to one or multiple measurement application devices. The measurement application processing device may e.g., be provided as a server, or server-based or cloud-based application, that may be accessed by a user via a web interface. Any control data that the measurement application processing device may generate for the one or more measurement application devices may be provided to the one or more measurement application devices via a respective network or data connection. To this end, the measurement application device may comprise a respective communication interface.

[0408] In embodiments, the communication interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0409] Further the server or cloud server and the one or more measurement application devices may communicate via an intermediary network with each other, and that such a network may comprise any type of network devices, like switches, hubs, routers, firewalls, and different types of network technologies.

[0410] Generally, such a server may be a dedicated server that may be implemented as a single hardware device. The server may also be implemented as a distributed system comprising a plurality of servers, optionally with a load balancer, that distributes the load over the servers. The server may also be provided as a so-called cloud or cloud-server system that implements the server via virtualization methods independently of the underlying hardware.

[0411] In embodiments, the measurement application processing device may be operated independently of the one or more measurement application devices, and may not even be communicatively coupled to the one or more measurement application devices. In such embodiments the user may be the sole recipient of the generated response data, or response data that may be directly interpreted by the one or more measurement application devices may be stored on a respective data carrier.

[0412] The measurement application processing device may, in embodiments, comprise or may be provided in or as part of at least one of a dedicated processing element e.g., a processing unit, a microcontroller, a field programmable gate array, FPGA, a complex programmable logic device, CPLD, an application specific integrated circuit, ASIC, or the like. A respective program or configuration may be provided to implement the required functionality. The measurement application processing device may at least in part also be provided as a computer program product comprising computer readable instructions that may be executed by a processing element. In a further embodiment, the measurement application processing device may be provided as addition or additional function or method to the firmware or operating system of a processing element that is already present in the respective application as respective computer readable instructions e.g., in the measurement application device. Such computer readable instructions may be stored in a memory that is coupled to or integrated into the processing element, like the measurement application device controller of the measurement application device. The processing element may load the computer readable instructions from the memory and execute them. The same applies to any other element, unit or function disclosed herein as part of the measurement application processing device, the measurement application device, and the method for controlling a measurement application device.

[0413] In addition, it is understood, that any required supporting or additional hardware may be provided like e.g., a power supply circuitry and clock generation circuitry.

[0414] In the context of the present disclosure, a measurement application device may comprise any device that may be used in a measurement application to acquire an input signal or to generate an output signal, or to perform additional or supporting functions in a measurement application. A measurement application device may also comprise or be implemented as application orapplications, also called measurement application or measurement applications, that may be executed on a computer device and that may communicate with other measurement application devices in order to perform a measurement task. A measurement application, also called measurement setup, may e.g., comprise at least one or multiple different measurement application devices for performing electric, magnetic, or electromagnetic measurements, especially on single devices under test. Such electric, magnetic, or electromagnetic measurements may be performed in a measurement laboratory or in a production facility in the respective production line. A measurement application or measurement setup may serve to qualify the single devices under test i.e. , to determine the proper electrical operation of the respective devices under test.

[0415] Measurement application devices to this end may comprise at least one signal acquisition section for acquiring electric, magnetic, or electromagnetic signals to be measured from a device under test, or at least one signal generation section for generating electric, magnetic, or electromagnetic signals that may be provided to the device under test. Such a signal acquisition section may comprise, but is not limited to, a front-end for acquiring, filtering, and attenuating or amplifying electrical signals. The signal generation section may comprise, but is not limited to, respective signal generators, amplifiers, and filters.

[0416] Further, when acquiring signals, measurement application devices may comprise a signal processing section that may process the acquired signals. Processing may comprise converting the acquired signals from analog to digital signals, and any other type of digital signal processing, for example, converting signals from the time-domain into the frequency-domain.

[0417] The measurement application devices may also comprise a user interface to display the acquired signals to a user and allow a user to control the measurement application devices. Of course, a housing may be provided that comprises the elements of the measurement application device. It is understood, that further elements, like power supply circuitry, and communication interfaces may be provided.

[0418] A measurement application device may be a stand-alone device that may be operated without any further element in a measurement application to perform tests on a device under test. Of course, communication capabilities may also be provided for the measurement application device to interact with other measurement application devices.

[0419] A measurement application device may comprise, for example, a signal acquisition device e.g., an oscilloscope, especially a digital oscilloscope, a spectrum analyzer, or a vector network analyzer. Such a measurement application device may also comprise a signal generation de-vice e.g., a signal generator, especially an arbitrary signal generator, also called arbitrary waveform generator, or a vector signal generator. Further possible measurement application devices comprise devices like calibration standards, or measurement probe tips.

[0420] Of course, at least some of the possible functions, like signal acquisition and signal generation, may be combined in a single measurement application device.

[0421] In embodiments, the measurement application device may comprise pure data acquisition devices that are capable of acquiring an input signal and of providing the acquired input signal as digital input signal to a respective data storage or application server. Such pure data acquisition devices not necessarily comprise a user interface or display. Instead, such pure data acquisition devices may be controlled remotely e.g., via a respective data interface, like a network interface or a USB interface. The same applies to pure signal generation devices that may generate an output signal without comprising any user interface or configuration input elements. Instead, such signal generation devices may be operated remotely via a data connection.

[0422] Further embodiments of the present disclosure are subject of the further dependent claims and of the following description, referring to the drawings.

[0423] In the following, the dependent claims referring directly or indirectly to claim 98 according to the fourth aspect of the present disclosure are described in more detail. For the avoidance of doubt, the features of the dependent claims relating to the measurement application processing device can be combined in all variations with each other and the disclosure of the description is not limited to the claim dependencies as specified in the claim set. Further, the features of the other independent claims may be combined with any of the features of the dependent claims relating to the measurement application processing device in all variations, wherein in the method respective method steps perform the function of the respective measurement application processing device elements.

[0424] In an embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the feedback data analyzer may be configured to identify at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data, and to provide a respective text-based feedback description.

[0425] A user may lack the experience to identify relevant data in the measurement results, and especially to identify anomalies.

[0426] Therefore, with the feedback data analyzer being configured to identify such anomalies, relevant measurement values, or relevant measurement waveform sections, the text-based feedback description may provide valuable information to such a user.

[0427] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise a text-based input interface configured to receive textbased user requests regarding the text-based feedback description, and a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate user information data regarding the measurement feedback data based on received measurement feedback data, and the text-based user requests. The pre-trained artificial-intelligence algorithm may be configured to output the user information data.

[0428] The text-based input interface may be implemented as a data interface that receives the text in a binary form e.g., as ASCII encoded text, or as text encoded in any other character encoding scheme. Such a data interface may be provided as hardware interface e.g., as a network interface, and a program interface, like an API or a callback function, or a combination of both.

[0429] The text-based input interface may comprise any other type of interface, like an API, callback functions, shared memory, a web-based III, a REST-API, a class or interface, like a Python class or interface, without being limited to these examples.

[0430] The text-based user requests are then provided to the pre-trained artificial-intelligence algorithm that generates respective response data based on the provided text-based user requests.

[0431] The pre-trained artificial-intelligence algorithm may, especially, be trained to receive text-based user requests that are formulated in a natural language style e.g., as a user would speak the text-based user requests to other users, or as a user would formulate the text-based user requests e.g., in an online forum.

[0432] The response data may be provided in different forms, as will be explained in more detail below. Generally, in embodiments the response data may be provided in textual form. The term “textual form” in the context of the present disclosure may refer to any type of text that may include, but is not limited to, natural language text, and text in a programming or scripting language, or a combination of both.

[0433] The measurement application processing device further comprises an output interface that outputs the response data.

[0434] In a further embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement feedback data may comprise at least one of display information, like screenshots of a display of the measurement application device showing a waveform of the acquired signal, measurement values, waveforms, marker information, and trigger information.

[0435] When assessing the results of a measurement that is performed with a measurement application device, not only the actually measured data may be relevant.

[0436] Instead, display information, like a screenshot of the display of a measurement application device may comprise additional information. Such information may relate to, but is not limited to, value or signal ranges, acquisition times, triggers and trigger levels.

[0437] As explained above, an inexperienced user may have difficulties in interpreting what he is seeing on the display of the measurement application device.

[0438] Therefore, using the display information, like the screenshot, and not only acquired measurement signals as input for the feedback data analyzer, allows to provide the user with a comprehensive explanation of the measurement results.

[0439] In a further embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the feedback data analyzer may comprise a trained image-to-text algorithm that is configured to generate the text-based feedback description based on the display information, like the screenshot, and the other information.

[0440] In order to generate the text-based feedback description, the display information, and other information needs to be interpreted correctly. To this end, the feedback data analyzer may comprise the trained image-to-text algorithm.

[0441] The trained image-to-text algorithm may comprise any adequate type of trained artificial-intelligence algorithm that may be trained to convert display information, like screenshots of measurement application devices, and other information into the text-based feedback description.

[0442] Such an algorithm may be trained to provide a text-based description of image or waveform data, and specific details that may be present in a screenshot of a measurement application device. The algorithm may, especially, be trained to describe details of the measurement data that a user may easily miss. Such an algorithm may comprise or be based on a respective visual foundation model.

[0443] Training data for the feedback data analyzer i.e., the trained image-to-text algorithm, may be taken e.g., from manuals, datasheets, and available measurement reports, that comprise descriptions of measurement results, and e.g., screenshots with measured waveforms. In embodiments, the feedback data analyzer may comprise an image-to-text conversion algorithm that is trained on general image and text data, and fine-tuned based on data from manuals, datasheets, and available measurement reports, as indicated above.

[0444] In other embodiments, the measurement application device may comprise the feedback data analyzer, and the feedback data analyzer may provide the text-based feedback description to the measurement application processing device.

[0445] In another embodiment, which can be combined with all other embodiments of the measurement application processing device mentioned above or below, the measurement application processing device may further comprise a pre-trained artificial-intelligence documentation algorithm configured to create a text-based measurement documentation based on the measurement feedback, or alternatively based on the measurement feedback, the text-based user requests, and the generated user information.

[0446] As explained above, the feedback data analyzer with the trained image-to-text algorithm may generate a description of the screenshot for an inexperienced user.

[0447] The pre-trained artificial-intelligence documentation algorithm in contrast may be trained to create a measurement documentation or a measurement report for a respective measurement.

[0448] To this end, the pre-trained artificial-intelligence documentation algorithm may be trained based on measurement reports or measurement documentation that may be provided e.g., by a manufacturer of the respective measurement application device.

[0449] The pre-trained artificial-intelligence documentation algorithm may be a generally trained language model that is fine-tuned with the respective training data in order to generate the required format for the measurement documentation.Fifth aspect:

[0450] A fifth aspect of the disclosure relates to circuit development device, and a respective computer-implemented method.

[0451] Although applicable to any type of electronic device, the present disclosure according to the fifth aspect will mainly be described in conjunction with development of electronic circuits.

[0452] When designing electronic circuits, inexperienced users may easily design circuits that comprise errors or that at least comprise design flaws. In certain situations, like under operating conditions at the extrema of the specified environmental operation conditions, such circuits may not behave as expected.

[0453] Accordingly, according to the fifth aspect there is a need for simplifying the design process for electronic circuits.

[0454] The above stated problem is solved by the features of the independent claims according to the fifth aspect. It is understood, that independent claims of a claim category may be formed in analogy to the dependent claims of another claim category.

[0455] A circuit development device comprising a text-based input interface configured to receive a text-based natural language circuit description regarding an electrical circuit, a pre-trained artificial-intelligence circuit generation algorithm coupled to the text-based input interface, and configured to generate at least one circuit design based on the text-based natural language circuit description, and an output interface coupled to the pre-trained artificial-intelligence circuit generation algorithm, and configured to output the at least one circuit design.

[0456] Further, it is provided:

[0457] A circuit development device comprising a pre-trained artificial-intelligence circuit verifying algorithm configured to receive at least one circuit design, wherein the pre-trained artificialintelligence circuit verifying algorithm is further configured to identify at least one of errors, and weaknesses in the at least one circuit design, and wherein the pre-trained artificial-intelligence circuit verifying algorithm is further configured to output the identified at least one of errors, and weaknesses.

[0458] Although in the claims, the pre-trained artificial-intelligence circuit verifying algorithm is only claimed in a dependent claim, it is understood, that a circuit development device comprising the pre-trained artificial-intelligence circuit verifying algorithm without the pre-trained artificial-intelligence circuit generation algorithm, or the pre-trained artificial-intelligence circuit verifying algorithmas such are also disclosed herein as separate entities or devices that may be claimed and may operate independently of the circuit development device comprising the pre-trained artificial-intelligence circuit generation algorithm.

[0459] Further it is provided:

[0460] A computer-implemented method comprising receiving a text-based natural language circuit description regarding an electrical circuit, generating at least one circuit design based on the text-based natural language circuit description with a pre-trained artificial-intelligence circuit generation algorithm, and outputting the at least one circuit design.

[0461] The present disclosure is based on the finding that designing electronic circuits may be a challenging task for inexperienced users that may lead to non-functional or instable electronic circuits. Inexperienced users may e.g., lack knowledge about any cross interference between different electronic elements. Especially, in the edges or extrema of the operating conditions defined for the respective electronic circuit, such cross interferences may cause difficult to detect anomalies or errors.

[0462] The present disclosure, therefore, provides the circuit development device, and the respective computer-implemented method.

[0463] The circuit development device comprises a text-based input interface that may receive a text-based natural language circuit description regarding an electrical circuit from a user. The circuit development device may also comprise e.g., an audio interface, and a translator as described for the measurement application device control units of other aspects of the present disclosure.

[0464] The text-based input interface may receive a written text-based natural language circuit description from a user. In addition, or as alternative, the circuit development device may receive a spoken natural language circuit description, and may convert the spoken natural language circuit description into a text-based natural language circuit description. Such a converted text-based natural language circuit description may then be provided to the text-based input interface in text form.

[0465] The text-based input interface may also be implemented as a data interface that receives the text in a binary form e.g., as ASCII encoded text, or as text encoded in any other character encoding scheme. Such a data interface may be provided as hardware interface e.g., as a network interface, and a program interface, like an API or a callback function, or a combination of both.

[0466] The text-based input interface may comprise any other type of interface, like an API, callback functions, shared memory, a web-based III, a REST-API, a class or interface, like a Python class or interface, without being limited to these examples.

[0467] This allows interfacing the circuit development device with any other device or applica- tion.

[0468] The text-based natural language circuit description may comprise an indication of a required electronic or electric circuit that a user may require. Exemplary text-based natural language circuit descriptions may comprise, but are not limited to, statements like “design an amplifier based on a Gilbert cell.”, or “design a mixer for two signals with frequencies up to 1GHz.”

[0469] The text-based natural language circuit description is then provided to a pre-trained artificial-intelligence circuit generation algorithm. The pre-trained artificial-intelligence circuit generation algorithm uses the text-based natural language circuit description to generate at least one circuit design based on the text-based natural language circuit description.

[0470] The pre-trained artificial-intelligence circuit generation algorithm may e.g., be executed in a processor of the circuit development device.

[0471] Such a processor may comprise or may be provided in or as part of at least one of a dedicated processing element e.g., a processing unit, a microcontroller, a field programmable gate array, FPGA, a complex programmable logic device, CPLD, an application specific integrated circuit, ASIC, or the like. A respective program or configuration may be provided to implement the required functionality. The processor may at least in part also be provided as a computer program product comprising computer readable instructions that may be executed by a processing element. In a further embodiment, the processor may be provided as addition or additional function or method to the firmware or operating system of a processing element that is already present in the respective application as respective computer readable instructions. Such computer readable instructions may be stored in a memory that is coupled to or integrated into the processing element. The processing element may load the computer readable instructions from the memory and execute them. The same applies to any other element, unit or function disclosed herein as part of the circuit development device, and the computer-implemented method, like the pre-conditioner, the image-to-text algorithm, and the pre-trained artificial-intelligence circuit verifying algorithm.

[0472] In addition, it is understood, that any required supporting or additional hardware may be provided like e.g., a power supply circuitry and clock generation circuitry.

[0473] Generally, the pre-trained artificial-intelligence circuit generation algorithm, and the pretrained artificial-intelligence circuit verifying algorithm may comprise a large language model based on at least one of a statistical model, like an N-gram, a recurrent neuronal network, like a long short term, LSTM, model, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0474] The circuit design may, in embodiments, be provided in a text-based or natural language like form to a user. In other embodiments, the circuit design may be provided in a computer readable form e.g., as file with a respective format for describing electrical circuits. A combination of both is of course possible.

[0475] The generated circuit design may then be output via the output interface. In embodiments, the generated circuit design may be output to a user e.g., visually via a display. In other embodiments, the output interface may be integrated with the text-based input interface as data interface. In such embodiments, the generated circuit design may be output as binary or digital data via the respective data interface. Of course, any combinations of the different types of textbased input interface, and output interfaces may be provided in embodiments of the circuit development device.

[0476] It is understood, that at least one of the input interface and the output interface may comprise any kind of wired and wireless communication interfaces, like for example a network communication interface, especially an Ethernet, wireless LAN or WIFI interface, a USB interface, a Bluetooth interface, an NFC interface, a visible or non-visible light-based interface, especially an infrared interface.

[0477] With the solution of the present disclosure, it is possible to easily create new circuits, even for inexperienced users.

[0478] In embodiments the fifth aspect of the present disclosure may be combined with any one of the other aspects of the present disclosure. For example, after providing the circuit design, a respective configuration for a measurement application device may be provided with the third aspect of the present disclosure, or a full measurement application may be designed for the circuit design with the second aspect according to the present disclosure. The results of respective measurements may be processed according to the fourth aspect of the present disclosure for a user.

[0479] Further embodiments of the present disclosure are subject of the further dependent claims and of the following description, referring to the drawings.

[0480] In the following, the dependent claims referring directly or indirectly to claim 112 according to the fifth aspect of the present disclosure are described in more detail. For the avoidance of doubt, the features of the dependent claims relating to the circuit development device can be combined in all variations with each other and the disclosure of the description is not limited to the claim dependencies as specified in the claim set. Further, the features of the other independent claims may be combined with any of the features of the dependent claims relating to the circuit development device in all variations, wherein respective method steps perform the function of the respective circuit development device elements.

[0481] In an embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificialintelligence circuit generation algorithm may be configured to generate multiple alternative circuit designs based on the text-based natural language circuit description, and to provide a natural-language description of each one of the alternative circuit designs.

[0482] For generating multiple circuit designs, the pre-trained artificial-intelligence circuit generation algorithm may e.g., be trained to automatically generate multiple circuit designs for a single text-based natural language circuit description. In other embodiments, the below-mentioned preconditioner may be used, to provide a pre-conditioning text-based request that indicates to the preconditioning text-based request that different circuit designs should be generated.

[0483] The natural-language description of each one of the alternative circuit designs may then comprise detailed explanations of the single variants of the respective circuit design. The natural-language description may e.g., explain why specific parts or circuit elements are used in a circuit, and what advantages and disadvantages these parts or circuit elements have compared to other parts or circuit elements. Further, the natural-language description may also comprise information about the expected behavior of the respective circuit design under specific operating conditions, and other information that may be relevant for a user to select a specific circuit design.

[0484] The user may, therefore, easily generated and assess different circuit designs for a given problem, and may chose the most adequate circuit design for his application.

[0485] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit generation algorithm is configured to output the circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

[0486] A SPICE-file may comprise file comprising a circuit description that may be processed by tools according to the “Simulation Program with Integrated Circuit Emphasis” (SPICE) general- purpose, open-source analog electronic circuit simulator, and any compatible tool.

[0487] A netlist may comprise a description of the connectivity of an electronic circuit. In its simplest form, a netlist consists of a list of the electronic components in a circuit and a list of the nodes they are connected to.

[0488] HDL is an acronym for the expression “Hardware Description Language”, and may generally refer to a specialized computer language used to describe the structure and behavior of electronic circuits, like digital logic circuits, without being limited to digital logic circuits. In the con-text of the present disclosure, the term HDL or HDL-file may, consequently, refer to a file comprising a description of the respective circuit design in the respective HDL, wherein any type of HDL may be used. VHDL may refer to Verilog-HDL, which is a special dialect of an HDL.

[0489] A bill of materials refers to a list of circuit elements that are required to build the circuit design.

[0490] The layout file comprises a description of a circuit layout e.g., a printed circuit board layout, in a respective format that may be read by specialized software programs that may then display the circuit layout, and may allow a user to modify the circuit layout. The circuit layout description may also be used to manufacture a PCB for a respective circuit layout. A possible file format comprises, but is not limited to, the Gerber file format.

[0491] In case of designing integrated circuits, the layout file may comprise a GDSII (Graphic Data System" ("GDS")) file format, or any other adequate file format for describing the layout of integrated circuits.

[0492] Providing the circuit design in at least one of, and especially in a combination of multiple of, a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file, allows easily simulating, modifying, and manufacturing the respective circuit design.

[0493] In embodiments, a specific pre-trained artificial-intelligence circuit generation algorithm may be provided for each type of possible output data format.

[0494] The circuit design may also be provided as a rendered image of the respective circuit. To this end, the types of circuit design mentioned above, that are required for rendering an image of the circuit, may be used as the basis for the rendering. The pre-trained artificial-intelligence circuit generation algorithm may use an external program, like a raytracing Tenderer, to render the image.

[0495] In a further embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the circuit development device may further comprise a pre-conditioner coupled to the pre-trained artificial-intelligence circuit generation algorithm. The pre-conditioner may be configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence circuit generation algorithm prior to the pre-trained artificial-intelligence circuit generation algorithm operating on the text-based natural language circuit description.

[0496] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-conditioning text-based request may indicate at least one of a list of available electrical circuit elements, a list ofpreferred electrical circuit elements, environmental conditions of an operating environment for the electrical circuit, delivery times of electrical circuit elements, costs of electrical circuit elements, suppliers for electrical circuit elements, and preferred suppliers for electrical circuit elements.

[0497] The pre-conditioner may serve to indicate general information to the pre-trained artificial-intelligence circuit generation algorithm that a user may e.g., skip or forget to include in the text-based user requests.

[0498] The pre-conditioning text-based request may be provided to the pre-trained artificialintelligence circuit generation algorithm preceding the actual text-based natural language circuit description.

[0499] In embodiments, the pre-conditioner may provide the pre-trained artificial-intelligence circuit generation algorithm with information about at least one of, without being limited to, a list of available electrical circuit elements, a list of preferred electrical circuit elements, environmental conditions of an operating environment for the electrical circuit, delivery times of electrical circuit elements, costs of electrical circuit elements, suppliers for electrical circuit elements, and preferred suppliers for electrical circuit elements.

[0500] In embodiments, the pre-conditioner may request information from a user prior to generating the pre-conditioning text-based request.

[0501] Exemplary pre-conditioning text-based requests formulated by the pre-conditioner may comprise, but are not limited to, requests like:

[0502] “Design a circuit for operating temperatures between -40°C and 150°C.”

[0503] “Design a circuit for signals with frequencies of up to 10GHz.”

[0504] “Design a circuit using circuit elements that may be bought from at least three different providers.”

[0505] Of course, the above examples are not limiting, and any other information may be provided by the pre-conditioner in the pre-conditioning text-based request.

[0506] The pre-conditioner may in embodiments comprise a further trained artificial-intelligence algorithm that is trained to generated pre-conditioning text-based requests as exemplarily indicated above. Such a trained artificial-intelligence algorithm may e.g., be trained based on sets of exemplary operating conditions or general design guidelines, and the respective pre-conditioning text-based requests. Such a trained artificial-intelligence algorithm may e.g., comprise any typeof language models, like statistical models like N-grams, recurrent neuronal network, like long short term, LSTM, models, and transformer models.

[0507] In other embodiments, the pre-conditioner may be implemented without a trained artificial intelligence algorithm. Such embodiments of the pre-conditioner may comprise e.g., a state machine, and may be configured to concatenate respective information about a measurement application setup into respective pre-conditioning text-based requests. The information about the operating conditions, or generally, the design guidelines, may e.g., be provided in tabular form. Generally, the information may be provided in any adequate, especially structured, format.

[0508] In such embodiments, the pre-conditioner may, for example, simply insert the information in template strings, and concatenate the template strings for all elements in the measurement application setup to generate the respective pre-conditioning text-based request.

[0509] In a further embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the circuit development device may further comprise an image-to-text algorithm that is configured to receive a visual representation of an electrical or electronic circuit, and to generate the text-based natural language circuit description based on the visual representation of an electrical circuit, and to provide the generated text-based natural language circuit description to the pre-trained artificial-intelligence circuit generation algorithm. The terms “electrical circuit” or “electronic circuit” are used interchangeably in this disclosure.

[0510] The image-to-text algorithm may support inexperienced users that are not capable of correctly describing an electrical circuit. The user may simply provide a visual representation e.g., an image or a video, to the image-to-text algorithm, of a similar electrical circuit, and the image-to- text algorithm will provide a respective text-based natural language circuit description.

[0511] In embodiments, the image-to-text algorithm may provide the user with a generated text-based natural language circuit description, and the user may amend or modify the text-based natural language circuit description prior to providing it to the pre-trained artificial-intelligence circuit generation algorithm.

[0512] The trained image-to-text algorithm may comprise any adequate type of trained artificial-intelligence algorithm that may be trained to convert the visual representation of an electrical circuit, like images or videos of such circuits, into the text-based natural language circuit description.

[0513] Such an algorithm may be trained to provide a text-based description of image or video data, and especially, the details of an electric or electronic circuit that may be present in the image or video.

[0514] Training data for the image-to-text algorithm may be generated by combining the development files, like at least one of the above-mentioned SPICE-file, a netlist, HDL-file, VHDL-file, bill of materials, and layout file with images and videos of the resulting integrated circuits.

[0515] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit generation algorithm may be pre-trained using at least one of manuals of measurement application devices, datasheets of measurement application devices, application notes regarding measurement application devices, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0516] The pre-trained artificial-intelligence circuit generation algorithm needs to be trained on data regarding circuit design. Of course, in embodiments, the pre-trained artificial-intelligence circuit generation algorithm may be trained on information regarding multiple different circuit designs.

[0517] Usually, a wide selection of training data is already available from the manufacturers of respective electronic circuits and other online sources, like forums. Such training data may be provided e.g., in the form of manuals, datasheets, and application notes that a manufacturer of a circuit may publish, or in the form of forum discussions and videos.

[0518] In embodiments, the pre-trained artificial-intelligence circuit generation algorithm may be pre-trained based on general data that is not specific to any circuit.

[0519] The training data relating specifically to electric or electronic circuits may then be used to perform a fine-tuning of the pre-trained artificial-intelligence circuit generation algorithm. Such a fine-tuning will then allow the pre-trained artificial-intelligence circuit generation algorithm to create adequate circuit designs.

[0520] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the circuit development device may further comprise a pre-trained artificial-intelligence circuit verifying algorithm coupled to the pre-trained artificial-intelligence circuit generation algorithm, and configured to receive the at least one circuit design. The pre-trained artificial-intelligence circuit verifying algorithm may further be configured to identify at least one of errors, and weaknesses in the at least one circuit design.The pre-trained artificial-intelligence circuit verifying algorithm may further be configured to output the identified at least one of errors, and weaknesses.

[0521] The pre-trained artificial-intelligence circuit verifying algorithm may be used in a standalone circuit development device, without the pre-trained artificial-intelligence circuit generation algorithm. Such a circuit development device may e.g., be used to verify existing circuit designs.

[0522] The pre-trained artificial-intelligence circuit verifying algorithm may be provided as a kind of antagonist to the pre-trained artificial-intelligence circuit generation algorithm. With that function, the pre-trained artificial-intelligence circuit generation algorithm identifies at least one of errors, and weaknesses in the at least one circuit design provided by the pre-trained artificial-intelligence circuit generation algorithm.

[0523] The term “errors” may refer to obvious errors that will lead to a non-functional electronic circuit being built based on the circuit design. Such an error may e.g., comprise a short-circuit, unconnected traces or ports, wrong circuit elements, or circuit elements with a wrong dimension or value.

[0524] Other types of “errors” may comprise non-obvious errors, that may e.g., result in a mainly functional electronic circuit built based on the circuit design, while the electronic circuit may not behave as expected under at least some operating conditions.

[0525] The term “weaknesses” may also refer to non-obvious details, that may e.g., result in a mainly functional electronic circuit built based on the circuit design, while the electronic circuit may not behave as expected under at least some operating conditions, or may comprise a reduce lifetime, or may only operate under limited environmental conditions.

[0526] Other types of “weaknesses” may refer to very specific and rare electric elements being used in the circuit.

[0527] Possible examples of errors and weaknesses comprise, but are not limited to, ESD (electrostatic discharge) protection not being adequately provided in the circuit designs, the circuit design using components with limited availability or a limited number of sources, or a limited regional availability, the circuit design using components with only a little number, like 2 or 3, of alternatives available.

[0528] Other types of errors and weaknesses comprise, but are not limited to, expected mechanical problems, and expected production problems.

[0529] Exemplary indications provided by the pre-trained artificial-intelligence circuit verifying algorithm may comprise, but are not limited to:

[0530] “The processor is positioned near the switched-mode power supply. Interference from the switched-mode power supply might influence the processor.”

[0531] “The used FR4 carrier might bend under high processor load and ambient temperatures in the upper range of the defined operating conditions.”

[0532] “The transformer might be difficult to mount at the intended position.”

[0533] With these indications, a user may modify the circuit accordingly. In embodiments, the identified at least one of errors, and weaknesses may be provided to the pre-trained artificial-intelligence circuit generation algorithm with an indication to re-design or re-create the circuit design taking the indications into account.

[0534] In a further embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit verifying algorithm may be configured to analyze the circuit design provided as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

[0535] The pre-trained artificial-intelligence circuit verifying algorithm may receive the circuit design in any of the formats that the pre-trained artificial-intelligence circuit generation algorithm may generate the circuit design in.

[0536] This allows the pre-trained artificial-intelligence circuit verifying algorithm to analyze the circuit design in detail based on all available design documents.

[0537] In embodiments, a specific pre-trained artificial-intelligence circuit verifying algorithm may be provided for each type of input data format.

[0538] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit verifying algorithm may further be configured to output natural language measurement recommendations regarding a measurement of the identified at least one of errors, and weaknesses in the at least one circuit design.

[0539] While at least one of errors, and weaknesses may be detected by the pre-trained artificial-intelligence circuit verifying algorithm, the pre-trained artificial-intelligence circuit verifying algorithm may be wrong with his assessment.

[0540] Therefore, the pre-trained artificial-intelligence circuit verifying algorithm may estimate an accuracy for the indicated at least one of errors, and weaknesses.

[0541] In embodiments, the pre-trained artificial-intelligence circuit verifying algorithm may, independently of the accuracy, provide a measurement instruction regarding at least one of the identified weaknesses or errors. In other embodiments, the pre-trained artificial-intelligence circuit verifying algorithm may, if the accuracy is below a predetermined threshold, provide a measurement instruction regarding at least one of the identified weaknesses or errors.

[0542] Such a measurement recommendation may exemplarily read as follows: “For verifying the stability of the power supply, measure the voltage at nodes X, and y, under operating conditions Z.” Wherein X and Y refer to nodes or measurement points in the electric circuit, and Z defines the operating conditions at the time of measurement.

[0543] In a further embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit verifying algorithm may be configured to output a circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file, and mark the identified at least one of errors, and weaknesses in the output.

[0544] The pre-trained artificial-intelligence circuit verifying algorithm may also output the identified at least one of errors, and weaknesses in the same formats as the pre-trained artificial-intelligence circuit generation algorithm provides the circuit designs.

[0545] The pre-trained artificial-intelligence circuit verifying algorithm may e.g., include respective comments or markers in the files provided by the pre-trained artificial-intelligence circuit generation algorithm. Such comments or markers may indicate the detected at least one of errors, and weaknesses.

[0546] Instead of exchanging the purely text-based indications as exemplarily provided above, the data exchange regarding the identified at least one of errors, and weaknesses may, therefore, be based on the construction or design documents that may also be used in other computer programs for viewing, modifying, and producing the circuit design.

[0547] In another embodiment of the present disclosure, which can be combined with all other embodiments of the circuit development device mentioned above or below, the pre-trained artificial-intelligence circuit verifying algorithm may be pre-trained using at least one of manuals of measurement application devices, datasheets of measurement application devices, application notes regarding measurement application devices, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0548] The pre-trained artificial-intelligence circuit verifying algorithm needs to be trained on data regarding circuit design. Of course, in embodiments, the pre-trained artificial-intelligence circuit verifying algorithm may be trained on information regarding multiple different circuit designs.

[0549] Usually, a wide selection of training data is already available from the manufacturers of respective electronic circuits and other online sources, like forums. Such training data may be provided e.g., in the form of manuals, datasheets, and application notes that a manufacturer of a circuit may publish, or in the form of forum discussions and videos.

[0550] In embodiments, the pre-trained artificial-intelligence circuit verifying algorithm may be pre-trained based on general data that is not specific to any circuit.

[0551] The training data relating specifically to electric or electronic circuits may then be used to perform a fine-tuning of the pre-trained artificial-intelligence circuit verifying algorithm. Such a fine-tuning will then allow the pre-trained artificial-intelligence circuit verifying algorithm to adequately identify the at least one of errors, and weaknesses.

[0552] BRIEF DESCRIPTION OF THE DRAWINGS

[0553] For a more complete understanding of the present disclosure and advantages thereof, reference is now made to the following description taken in conjunction with the accompanying drawings. The disclosure is explained in more detail below using exemplary embodiments which are specified in the schematic figures of the drawings, in which:

[0554] Figure 1 shows a block diagram of an embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0555] Figure 2 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0556] Figure 3 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0557] Figure 4 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0558] Figure 5 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0559] Figure 6 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0560] Figure 7 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0561] Figure 8 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0562] Figure 9 shows a block diagram of another embodiment of a measurement application device control unit according to the first aspect of the present disclosure;

[0563] Figure 10 shows a block diagram of an embodiment of a measurement application device according to the first aspect of the present disclosure;

[0564] Figure 11 shows a flow diagram of an embodiment of a method according to the first aspect of the present disclosure;

[0565] Figure 12 shows a block diagram of an oscilloscope that may be used with an embodiment of a measurement application device control unit, or method according to any one of the embodiments of the first aspect of the present disclosure;

[0566] Figure 13 shows a block diagram of another oscilloscope that may be used with an embodiment of a measurement application device control unit, or method according to any one of the embodiments of the first aspect of the present disclosure;

[0567] Figure 14 shows a block diagram of an embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0568] Figure 15 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0569] Figure 16 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0570] Figure 17 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0571] Figure 18 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0572] Figure 19 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0573] Figure 20 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0574] Figure 21 shows a block diagram of another embodiment of a measurement application processing device according to the second aspect of the present disclosure;

[0575] Figure 22 shows a block diagram of a measurement application according to the second aspect of the present disclosure;

[0576] Figure 23 shows a flow diagram of an embodiment of a method according to the second aspect of the present disclosure;

[0577] Figure 24 shows a block diagram of an embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0578] Figure 25 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0579] Figure 26 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0580] Figure 27 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0581] Figure 28 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0582] Figure 29 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0583] Figure 30 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0584] Figure 31 shows a block diagram of another embodiment of a measurement application device control unit according to the third aspect of the present disclosure;

[0585] Figure 32 shows a block diagram of an embodiment of a measurement application device according to the third aspect of the present disclosure;

[0586] Figure 33 shows a flow diagram of an embodiment of a method according to the third aspect of the present disclosure;

[0587] Figure 34 shows a block diagram of an embodiment of a measurement application processing device according to the fourth aspect of the present disclosure;

[0588] Figure 35 shows a block diagram of another embodiment of a measurement application processing device according to the fourth aspect of the present disclosure;

[0589] Figure 36 shows a block diagram of another embodiment of a measurement application processing device according to the fourth aspect of the present disclosure;

[0590] Figure 37 shows a block diagram of another embodiment of a measurement application processing device according to the fourth aspect of the present disclosure;

[0591] Figure 38 shows a block diagram of an embodiment of a measurement application device according to the fourth aspect of the present disclosure;

[0592] Figure 39 shows a flow diagram of an embodiment of a method according to the fourth aspect of the present disclosure;

[0593] Figure 40 shows a block diagram of an embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0594] Figure 41 shows a block diagram of another embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0595] Figure 42 shows a block diagram of another embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0596] Figure 43 shows a block diagram of another embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0597] Figure 44 shows a block diagram of another embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0598] Figure 45 shows a block diagram of another embodiment of a circuit development device according to the fifth aspect of the present disclosure;

[0599] Figure 46 shows a flow diagram of an embodiment of a method according to the fifth aspect of the present disclosure; and

[0600] Figure 47 shows a flow diagram of another embodiment of a method according to the fifth aspect of the present disclosure.

[0601] In the figures like reference signs denote like elements unless stated otherwise.DETAILED DESCRIPTION OF THE DRAWINGSFigures regarding the first aspect:

[0602] Figure 1 shows a block diagram a measurement application device control unit 100.

[0603] The measurement application device control unit 10100 comprises a text-based input interface 10101 that receives text-based user requests 10102 regarding a measurement application device. The measurement application device control unit 10100 further comprises a pretrained artificial-intelligence algorithm 10103 coupled to the text-based input interface 10101 that generates response data 10104 regarding the measurement application device based on the textbased user requests 10102. The measurement application device control unit 10100 further comprises an output interface 10105 coupled to the pre-trained artificial-intelligence algorithm 10103,and configured to output the response data 10104. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10200.

[0604] The measurement application device control unit 10100 may be implemented as a dedicated device e.g., laboratory equipment, similar to other laboratory measurement application devices, like oscilloscopes, network analyzers, signal generators, and the like. Such a measurement application device control unit 10100 may comprise housing, a user interface, like a display or touchscreen, buttons, knobs, a mouse, a keyboard, or the like, as text-based input interface 10101 , a respective processor or processing element, and respective data interfaces, like a network interface, possibly also as text-based input interface 10101. The measurement application device control unit 10100 may also be integrated into any type of laboratory measurement application device.

[0605] In embodiments, the measurement application device control unit 10100 may be implemented on a computer or server, that executes a computer-program product that comprises instructions that when executed by the computer or server, especially a processor in the computer or server, causes the computer or server to perform the computer-implemented method of the first aspect according to the present disclosure. Such a computer or server may be located remotely to other measurement application devices and coupled to other measurement application devices via a network connection.

[0606] In embodiments, the pre-trained artificial-intelligence algorithm 10103 may be pretrained using any combination of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0607] Figure 2 shows a block diagram of another embodiment of a measurement application device control unit 10200. The measurement application device control unit 10200 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10200 comprises a text-based input interface 10201 that receives text-based user requests 10202 regarding a measurement application device. The measurement application device control unit 10200 further comprises a pre-trained artificial-intelligence algorithm 10203 coupled to the text-based input interface 10201 that generates response data 10204 regarding the measurement application device based on the text-based user requests 10202. The measurement application device control unit 10200 further comprises an output interface 10205 coupled to the pretrained artificial-intelligence algorithm 10203, and configured to output the response data 10204.The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10200.

[0608] The measurement application device control unit 10200 further comprises an audio input interface 10228. The audio input interface 10228 is coupled to a speech recognition unit 10230, and the speech recognition unit 10230 is coupled to the text-based input interface 10201.

[0609] The audio input interface 10228 may receive spoken language requests 10229 e.g., from a user. It is understood, that the audio input interface 10228 may comprise e.g., a microphone for recording the spoken language requests 10229. In embodiments, the audio input interface10228 may comprise a data interface for receiving the spoken language requests 10229 in digital form that are recorded elsewhere. A combination of both is possible.

[0610] The speech recognition unit 10230 may then convert the spoken language requests10229 into text-based requests 10231. Such text-based requests 10231 may then be forwarded to the text-based input interface 10201 , as are the text-based user requests 10202. The text-based input interface 10201 may then process the text-based requests 10231 like any other text-based user requests 10202.

[0611] The speech recognition unit 10230 may comprise any type of adequate element. Exemplary embodiments of the speech recognition unit 10230 may e.g., be based on computer-implemented algorithms, especially respective artificial-intelligence based computer-implemented algorithms.

[0612] Figure 3 shows a block diagram of a measurement application device control unit 10300. The measurement application device control unit 10300 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10300 comprises a text-based input interface 10301 that receives text-based user requests 10302 regarding a measurement application device. The measurement application device control unit 10300 further comprises a pre-trained artificial-intelligence algorithm 10303 coupled to the textbased input interface 10301 that generates response data 10304 regarding the measurement application device based on the text-based user requests 10302. The measurement application device control unit 10300 further comprises an output interface 10305 coupled to the pre-trained artificial-intelligence algorithm 10303, and configured to output the response data 10304. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10300.

[0613] In the measurement application device control unit 10300, the pre-trained artificial-intelligence algorithm 10303 comprises a large language model 10335 for implementing the functionality of the pre-trained artificial-intelligence algorithm 10303.

[0614] The large language model 10335 may be based on any adequate type of algorithm, like a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0615] Figure 4 shows a block diagram of a measurement application device control unit 10400. The measurement application device control unit 10400 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10400 comprises a text-based input interface 10401 that receives text-based user requests 10402 regarding a measurement application device. The measurement application device control unit 10400 further comprises a pre-trained artificial-intelligence algorithm 10403 coupled to the textbased input interface 10401 that generates response data 10404 regarding the measurement application device based on the text-based user requests 10402. The measurement application device control unit 10400 further comprises an output interface 10405 coupled to the pre-trained artificial-intelligence algorithm 10403, and configured to output the response data 10404. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10400.

[0616] The measurement application device control unit 10400 further comprises a user input interface 10438. The user input interface 10438 is depicted only exemplarily as the front of a measurement application device with four buttons 10440-1 to 10440-n, two rotary knobs 10441-1, and 10441-2, and with a touchscreen 10442. The shown user input interface 10438 is just exemplarily shown. In other embodiments, any other type of input elements and / or arrangement may be chosen. Further, in other embodiments, the user input interface 10438 may comprise a software-based interface e.g., of a locally executed application or a respective website served by a server-implementation of the measurement application device control unit 10400.

[0617] With the user input interface 10438, a user may provide user input 10443 to the pretrained artificial-intelligence algorithm 10403 that refers to controlling a measurement application device.

[0618] The pre-trained artificial-intelligence algorithm 10403 is, consequently, provided with the text-based user requests 10402, and the user input 10443 at the same time.

[0619] The user input 10443 may e.g., refer to a user pointing to a specific element of a display of the respective measurement device on the touchscreen 10442. At the same time, the user may provide a text-based user requests 10402, like “what is this?”.

[0620] The pre-trained artificial-intelligence algorithm 10403 may then combine the text-based user requests 10402 with the user input 10443, and provide a respective response data 10404.

[0621] In embodiments, the user input 10443 may be provided to the pre-trained artificial-intelligence algorithm 10403 as a pre-conditioning text-based request, as will be explained in more detail below with regard to figure 6. To this end, the pre-conditioner may be coupled to the user input interface 10438 in order to generate the pre-conditioning text-based request. Such a pre-conditioning text-based request may e.g., comprise a text like “The user is pointing to the label of the Y axis of the diagram shown on the display of the oscilloscope, and asks:”, wherein the original textbased user requests 10402 may be provided directly after the pre-conditioning text-based request.

[0622] The pre-trained artificial-intelligence algorithm 10403 may then generate a respective response in the response data 10404. In case of the above example, the response data 10404 may comprise a response to the user’s question. In case of other text-based user requests 10402, the response data 10404 may comprise the respective content as requested in the text-based user requests 10402.

[0623] Figure 5 shows a block diagram of a measurement application device control unit 10500. The measurement application device control unit 10500 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10500 comprises a text-based input interface 10501 that receives text-based user requests 10502 regarding a measurement application device. The measurement application device control unit 10500 further comprises a pre-trained artificial-intelligence algorithm 10503 coupled to the textbased input interface 10501 that generates response data 10504 regarding the measurement application device based on the text-based user requests 10502. The measurement application device control unit 10500 further comprises an output interface 10505 coupled to the pre-trained artificial-intelligence algorithm 10503, and configured to output the response data 10504. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10500.

[0624] The measurement application device control unit 10500 further comprises a translator 10546. The translator 10546 is arranged between the text-based input interface 10501 , and the pre-trained artificial-intelligence algorithm 10503.

[0625] The translator 10546 may translate text-based user requests 10502 in a language that the pre-trained artificial-intelligence algorithm 10503 is not trained to operate on into translated text-based user requests 10546 in a language that the pre-trained artificial-intelligence algorithm 10503 may operate on.

[0626] The translator 10546 may also identify the language of the original text-based user requests 10502, and may pass any text-based user requests 10502 directly to the pre-trained artificial-intelligence algorithm 10503 that are provided in a language that the pre-trained artificial-intelligence algorithm 10503 may operate on.

[0627] Alternatively, the text-based input interface 10501 may be adapted to identify the language of the text-based user requests 10502, and may directly provide a text-based user request 10502 to the pre-trained artificial-intelligence algorithm 10503 that is provided in a language that the pre-trained artificial-intelligence algorithm 10503 may operate on.

[0628] Figure 6 shows a block diagram of a measurement application device control unit 10600. The measurement application device control unit 10600 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10600 comprises a text-based input interface 10601 that receives text-based user requests 10602 regarding a measurement application device. The measurement application device control unit 10600 further comprises a pre-trained artificial-intelligence algorithm 10603 coupled to the textbased input interface 10601 that generates response data 10604 regarding the measurement application device based on the text-based user requests 10602. The measurement application device control unit 10600 further comprises an output interface 10605 coupled to the pre-trained artificial-intelligence algorithm 10603, and configured to output the response data 10604. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10600.

[0629] The measurement application device control unit 10600 further comprises a pre-condi- tioner 10648. The pre-conditioner 10648 is coupled to the text-based input interface 10601 and may provide a pre-conditioning text-based request 10649 to the text-based input interface 10601.

[0630] The pre-conditioning text-based request 10649 serves for pre-conditioning the pretrained artificial-intelligence algorithm 10603, and may be provided to the pre-trained artificial-intelligence algorithm 10603 prior to providing the text-based user requests 10602 to the pre-trained artificial-intelligence algorithm 10603.

[0631] In embodiments, the text-based user requests 10602 may be provided to the pre-condi- tioner 10648 instead of the text-based input interface 10601. The pre-conditioner 10648 may thencombine the pre-conditioning text-based request 10649 with the received text-based user requests 10602.

[0632] Figure 7 shows a block diagram of a measurement application device control unit 10700. The measurement application device control unit 10700 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10700 comprises a text-based input interface 10701 that receives text-based user requests 10702- 1 , 10702-2 regarding a measurement application device. The measurement application device control unit 10700 further comprises a pre-trained artificial-intelligence algorithm 10703 coupled to the text-based input interface 10701 that generates response data 10704 regarding the measurement application device based on the text-based user requests 10702. The measurement application device control unit 10700 further comprises an output interface 10705 coupled to the pretrained artificial-intelligence algorithm 10703, and configured to output the response data 10704. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10700.

[0633] The measurement application device control unit 10700 receives a first type of textbased user requests 10702-1 that comprise user requests regarding the usage of the measurement application device. Further, the measurement application device control unit 10700 receives a second type of text-based user requests 10702-2 that comprise user requests regarding the control of the measurement application device.

[0634] Accordingly, the pre-trained artificial-intelligence algorithm 10703 will provide usage response data 10752, and control response data 10753 in the response data 10704.

[0635] The measurement application device control unit 10700 further comprises a code generator 10754. The code generator 10754 may receive the control response data 10753 or other respective indications from the pre-trained artificial-intelligence algorithm 10703 and may generate respective configuration and control commands 10755 in any adequate scripting or programming language.

[0636] In embodiments, the code generator 10754 may be provided as function of, or as element of, or included in the pre-trained artificial-intelligence algorithm 10703.

[0637] Figure 8 shows a block diagram of a measurement application device control unit 10800. The measurement application device control unit 10800 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10800 comprises a text-based input interface 10801 that receives text-based user requests 10802 regarding a measurement application device. The measurement application device control unit10800 further comprises a pre-trained artificial-intelligence algorithm 10803 that generates response data 10804 regarding the measurement application device based on the text-based user requests 10802. The measurement application device control unit 10800 further comprises an output interface 10805 coupled to the pre-trained artificial-intelligence algorithm 10803, and configured to output the response data 10804. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10800.

[0638] The measurement application device control unit 10800 further comprises a complexity estimator 10858. The complexity estimator 10858 serves for estimating or calculating a complexity of the text-based user requests 10802. In the measurement application device control unit 10800 the complexity is calculated by unit C. The calculation of the complexity may e.g., be performed based on a length of the text-based user requests 10802, or the number of main and / or subordinate clauses, or a combination of both.

[0639] The complexity may then be compared to a complexity threshold 10860. If the complexity is higher than the threshold 10860, the complexity estimator 10858 may, instead of providing the text-based user requests 10802 to the local pre-trained artificial-intelligence algorithm 10803 in the measurement application device control unit 10800, provide the text-based user requests 10802 to an external pre-trained artificial-intelligence algorithm 10859. The external pretrained artificial-intelligence algorithm 10859 may be provided in a cloud system, or a server, or any other element, and may be coupled to the measurement application device control unit 10800 e.g., the complexity estimator 10858 via a data network. The complexity estimator 10858 may request a user consent 10861 via a user interface 10862 for providing the provide the text-based user requests 10802 to the external pre-trained artificial-intelligence algorithm 10859.

[0640] As optional element, a request anonymizer 10863 is provided between the measurement application device control unit 10800, and the external pre-trained artificial-intelligence algorithm 10859. The request anonymizer 10863 may serve to anonymize the text-based user requests 10802 prior to providing the text-based user requests 10802 to the external pre-trained artificialintelligence algorithm 10859.

[0641] Figure 9 shows a block diagram of a measurement application device control unit 10900. The measurement application device control unit 10900 is based on the measurement application device control unit 10100. Therefore, the measurement application device control unit 10900 comprises a text-based input interface 10901 that receives text-based user requests 10902 regarding a measurement application device. The measurement application device control unit 10900 further comprises a pre-trained artificial-intelligence algorithm 10903 coupled to the text-based input interface 10901 that generates response data 10904 regarding the measurement application device based on the text-based user requests 10902. The measurement application device control unit 10900 further comprises an output interface 10905 coupled to the pre-trained artificial-intelligence algorithm 10903, and configured to output the response data 10904. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application device control unit 10900.

[0642] The measurement application device control unit 10900 further comprises a text-based request generator 10970. The text-based request generator 10970 may receive a non-textual description 10971 , like an image, a video, or any other non-text-based, especially visual, description. Such a non-textual description may comprise e.g., an image or a video of a measurement application setup.

[0643] The text-based request generator 10970 may then generate a respective text-based user request 10902 based on the non-textual description 10971.

[0644] The text-based request generator 10970 may comprise any adequate algorithm, like an artificial-intelligence based algorithm, that may convert the non-textual description 10971 into a text-based user request 10902.

[0645] Figure 10 shows a measurement application device 11075. The measurement application device 11075 comprises a measurement application device control unit 11000, and a measurement application device controller 11076.

[0646] The measurement application device controller 11076 is coupled to the text-based input interface 11001 to provide text-based user requests 11002 to the measurement application device control unit 11000. The measurement application device controller 11076 is further coupled to the output interface 11005 in order to receive response data 11004 from the measurement application device control unit 11000.

[0647] The measurement application device controller 11076, and the measurement application device control unit 11000 are provided as separate devices, or elements in the measurement application device 11075.

[0648] In other embodiments, the measurement application device control unit 11000 may be provided as a function of, or addition to the functionality of the measurement application device controller 11076.

[0649] Figure 11 shows a flow diagram of a computer-implemented method for controlling a measurement application device. The method comprises receiving S1 text-based user requests102 regarding a measurement application device, generating S2 response data regarding the measurement application device based on the text-based user requests with a pre-trained artificialintelligence algorithm, and outputting S2 the response data.

[0650] The pre-trained artificial-intelligence algorithm may be pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes regarding electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0651] The pre-trained artificial-intelligence algorithm may comprise a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0652] The method may further comprise receiving user input, wherein the pre-trained artificial-intelligence algorithm may generate the response data regarding the measurement application device based on the text-based user requests and the user input. The user input may e.g., be provided via at least one of a button, a switch, a knob, a touchscreen, a keyboard, a mouse, a camera, and a gesture sensor.

[0653] The method may further comprise translating text-based user requests in a language that the pre-trained artificial-intelligence algorithm is not trained to operate on into translated textbased user requests in a language that the pre-trained artificial-intelligence algorithm is trained to operate on, the translated text-based user requests may then be provided to the pre-trained artificial-intelligence algorithm.

[0654] The method may further comprises providing a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0655] In embodiments, the text-based user requests may refer to the usage of the measurement application device. Usage response data may, therefore, be generated by the pre-trained artificial-intelligence algorithm that comprises a respective explanation regarding the usage of the measurement application device.

[0656] The text-based user requests may refer to the control of the measurement application device. Control response data may, therefore, be generated by the pre-trained artificial-intelligence algorithm that comprises respective control commands regarding the measurement application de-vice. Further, configuration and control commands may be generated for the measurement application device in a predetermined control or programming language, and may be provided to a controller of the measurement application device.

[0657] The pre-trained artificial-intelligence algorithm may be locally executed on the measurement application device. The method may further comprise estimating the complexity of the text-based user requests, and forwarding the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold. In such an embodiment, a user consent may be requested via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm. The text-based user requests may be provided indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

[0658] In an embodiment, the method may further comprise receiving at least one of an image, a video, or a non-textual description of at least one of measurement application device, a device under test, and a measurement application setup, generating text-based user requests based on the at least one of the image or the video, and providing the generated text-based user requests to the pre-trained artificial-intelligence algorithm.

[0659] Figure 12 shows a block diagram of an oscilloscope OSC1 that may be used with, or implement an embodiment of a measurement application device or method according to the first aspect of the present disclosure.

[0660] The oscilloscope OSC1 comprises a housing HO that accommodates four measurement inputs MIP1 , MIP2, MIP3, MIP4 that are coupled to a signal processor SIP for processing any measured signals. The signal processor SIP is coupled to a display DISP1 for displaying the measured signals to a user.

[0661] Although not explicitly shown, it is understood, that the oscilloscope OSC1 may also comprise signal outputs that may also be coupled to the differential measurement probe. Such signal outputs may for example serve to output calibration signals. Such calibration signals allow calibrating the measurement setup prior to performing any measurement. The process of calibrating and correcting any measurement signals based on the calibration may also be called de-embed- ding and may comprise applying respective algorithms on the measured signals.

[0662] In the oscilloscope OSC1 the signal processor SIP or an additional processing element may perform the function of the measurement application device control unit or the method according to the present disclosure, or may implement the the measurement application device control unit, or method. Of course, a communication interface may be provided in the oscilloscope OSC1 for communication with other measurement application devices.

[0663] Figure 13 shows a block diagram of an oscilloscope OSC that may be used with, or implement an embodiment of a measurement application device or method according to the first aspect of the present disclosure. The oscilloscope OSC is implemented as a digital oscilloscope. However, the present disclosure may also be implemented with any other type of oscilloscope.

[0664] The oscilloscope OSC exemplarily comprises five general sections, the vertical system VS, the triggering section TS, the horizontal system HS, the processing section PS and the display DISP. It is understood, that the partitioning into five general sections is a logical partitioning and does not limit the placement and implementation of any of the elements of the oscilloscope OSC in any way.

[0665] The vertical system VS mainly serves for offsetting, attenuating and amplifying a signal to be acquired. The signal may for example be modified to fit in the available space on the display DISP or to comprise a vertical size as configured by a user.

[0666] To this end, the vertical system VS comprises a signal conditioning section SC with an attenuator ATT and a digital-to-analog-converter DAC that are coupled to an amplifier AMP1. The amplifier AMP1 is coupled to a filter FI1 , which in the shown example is provided as a low pass filter. The vertical system VS also comprises an analog-to-digital converter ADC that receives the output from the filter FI1 and converts the received analog signal into a digital signal.

[0667] The attenuator ATT and the amplifier AMP1 serve to scale the amplitude of the signal to be acquired to match the operation range of the analog-to-digital converter ADC. The digital-to- analog-converter DAC serves to modify the DC component of the input signal to be acquired to match the operation range of the analog-to-digital converter ADC. The filter FI1 serves to filter out unwanted high frequency components of the signal to be acquired.

[0668] The triggering section TS operates on the signal as provided by the amplifier AMP. The triggering section TS comprises a filter FI2, which in this embodiment is implemented as a low pass filter. The filter FI2 is coupled to a trigger system TS1.

[0669] The triggering section TS serves to capture predefined signal events and allows the horizontal system HS to e.g., display a stable view of a repeating waveform, or to simply display waveform sections that comprise the respective signal event. It is understood, that the predefined signal event may be configured by a user via a user input of the oscilloscope OSC.

[0670] Possible predefined signal events may for example include, but are not limited to, when the signal crosses a predefined trigger threshold in a predefined direction i.e. , with a rising or falling slope. Such a trigger condition is also called an edge trigger. Another trigger condition is called“glitch triggering” and triggers, when a pulse occurs in the signal to be acquired that has a width that is greater than or less than a predefined amount of time.

[0671] In order to allow an exact matching of the trigger event and the waveform that is shown on the display DISP, a common time base may be provided for the analog-to-digital converter ADC and the trigger system TS1.

[0672] It is understood, that although not explicitly shown, the trigger system TS1 may comprise at least one of configurable voltage comparators for setting the trigger threshold voltage, fixed voltage sources for setting the required slope, respective logic gates like e.g., a XOR gate, and FlipFlops to generate the triggering signal.

[0673] The triggering section TS is exemplarily provided as an analog trigger section. It is understood, that the oscilloscope OSC may also be provided with a digital triggering section. Such a digital triggering section will not operate on the analog signal as provided by the amplifier AMP but will operate on the digital signal as provided by the analog-to-digital converter ADC.

[0674] A digital triggering section may comprise a processing element, like a processor, a DSP, a CPLD, an ASIC or an FPGA to implement digital algorithms that detect a valid trigger event.

[0675] The horizontal system HS is coupled to the output of the trigger system TS1 and mainly serves to position and scale the signal to be acquired horizontally on the display DISP.

[0676] The oscilloscope OSC further comprises a processing section PS that implements digital signal processing and data storage for the oscilloscope OSC. The processing section PS comprises an acquisition processing element ACP that is couple to the output of the analog-to-digital converter ADC and the output of the horizontal system HS as well as to a memory MEM and a post processing element PPE.

[0677] The acquisition processing element ACP manages the acquisition of digital data from the analog-to-digital converter ADC and the storage of the data in the memory MEM. The acquisition processing element ACP may for example comprise a processing element with a digital interface to the analog-to-digital converter ADC2 and a digital interface to the memory MEM. The processing element may for example comprise a microcontroller, a DSP, a CPLD, an ASIC or an FPGA with respective interfaces. In a microcontroller or DSP, the functionality of the acquisition processing element ACP may be implemented as computer readable instructions that are executed by a CPU. In a CPLD or FPGA the functionality of the acquisition processing element ACP may be configured in to the CPLD or FPGA opposed to software being executed by a processor.

[0678] The processing section PS further comprises a communication processor CP and a communication interface COM.

[0679] The communication processor CP may be a device that manages data transfer to and from the oscilloscope OSC. The communication interface COM for any adequate communication standard like for example, Ethernet, WIFI, Bluetooth, NFC, an infra-red communication standard, and a visible-light communication standard.

[0680] The communication processor CP is coupled to the memory MEM and may use the memory MEM to store and retrieve data.

[0681] Of course, the communication processor CP may also be coupled to any other element of the oscilloscope OSC to retrieve device data or to provide device data that is received from the management server.

[0682] The post processing element PPE may be controlled by the acquisition processing element ACP and may access the memory MEM to retrieve data that is to be displayed on the display DISP. The post processing element PPE may condition the data stored in the memory MEM such that the display DISP may show the data e.g., as waveform to a user. The post processing element PPE may also realize analysis functions like cursors, waveform measurements, histograms, or math functions.

[0683] The display DISP controls all aspects of signal representation to a user, although not explicitly shown, may comprise any component that is required to receive data to be displayed and control a display device to display the data as required.

[0684] It is understood, that even if it is not shown, the oscilloscope OSC may also comprise a user interface for a user to interact with the oscilloscope OSC. Such a user interface may comprise dedicated input elements like for example knobs and switches. At least in part the user interface may also be provided as a touch sensitive display device.

[0685] In the oscilloscope OSC, any one of the processing elements in the processing section PS or an additional processing element may perform the function of the measurement application device control unit, or the method according to the first aspect of the present disclosure.

[0686] It is understood, that all elements of the oscilloscope OSC that perform digital data processing may be provided as dedicated elements. As alternative, at least some of the above-described functions may be implemented in a single hardware element, like for example a microcontroller, DSP, CPLD or FPGA. Generally, the above-describe logical functions may be implemented in any adequate hardware element of the oscilloscope OSC and not necessarily need to be partitioned into the different sections explained above.Figures regarding the second aspect:

[0687] Figure 14 shows a block diagram of a measurement application processing device 20100. The measurement application processing device 20100 comprises a text-based input interface 20101 that receives text-based user requests 20102 regarding a measurement application. The measurement application processing device 20100 further comprises a pre-trained artificialintelligence algorithm 20103 coupled to the text-based input interface 20101 , and configured to generate response data 20104 regarding the measurement application based on the text-based user requests 20102. The measurement application processing device 20100 further comprise an output interface 20105 coupled to the pre-trained artificial-intelligence algorithm 20103, and configured to output the response data 20104. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20100.

[0688] In embodiments, the measurement application processing device 20100 may be implemented on a computer or server, that executes a computer-program product that comprises instructions that when executed by the computer or server, especially a processor in the computer or server, causes the computer or server to perform the computer-implemented method, or the function of the measurement application processing device 20100 of the second aspect according to the present disclosure. Such a computer or server may be located remotely to measurement application devices and coupled to measurement application devices via a network connection.

[0689] In embodiments, the pre-trained artificial-intelligence algorithm 20103 may be pretrained using any combination of manuals regarding one or more measurement application devices or measurement applications, datasheets regarding one or more measurement application devices or measurement applications, application notes regarding one or more measurement application devices or measurement applications, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0690] Figure 15 shows a block diagram of a measurement application processing device 20200. The measurement application processing device 20200 is based on the measurement application processing device 20100. The measurement application processing device 20200 comprises a text-based input interface 20201 that receives text-based user requests 20202 regarding a measurement application. The measurement application processing device 20200 further comprises a pre-trained artificial-intelligence algorithm 20203 coupled to the text-based input interface 20201 , and configured to generate response data 20204 regarding the measurement application based on the text-based user requests 20202. The measurement application processing device 20200 further comprise an output interface 20205 coupled to the pre-trained artificial-intelligencealgorithm 20203, and configured to output the response data 20204. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20200.

[0691] The measurement application processing device 20200 further comprises an audio input interface 20228. The audio input interface 20228 is coupled to a speech recognition unit 20230, and the speech recognition unit 20230 is coupled to the text-based input interface 20201.

[0692] The audio input interface 20228 may receive spoken language requests 20229 e.g., from a user. It is understood, that the audio input interface 20228 may comprise e.g., a microphone for recording the spoken language requests 20229. In embodiments, the audio input interface20228 may comprise a data interface for receiving the spoken language requests 20229 in digital form that are recorded elsewhere. A combination of both is possible.

[0693] The speech recognition unit 20230 may then convert the spoken language requests20229 into text-based requests 20231. Such text-based requests 20231 may then be forwarded to the text-based input interface 20201 , as are the text-based user requests 20202. The text-based input interface 20201 may then process the text-based requests 20231 like any other text-based user requests 20202.

[0694] The speech recognition unit 20230 may comprise any type of adequate element. Exemplary embodiments of the speech recognition unit 20230 may e.g., be based on computer-implemented algorithms or methods, especially respective artificial-intelligence based computer-implemented algorithms.

[0695] Figure 16 shows a block diagram of a measurement application processing device 20300. The measurement application processing device 20300 is based on the measurement application processing device 20100. The measurement application processing device 20300 comprises a text-based input interface 20301 that receives text-based user requests 20302 regarding a measurement application. The measurement application processing device 20300 further comprises a pre-trained artificial-intelligence algorithm 20303 coupled to the text-based input interface 20301 , and configured to generate response data 20304 regarding the measurement application based on the text-based user requests 20302. The measurement application processing device 20300 further comprise an output interface 20305 coupled to the pre-trained artificial-intelligence algorithm 20303, and configured to output the response data 20304. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20300.

[0696] In the measurement application processing device 20300, the pre-trained artificial-intelligence algorithm 20303 comprises a large language model 20335 for implementing the functionality of the pre-trained artificial-intelligence algorithm 20303.

[0697] The large language model 20335 may be based on any adequate type of algorithm, like a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0698] Figure 17 shows a block diagram of a measurement application processing device 20400. The measurement application processing device 20400 is based on the measurement application processing device 20100. The measurement application processing device 20400 comprises a text-based input interface 20401 that receives text-based user requests 20402 regarding a measurement application. The measurement application processing device 20400 further comprises a pre-trained artificial-intelligence algorithm 20403 coupled to the text-based input interface 20401 , and configured to generate response data 20404 regarding the measurement application based on the text-based user requests 20402. The measurement application processing device 20400 further comprise an output interface 20405 coupled to the pre-trained artificial-intelligence algorithm 20403, and configured to output the response data 20404. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20400.

[0699] The pre-trained artificial-intelligence algorithm 20403 of the measurement application processing device 20400 provides the response data 20404 in a visual form.

[0700] To this end, the pre-trained artificial-intelligence algorithm 20403 provides a block diagram 20438. In the response data 20404.

[0701] The exemplary block diagram 20438 shows two measurement application devices. The left measurement application device comprises four ports arranged in a row, generates two test signals, and provides one of the test signals from the leftmost port, and one of the test signals from the rightmost port to a device under test. The right measurement application device also comprises four ports arranged in a row, and acquires one signal from the device under test with the second port from the left.

[0702] Since the block diagram 20438 is only exemplary, no further indications are provided. In other embodiments, the block diagram 20438 may comprise further information, like a textual description of the single elements, and a description of the steps that need to be performed to setup the measurement application as it is shown in the block diagram 20438. In other embodiments, the number of measurement devices, connections, and devices under test may be different, andother types, or additional types of elements may be provided in the measurement application, and the block diagram 20438.

[0703] Figure 18 shows a block diagram of a measurement application processing device 20500. The measurement application processing device 20500 is based on the measurement application processing device 20100. The measurement application processing device 20500 comprises a text-based input interface 20501-1, 20501-2 that receives text-based user requests 20502 regarding a measurement application. The measurement application processing device 20500 further comprises a pre-trained artificial-intelligence algorithm 20503 coupled to the text-based input interface 20501 , and configured to generate response data 20504 regarding the measurement application based on the text-based user requests 20502. The measurement application processing device 20500 further comprise an output interface 20505 coupled to the pre-trained artificial-intelligence algorithm 20503, and configured to output the response data 20504. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20500.

[0704] The measurement application device control unit 20500 receives a first type of textbased user requests 20502-1 that comprise user requests regarding the setup of the measurement application. Further, the measurement application device control unit 20500 receives a second type of text-based user requests 20502-2 that comprise user requests regarding the control of the measurement application, or single measurement application devices in the measurement application.

[0705] Accordingly, the pre-trained artificial-intelligence algorithm 20503 will provide set-up response data 20540, and control response data 20541 in the response data 20504.

[0706] The measurement application device control unit 20500 further comprises a code generator 20542. The code generator 20542 may receive the control response data 20541 or other respective indications from the pre-trained artificial-intelligence algorithm 20503 and may generate respective configuration and control commands 20543 in any adequate scripting or programming language.

[0707] In embodiments, the code generator 20542 may be provided as function of, or as element of, or included in the pre-trained artificial-intelligence algorithm 20503.

[0708] Figure 19 shows a block diagram of a measurement application processing device 20600. The measurement application processing device 20600 is based on the measurement application processing device 20100. The measurement application processing device 20600 comprises a text-based input interface 20601 that receives text-based user requests 20602 regarding ameasurement application. The measurement application processing device 20600 further comprises a pre-trained artificial-intelligence algorithm 20603 coupled to the text-based input interface 20601 , and configured to generate response data 20604 regarding the measurement application based on the text-based user requests 20602. The measurement application processing device 20600 further comprise an output interface 20605 coupled to the pre-trained artificial-intelligence algorithm 20603, and configured to output the response data 20604. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20600.

[0709] The measurement application device control unit 20600 further comprises a pre-condi- tioner 20645. The pre-conditioner 20645 is coupled to the text-based input interface 20601 and may provide a pre-conditioning text-based request 20646 to the text-based input interface 20601.

[0710] The pre-conditioning text-based request 20646 serves for pre-conditioning the pretrained artificial-intelligence algorithm 20603, and may be provided to the pre-trained artificial-intelligence algorithm 20603 prior to providing the text-based user requests 20602 to the pre-trained artificial-intelligence algorithm 20603.

[0711] In embodiments, the text-based user requests 20602 may be provided to the pre-condi- tioner 20645 instead of the text-based input interface 20601. The pre-conditioner 20645 may then combine the pre-conditioning text-based request 20646 with the received text-based user requests 20602.

[0712] Figure 20 shows a block diagram of a measurement application processing device 20700. The measurement application processing device 20700 is based on the measurement application processing device 20100. The measurement application processing device 20700 comprises a text-based input interface 20701 that receives text-based user requests 20702 regarding a measurement application. The measurement application processing device 20700 further comprises a pre-trained artificial-intelligence algorithm 20703 coupled to the text-based input interface 20701 , and configured to generate response data 20704 regarding the measurement application based on the text-based user requests 20702. The measurement application processing device 20700 further comprise an output interface 20705 coupled to the pre-trained artificial-intelligence algorithm 20703, and configured to output the response data 20704. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20700.

[0713] In the measurement application processing device 20700 the pre-trained artificial-intelligence algorithm 20703 may generate an augmented text-based user request 20750. This augmented text-based user request 20750 may be provided to a user via the output interface 20705. The user may then review the augmented text-based user request 20750, and provide a respective confirmation 20751 to the measurement application processing device 20700, especially via the text-based input interface 20701.

[0714] After receiving the confirmation 20751 , the pre-trained artificial-intelligence algorithm 20703 may generate the response data 20704, and output the response data 20704 via the output interface 20705.

[0715] Figure 21 shows a block diagram of a measurement application processing device 20800. The measurement application processing device 20800 is based on the measurement application processing device 20100. The measurement application processing device 20800 comprises a text-based input interface 20801 that receives text-based user requests 20802 regarding a measurement application. The measurement application processing device 20800 further comprises a pre-trained artificial-intelligence algorithm 20803 coupled to the text-based input interface 20801 , and configured to generate response data 20804 regarding the measurement application based on the text-based user requests 20802. The measurement application processing device 20800 further comprise an output interface 20805 coupled to the pre-trained artificial-intelligence algorithm 20803, and configured to output the response data 20804. The explanations provided herein regarding any embodiment of the measurement application processing device disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 20800.

[0716] The measurement application device control unit 20800 further comprises a text-based request generator 20855. The text-based request generator 20855 may receive a non-textual description 20856, like an image, a video, or any other non-text-based, especially visual, description. Such a non-textual description may comprise e.g., an image or a video of a measurement application setup.

[0717] The text-based request generator 20855 may then generate a respective text-based user requests 20802 based on the non-textual description 20856.

[0718] The text-based request generator 20855 may comprise any adequate algorithm, like an artificial-intelligence based algorithm, that may convert the non-textual description 20856 into a text-based user request 20802.

[0719] Figure 22 shows a measurement application 20960. The measurement application 20960 exemplarily comprises a measurement application processing device 20900 that is coupledto three measurement application devices 20961-1 , 20961-2, 20961-3. In other embodiments, more or less measurement application devices are possible, and the measurement application processing device 20900 may be integrated into any one of the measurement application devices.

[0720] The measurement application processing device 20900 comprises a text-based input interface 20901 , a pre-trained artificial-intelligence algorithm 20903, and an output interface 20905, like the measurement application processing device 20100 of figure 1. Of course, any other embodiment of the measurement application processing device may be used in the measurement application 20960.

[0721] The measurement application devices 20961-1 , 20961-2, 20961-3 may be coupled to the measurement application processing device 20900 via a private or public data network. In embodiments with a code generator in the measurement application processing device 20900, the measurement application processing device 20900 may directly provide the generated configuration and control commands to the measurement application devices 20961-1 , 20961-2, 20961-3.

[0722] In embodiments, the measurement application processing device 20900 may serve as a remote-control device for the measurement application devices 20961-1 , 20961-2, 20961-3, and may provide a user with a respective control interface. The measurement application devices 20961-1 , 20961-2, 20961-3 may e.g., be measurement application devices 20961-1 , 20961-2, 20961-3 according to any one of the embodiments described herein.

[0723] Figure 23 shows a flow diagram of an embodiment of a computer-implemented method according to the present disclosure. The method comprises receiving S1 text-based user requests regarding a measurement application, generating S2 with a pre-trained artificial-intelligence algorithm response data regarding the measurement application based on the text-based user requests, and outputting S3 the response data.

[0724] The pre-trained artificial-intelligence algorithm may be pre-trained using at least one of manuals regarding one or more measurement application devices or measurement applications, datasheets regarding one or more measurement application devices or measurement applications, and application notes regarding one or more measurement application devices or measurement applications. The pre-trained artificial-intelligence algorithm may e.g., comprise a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0725] In embodiments, the method may further comprise receiving spoken language requests, converting the spoken language requests into text-based requests, and providing the textbased requests to the text-based input interface as text-based user requests.

[0726] In further embodiments, the pre-trained artificial-intelligence algorithm may augment the text-based user requests, and output the augmented text-based user requests via the output interface. The pre-trained artificial-intelligence algorithm may further generate the response data after confirmation of the augmented text-based user requests by the user.

[0727] The pre-trained artificial-intelligence algorithm may receive text-based user requests regarding the setup of the measurement application, and may generate set-up response data that comprises a respective explanation regarding the setup of the measurement application. The pretrained artificial-intelligence algorithm may then generate set-up response data that indicates which measurement application devices to use, and how to connect the measurement application devices to set-up the measurement application. The pre-trained artificial-intelligence algorithm may e.g., generate a block diagram of the set-up of the measurement application.

[0728] The pre-trained artificial-intelligence algorithm may also generate control response data that may comprise respective control commands for at least one measurement application device. The control commands may e.g., comprise instructions to a user. Alternatively, or in addition, the control commands may comprise generating configuration and control commands for the measurement application device in a predetermined control or programming language.

[0729] The method may also comprise providing a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0730] The method may further comprise receiving at least one of a non-text based description, an image or a video of a measurement application device, a device under test, or a measurement application set-up, and generating text-based user requests based on the at least one of the image or the video.

[0731] Further, the pre-trained artificial-intelligence algorithm may receive text-based user requests regarding the maintenance of a measurement application device in a measurement application, and may generate respective maintenance response data that comprises a respective explanation regarding the maintenance of the respective measurement application device.

[0732] Figure 12 shows a block diagram of an oscilloscope OSC1 that may be used with or implement an embodiment of a measurement application processing device or method according to the second aspect of the present disclosure.

[0733] The oscilloscope OSC1 comprises a housing HO that accommodates four measurement inputs MIP1 , MIP2, MIP3, MIP4 that are coupled to a signal processor SIP for processing anymeasured signals. The signal processor SIP is coupled to a display DISP1 for displaying the measured signals to a user.

[0734] Although not explicitly shown, it is understood, that the oscilloscope OSC1 may also comprise signal outputs that may also be coupled to the differential measurement probe. Such signal outputs may for example serve to output calibration signals. Such calibration signals allow calibrating the measurement setup prior to performing any measurement. The process of calibrating and correcting any measurement signals based on the calibration may also be called de-embed- ding and may comprise applying respective algorithms on the measured signals.

[0735] In the oscilloscope OSC1 the signal processor SIP or an additional processing element may perform the function of the measurement application processing device, or the method according to the present disclosure, or may implement the measurement application processing device, or the method. Of course, a communication interface may be provided in the oscilloscope OSC1 for communication with other measurement application devices.

[0736] Figure 13 shows a block diagram of an oscilloscope OSC that may be an comprise or implement a measurement application processing device or method according to the second aspect of the present disclosure. The oscilloscope OSC is implemented as a digital oscilloscope. However, the present disclosure may also be implemented with any other type of oscilloscope.

[0737] The oscilloscope OSC exemplarily comprises five general sections, the vertical system VS, the triggering section TS, the horizontal system HS, the processing section PS and the display DISP. It is understood, that the partitioning into five general sections is a logical partitioning and does not limit the placement and implementation of any of the elements of the oscilloscope OSC in any way.

[0738] The vertical system VS mainly serves for offsetting, attenuating and amplifying a signal to be acquired. The signal may for example be modified to fit in the available space on the display DISP or to comprise a vertical size as configured by a user.

[0739] To this end, the vertical system VS comprises a signal conditioning section SC with an attenuator ATT and a digital-to-analog-converter DAC that are coupled to an amplifier AMP1 . The amplifier AMP1 is coupled to a filter FI1 , which in the shown example is provided as a low pass filter. The vertical system VS also comprises an analog-to-digital converter ADC that receives the output from the filter FI1 and converts the received analog signal into a digital signal.

[0740] The attenuator ATT and the amplifier AMP1 serve to scale the amplitude of the signal to be acquired to match the operation range of the analog-to-digital converter ADC. The digital-to- analog-converter DAC serves to modify the DC component of the input signal to be acquired tomatch the operation range of the analog-to-digital converter ADC. The filter FI1 serves to filter out unwanted high frequency components of the signal to be acquired.

[0741] The triggering section TS operates on the signal as provided by the amplifier AMP. The triggering section TS comprises a filter FI2, which in this embodiment is implemented as a low pass filter. The filter FI2 is coupled to a trigger system TS1.

[0742] The triggering section TS serves to capture predefined signal events and allows the horizontal system HS to e.g., display a stable view of a repeating waveform, or to simply display waveform sections that comprise the respective signal event. It is understood, that the predefined signal event may be configured by a user via a user input of the oscilloscope OSC.

[0743] Possible predefined signal events may for example include, but are not limited to, when the signal crosses a predefined trigger threshold in a predefined direction i.e. , with a rising or falling slope. Such a trigger condition is also called an edge trigger. Another trigger condition is called “glitch triggering” and triggers, when a pulse occurs in the signal to be acquired that has a width that is greater than or less than a predefined amount of time.

[0744] In order to allow an exact matching of the trigger event and the waveform that is shown on the display DISP, a common time base may be provided for the analog-to-digital converter ADC and the trigger system TS1.

[0745] It is understood, that although not explicitly shown, the trigger system TS1 may comprise at least one of configurable voltage comparators for setting the trigger threshold voltage, fixed voltage sources for setting the required slope, respective logic gates like e.g., a XOR gate, and FlipFlops to generate the triggering signal.

[0746] The triggering section TS is exemplarily provided as an analog trigger section. It is understood, that the oscilloscope OSC may also be provided with a digital triggering section. Such a digital triggering section will not operate on the analog signal as provided by the amplifier AMP but will operate on the digital signal as provided by the analog-to-digital converter ADC.

[0747] A digital triggering section may comprise a processing element, like a processor, a DSP, a CPLD, an ASIC or an FPGA to implement digital algorithms that detect a valid trigger event.

[0748] The horizontal system HS is coupled to the output of the trigger system TS1 and mainly serves to position and scale the signal to be acquired horizontally on the display DISP.

[0749] The oscilloscope OSC further comprises a processing section PS that implements digital signal processing and data storage for the oscilloscope OSC. The processing section PS comprises an acquisition processing element ACP that is couple to the output of the analog-to-digital converter ADC and the output of the horizontal system HS as well as to a memory MEM and a post processing element PPE.

[0750] The acquisition processing element ACP manages the acquisition of digital data from the analog-to-digital converter ADC and the storage of the data in the memory MEM. The acquisition processing element ACP may for example comprise a processing element with a digital interface to the analog-to-digital converter ADC2 and a digital interface to the memory MEM. The processing element may for example comprise a microcontroller, a DSP, a CPLD, an ASIC or an FPGA with respective interfaces. In a microcontroller or DSP, the functionality of the acquisition processing element ACP may be implemented as computer readable instructions that are executed by a CPU. In a CPLD or FPGA the functionality of the acquisition processing element ACP may be configured in to the CPLD or FPGA opposed to software being executed by a processor.

[0751] The processing section PS further comprises a communication processor CP and a communication interface COM.

[0752] The communication processor CP may be a device that manages data transfer to and from the oscilloscope OSC. The communication interface COM for any adequate communication standard like for example, Ethernet, WIFI, Bluetooth, NFC, an infra-red communication standard, and a visible-light communication standard.

[0753] The communication processor CP is coupled to the memory MEM and may use the memory MEM to store and retrieve data.

[0754] Of course, the communication processor CP may also be coupled to any other element of the oscilloscope OSC to retrieve device data or to provide device data that is received from the management server.

[0755] The post processing element PPE may be controlled by the acquisition processing element ACP and may access the memory MEM to retrieve data that is to be displayed on the display DISP. The post processing element PPE may condition the data stored in the memory MEM such that the display DISP may show the data e.g., as waveform to a user. The post processing element PPE may also realize analysis functions like cursors, waveform measurements, histograms, or math functions.

[0756] The display DISP controls all aspects of signal representation to a user, although not explicitly shown, may comprise any component that is required to receive data to be displayed and control a display device to display the data as required.

[0757] It is understood, that even if it is not shown, the oscilloscope OSC may also comprise a user interface for a user to interact with the oscilloscope OSC. Such a user interface may comprise dedicated input elements like for example knobs and switches. At least in part the user interface may also be provided as a touch sensitive display device.

[0758] In the oscilloscope OSC, any one of the processing elements in the processing section PS or an additional processing element may perform the function of the measurement application processing device, or the method according to the present disclosure.

[0759] It is understood, that all elements of the oscilloscope OSC that perform digital data processing may be provided as dedicated elements. As alternative, at least some of the above-described functions may be implemented in a single hardware element, like for example a microcontroller, DSP, CPLD or FPGA. Generally, the above-describe logical functions may be implemented in any adequate hardware element of the oscilloscope OSC and not necessarily need to be partitioned into the different sections explained above.Figures regarding the third aspect:

[0760] Figure 24 shows a block diagram a measurement application device control unit 30100 according to the third aspect of the present disclosure. The measurement application device control unit 30100 comprises a text-based input interface 30101 that receives text-based user requests 30102 regarding a measurement application device. The measurement application device control unit 30100 further comprises a pre-trained artificial-intelligence algorithm 30103 coupled to the text-based input interface 30101 that generates configuration data 30104 regarding the measurement application device based on the text-based user requests 30102. The measurement application device control unit 30100 further comprises an output interface 30105 coupled to the pretrained artificial-intelligence algorithm 30103, and configured to output the configuration data 30104. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30100.

[0761] The measurement application device control unit 30100 may be implemented as a dedicated device e.g., laboratory equipment, similar to other laboratory measurement application devices, like oscilloscopes, network analyzers, signal generators, and the like. Such a measurement application device control unit 30100 may comprise housing, a user interface, like a display or touchscreen, buttons, knobs, a mouse, a keyboard, or the like, as text-based input interface 30101 ,a respective processor or processing element, and respective data interfaces, like a network interface, possibly also as text-based input interface 30101. The measurement application device control unit 30100 may also be integrated into any type of laboratory measurement application device.

[0762] In embodiments, the measurement application device control unit 30100 may be implemented on a computer or server, that executes a computer-program product that comprises instructions that when executed by the computer or server, especially a processor in the computer or server, causes the computer or server to perform the computer-implemented method of the third aspect according to the present disclosure. Such a computer or server may be located remotely to other measurement application devices and coupled to other measurement application devices via a network connection.

[0763] In embodiments, the pre-trained artificial-intelligence algorithm 30103 may be pretrained using any combination of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0764] Figure 25 shows a block diagram of another measurement application device control unit 30200. The measurement application device control unit 30200 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30200 comprises a text-based input interface 30201 that receives text-based user requests 30202 regarding a measurement application device. The measurement application device control unit 30200 further comprises a pre-trained artificial-intelligence algorithm 30203 coupled to the textbased input interface 30201 that generates configuration data 30204 regarding the measurement application device based on the text-based user requests 30202. The measurement application device control unit 30200 further comprises an output interface 30205 coupled to the pre-trained artificial-intelligence algorithm 30203, and configured to output the configuration data 30204. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30200.

[0765] The measurement application device control unit 30200 further comprises a feedback interface 30210 that receives measurement feedback data 30211 . The measurement feedback data 30211 may in embodiments be provided directly to the pre-trained artificial-intelligence algorithm 30203. In the measurement application device control unit 30200, a feedback data analyzer 30212 is arranged between the feedback interface 30210, and the pre-trained artificial-intelligence algorithm 30203.

[0766] In the measurement application device control unit 30200 the feedback data analyzer 30212 generates a text-based feedback description 30213 of the measurement feedback data 30211 , and provides the text-based feedback description 30213 to the pre-trained artificial-intelligence algorithm 30203.

[0767] The pre-trained artificial-intelligence algorithm 30203 generates further sets of configuration data 30204 based on the original text-based user request 30202, and the received measurement feedback data 30211, especially the text-based feedback description 30213.

[0768] The feedback data analyzer 30212 may identify at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data. The raw measurement data may be provided in, or as the measurement feedback data 30211. The feedback data analyzer 30212 may then provide a respective text-based feedback description 30213.

[0769] Such a text-based feedback description 30213 may e.g., be comprise statements like “the relevant information in the acquired waveform is in the frequency range between 1GHz and 5GHz. Please zoom into this section of the acquired waveform for a measurement”.

[0770] Figure 26 shows a block diagram of another measurement application device control unit 30300. The measurement application device control unit 30300 is based on the measurement application device control unit 30200. Therefore, the measurement application device control unit 30300 comprises a text-based input interface 30301 that receives text-based user requests 30302 regarding a measurement application device. The measurement application device control unit 30300 further comprises a pre-trained artificial-intelligence algorithm 30303 coupled to the textbased input interface 30301 that generates configuration data 30304 regarding the measurement application device based on the text-based user requests 30302. The measurement application device control unit 30300 further comprises an output interface 30305 coupled to the pre-trained artificial-intelligence algorithm 30303, and configured to output the configuration data 30304. The measurement application device control unit 30300 further comprises a feedback interface 30310 that receives measurement feedback data 30311. A feedback data analyzer 30312 is arranged between the feedback interface 30310, and the pre-trained artificial-intelligence algorithm 30303, and generates a text-based feedback description 30313 of the measurement feedback data 30311, and provides the text-based feedback description 30313 to the pre-trained artificial-intelligence algorithm 30303. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30300.

[0771] The measurement application device control unit 30300 further comprises a feedback output interface 30314 that is coupled to the feedback data analyzer 30312.

[0772] The feedback output interface 30314 outputs the measurement feedback data 30311 , especially the text-based feedback description 30213, to a user.

[0773] The pre-trained artificial-intelligence algorithm 30303 may then generate a further set of configuration data 30304 based on the original text-based user request 30302, and a further textbased user request 30302 received after the measurement feedback data 30311 is output to the user.

[0774] The pre-trained artificial-intelligence algorithm 30303 may also perform reinforced learning based on the further text-based user request 30302 received after the measurement feedback data 30311 is output to the user. It is understood, that this is just an example of performing further training with the pre-trained artificial-intelligence algorithm 30303. Any other form of further or continuous learning may be implemented for the pre-trained artificial-intelligence algorithm 30303.

[0775] Figure 27 shows a block diagram of another measurement application device control unit 30400. The measurement application device control unit 30400 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30400 comprises a text-based input interface 30401 that receives text-based user requests 30402 regarding a measurement application device. The measurement application device control unit 30400 further comprises a pre-trained artificial-intelligence algorithm 30403 coupled to the textbased input interface 30401 that generates configuration data 30404 regarding the measurement application device based on the text-based user requests 30402. The measurement application device control unit 30400 further comprises an output interface 30405 coupled to the pre-trained artificial-intelligence algorithm 30403, and configured to output the configuration data 30404. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30400.

[0776] The measurement application device control unit 30400 further comprises an audio input interface 30428. The audio input interface 30428 is coupled to a speech recognition unit 30430, and the speech recognition unit 30430 is coupled to the text-based input interface 30401 .

[0777] The audio input interface 30428 may receive spoken language requests 30429 e.g., from a user. It is understood, that the audio input interface 30428 may comprise e.g., a microphone for recording the spoken language requests 30429. In embodiments, the audio input interface30428 may comprise a data interface for receiving the spoken language requests 30429 in digital form that are recorded elsewhere. A combination of both is possible.

[0778] The speech recognition unit 30430 may then convert the spoken language requests 30429 into text-based requests 30431. Such text-based requests 30431 may then be forwarded to the text-based input interface 30401 , as are the text-based user requests 30402. The text-based input interface 30401 may then process the text-based requests 30431 like any other text-based user requests 30402.

[0779] The speech recognition unit 30430 may comprise any type of adequate element. Exemplary embodiments of the speech recognition unit 30430 may e.g., be based on computer-implemented algorithms, especially respective artificial-intelligence based computer-implemented algorithms.

[0780] Although not explicitly shown, the measurement application device control unit 30400 may also comprise a translator that may translate either the spoken language requests 30429, or the text-based requests 30431 that are provided in a language that the pre-trained artificial-intelligence algorithm 30403 may not operate on, into a language that the pre-trained artificial-intelligence algorithm 30403 may operate on.

[0781] Figure 28 shows a block diagram of another measurement application device control unit 30500. The measurement application device control unit 30500 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30500 comprises a text-based input interface 30501 that receives text-based user requests 30502 regarding a measurement application device. The measurement application device control unit 30500 further comprises a pre-trained artificial-intelligence algorithm 30503 coupled to the textbased input interface 30501 that generates configuration data 30504 regarding the measurement application device based on the text-based user requests 30502. The measurement application device control unit 30500 further comprises an output interface 30505 coupled to the pre-trained artificial-intelligence algorithm 30503, and configured to output the configuration data 30504. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30500.

[0782] In the measurement application device control unit 30500, the pre-trained artificial-intelligence algorithm 30503 comprises a large language model 30535 for implementing the functionality of the pre-trained artificial-intelligence algorithm 30503.

[0783] The large language model 30535 may be based on any adequate type of algorithm, like a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0784] Figure 29 shows a block diagram of another measurement application device control unit 30600. The measurement application device control unit 30600 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30600 comprises a text-based input interface 30601 that receives text-based user requests 30602 regarding a measurement application device. The measurement application device control unit 30600 further comprises a pre-trained artificial-intelligence algorithm 30603 coupled to the textbased input interface 30601 that generates configuration data 30604 regarding the measurement application device based on the text-based user requests 30602. The measurement application device control unit 30600 further comprises an output interface 30605 coupled to the pre-trained artificial-intelligence algorithm 30603, and configured to output the configuration data 30604. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30600.

[0785] The measurement application device control unit 30600 further comprises a pre-condi- tioner 30648. The pre-conditioner 30648 is coupled to the text-based input interface 30601 and may provide a pre-conditioning text-based request 30649 to the text-based input interface 30601.

[0786] The pre-conditioning text-based request 30649 serves for pre-conditioning the pretrained artificial-intelligence algorithm 30603, and may be provided to the pre-trained artificial-intelligence algorithm 30603 prior to providing the text-based user requests 30602 to the pre-trained artificial-intelligence algorithm 30603.

[0787] In embodiments, the text-based user requests 30602 may be provided to the pre-condi- tioner 30648 instead of the text-based input interface 30601. The pre-conditioner 30648 may then combine the pre-conditioning text-based request 30649 with the received text-based user requests 30602.

[0788] Figure 30 shows a block diagram of another measurement application device control unit 30700. The measurement application device control unit 30700 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30700 comprises a text-based input interface 30701 that receives text-based user requests 30702 regarding a measurement application device. The measurement application device control unit 30700 further comprises a pre-trained artificial-intelligence algorithm 30703 coupled to the textbased input interface 30701 that generates configuration data 30704 regarding the measurementapplication device based on the text-based user requests 30702. The measurement application device control unit 30700 further comprises an output interface 30705 coupled to the pre-trained artificial-intelligence algorithm 30703, and configured to output the configuration data 30704. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30700.

[0789] The measurement application device control unit 30700 may optionally receive a first type of text-based user requests 30702-1 that comprise user requests regarding the usage of the measurement application device. Further, the measurement application device control unit 30700 may receive a second type of text-based user requests 30702-2 that comprise user requests regarding the control of the measurement application device.

[0790] The pre-trained artificial-intelligence algorithm 30703 may provide usage response data 30752 that may e.g., be provided to a user. The pre-trained artificial-intelligence algorithm 30703 may further provide control response data 30753 that may then form the configuration data 30704. The usage response data 30752 may be included in the configuration data 30704 e.g., as additional information that may be shown to a user. The usage response data 30752 may also be output independently of the configuration data 30704.

[0791] The measurement application device control unit 30700 further comprises a code generator 30754. The code generator 30754 may receive the control response data 30753 or other respective indications from the pre-trained artificial-intelligence algorithm 30703 and may generate respective configuration and control commands 30755 in any adequate scripting or programming language. The configuration and control commands 30755 may then be output as the configuration data 30704.

[0792] In embodiments, the code generator 30754 may be provided as function of, or as element of, or included in the pre-trained artificial-intelligence algorithm 30703.

[0793] Figure 31 shows a block diagram of another measurement application device control unit 30800. The measurement application device control unit 30800 is based on the measurement application device control unit 30100. Therefore, the measurement application device control unit 30800 comprises a text-based input interface 30801 that receives text-based user requests 30802 regarding a measurement application device. The measurement application device control unit 30800 further comprises a pre-trained artificial-intelligence algorithm 30803 coupled to the textbased input interface 30801 that generates configuration data 30804 regarding the measurement application device based on the text-based user requests 30802. The measurement application de-vice control unit 30800 further comprises an output interface 30805 coupled to the pre-trained artificial-intelligence algorithm 30803, and configured to output the configuration data 30804. The explanations provided herein regarding any embodiment of the measurement application device control unit disclosed herein may, mutatis mutandis, also be applied to the measurement application processing device 30800.

[0794] The measurement application device control unit 30800 further comprises a complexity estimator 30858. The complexity estimator 30858 serves for estimating or calculating a complexity of the text-based user requests 30802. In the measurement application device control unit 30800 the complexity is calculated by unit C. The calculation of the complexity may e.g., be performed based on a length of the text-based user requests 30802, or the number of main and / or subordinate clauses, or a combination of both.

[0795] The complexity may then be compared to a complexity threshold 30860. If the complexity is higher than the threshold 30860, the complexity estimator 30858 may, instead of providing the text-based user requests 30802 to the local pre-trained artificial-intelligence algorithm 30803 in the measurement application device control unit 30800, provide the text-based user requests 30802 to an external pre-trained artificial-intelligence algorithm 30859. The external pretrained artificial-intelligence algorithm 30859 may be provided in a cloud system, or a server, or any other element, and may be coupled to the measurement application device control unit 30800 e.g., the complexity estimator 30858 via a data network. The complexity estimator 30858 may request a user consent 30861 via a user interface 30862 for providing the provide the text-based user requests 30802 to the external pre-trained artificial-intelligence algorithm 30859.

[0796] As optional element, a request anonymizer 30863 is provided between the measurement application device control unit 30800, and the external pre-trained artificial-intelligence algorithm 30859. The request anonymizer 30863 may serve to anonymize the text-based user requests 30802 prior to providing the text-based user requests 30802 to the external pre-trained artificialintelligence algorithm 30859.

[0797] Figure 32 shows a measurement application device 30975. The measurement application device 30975 comprises a measurement application device control unit 30900, and a measurement application device controller 30976.

[0798] The measurement application device controller 30976 is coupled to the text-based input interface 30901 to provide text-based user requests 30902 to the measurement application device control unit 30900. The measurement application device controller 30976 is further coupled to the output interface 30905 in order to receive response data 30904 from the measurement application device control unit 30900.

[0799] The measurement application device controller 30976, and the measurement application device control unit 30900 are provided as separate devices, or elements in the measurement application device 30975.

[0800] In other embodiments, the measurement application device control unit 30900 may be provided as a function of, or addition to the functionality of the measurement application device controller 30976.

[0801] Figure 33 shows a flow diagram of an embodiment of a computer-implemented method according to the present disclosure. The method comprises receiving S1 text-based user requests regarding a measurement application device, generating S2 configuration data regarding the measurement application device based on the text-based user requests with a pre-trained artificialintelligence algorithm, and outputting S3 the configuration data.

[0802] The pre-trained artificial-intelligence algorithm may be pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes regarding electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

[0803] The pre-trained artificial-intelligence algorithm may comprise a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

[0804] When generating S2 the configuration data, control commands may be generated in a predetermined control or programming language. The generated control commands may be provided to a controller of a measurement application device.

[0805] The method may further comprise receiving measurement feedback data, and generating a further set of configuration data based on the original text-based user request, and the received measurement feedback data. A text-based feedback description may be generated based on the measurement feedback data, and may be provided to the pre-trained artificial-intelligence algorithm. For example, at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections may be identified in raw measurement data provided in the measurement feedback data. A respective text-based feedback description may be generated based on the identified at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections.Ill

[0806] The measurement feedback data may be output to a user, and a further set of configuration data may be generated based on the original text-based user request, and a further textbased user request received after the measurement feedback data is output to the user. For example, the text-based feedback description may be output to the user.

[0807] The pre-trained artificial-intelligence algorithm may e.g., perform reinforced learning based on the further text-based user request received after the measurement feedback data is output to the user.

[0808] The method may further comprise receiving spoken language requests, converting the spoken language requests into text-based requests, and providing the text-based requests to the text-based input interface as text-based user requests.

[0809] The method may further comprise providing a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

[0810] The pre-trained artificial-intelligence algorithm may be locally executed on the measurement application device. The method may further comprise estimating the complexity of the text-based user requests, and forwarding the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold. In such an embodiment, a user consent may be requested via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm. The text-based user requests may be provided indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

[0811] Figure 12 shows a block diagram of an oscilloscope OSC1 that may be used with or implement an embodiment of a measurement application device control unit, or the method according to the third aspect of the present disclosure.

[0812] The oscilloscope OSC1 comprises a housing HO that accommodates four measurement inputs MIP1 , MIP2, MIP3, MIP4 that are coupled to a signal processor SIP for processing any measured signals. The signal processor SIP is coupled to a display DISP1 for displaying the measured signals to a user.

[0813] Although not explicitly shown, it is understood, that the oscilloscope OSC1 may also comprise signal outputs that may also be coupled to the differential measurement probe. Such signal outputs may for example serve to output calibration signals. Such calibration signals allow calibrating the measurement setup prior to performing any measurement. The process of calibratingand correcting any measurement signals based on the calibration may also be called de-embed- ding and may comprise applying respective algorithms on the measured signals.

[0814] In the oscilloscope OSC1 the signal processor SIP or an additional processing element may perform the function of the measurement application device control unit or the method according to the third aspect of the present disclosure, or may implement the measurement application device control unit or the method according to the third aspect. Of course, a communication interface may be provided in the oscilloscope OSC1 for communication with other measurement application devices.

[0815] Figure 13 shows a block diagram of an oscilloscope OSC that may be used with or implement a measurement application device control unit or method according to the third aspect of the present disclosure. The oscilloscope OSC is implemented as a digital oscilloscope. However, the present disclosure may also be implemented with any other type of oscilloscope.

[0816] The oscilloscope OSC exemplarily comprises five general sections, the vertical system VS, the triggering section TS, the horizontal system HS, the processing section PS and the display DISP. It is understood, that the partition...

Claims

CLAIMS1. Measurement application device control unit comprising: a text-based input interface configured to receive text-based user requests regarding a measurement application device; a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate response data regarding the measurement application device based on the textbased user requests; and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the response data.

2. Measurement application device control unit according to claim 1, further comprising an audio input interface configured to receive spoken language requests; and further comprising a speech recognition unit coupled to the audio input interface and the text-based input interface; wherein the speech recognition unit is configured to convert the spoken language requests into text-based requests, and to provide the text-based requests to the text-based input interface as text-based user requests.

3. Measurement application device control unit according to any one of the preceding claims, wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

4. Measurement application device control unit according to any one of the preceding claims, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

5. Measurement application device control unit according to any one of the preceding claims, further comprising a user input interface coupled to the pre-trained artificial-intelligence algorithm, and configured to receive user input;wherein the pre-trained artificial-intelligence algorithm is configured to generate the response data regarding the measurement application device based on the text-based user requests and the user input.

6. Measurement application device control unit according to claim 5, wherein the user input interface comprises at least one of a button, a switch, a knob, a touchscreen, a keyboard, a mouse, a camera, and a gesture sensor.

7. Measurement application device control unit according to any one of the preceding claims, further comprising a translator coupled to the text-based input interface, and the pre-trained artificial-intelligence algorithm; wherein the translator is configured to translate text-based user requests in a language that the pre-trained artificial-intelligence algorithm is not trained to operate on into translated text-based user requests in a language that the pre-trained artificial-intelligence algorithm is trained to operate on, and to provide the translated text-based user requests to the pre-trained artificial-intelligence algorithm.

8. Measurement application device control unit according to any one of the preceding claims, further comprising a pre-conditioner coupled to the pre-trained artificial-intelligence algorithm; wherein the pre-conditioner is configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

9. Measurement application device control unit according to any one of the preceding claims, wherein the pre-trained artificial-intelligence algorithm is configured to receive text-based user requests regarding the usage of the measurement application device, and to generate usage response data that comprises a respective explanation regarding the usage of the measurement application device.

10. Measurement application device control unit according to any one of the preceding claims, wherein the pre-trained artificial-intelligence algorithm is configured to receive text-based user requests regarding the control of the measurement application device, and to generate control response data that comprises respective control commands regarding the usage of the measurement application device.11 . Measurement application device control unit according to claim 10, further comprising a code generator that is coupled to or integrated into the pre-trained artificial-intelligence algorithm;wherein the code generator is configured to generate configuration and control commands for the measurement application device in a predetermined control or programming language, and to provide the generated configuration and control commands to a controller of the measurement application device.

12. Measurement application device control unit according to any one of the preceding claims, wherein the pre-trained artificial-intelligence algorithm comprises an algorithm that is locally executed on the measurement application device.

13. Measurement application device control unit according to any one of the preceding claims, further comprising a complexity estimator coupled to the text-based input interface; wherein the complexity estimator is configured to estimate the complexity of the text-based user requests, and to forward the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.

14. Measurement application device control unit according to claim 13, wherein the complexity estimator is configured to request a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm.

15. Measurement application device control unit according to any one of the preceding claims 13 and 14, wherein the complexity estimator is configured to provide the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

16. Measurement application device control unit according to any one of the preceding claims, further comprising a text-based user request generator configured to receive at least one of an image, a video, or a non-textual description of at least one of a measurement application device, a device under test, and a measurement application setup; wherein the text-based user request generator is further configured to generate text-based user requests based on the at least one of the image or the video, and to provide the generated textbased user requests to the pre-trained artificial-intelligence algorithm.

17. Measurement application device comprising: a measurement application device control unit according to any one of the preceding claims; and a measurement application device controller coupled to the measurement application device control unit.

18. Computer-implemented method comprising:receiving text-based user requests regarding a measurement application device; generating response data regarding the measurement application device based on the text-based user requests with a pre-trained artificial-intelligence algorithm; and outputting the response data.

19. Computer-implemented method according to claim 18, further comprising: receiving spoken language requests; converting the spoken language requests into text-based requests; and providing the text-based requests to the pre-trained artificial-intelligence algorithm as text-based user requests.

20. Computer-implemented method according to any one of the preceding method-based claims, wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes regarding electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

21. Computer-implemented method according to any one of the preceding method-based claims, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

22. Computer-implemented method according to any one of the preceding method-based claims, further comprising: receiving user input; wherein the pre-trained artificial-intelligence algorithm generates the response data regarding the measurement application device based on the text-based user requests and the user input.

23. Computer-implemented method according to claim 22, wherein the user input is provided via at least one of a button, a switch, a knob, a touchscreen, a keyboard, a mouse, a camera, and a gesture sensor.

24. Computer-implemented method according to any one of the preceding method-based claims, further comprising: translating text-based user requests in a language that the pre-trained artificial-intelligence algorithm is not trained to operate on into translated text-based user requests in a language that the pre-trained artificial-intelligence algorithm is trained to operate on; and providing the translated text-based user requests to the pre-trained artificial-intelligence algorithm.

25. Computer-implemented method according to any one of the preceding method-based claims, further comprising: providing a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

26. Computer-implemented method according to any one of the preceding method-based claims, wherein the text-based user requests refer to the usage of the measurement application device; and wherein usage response data is generated by the pre-trained artificial-intelligence algorithm that comprises a respective explanation regarding the usage of the measurement application device.

27. Computer-implemented method according to any one of the preceding method-based claims, wherein the text-based user requests refer to the control of the measurement application device: wherein control response data is generated by the pre-trained artificial-intelligence algorithm that comprises respective control commands regarding the usage of the measurement application device.

28. Computer-implemented method according to claim 27, further comprising: generating configuration and control commands for the measurement application device in a predetermined control or programming language; and providing the generated configuration and control commands to a controller of the measurement application device.

29. Computer-implemented method according to any one of the preceding method-based claims, wherein the pre-trained artificial-intelligence algorithm is locally executed on the measurement application device.

30. Computer-implemented method according to any one of the preceding method-based claims, further comprising: estimating the complexity of the text-based user requests; and forwarding the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.31 . Computer-implemented method according to claim 30, further comprising: requesting a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm.

32. Computer-implemented method according to any one of the preceding claims 30 and 31 , further comprising: providing the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

33. Computer-implemented method according to any one of the preceding method-based claims, further comprising: receiving at least one of an image, a video, or a non-textual description of at least one of a measurement application device, a device under test, and a measurement application setup; generating text-based user requests based on the at least one of the image or the video; and providing the generated text-based user requests to the pre-trained artificial-intelligence algorithm.

34. Computer program product comprising instructions that when executed by a processor cause the processor to perform a method according to any one of the preceding method-based claims.

35. Measurement application processing device comprising: a text-based input interface configured to receive text-based user requests regarding a measurement application; a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate response data regarding the measurement application based on the text-based user requests; andan output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the response data.

36. Measurement application processing device according to claim 35, further comprising an audio input interface configured to receive spoken language requests; and further comprising a speech recognition unit coupled to the audio input interface and the text-based input interface; wherein the speech recognition unit is configured to convert the spoken language requests into text-based requests, and to provide the text-based requests to the text-based input interface as text-based user requests.

37. Measurement application processing device according to any one of the preceding claims 35 to 36, wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding one or more measurement application devices or measurement applications, datasheets regarding one or more measurement application devices or measurement applications, application notes regarding one or more measurement application devices or measurement applications, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

38. Measurement application processing device according to any one of the preceding claims 35 to 37, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

39. Measurement application processing device according to any one of the preceding claims 35 to 38, wherein the pre-trained artificial-intelligence algorithm is configured to receive text-based user requests regarding the setup of the measurement application, and to generate set-up response data that comprises a respective explanation regarding the setup of the measurement application.

40. Measurement application processing device according to claim 39, wherein the pre-trained artificial-intelligence algorithm is configured generate set-up response data that indicates at least one of which measurement application devices to use, and how to connect the measurement application devices to set-up the measurement application.41 . Measurement application processing device according to claim 40, wherein the pre-trained artificial-intelligence algorithm is configured to generate a block diagram of the set-up of the measurement application.

42. Measurement application processing device according to any one of the preceding claims 35 to 41, wherein the pre-trained artificial-intelligence algorithm is further configured to generate control response data that comprises respective control commands for at least one measurement application device.

43. Measurement application processing device according to claim 42, further comprising a code generator that is coupled to or integrated into the pre-trained artificial-intelligence algorithm; wherein the code generator is configured to generate configuration and control commands for the at least one measurement application device in a predetermined control or programming language.

44. Measurement application processing device according to any one of the preceding claims 35 to 43, further comprising a pre-conditioner coupled to the pre-trained artificial-intelligence algorithm; wherein the pre-conditioner is configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

45. Measurement application processing device according to any one of the preceding claims 35 to 44, wherein the pre-trained artificial-intelligence algorithm is configured to augment the textbased user requests and output the augmented text-based user requests via the output interface; wherein the pre-trained artificial-intelligence algorithm is configured to generate the response data after confirmation of the augmented text-based user requests by the user.

46. Measurement application processing device according to any one of the preceding claims 35 to 45, further comprising a text-based user request generator configured to receive at least one of an image, a video, and a non-textual description of a measurement application device, a device under test, or a measurement application set-up; wherein the text-based user request generator is further configured to generate text-based user requests based on the at least one of the image, the video, and the non-textual description, and to provide the generated text-based user requests to the pre-trained artificial-intelligence algorithm.

47. Measurement application processing device according to any one of the preceding claims 35 to 46, wherein the pre-trained artificial-intelligence algorithm is configured to receive text-based user requests regarding the maintenance of a measurement application device in a measurement application, and to generate maintenance response data that comprises a respective explanation regarding the maintenance of the respective measurement application device.

48. Measurement application comprising: a measurement application processing device according to any one of the preceding claims 35 to 47; and at least one measurement application device.

49. Measurement application according to claim 48, wherein the at least one measurement application device is communicatively coupled to the measurement application processing device for receiving configuration and control commands from the measurement application processing device.

50. Computer-implemented method comprising: receiving text-based user requests regarding a measurement application; generating with a pre-trained artificial-intelligence algorithm response data regarding the measurement application based on the text-based user requests; and outputting the response data.

51. Computer-implemented method according to claim 50, further comprising: receiving spoken language requests; converting the spoken language requests into text-based requests; and providing the text-based requests to the text-based input interface as text-based user requests.

52. Computer-implemented method according to any one of the preceding method-based claims 50 to 51 , wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding one or more measurement application devices or measurement applications, datasheets regarding one or more measurement application devices or measurement applications, application notes regarding one or more measurement application devices or measurement applications, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

53. Measurement application processing device according to any one of the preceding claims 50 to 52, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a statistical model, a recurrent neuronal network, a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

54. Computer-implemented method according to any one of the preceding method-based claims 50 to 53, wherein the pre-trained artificial-intelligence algorithm receives text-based user requests regarding the setup of the measurement application, and generates set-up response data that comprises a respective explanation regarding the setup of the measurement application.

55. Computer-implemented method according to claim 54, wherein the pre-trained artificial-intelligence algorithm generates set-up response data that indicates which measurement application devices to use, and how to connect the measurement application devices to set-up the measurement application.

56. Computer-implemented method according to claim 55, wherein the pre-trained artificial-intelligence algorithm generates a block diagram of the set-up of the measurement application.

57. Computer-implemented method according to any one of the preceding method-based claims 50 to 56, wherein the pre-trained artificial-intelligence algorithm generates control response data that comprises respective control commands for at least one measurement application device.

58. Computer-implemented method according to claim 57, further comprising generating configuration and control commands for the measurement application device in a predetermined control or programming language.

59. Computer-implemented method according to any one of the preceding method-based claims 50 to 58, further comprising providing a pre-conditioning text-based request to the pretrained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

60. Computer-implemented method according to any one of the preceding method-based claims 50 to 59, wherein the pre-trained artificial-intelligence algorithm augments the text-based user requests, and outputs the augmented text-based user requests via the output interface; and wherein the pre-trained artificial-intelligence algorithm generates the response data after confirmation of the augmented text-based user requests by the user.61 . Computer-implemented method according to any one of the preceding method-based claims 50 to 60, further comprising: receiving at least one of an image or a video of a measurement application device, a device under test, or a measurement application set-up; and generating text-based user requests based on the at least one of the image or the video.

62. Computer-implemented method according to any one of the preceding method-based claims 50 to 61 , wherein the pre-trained artificial-intelligence algorithm receives text-based user requests regarding the maintenance of a measurement application device in a measurement application, and generates maintenance response data that comprises a respective explanation regarding the maintenance of the respective measurement application device.

63. Computer program product comprising instructions that when executed by a processor cause the processor to perform a method according to any one of the preceding method-based claims 50 to 62.

64. Measurement application device control unit comprising: a text-based input interface configured to receive text-based user requests regarding a measurement application device; a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate configuration data regarding the measurement application device based on the text-based user requests; and an output interface coupled to the pre-trained artificial-intelligence algorithm, and configured to output the configuration data.

65. Measurement application device control unit according to claim 64, further comprising a feedback interface configured to receive measurement feedback data; wherein the pre-trained artificial-intelligence algorithm is configured to generate a further set of configuration data based on the original text-based user request, and the received measurement feedback data.

66. Measurement application device control unit according to claim 65, further comprising a feedback data analyzer arranged between the feedback interface, and the pre-trained artificial-intelligence algorithm; wherein the feedback data analyzer is configured to generate a text-based feedback description of the measurement feedback data, and to provide the text-based feedback description to the pretrained artificial-intelligence algorithm.

67. Measurement application device control unit according to claim 66, wherein the feedback data analyzer is configured to identify at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data, and to provide a respective text-based feedback description.

68. Measurement application device control unit according to any one of the preceding claims 65 - 67, further comprising a feedback output interface that is configured to output the measurement feedback data to a user; wherein the pre-trained artificial-intelligence algorithm is configured to generate a further set of configuration data based on the original text-based user request, and a further text-based user request received after the measurement feedback data is output to the user.

69. Measurement application device control unit according to claims 66 and 68, wherein the feedback output interface outputs the text-based feedback description to the user.

70. Measurement application device control unit according to any one of the preceding claims 68 and 69, wherein the pre-trained artificial-intelligence algorithm is configured to perform reinforced learning based on the further text-based user request received after the measurement feedback data is output to the user.

71. Measurement application device control unit according to any one of the preceding claims 64 to 70, further comprising an audio input interface configured to receive spoken language requests; and further comprising a speech recognition unit coupled to the audio input interface and the text-based input interface; wherein the speech recognition unit is configured to convert the spoken language requests into text-based requests, and to provide the text-based requests to the text-based input interface as text-based user requests.

72. Measurement application device control unit according to any one of the preceding claims 64 to 71, wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

73. Measurement application device control unit according to any one of the preceding claims 64 to 72, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.

74. Measurement application device control unit according to any one of the preceding claims 64 to 73, further comprising a pre-conditioner coupled to the pre-trained artificial-intelligence algorithm; wherein the pre-conditioner is configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

75. Measurement application device control unit according to any one of the preceding claims 64 to 74, further comprising a code generator that is coupled to or integrated into the pre-trained artificial-intelligence algorithm; wherein the code generator is configured to generate control commands in a predetermined control or programming language, and to provide the generated control commands in the respective control or programming language to a controller of a measurement application device.

76. Measurement application device control unit according to any one of the preceding claims 64 to 75, wherein the pre-trained artificial-intelligence algorithm comprises an algorithm that is locally executed on the measurement application device.

77. Measurement application device control unit according to any one of the preceding claims 64 to 76, further comprising a complexity estimator coupled to the text-based input interface; wherein the complexity estimator is configured to estimate the complexity of the text-based user requests, and forward the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.

78. Measurement application device control unit according to claim 77, wherein the complexity estimator is configured to request a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm.

79. Measurement application device control unit according to any one of the preceding claims 77 and 78, wherein the complexity estimator is configured to provide the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

80. Measurement application device comprising: a measurement application device control unit according to any one of the preceding claims 64 to 79; anda measurement application device controller coupled to the measurement application device control unit.

81. Computer-implemented method comprising: receiving text-based user requests regarding a measurement application device; generating configuration data regarding the measurement application device based on the textbased user requests with a pre-trained artificial-intelligence algorithm; and outputting the configuration data.

82. Computer-implemented method according to claim 81 , further comprising receiving measurement feedback data; and generating a further set of configuration data based on the original text-based user request, and the received measurement feedback data.

83. Computer-implemented method according to claim 82, further comprising generating a textbased feedback description of the measurement feedback data; and providing the text-based feedback description to the pre-trained artificial-intelligence algorithm.

84. Computer-implemented method according to claim 83, further comprising identifying at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data; and providing a respective text-based feedback description based on the identified at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data.

85. Computer-implemented method according to any one of the preceding claims 82 - 84, further comprising outputting the measurement feedback data to a user; and generating a further set of configuration data based on the original text-based user request, and a further text-based user request received after the measurement feedback data is output to the user.

86. Computer-implemented method according to claims 83 and 85, further comprising outputting the text-based feedback description to the user.

87. Computer-implemented method according to any one of the preceding claims 85 and 86, wherein the pre-trained artificial-intelligence algorithm performs reinforced learning based on thefurther text-based user request received after the measurement feedback data is output to the user.

88. Computer-implemented method according to any one of the preceding method-based claims 81 to 87, further comprising receiving spoken language requests; converting the spoken language requests into text-based requests; and providing the text-based requests to the text-based input interface as text-based user requests.

89. Computer-implemented method according to any one of the preceding method-based claims 81 to 88, wherein the pre-trained artificial-intelligence algorithm is pre-trained using at least one of manuals regarding the measurement application device, datasheets regarding the measurement application device, application notes regarding the measurement application device, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

90. Computer-implemented method according to any one of the preceding method-based claims 81 to 89, wherein the pre-trained artificial-intelligence algorithm comprises a large language model based on at least one of a Bidirectional Encoder Representations from Transformers model, and a Generative Pre-trained Transformer model.91 . Computer-implemented method according to any one of the preceding method-based claims 81 to 90, further comprising providing a pre-conditioning text-based request to the pretrained artificial-intelligence algorithm prior to the pre-trained artificial-intelligence algorithm operating on the text-based user requests.

92. Computer-implemented method according to any one of the preceding method-based claims 81 to 91 , further comprising generating control commands in a predetermined control or programming language; and providing the generated control commands in the respective control or programming language to a controller of a measurement application device.

93. Computer-implemented method according to any one of the preceding method-based claims 81 to 92, wherein the pre-trained artificial-intelligence algorithm is locally executed on the measurement application device.

94. Computer-implemented method according to any one of the preceding method-based claims 81 to 93, further comprising estimating the complexity of the text-based user requests; andforwarding the text-based user requests to an external pre-trained artificial-intelligence algorithm if the estimated complexity is higher than a predetermined threshold.

95. Computer-implemented method according to claim 94, further comprising requesting a user consent via a user interface of the measurement application device prior to providing one of the text-based user requests to the external pre-trained artificial-intelligence algorithm.

96. Computer-implemented method according to any one of the preceding claims 94 and 95, further comprising providing the text-based user requests indirectly to the external pre-trained artificial-intelligence algorithm via a request anonymizer.

97. Computer program product comprising instructions that when executed by a processor cause the processor to perform a method according to any one of the preceding method-based claims 81 to 96.

98. Measurement application processing device comprising: a feedback interface configured to receive measurement feedback data from at least one measurement application device; a feedback data analyzer coupled to the feedback interface, and configured to generate a textbased feedback description of the measurement feedback data; and an output interface coupled to the feedback data analyzer and configured to output the text-based feedback description to a user.

99. Measurement application processing device according to claim 98, wherein the feedback data analyzer is configured to identify at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data, and to provide a respective text-based feedback description.

100. Measurement application processing device according to any one of the preceding claims 98 to 99, further comprising: a text-based input interface configured to receive text-based user requests regarding the textbased feedback description; and a pre-trained artificial-intelligence algorithm coupled to the text-based input interface, and configured to generate user information data regarding the measurement feedback data based on received measurement feedback data, and the text-based user requests; andwherein the pre-trained artificial-intelligence algorithm is configured to output the user information data.

101. Measurement application processing device according to any one of the preceding claims 98 to 100, wherein the measurement feedback data comprises at least one of display information of a display of the measurement application device, measurement values, waveforms, marker information, and trigger information.

102. Measurement application processing device according to claim 101 , wherein the feedback data analyzer comprises a trained image-to-text algorithm that is configured to generate the textbased feedback description based on a screenshot comprising the at least one of display information of a display of the measurement application device, measurement values, waveforms, marker information, and trigger information.

103. Measurement application processing device according to any one of the preceding claims 98 to 102, further comprising a pre-trained artificial-intelligence documentation algorithm configured to create a text-based measurement documentation based on the measurement feedback, and when depending on claim 5, based on at least one of the measurement feedback, the textbased user requests, and the generated user information.

104. Measurement application device comprising: a measurement interface; and a measurement application processing device according to any one of the preceding claims 98 to 103, and coupled to the measurement interface; wherein the measurement interface is configured to provide measurement data to the measurement application processing device as measurement feedback data.

105. Computer-implemented method comprising: receiving measurement feedback data from at least one measurement application device; generating a text-based feedback description of the measurement feedback data; and outputting the text-based feedback description to a user.

106. Computer-implemented method according to claim 105, further comprising identifying at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data;wherein the text-based feedback description is provided based on the identified at least one of anomalies, and relevant measurement values, and relevant measurement waveform sections in raw measurement data provided in the measurement feedback data.

107. Computer-implemented method according to any one of the preceding method-based claims 105 to 106, further comprising: receiving text-based user requests regarding the text-based feedback description; generating user information data regarding the measurement feedback data based on received measurement feedback data, and the text-based user requests with a pre-trained artificial-intelligence algorithm; and outputting the user information data.

108. Computer-implemented method according to any one of the preceding method-based claims 105 to 107, wherein the measurement feedback data comprises a screenshot of the at least one measurement device.

109. Computer-implemented method according to claim 108, wherein a text-based description of the screenshot is generated with a trained image-to-text algorithm.

110. Computer-implemented method according to any one of the preceding method-based claims 105 to 109, further comprising creating a text-based measurement documentation with a pre-trained artificial-intelligence documentation algorithm based on the measurement feedback, and when depending on claim 10, based on at least one of the measurement feedback, the textbased user requests, and the generated user information.

111. Computer program product comprising instructions that when executed by a processor cause the processor to perform a method according to any one of the preceding method-based claims 105 to 110.

112. Circuit development device comprising: a text-based input interface configured to receive a text-based natural language circuit description regarding an electrical circuit; a pre-trained artificial-intelligence circuit generation algorithm coupled to the text-based input interface, and configured to generate at least one circuit design based on the text-based natural language circuit description; andan output interface coupled to the pre-trained artificial-intelligence circuit generation algorithm, and configured to output the at least one circuit design.

113. Circuit development device according to claim 112, wherein the pre-trained artificial-intelligence circuit generation algorithm is configured to generate multiple alternative circuit designs based on the text-based natural language circuit description, and to provide a natural-language description of each one of the alternative circuit designs.

114. Circuit development device according to any one of the preceding claims 112 to 113, wherein the pre-trained artificial-intelligence circuit generation algorithm is configured to output the circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

115. Circuit development device according to any one of the preceding claims 112 to 114, further comprising a pre-conditioner coupled to the pre-trained artificial-intelligence circuit generation algorithm; wherein the pre-conditioner is configured to provide a pre-conditioning text-based request to the pre-trained artificial-intelligence circuit generation algorithm prior to the pre-trained artificial-intelligence circuit generation algorithm operating on the text-based natural language circuit description.

116. Circuit development device according to any one of the preceding claims 112 to 115, wherein the pre-conditioning text-based request indicates at least one of a list of available electrical circuit elements, a list of preferred electrical circuit elements, environmental conditions of an operating environment for the electrical circuit, delivery times of electrical circuit elements, costs of electrical circuit elements, suppliers for electrical circuit elements, and preferred suppliers for electrical circuit elements.

117. Circuit development device according to any one of the preceding claims 112 to 116, further comprising an image-to-text algorithm that is configured to receive a visual representation of an electrical circuit, and to generate the text-based natural language circuit description based on the visual representation of an electrical circuit, and to provide the generated text-based natural language circuit description to the pre-trained artificial-intelligence circuit generation algorithm.

118. Circuit development device according to any one of the preceding claims 112 to 117, wherein the pre-trained artificial-intelligence circuit generation algorithm is pre-trained using at least one of manuals of measurement application devices, datasheets of measurement application devices, application notes of measurement application devices, and manuals of measurement application devices, datasheets of electronic devices, application notes of electronic devices, articlesregarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

119. Circuit development device according to any one of the preceding claims 112 to 118, further comprising: a pre-trained artificial-intelligence circuit verifying algorithm coupled to the pre-trained artificial-intelligence circuit generation algorithm, and configured to receive the at least one circuit design; wherein the pre-trained artificial-intelligence circuit verifying algorithm is further configured to identify at least one of errors, and weaknesses in the at least one circuit design; wherein the pre-trained artificial-intelligence circuit verifying algorithm is further configured to output the identified at least one of errors, and weaknesses.

120. Circuit development device according to claim 119, wherein the pre-trained artificial-intelligence circuit verifying algorithm is configured to analyze the circuit design provided as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

121. Circuit development device according to any one of the preceding claims 119 and 120, wherein the pre-trained artificial-intelligence circuit verifying algorithm is further configured to output natural language measurement recommendations regarding a measurement of the identified at least one of errors, and weaknesses in the at least one circuit design.

122. Circuit development device according to any one of the preceding claims 119 to 121 , wherein the pre-trained artificial-intelligence circuit verifying algorithm is configured to output a circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file, and mark the identified at least one of errors, and weaknesses in the output.

123. Circuit development device according to any one of the preceding claims 119 to 122, wherein the pre-trained artificial-intelligence circuit verifying algorithm is pre-trained using at least one of manuals of measurement application devices, datasheets of measurement application devices, application notes regarding measurement application devices, manuals of electronic devices, datasheets of electronic devices, application notes regarding electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

124. Computer-implemented method comprising: receiving a text-based natural language circuit description regarding an electrical circuit;generating at least one circuit design based on the text-based natural language circuit description with a pre-trained artificial-intelligence circuit generation algorithm; and outputting the at least one circuit design.

125. Computer-implemented method according to claim 124, wherein multiple alternative circuit designs are generated based on the text-based natural language circuit description; and wherein a natural-language description of each one of the alternative circuit designs is generated and output.

126. Computer-implemented method according to any one of the preceding method-based claims 124 to 125, wherein the pre-trained artificial-intelligence circuit generation algorithm outputs the circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

127. Computer-implemented method according to any one of the preceding method-based claims 124 to 126, further comprising providing a pre-conditioning text-based request to the pretrained artificial-intelligence circuit generation algorithm prior to the pre-trained artificial-intelligence circuit generation algorithm operating on the text-based natural language circuit description.

128. Computer-implemented method according to any one of the preceding method-based claims 124 to 127, wherein the pre-conditioning text-based request indicates at least one of a list of available electrical circuit elements, a list of preferred electrical circuit elements, environmental conditions of an operating environment for the electrical circuit, delivery times of electrical circuit elements, costs of electrical circuit elements, suppliers for electrical circuit elements, and preferred suppliers for electrical circuit elements.

129. Computer-implemented method according to any one of the preceding method-based claims 124 to 128, further comprising: receiving a visual representation of an electrical circuit; generating the text-based natural language circuit description based on the visual representation of an electrical circuit; and providing the generated text-based natural language circuit description to the pre-trained artificialintelligence circuit generation algorithm.

130. Computer-implemented method according to any one of the preceding method-based claims 124 to 129, wherein the pre-trained artificial-intelligence circuit generation algorithm is pretrained using at least one of manuals of electronic devices, datasheets of electronic devices, application notes regarding electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.131 . Computer-implemented method according to any one of the preceding method-based claims 124 to 130, further comprising: identifying at least one of errors, and weaknesses in the at least one circuit design with a pretrained artificial-intelligence circuit verifying algorithm; and outputting the identified at least one of errors, and weaknesses.

132. Computer-implemented method according to preceding claim 131 , wherein the pre-trained artificial-intelligence circuit verifying algorithm analyzes the circuit design provided as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file.

133. Computer-implemented method according to any one of the preceding method-based claims 131 and 132, wherein the pre-trained artificial-intelligence circuit verifying algorithm further outputs natural language measurement recommendations regarding a measurement of the identified at least one of errors, and weaknesses in the at least one circuit design.

134. Computer-implemented method according to any one of the preceding method-based claims 131 to 133, wherein the pre-trained artificial-intelligence circuit verifying algorithm is configured to output a circuit design as at least one of a SPICE-file, a netlist, an HDL-file, a VHDL-file, a bill of materials, and a layout file, and mark the identified at least one of errors, and weaknesses in the output.

135. Computer-implemented method according to any one of the preceding method-based claims 131 to 134, wherein the pre-trained artificial-intelligence circuit verifying algorithm is pretrained using at least one of manuals of measurement application devices, datasheets of measurement application devices, application notes regarding measurement application devices, manuals of electronic devices, datasheets of electronic devices, application notes of electronic devices, articles regarding electronic circuits, online forum discussions regarding electronic circuits, and videos regarding electronic circuits.

136. Computer program product comprising instructions that when executed by a processor cause the processor to perform a method according to any one of the preceding method-based claims 124 to 135.

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