System

Abstract

An object of a system according to an embodiment is to efficiently perform selection, combination, and simulation of electronic components.SOLUTION: A system according to an embodiment includes a selection unit, a simulation unit, an acquisition unit, and a proposal unit. The selection unit selects and combines virtual electronic components. The simulation part simulates the operation of the virtual electronic component selected by the selection part. The intake part takes in a data sheet of a new component. The proposal unit proposes an improvement point and efficiency improvement of the circuit based on the simulation result obtained by the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to efficiently select, combine, and simulate electronic components, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently select, combine, and simulate electronic components. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a simulation unit, an import unit, and a proposal unit. The selection unit selects and combines virtual electronic components. The simulation unit simulates the operation of the virtual electronic components selected by the selection unit. The import unit imports data sheets for new components. The proposal unit proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select, combine, and simulate electronic components. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention combines virtual electronic components in software and simulates their operation. This system can generate new components by importing data sheets and other information, and proposes functional improvements and efficient circuit configurations. For example, a user selects and combines virtual electronic components in the software. For example, the user selects electronic components such as resistors, capacitors, and transistors and creates a circuit diagram. The user can specify the component characteristics and connection method. The software then simulates the operation of the combined virtual electronic components. The simulation is performed based on the component characteristics and connection method, and analyzes the operation of the entire circuit. For example, it can simulate changes in voltage and current, signal transmission, etc. Furthermore, new components can be generated by importing data sheets and other information. The user can input data sheets into the software to generate new virtual electronic components. This allows the latest components and custom components to be incorporated into the simulation. The software also proposes functional improvements and efficient circuit configurations. Based on the simulation results, the system proposes improvements and optimizations for the circuit. For example, it can propose changes to component placement and connection methods, component selection, etc. This allows the system to help users design circuits efficiently and improve circuit performance. For example, errors in the design stage can be detected and corrected early. In addition, the system can improve circuit efficiency by proposing optimal circuit configurations.

[0029] The system according to the embodiment includes a selection unit, a simulation unit, an import unit, and a proposal unit. The selection unit selects and combines virtual electronic components. For example, the selection unit selects virtual electronic components such as resistors, capacitors, and transistors. The selection unit also allows a user to specify component characteristics and connection methods. The simulation unit simulates the operation of the virtual electronic components selected by the selection unit. For example, the simulation unit simulates voltage and current changes and signal transmission. The simulation unit can also analyze the operation of the entire circuit based on the component characteristics and connection methods. The import unit imports data sheets for new components. For example, the import unit can import data sheets in PDF or CSV format. The import unit can also analyze the data sheets and generate new virtual electronic components. The proposal unit proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit. For example, the proposal unit can propose changes to component placement and connection methods, and component selection. This enables the system according to the embodiment to select virtual electronic components, perform simulations, import data sheets, and propose improvements and optimizations to the circuit.

[0030] The import unit can import data sheets in PDF or CSV format. The import unit imports, for example, a data sheet in PDF format. The import unit analyzes the structure of the PDF file and extracts the necessary information. The import unit can also import data sheets in CSV format. The import unit formats the data in the CSV file and extracts the necessary information. This makes it possible to import data sheets in PDF or CSV format.

[0031] The simulation unit can simulate changes in voltage and current, and signal transmission. The simulation unit, for example, simulates changes in voltage. The simulation unit analyzes changes in voltage and evaluates the operation of the entire circuit. The simulation unit can also simulate changes in current. The simulation unit can also analyze changes in current and evaluate the operation of the entire circuit. The simulation unit can also simulate signal transmission. The simulation unit can analyze the signal transmission speed and transmission accuracy and evaluate the operation of the entire circuit. This makes it possible to simulate changes in voltage and current, and signal transmission.

[0032] The proposal unit can propose changes to component placement and connection methods, and component selection, based on the simulation results. The proposal unit proposes component placement, for example, based on the simulation results. The proposal unit optimizes component placement to improve circuit efficiency. The proposal unit can also propose changes to connection methods. The proposal unit can also change connection methods to improve circuit performance. The proposal unit can also propose component selection. The proposal unit can select optimal components to improve circuit performance. This makes it possible to propose changes to component placement and connection methods, and component selection, based on the simulation results.

[0033] The selection unit can select virtual electronic components such as resistors, capacitors, and transistors. The selection unit selects, for example, a resistor. The selection unit specifies the characteristics of the resistor and incorporates it into the circuit. The selection unit can also select a capacitor. The selection unit can also specify the characteristics of the capacitor and incorporate it into the circuit. The selection unit can also select a transistor. The selection unit can also specify the characteristics of the transistor and incorporate it into the circuit. This makes it possible to select virtual electronic components such as resistors, capacitors, and transistors.

[0034] The simulation unit can analyze the operation of the entire circuit based on the component characteristics and connection method. The simulation unit, for example, analyzes the component characteristics. The simulation unit analyzes resistance values, capacitance, transistor characteristics, etc., and evaluates the operation of the entire circuit. The simulation unit can also analyze the connection method. The simulation unit can also analyze the type of connection and the connection standard, and evaluate the operation of the entire circuit. This makes it possible to analyze the operation of the entire circuit based on the component characteristics and connection method.

[0035] The selection unit can analyze the user's past selection history at the time of selection and automatically suggest the most suitable parts. For example, the selection unit automatically displays as candidates virtual electronic parts that the user has frequently selected in the past. The selection unit analyzes the past selection history and displays the frequently selected parts. The selection unit can also preferentially suggest a selection method (voice, text, etc.) that the user has used in the past. The selection unit can analyze the past selection history and suggest a used selection method. The selection unit can also predict and suggest parts to be used for a specific project from the user's past selection history. The selection unit can analyze the past selection history and suggest parts related to the project. In this way, the selection unit can analyze the user's past selection history and automatically suggest the most suitable parts.

[0036] At the time of selection, the selection unit can perform filtering based on the user's current project or field of interest. For example, the selection unit preferentially displays virtual electronic components related to a project the user is currently working on. The selection unit analyzes the current project and displays related components. The selection unit can also filter and suggest related virtual electronic components based on the user's field of interest. The selection unit can analyze the field of interest and suggest related components. The selection unit can also suggest related virtual electronic components based on projects in which the user has shown interest in the past. The selection unit can analyze past fields of interest and suggest related components. This makes it possible to perform filtering based on the user's current project or field of interest.

[0037] The selection unit can provide an appropriate selection means according to the user's input method at the time of selection. For example, when the user simply inputs "resistor" by voice, the selection unit automatically displays related virtual electronic components. The selection unit analyzes the voice input and displays related components. The selection unit can also display candidate virtual electronic components in real time when the user inputs a component name by text. The selection unit can analyze text input and display candidate components. The selection unit can also suggest related virtual electronic components based on an image uploaded by the user. The selection unit can extract and suggest related components from the image using image analysis technology. This makes it possible to provide an optimal selection means according to the user's input method.

[0038] When making a selection, the selection unit can prioritize displaying highly relevant parts by taking into account the user's geographical location information. For example, if the user is in a specific region, the selection unit prioritizes displaying virtual electronic parts available in that region. The selection unit analyzes the geographical location information and displays parts based on the region. Furthermore, if the user is in a specific country, the selection unit can suggest virtual electronic parts commonly used in that country. The selection unit can analyze country-specific information and suggest common parts. Furthermore, if the user is traveling, the selection unit can suggest the most appropriate virtual electronic part based on the user's current location. The selection unit can analyze location information during travel and suggest the most appropriate part. In this way, highly relevant parts can be prioritized and displayed by taking into account the user's geographical location information.

[0039] At the time of selection, the selection unit can analyze the user's social media activity and suggest related parts. For example, the selection unit can suggest virtual electronic parts related to a project shared by the user on social media. The selection unit can analyze social media posts and suggest related parts. The selection unit can also analyze the content of the user's social media posts and suggest related virtual electronic parts. The selection unit can analyze the content of the posts and suggest related parts. The selection unit can also suggest related virtual electronic parts by referring to the activities of the user's friends on social media. The selection unit can analyze the friends' activities and suggest related parts. In this way, the user's social media activity can be analyzed and related parts can be suggested.

[0040] The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. The selection unit customizes the selection interface, for example, based on feedback provided by the user in the past. The selection unit analyzes the past feedback and adjusts the interface. The selection unit can also preferentially display specific parts based on the user's past feedback. The selection unit can analyze the feedback and display the prioritized parts. The selection unit can also analyze the user's feedback and optimize the selection method. The selection unit can analyze the feedback and improve the selection method. This makes it possible to customize the selection method by reflecting the user's past feedback.

[0041] The simulation unit can adjust the level of detail of the simulation based on the importance of the parts during the simulation. For example, the simulation unit performs a detailed simulation and analyzes the operation of important parts. The simulation unit evaluates the importance of the parts and performs a detailed analysis. The simulation unit can also perform a simplified simulation for parts with low importance. The simulation unit can evaluate the importance of the parts and perform a simplified analysis. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the parts. The simulation unit can evaluate the importance of the parts and adjust the level of detail. This makes it possible to adjust the level of detail of the simulation based on the importance of the parts.

[0042] The simulation unit can apply different simulation algorithms depending on the category of the component during simulation. For example, the simulation unit applies a specific simulation algorithm to passive components such as resistors and capacitors. The simulation unit evaluates the category of the component and applies an appropriate algorithm. The simulation unit can also apply a different simulation algorithm to active components such as transistors and ICs. The simulation unit can evaluate the category of the component and apply a different algorithm. The simulation unit can also select an optimal simulation algorithm depending on the category of the component. The simulation unit can evaluate the category of the component and select an optimal algorithm. This makes it possible to apply different simulation algorithms depending on the category of the component.

[0043] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. The simulation unit improves the accuracy of the simulation, for example, based on the results of simulations performed by the user in the past. The simulation unit analyzes the past results and improves the accuracy. The simulation unit can also optimize specific parameters from the user's past simulation results. The simulation unit can analyze the past results and optimize parameters. The simulation unit can also analyze the user's past simulation results and improve the simulation algorithm. The simulation unit can analyze the past results and improve the algorithm. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0044] During simulation, the simulation unit can determine the priority of the simulation based on the submission date of the parts. For example, the simulation unit prioritizes simulation of recently submitted parts. The simulation unit evaluates the submission date and determines the priority. The simulation unit can also postpone simulation of parts that were submitted earlier. The simulation unit can evaluate the submission date and postpone. The simulation unit can also dynamically adjust the priority of the simulation based on the submission date. The simulation unit can evaluate the submission date and dynamically adjust the priority. In this way, the priority of the simulation can be determined based on the submission date of the parts.

[0045] The simulation unit can adjust the order of simulations based on the relevance of parts during simulation. For example, the simulation unit prioritizes simulation of parts with high relevance. The simulation unit evaluates the relevance of parts and determines the priority order. The simulation unit can also postpone simulation of parts with low relevance. The simulation unit can evaluate the relevance of parts and postpone them. The simulation unit can also dynamically adjust the order of simulations based on the relevance of parts. The simulation unit can evaluate the relevance of parts and dynamically adjust the order. In this way, the order of simulations can be adjusted based on the relevance of parts.

[0046] The simulation unit can adjust the use of technical terms in the simulation during the simulation according to the user's level of expertise. For example, the simulation unit displays simulation results using simple technical terms for a novice user. The simulation unit evaluates the user's level of expertise and adjusts the terminology. The simulation unit can also display simulation results using detailed technical terms for an advanced user. The simulation unit can evaluate the user's level of expertise and use detailed terminology. The simulation unit can also customize the display method of the simulation results according to the user's level of expertise. The simulation unit can evaluate the user's level of expertise and customize the display method. This makes it possible to adjust the use of technical terms in the simulation according to the user's level of expertise.

[0047] The import unit can select the optimal import means depending on the format of the data sheet when importing. For example, for a data sheet in PDF format, the import unit imports it using a dedicated analysis algorithm. The import unit analyzes the structure of the PDF file and selects the optimal means. For a data sheet in CSV format, the import unit can also import it while reformatting the data. The import unit can analyze the data in the CSV file and select the optimal means. The import unit can also dynamically select the optimal import means depending on the format of the data sheet. The import unit can evaluate the format of the data sheet and dynamically select the means. This makes it possible to select the optimal import means depending on the format of the data sheet.

[0048] The import unit can automatically analyze the contents of the data sheet when importing and extract necessary information. For example, the import unit can automatically analyze the contents of the data sheet and extract characteristic information of components. The import unit can analyze the data sheet and extract characteristic information. The import unit can also analyze the contents of the data sheet and extract information necessary for circuit design. The import unit can analyze the data sheet and extract design information. The import unit can also automatically analyze the contents of the data sheet and extract parameters necessary for simulation. The import unit can analyze the data sheet and extract simulation parameters. In this way, the contents of the data sheet can be automatically analyzed and necessary information can be extracted.

[0049] The import unit can improve the accuracy of importing by referring to the user's past import history when importing. The import unit improves the accuracy of importing, for example, based on the history of data sheets that the user has imported in the past. The import unit analyzes the past history and improves the accuracy. The import unit can also select the optimal import method for a specific format from the user's past import history. The import unit can analyze the past history and select the optimal method. The import unit can also analyze the user's past import history and improve the import algorithm. The import unit can analyze the past history and improve the algorithm. In this way, the accuracy of importing can be improved by referring to the user's past import history.

[0050] The import unit can determine the import priority based on the submission time of the data sheet at the time of import. For example, the import unit prioritizes importing of recently submitted data sheets. The import unit evaluates the submission time and determines the priority. The import unit can also postpone importing of data sheets that were submitted earlier. The import unit can evaluate the submission time and postpone. The import unit can also dynamically adjust the import priority based on the submission time. The import unit can evaluate the submission time and dynamically adjust the priority. In this way, the import priority can be determined based on the submission time of the data sheet.

[0051] The import unit can adjust the import order based on the relevance of the data sheets when importing. For example, the import unit prioritizes importing highly relevant data sheets. The import unit evaluates the relevance of the data sheets and determines the priority order. The import unit can also postpone importing less relevant data sheets. The import unit can evaluate the relevance of the data sheets and postpone them. The import unit can also dynamically adjust the import order based on the relevance of the data sheets. The import unit can evaluate the relevance of the data sheets and dynamically adjust the order. This makes it possible to adjust the import order based on the relevance of the data sheets.

[0052] The capture unit can adjust the capture method according to the user's level of expertise during capture. For example, the capture unit provides a simple capture procedure to a novice user. The capture unit evaluates the user's level of expertise and adjusts the procedure. The capture unit can also provide detailed capture options to an advanced user. The capture unit can evaluate the user's level of expertise and provide detailed options. The capture unit can also customize the capture procedure according to the user's level of expertise. The capture unit can evaluate the user's level of expertise and customize the procedure. This makes it possible to adjust the capture method according to the user's level of expertise.

[0053] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result when making a proposal. For example, the proposal unit makes a detailed proposal for an important simulation result. The proposal unit evaluates the importance of the simulation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for a simulation result with a low importance. The proposal unit can evaluate the importance of the simulation result and make a simplified proposal. The proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the simulation result. The proposal unit can evaluate the importance of the simulation result and dynamically adjust the level of detail. This makes it possible to adjust the level of detail of the proposal based on the importance of the simulation result.

[0054] The proposal unit can apply different proposal algorithms depending on the category of the circuit when making a proposal. For example, the proposal unit applies a specific proposal algorithm to an analog circuit. The proposal unit evaluates the category of the circuit and applies an appropriate algorithm. The proposal unit can also apply a different proposal algorithm to a digital circuit. The proposal unit can evaluate the category of the circuit and apply a different algorithm. The proposal unit can also select an optimal proposal algorithm depending on the category of the circuit. The proposal unit can evaluate the category of the circuit and select an optimal algorithm. This makes it possible to apply different proposal algorithms depending on the category of the circuit.

[0055] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results the user has received in the past. The suggestion unit analyzes the past suggestion results and improves the accuracy. The suggestion unit can also optimize specific parameters from the user's past suggestion results. The suggestion unit can analyze the past suggestion results and optimize parameters. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. The suggestion unit can analyze the past suggestion results and improve the algorithm. This makes it possible to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0056] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the simulation results. For example, the proposal unit gives priority to the most recently submitted simulation results. The proposal unit evaluates the submission time and determines the priority. The proposal unit can also postpone the proposal of older submitted simulation results. The proposal unit can evaluate the submission time and postpone. The proposal unit can also dynamically adjust the priority of the proposal based on the submission time. The proposal unit can evaluate the submission time and dynamically adjust the priority. In this way, the priority of the proposal can be determined based on the submission time of the simulation results.

[0057] The proposal unit can adjust the order of proposals based on the relevance of the simulation results when making a proposal. For example, the proposal unit gives priority to proposals for highly relevant simulation results. The proposal unit evaluates the relevance of the simulation results and determines the priority order. The proposal unit can also postpone proposals for less relevant simulation results. The proposal unit can evaluate the relevance of the simulation results and postpone them. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the simulation results. The proposal unit can evaluate the relevance of the simulation results and dynamically adjust the order. This makes it possible to adjust the order of proposals based on the relevance of the simulation results.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes a suggestion using simple technical terminology for a novice user. The suggestion unit evaluates the user's level of expertise and adjusts the terminology. The suggestion unit can also make a suggestion using detailed technical terminology for an advanced user. The suggestion unit can evaluate the user's level of expertise and use detailed terminology. The suggestion unit can also customize the content of the suggestion according to the user's level of expertise. The suggestion unit can evaluate the user's level of expertise and customize the content. This makes it possible to adjust the use of technical terminology in the suggestion according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The selection unit can analyze the user's past selection history and automatically suggest optimal parts. For example, virtual electronic parts that the user has frequently selected in the past are automatically displayed as candidates. The selection unit can analyze the past selection history and display frequently selected parts. The selection unit can also prioritize and suggest selection methods (voice, text, etc.) that the user has used in the past. The selection unit can analyze the past selection history and suggest used selection methods. The selection unit can also predict and suggest parts to be used for a specific project from the user's past selection history. The selection unit can analyze the past selection history and suggest parts related to the project. In this way, the user's past selection history can be analyzed and optimal parts can be automatically suggested.

[0061] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result. For example, a detailed proposal is made for an important simulation result. The proposal unit evaluates the importance of the simulation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for a simulation result with a low importance. The proposal unit can evaluate the importance of the simulation result and make a simplified proposal. The proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the simulation result. The proposal unit can evaluate the importance of the simulation result and dynamically adjust the level of detail. This makes it possible to adjust the level of detail of the proposal based on the importance of the simulation result.

[0062] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the parts. For example, for important parts, a detailed simulation is performed and the operation is analyzed. The simulation unit evaluates the importance of the parts and performs a detailed analysis. The simulation unit can also perform a simplified simulation for parts with low importance. The simulation unit can evaluate the importance of the parts and perform a simplified analysis. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the parts. The simulation unit can evaluate the importance of the parts and adjust the level of detail. In this way, the level of detail of the simulation can be adjusted based on the importance of the parts.

[0063] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the circuit. For example, a specific proposal algorithm is applied to an analog circuit. The proposal unit evaluates the category of the circuit and applies an appropriate algorithm. The proposal unit can also apply a different proposal algorithm to a digital circuit. The proposal unit can evaluate the category of the circuit and apply a different algorithm. The proposal unit can also select an optimal proposal algorithm depending on the category of the circuit. The proposal unit can evaluate the category of the circuit and select an optimal algorithm. This makes it possible to apply different proposal algorithms depending on the category of the circuit.

[0064] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the accuracy of the simulation is improved based on the results of simulations performed by the user in the past. The simulation unit analyzes the past results and improves the accuracy. The simulation unit can also optimize specific parameters from the user's past simulation results. The simulation unit can analyze the past results and optimize parameters. The simulation unit can also analyze the user's past simulation results and improve the simulation algorithm. The simulation unit can analyze the past results and improve the algorithm. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0065] The import unit can select the optimal import method depending on the format of the data sheet when importing. For example, for a data sheet in PDF format, it imports using a dedicated analysis algorithm. The import unit analyzes the structure of the PDF file and selects the optimal method. For a data sheet in CSV format, the import unit can also import while reformatting the data. The import unit can analyze the data in the CSV file and select the optimal method. The import unit can also dynamically select the optimal import method depending on the format of the data sheet. The import unit can evaluate the format of the data sheet and dynamically select the method. This makes it possible to select the optimal import method depending on the format of the data sheet.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The selection unit selects and combines virtual electronic components. For example, the selection unit selects virtual electronic components such as resistors, capacitors, and transistors. The selection unit also allows the user to specify the characteristics and connection method of the components. Step 2: The simulation unit simulates the operation of the virtual electronic components selected by the selection unit. For example, the simulation unit simulates changes in voltage and current, and signal transmission. The simulation unit can also analyze the operation of the entire circuit based on the characteristics and connection method of the components. Step 3: The importer imports the datasheet for the new component. For example, the importer can import a datasheet in PDF or CSV format. The importer can also analyze the datasheet and generate a new virtual electronic component. Step 4: The proposal unit proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit. For example, the proposal unit can propose changes to the component placement and connection methods, or component selection.

[0068] (Example 2) A system according to an embodiment of the present invention combines virtual electronic components in software and simulates their operation. This system can generate new components by importing data sheets and other information, and proposes functional improvements and efficient circuit configurations. For example, a user selects and combines virtual electronic components in the software. For example, the user selects electronic components such as resistors, capacitors, and transistors and creates a circuit diagram. The user can specify the component characteristics and connection method. The software then simulates the operation of the combined virtual electronic components. The simulation is performed based on the component characteristics and connection method, and analyzes the operation of the entire circuit. For example, it can simulate changes in voltage and current, signal transmission, etc. Furthermore, new components can be generated by importing data sheets and other information. The user can input data sheets into the software to generate new virtual electronic components. This allows the latest components and custom components to be incorporated into the simulation. The software also proposes functional improvements and efficient circuit configurations. Based on the simulation results, the system proposes improvements and optimizations for the circuit. For example, it can propose changes to component placement and connection methods, component selection, etc. This allows the system to help users design circuits efficiently and improve circuit performance. For example, errors in the design stage can be detected and corrected early. In addition, the system can improve circuit efficiency by proposing optimal circuit configurations.

[0069] The system according to the embodiment includes a selection unit, a simulation unit, an import unit, and a proposal unit. The selection unit selects and combines virtual electronic components. For example, the selection unit selects virtual electronic components such as resistors, capacitors, and transistors. The selection unit also allows a user to specify component characteristics and connection methods. The simulation unit simulates the operation of the virtual electronic components selected by the selection unit. For example, the simulation unit simulates voltage and current changes and signal transmission. The simulation unit can also analyze the operation of the entire circuit based on the component characteristics and connection methods. The import unit imports data sheets for new components. For example, the import unit can import data sheets in PDF or CSV format. The import unit can also analyze the data sheets and generate new virtual electronic components. The proposal unit proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit. For example, the proposal unit can propose changes to component placement and connection methods, and component selection. This enables the system according to the embodiment to select virtual electronic components, perform simulations, import data sheets, and propose improvements and optimizations to the circuit.

[0070] The import unit can import data sheets in PDF or CSV format. The import unit imports, for example, a data sheet in PDF format. The import unit analyzes the structure of the PDF file and extracts the necessary information. The import unit can also import data sheets in CSV format. The import unit formats the data in the CSV file and extracts the necessary information. This makes it possible to import data sheets in PDF or CSV format.

[0071] The simulation unit can simulate changes in voltage and current, and signal transmission. The simulation unit, for example, simulates changes in voltage. The simulation unit analyzes changes in voltage and evaluates the operation of the entire circuit. The simulation unit can also simulate changes in current. The simulation unit can also analyze changes in current and evaluate the operation of the entire circuit. The simulation unit can also simulate signal transmission. The simulation unit can analyze the signal transmission speed and transmission accuracy and evaluate the operation of the entire circuit. This makes it possible to simulate changes in voltage and current, and signal transmission.

[0072] The proposal unit can propose changes to component placement and connection methods, and component selection, based on the simulation results. The proposal unit proposes component placement, for example, based on the simulation results. The proposal unit optimizes component placement to improve circuit efficiency. The proposal unit can also propose changes to connection methods. The proposal unit can also change connection methods to improve circuit performance. The proposal unit can also propose component selection. The proposal unit can select optimal components to improve circuit performance. This makes it possible to propose changes to component placement and connection methods, and component selection, based on the simulation results.

[0073] The selection unit can select virtual electronic components such as resistors, capacitors, and transistors. The selection unit selects, for example, a resistor. The selection unit specifies the characteristics of the resistor and incorporates it into the circuit. The selection unit can also select a capacitor. The selection unit can also specify the characteristics of the capacitor and incorporate it into the circuit. The selection unit can also select a transistor. The selection unit can also specify the characteristics of the transistor and incorporate it into the circuit. This makes it possible to select virtual electronic components such as resistors, capacitors, and transistors.

[0074] The simulation unit can analyze the operation of the entire circuit based on the component characteristics and connection method. The simulation unit, for example, analyzes the component characteristics. The simulation unit analyzes resistance values, capacitance, transistor characteristics, etc., and evaluates the operation of the entire circuit. The simulation unit can also analyze the connection method. The simulation unit can also analyze the type of connection and the connection standard, and evaluate the operation of the entire circuit. This makes it possible to analyze the operation of the entire circuit based on the component characteristics and connection method.

[0075] The selection unit can estimate the user's emotions and adjust the selection method for virtual electronic components based on the estimated user emotions. For example, when the user is stressed, the selection unit provides a simple interface and minimizes the selection procedure. The selection unit can estimate the user's emotions and simplify the selection procedure. Furthermore, when the user is relaxed, the selection unit can provide detailed selection options and suggest a customizable selection method. The selection unit can estimate the user's emotions and increase the selection options. Furthermore, when the user is in a hurry, the selection unit can prioritize voice input to enable quick selection of virtual electronic components. The selection unit can estimate the user's emotions and prioritize voice input. This allows the selection method for virtual electronic components to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The selection unit can analyze the user's past selection history at the time of selection and automatically suggest the most suitable parts. For example, the selection unit automatically displays as candidates virtual electronic parts that the user has frequently selected in the past. The selection unit analyzes the past selection history and displays the frequently selected parts. The selection unit can also preferentially suggest a selection method (voice, text, etc.) that the user has used in the past. The selection unit can analyze the past selection history and suggest a used selection method. The selection unit can also predict and suggest parts to be used for a specific project from the user's past selection history. The selection unit can analyze the past selection history and suggest parts related to the project. In this way, the selection unit can analyze the user's past selection history and automatically suggest the most suitable parts.

[0077] At the time of selection, the selection unit can perform filtering based on the user's current project or field of interest. For example, the selection unit preferentially displays virtual electronic components related to a project the user is currently working on. The selection unit analyzes the current project and displays related components. The selection unit can also filter and suggest related virtual electronic components based on the user's field of interest. The selection unit can analyze the field of interest and suggest related components. The selection unit can also suggest related virtual electronic components based on projects in which the user has shown interest in the past. The selection unit can analyze past fields of interest and suggest related components. This makes it possible to perform filtering based on the user's current project or field of interest.

[0078] The selection unit can provide an appropriate selection means according to the user's input method at the time of selection. For example, when the user simply inputs "resistor" by voice, the selection unit automatically displays related virtual electronic components. The selection unit analyzes the voice input and displays related components. The selection unit can also display candidate virtual electronic components in real time when the user inputs a component name by text. The selection unit can analyze text input and display candidate components. The selection unit can also suggest related virtual electronic components based on an image uploaded by the user. The selection unit can extract and suggest related components from the image using image analysis technology. This makes it possible to provide an optimal selection means according to the user's input method.

[0079] The selection unit can estimate the user's emotions and determine the priority of parts to be selected based on the estimated user emotions. For example, if the user is nervous, the selection unit can prioritize displaying important parts to simplify selection. The selection unit can estimate the user's emotions and prioritize important parts. Furthermore, if the user is relaxed, the selection unit can display a detailed parts list to broaden the range of choices. The selection unit can estimate the user's emotions and display a detailed list. Furthermore, if the user is in a hurry, the selection unit can prioritize displaying the most frequently used parts. The selection unit can estimate the user's emotions and prioritize the most frequently used parts. This allows the priority of parts to be selected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When making a selection, the selection unit can prioritize displaying highly relevant parts by taking into account the user's geographical location information. For example, if the user is in a specific region, the selection unit prioritizes displaying virtual electronic parts available in that region. The selection unit analyzes the geographical location information and displays parts based on the region. Furthermore, if the user is in a specific country, the selection unit can suggest virtual electronic parts commonly used in that country. The selection unit can analyze country-specific information and suggest common parts. Furthermore, if the user is traveling, the selection unit can suggest the most appropriate virtual electronic part based on the user's current location. The selection unit can analyze location information during travel and suggest the most appropriate part. In this way, highly relevant parts can be prioritized and displayed by taking into account the user's geographical location information.

[0081] At the time of selection, the selection unit can analyze the user's social media activity and suggest related parts. For example, the selection unit can suggest virtual electronic parts related to a project shared by the user on social media. The selection unit can analyze social media posts and suggest related parts. The selection unit can also analyze the content of the user's social media posts and suggest related virtual electronic parts. The selection unit can analyze the content of the posts and suggest related parts. The selection unit can also suggest related virtual electronic parts by referring to the activities of the user's friends on social media. The selection unit can analyze the friends' activities and suggest related parts. In this way, the user's social media activity can be analyzed and related parts can be suggested.

[0082] The selection unit can customize the selection method by reflecting the user's past feedback when making a selection. The selection unit customizes the selection interface, for example, based on feedback provided by the user in the past. The selection unit analyzes the past feedback and adjusts the interface. The selection unit can also preferentially display specific parts based on the user's past feedback. The selection unit can analyze the feedback and display the prioritized parts. The selection unit can also analyze the user's feedback and optimize the selection method. The selection unit can analyze the feedback and improve the selection method. This makes it possible to customize the selection method by reflecting the user's past feedback.

[0083] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user's emotions. For example, if the user is nervous, the simulation unit provides a simple, highly visible display method. The simulation unit can estimate the user's emotions and simplify the display method. Furthermore, if the user is relaxed, the simulation unit can provide a display method including detailed information. The simulation unit can estimate the user's emotions and provide a detailed display. Furthermore, if the user is in a hurry, the simulation unit can provide a display method that focuses on the main points. The simulation unit can estimate the user's emotions and provide a display that emphasizes the main points. This makes it possible to adjust the display method of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The simulation unit can adjust the level of detail of the simulation based on the importance of the parts during the simulation. For example, the simulation unit performs a detailed simulation and analyzes the operation of important parts. The simulation unit evaluates the importance of the parts and performs a detailed analysis. The simulation unit can also perform a simplified simulation for parts with low importance. The simulation unit can evaluate the importance of the parts and perform a simplified analysis. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the parts. The simulation unit can evaluate the importance of the parts and adjust the level of detail. This makes it possible to adjust the level of detail of the simulation based on the importance of the parts.

[0085] The simulation unit can apply different simulation algorithms depending on the category of the component during simulation. For example, the simulation unit applies a specific simulation algorithm to passive components such as resistors and capacitors. The simulation unit evaluates the category of the component and applies an appropriate algorithm. The simulation unit can also apply a different simulation algorithm to active components such as transistors and ICs. The simulation unit can evaluate the category of the component and apply a different algorithm. The simulation unit can also select an optimal simulation algorithm depending on the category of the component. The simulation unit can evaluate the category of the component and select an optimal algorithm. This makes it possible to apply different simulation algorithms depending on the category of the component.

[0086] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. The simulation unit improves the accuracy of the simulation, for example, based on the results of simulations performed by the user in the past. The simulation unit analyzes the past results and improves the accuracy. The simulation unit can also optimize specific parameters from the user's past simulation results. The simulation unit can analyze the past results and optimize parameters. The simulation unit can also analyze the user's past simulation results and improve the simulation algorithm. The simulation unit can analyze the past results and improve the algorithm. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0087] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user's emotions. For example, if the user is in a hurry, the simulation unit provides a short, to-the-point simulation. The simulation unit can estimate the user's emotions and shorten the length of the simulation. If the user is relaxed, the simulation unit can also provide a longer simulation with detailed explanations. The simulation unit can estimate the user's emotions and extend the length of the simulation. If the user is excited, the simulation unit can also provide a simulation with visually stimulating effects. The simulation unit can estimate the user's emotions and add effects. This allows the length of the simulation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During simulation, the simulation unit can determine the priority of the simulation based on the submission date of the parts. For example, the simulation unit prioritizes simulation of recently submitted parts. The simulation unit evaluates the submission date and determines the priority. The simulation unit can also postpone simulation of parts that were submitted earlier. The simulation unit can evaluate the submission date and postpone. The simulation unit can also dynamically adjust the priority of the simulation based on the submission date. The simulation unit can evaluate the submission date and dynamically adjust the priority. In this way, the priority of the simulation can be determined based on the submission date of the parts.

[0089] The simulation unit can adjust the order of simulations based on the relevance of parts during simulation. For example, the simulation unit prioritizes simulation of parts with high relevance. The simulation unit evaluates the relevance of parts and determines the priority order. The simulation unit can also postpone simulation of parts with low relevance. The simulation unit can evaluate the relevance of parts and postpone them. The simulation unit can also dynamically adjust the order of simulations based on the relevance of parts. The simulation unit can evaluate the relevance of parts and dynamically adjust the order. In this way, the order of simulations can be adjusted based on the relevance of parts.

[0090] The simulation unit can adjust the use of technical terms in the simulation during the simulation according to the user's level of expertise. For example, the simulation unit displays simulation results using simple technical terms for a novice user. The simulation unit evaluates the user's level of expertise and adjusts the terminology. The simulation unit can also display simulation results using detailed technical terms for an advanced user. The simulation unit can evaluate the user's level of expertise and use detailed terminology. The simulation unit can also customize the display method of the simulation results according to the user's level of expertise. The simulation unit can evaluate the user's level of expertise and customize the display method. This makes it possible to adjust the use of technical terms in the simulation according to the user's level of expertise.

[0091] The capture unit can estimate the user's emotions and adjust the data sheet capture method based on the estimated user emotions. For example, if the user is feeling stressed, the capture unit can provide a simple interface and minimize the capture procedure. The capture unit can estimate the user's emotions and simplify the capture procedure. Furthermore, if the user is relaxed, the capture unit can provide detailed capture options and suggest a customizable capture method. The capture unit can estimate the user's emotions and provide detailed options. Furthermore, if the user is in a hurry, the capture unit can prioritize voice input and quickly capture the data sheet. The capture unit can estimate the user's emotions and prioritize voice input. This allows the data sheet capture method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The import unit can select the optimal import means depending on the format of the data sheet when importing. For example, for a data sheet in PDF format, the import unit imports it using a dedicated analysis algorithm. The import unit analyzes the structure of the PDF file and selects the optimal means. For a data sheet in CSV format, the import unit can also import it while reformatting the data. The import unit can analyze the data in the CSV file and select the optimal means. The import unit can also dynamically select the optimal import means depending on the format of the data sheet. The import unit can evaluate the format of the data sheet and dynamically select the means. This makes it possible to select the optimal import means depending on the format of the data sheet.

[0093] The import unit can automatically analyze the contents of the data sheet when importing and extract necessary information. For example, the import unit can automatically analyze the contents of the data sheet and extract characteristic information of components. The import unit can analyze the data sheet and extract characteristic information. The import unit can also analyze the contents of the data sheet and extract information necessary for circuit design. The import unit can analyze the data sheet and extract design information. The import unit can also automatically analyze the contents of the data sheet and extract parameters necessary for simulation. The import unit can analyze the data sheet and extract simulation parameters. In this way, the contents of the data sheet can be automatically analyzed and necessary information can be extracted.

[0094] The import unit can improve the accuracy of importing by referring to the user's past import history when importing. The import unit improves the accuracy of importing, for example, based on the history of data sheets that the user has imported in the past. The import unit analyzes the past history and improves the accuracy. The import unit can also select the optimal import method for a specific format from the user's past import history. The import unit can analyze the past history and select the optimal method. The import unit can also analyze the user's past import history and improve the import algorithm. The import unit can analyze the past history and improve the algorithm. In this way, the accuracy of importing can be improved by referring to the user's past import history.

[0095] The capture unit can estimate the user's emotions and determine the priority of data sheets to be captured based on the estimated user emotions. For example, when the user is nervous, the capture unit prioritizes importing important data sheets. The capture unit can estimate the user's emotions and prioritize important data sheets. Furthermore, when the user is relaxed, the capture unit can prioritize importing detailed data sheets. The capture unit can estimate the user's emotions and prioritize detailed data sheets. Furthermore, when the user is in a hurry, the capture unit can prioritize importing the most frequently used data sheets. The capture unit can estimate the user's emotions and prioritize the most frequently used data sheets. This makes it possible to determine the priority of data sheets to be captured based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The import unit can determine the import priority based on the submission time of the data sheet at the time of import. For example, the import unit prioritizes importing of recently submitted data sheets. The import unit evaluates the submission time and determines the priority. The import unit can also postpone importing of data sheets that were submitted earlier. The import unit can evaluate the submission time and postpone. The import unit can also dynamically adjust the import priority based on the submission time. The import unit can evaluate the submission time and dynamically adjust the priority. In this way, the import priority can be determined based on the submission time of the data sheet.

[0097] The import unit can adjust the import order based on the relevance of the data sheets when importing. For example, the import unit prioritizes importing highly relevant data sheets. The import unit evaluates the relevance of the data sheets and determines the priority order. The import unit can also postpone importing less relevant data sheets. The import unit can evaluate the relevance of the data sheets and postpone them. The import unit can also dynamically adjust the import order based on the relevance of the data sheets. The import unit can evaluate the relevance of the data sheets and dynamically adjust the order. This makes it possible to adjust the import order based on the relevance of the data sheets.

[0098] The capture unit can adjust the capture method according to the user's level of expertise during capture. For example, the capture unit provides a simple capture procedure to a novice user. The capture unit evaluates the user's level of expertise and adjusts the procedure. The capture unit can also provide detailed capture options to an advanced user. The capture unit can evaluate the user's level of expertise and provide detailed options. The capture unit can also customize the capture procedure according to the user's level of expertise. The capture unit can evaluate the user's level of expertise and customize the procedure. This makes it possible to adjust the capture method according to the user's level of expertise.

[0099] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit provides a simple, highly visible suggestion. The suggestion unit can estimate the user's emotions and simplify the way the suggestions are expressed. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion including detailed information. The suggestion unit can estimate the user's emotions and provide a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion that focuses on the main points. The suggestion unit can estimate the user's emotions and provide a suggestion that emphasizes the main points. This makes it possible to adjust the way the suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result when making a proposal. For example, the proposal unit makes a detailed proposal for an important simulation result. The proposal unit evaluates the importance of the simulation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for a simulation result with a low importance. The proposal unit can evaluate the importance of the simulation result and make a simplified proposal. The proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the simulation result. The proposal unit can evaluate the importance of the simulation result and dynamically adjust the level of detail. This makes it possible to adjust the level of detail of the proposal based on the importance of the simulation result.

[0101] The proposal unit can apply different proposal algorithms depending on the category of the circuit when making a proposal. For example, the proposal unit applies a specific proposal algorithm to an analog circuit. The proposal unit evaluates the category of the circuit and applies an appropriate algorithm. The proposal unit can also apply a different proposal algorithm to a digital circuit. The proposal unit can evaluate the category of the circuit and apply a different algorithm. The proposal unit can also select an optimal proposal algorithm depending on the category of the circuit. The proposal unit can evaluate the category of the circuit and select an optimal algorithm. This makes it possible to apply different proposal algorithms depending on the category of the circuit.

[0102] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the suggestion results the user has received in the past. The suggestion unit analyzes the past suggestion results and improves the accuracy. The suggestion unit can also optimize specific parameters from the user's past suggestion results. The suggestion unit can analyze the past suggestion results and optimize parameters. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. The suggestion unit can analyze the past suggestion results and improve the algorithm. This makes it possible to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0103] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit provides a short and to-the-point suggestion. The suggestion unit can estimate the user's emotion and shorten the length of the suggestion. If the user is relaxed, the suggestion unit can also provide a longer suggestion with detailed explanations. The suggestion unit can estimate the user's emotion and extend the length of the suggestion. If the user is excited, the suggestion unit can also provide a suggestion with visually stimulating effects. The suggestion unit can estimate the user's emotion and add effects. This allows the length of the suggestion to be adjusted based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the simulation results. For example, the proposal unit gives priority to the most recently submitted simulation results. The proposal unit evaluates the submission time and determines the priority. The proposal unit can also postpone the proposal of older submitted simulation results. The proposal unit can evaluate the submission time and postpone. The proposal unit can also dynamically adjust the priority of the proposal based on the submission time. The proposal unit can evaluate the submission time and dynamically adjust the priority. In this way, the priority of the proposal can be determined based on the submission time of the simulation results.

[0105] The proposal unit can adjust the order of proposals based on the relevance of the simulation results when making a proposal. For example, the proposal unit gives priority to proposals for highly relevant simulation results. The proposal unit evaluates the relevance of the simulation results and determines the priority order. The proposal unit can also postpone proposals for less relevant simulation results. The proposal unit can evaluate the relevance of the simulation results and postpone them. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the simulation results. The proposal unit can evaluate the relevance of the simulation results and dynamically adjust the order. This makes it possible to adjust the order of proposals based on the relevance of the simulation results.

[0106] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit makes a suggestion using simple technical terminology for a novice user. The suggestion unit evaluates the user's level of expertise and adjusts the terminology. The suggestion unit can also make a suggestion using detailed technical terminology for an advanced user. The suggestion unit can evaluate the user's level of expertise and use detailed terminology. The suggestion unit can also customize the content of the suggestion according to the user's level of expertise. The suggestion unit can evaluate the user's level of expertise and customize the content. This makes it possible to adjust the use of technical terminology in the suggestion according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned selection unit, simulation unit, capture unit, and proposal unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. For example, the capture unit is realized by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned selection unit, simulation unit, capture unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. For example, the capture unit is realized by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, simulation unit, capture unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. For example, the capture unit is realized by the communication I / F 44 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned selection unit, simulation unit, capture unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the simulation unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. For example, the capture unit is realized by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The selection unit can analyze the user's past selection history and automatically suggest optimal parts. For example, virtual electronic parts that the user has frequently selected in the past are automatically displayed as candidates. The selection unit can analyze the past selection history and display frequently selected parts. The selection unit can also prioritize and suggest selection methods (voice, text, etc.) that the user has used in the past. The selection unit can analyze the past selection history and suggest used selection methods. The selection unit can also predict and suggest parts to be used for a specific project from the user's past selection history. The selection unit can analyze the past selection history and suggest parts related to the project. In this way, the user's past selection history can be analyzed and optimal parts can be automatically suggested.

[0109] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method is provided. The simulation unit can estimate the user's emotions and simplify the display method. Furthermore, if the user is relaxed, the simulation unit can also provide a display method including detailed information. The simulation unit can estimate the user's emotions and provide a detailed display. Furthermore, if the user is in a hurry, the simulation unit can also provide a display method that focuses on the main points. The simulation unit can estimate the user's emotions and provide a display that emphasizes the main points. This makes it possible to adjust the display method of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The proposal unit can adjust the level of detail of the proposal based on the importance of the simulation result. For example, a detailed proposal is made for an important simulation result. The proposal unit evaluates the importance of the simulation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for a simulation result with a low importance. The proposal unit can evaluate the importance of the simulation result and make a simplified proposal. The proposal unit can also dynamically adjust the level of detail of the proposal according to the importance of the simulation result. The proposal unit can evaluate the importance of the simulation result and dynamically adjust the level of detail. This makes it possible to adjust the level of detail of the proposal based on the importance of the simulation result.

[0111] The capture unit can estimate the user's emotions and adjust the data sheet capture method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the capture procedure. The capture unit can estimate the user's emotions and simplify the capture procedure. Furthermore, if the user is relaxed, the capture unit can provide detailed capture options and suggest a customizable capture method. The capture unit can estimate the user's emotions and provide detailed options. Furthermore, if the user is in a hurry, the capture unit can prioritize voice input to quickly capture the data sheet. The capture unit can estimate the user's emotions and prioritize voice input. This allows the data sheet capture method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the parts. For example, for important parts, a detailed simulation is performed and the operation is analyzed. The simulation unit evaluates the importance of the parts and performs a detailed analysis. The simulation unit can also perform a simplified simulation for parts with low importance. The simulation unit can evaluate the importance of the parts and perform a simplified analysis. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the parts. The simulation unit can evaluate the importance of the parts and adjust the level of detail. In this way, the level of detail of the simulation can be adjusted based on the importance of the parts.

[0113] The selection unit can estimate the user's emotions and prioritize the parts to be selected based on the estimated user's emotions. For example, if the user is nervous, important parts can be displayed preferentially to simplify selection. The selection unit can estimate the user's emotions and prioritize important parts. Furthermore, if the user is relaxed, the selection unit can display a detailed parts list to broaden the range of choices. The selection unit can estimate the user's emotions and display a detailed list. Furthermore, if the user is in a hurry, the selection unit can prioritize the most frequently used parts. The selection unit can estimate the user's emotions and prioritize the most frequently used parts. This allows the prioritization of the parts to be selected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the circuit. For example, a specific proposal algorithm is applied to an analog circuit. The proposal unit evaluates the category of the circuit and applies an appropriate algorithm. The proposal unit can also apply a different proposal algorithm to a digital circuit. The proposal unit can evaluate the category of the circuit and apply a different algorithm. The proposal unit can also select an optimal proposal algorithm depending on the category of the circuit. The proposal unit can evaluate the category of the circuit and select an optimal algorithm. This makes it possible to apply different proposal algorithms depending on the category of the circuit.

[0115] During a simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. For example, the accuracy of the simulation is improved based on the results of simulations performed by the user in the past. The simulation unit analyzes the past results and improves the accuracy. The simulation unit can also optimize specific parameters from the user's past simulation results. The simulation unit can analyze the past results and optimize parameters. The simulation unit can also analyze the user's past simulation results and improve the simulation algorithm. The simulation unit can analyze the past results and improve the algorithm. In this way, the accuracy of the simulation can be improved by referring to the user's past simulation results.

[0116] The import unit can select the optimal import method depending on the format of the data sheet when importing. For example, for a data sheet in PDF format, it imports using a dedicated analysis algorithm. The import unit analyzes the structure of the PDF file and selects the optimal method. For a data sheet in CSV format, the import unit can also import while reformatting the data. The import unit can analyze the data in the CSV file and select the optimal method. The import unit can also dynamically select the optimal import method depending on the format of the data sheet. The import unit can evaluate the format of the data sheet and dynamically select the method. This makes it possible to select the optimal import method depending on the format of the data sheet.

[0117] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible suggestion is provided. The suggestion unit can estimate the user's emotions and simplify the way the suggestions are expressed. Furthermore, if the user is relaxed, the suggestion unit can also provide a suggestion including detailed information. The suggestion unit can estimate the user's emotions and provide a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. The suggestion unit can estimate the user's emotions and provide a suggestion that emphasizes the main points. This makes it possible to adjust the way the suggestions are expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The selection unit selects and combines virtual electronic components. For example, the selection unit selects virtual electronic components such as resistors, capacitors, and transistors. The selection unit also allows the user to specify the characteristics and connection method of the components. Step 2: The simulation unit simulates the operation of the virtual electronic components selected by the selection unit. For example, the simulation unit simulates changes in voltage and current, and signal transmission. The simulation unit can also analyze the operation of the entire circuit based on the characteristics and connection method of the components. Step 3: The importer imports the datasheet for the new component. For example, the importer can import a datasheet in PDF or CSV format. The importer can also analyze the datasheet and generate a new virtual electronic component. Step 4: The proposal unit proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit. For example, the proposal unit can propose changes to the component placement and connection methods, or component selection.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection unit for selecting and combining virtual electronic components; a simulation unit that simulates the operation of the virtual electronic component selected by the selection unit; an import unit for importing data sheets for new parts; a proposal unit that proposes improvements and efficiency improvements to the circuit based on the simulation results obtained by the simulation unit; Equipped with A system characterized by:

2. The capture unit is Import a datasheet in PDF or CSV format 2. The system of claim 1.

3. The simulation unit Simulates voltage and current changes and signal transmission 2. The system of claim 1.

4. The proposal unit Based on the simulation results, we propose changes to component placement and connection methods, as well as component selection.

2. The system of claim 1.

5. The selection unit Select virtual electronic components: resistors, capacitors, and transistors 2. The system of claim 1.

6. The simulation unit Analyze the behavior of the entire circuit based on component characteristics and connection methods 2. The system of claim 1.

7. The selection unit Estimating a user's emotion and adjusting a method for selecting virtual electronic components based on the estimated user's emotion 2. The system of claim 1.

8. The selection unit When selecting, the system analyzes the user's past selection history and automatically suggests the most suitable parts.

2. The system of claim 1.

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