Intelligent maintenance of scientific instruments

Abstract

And intelligent maintenance of scientific instruments is realized. Systems or techniques are provided for facilitating intelligent maintenance of scientific instruments. In various embodiments, the scientific instrument may include a mass spectrometer equipped with a chromatograph. In various aspects, the scientific instrument may determine whether maintenance tasks are necessary to be performed on the chromatograph-equipped mass spectrometer based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to meet a threshold. In various instances, the scientific instrument may schedule a time or date for performing the maintenance task in response to determining that it is necessary to perform the maintenance task, where the time or date may be predicted by a machine learning model based on an operation history of the chromatograph equipped mass spectrometer. In various cases, the scientific instrument may prepare for performing the maintenance task by adjusting actuatable hardware of the chromatograph equipped mass spectrometer prior to the time or date.

Description

Background Art

[0001] Scientific instruments may include complex arrangements of actuatable components, sensors or consumables. Throughout the useful life of a scientific instrument, any part of such a complex device may gradually or suddenly degrade, which may be undesirable. Summary of the invention

[0002] The following presents a summary of the invention to provide a basic understanding of one or more embodiments. The summary of the invention is not intended to identify key or important elements, or to delineate any scope of a particular embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to a more detailed description presented later. In one or more embodiments described herein, an apparatus, system, computer-implemented method, device, or computer program product that facilitates intelligent maintenance of scientific instruments is described.

[0003] According to one or more embodiments, a scientific instrument is provided. The scientific instrument may include a mass spectrometer equipped with a chromatograph. The scientific instrument may also include a non-transitory computer readable memory that can store computer executable components. The scientific instrument may also include a processor that can be operably coupled to the non-transitory computer readable memory and can execute the computer executable components stored in the non-transitory computer readable memory. In various embodiments, the computer executable component may include a determination component that can determine whether it is necessary to perform a maintenance task on the mass spectrometer equipped with the chromatograph based on an electronic counter or a readback sensor associated with the mass spectrometer equipped with the chromatograph failing to meet a threshold. In various aspects, the computer executable component may include a scheduling component that can arrange a time or date for performing a maintenance task in response to determining that it is necessary to perform a maintenance task, wherein the time or date can be predicted by a machine learning model based on the operation history of the mass spectrometer equipped with the chromatograph.

[0004] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method may include scheduling, by a scientific instrument including a mass spectrometer equipped with a chromatograph, a time or date for performing a maintenance task on the mass spectrometer equipped with a chromatograph. In [TP386185CNSEC1]

[0005] In various aspects, the computer-implemented method may include identifying, by the scientific instrument, a hardware actuator of a mass spectrometer equipped with a chromatograph that is associated with a maintenance task. In various cases, the computer-implemented method may include adjusting, by the scientific instrument, the hardware actuator in preparation for the maintenance task in response to less than a threshold amount of time remaining before the time or date.

[0006] According to one or more embodiments, a computer program product for facilitating the intelligent maintenance of scientific instruments is provided. In various embodiments, the computer program product may include a non-transient computer readable memory containing program instructions. In various aspects, the program instructions may be executable by a processor to enable the processor to predict the time or date for performing maintenance tasks on the mass spectrometer via a log of graphical user interface interactions experienced by the mass spectrometer. In various cases, the program instructions may also be executable to enable the processor to prepare the mass spectrometer for maintenance tasks by changing the internal temperature, fluid flow rate or voltage of the mass spectrometer in response to the arrival of less than a threshold amount of time before the time or date. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Various embodiments will be easily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, the same reference numerals indicate the same structural elements. The embodiments are shown in the figures by way of example and not by way of limitation. These figures are not necessarily drawn to scale.

[0008] Figure 1 An example non-limiting block diagram of a scientific instrument module is shown in accordance with various embodiments described herein.

[0009] Figure 2 An example non-limiting flow chart of a computer-implemented method according to various embodiments described herein is shown.

[0010] Figure 3 A block diagram of an example non-limiting scientific instrument that facilitates smart maintenance according to one or more embodiments described herein is shown.

[0011] Figure 4 A block diagram of an example non-limiting scientific instrument including maintenance mapping and maintenance tasks that facilitate smart maintenance according to one or more embodiments described herein is shown.

[0012] Figure 5 An example non-limiting block diagram of a maintenance map is shown in accordance with one or more embodiments described herein.

[0013] Figure 6 A block diagram of an example non-limiting scientific instrument including maintenance times or dates predicted by a machine learning model that facilitates smart maintenance is shown according to one or more embodiments described herein.

[0014] Figures 7 and 8 An example non-limiting block diagram illustrating how a machine learning model may predict a maintenance time or date is shown according to one or more embodiments described herein.

[0015] [TP386185CNSEC1]

[0016] Fig. 9 An example non-limiting block diagram of a training data set according to one or more embodiments described herein is shown.

[0017] Fig.10 An example non-limiting block diagram illustrating how a machine learning model may be trained according to one or more embodiments described herein is shown.

[0018] Fig.11 A block diagram of an example non-limiting scientific instrument including hardware adjustments to facilitate smart maintenance according to one or more embodiments described herein is shown.

[0019] Fig.12 An example non-limiting block diagram illustrating how hardware adjustments may be performed in preparation for maintenance is shown in accordance with one or more embodiments described herein.

[0020] Fig.13 A block diagram of an example non-limiting scientific instrument including auxiliary components that facilitate smart maintenance according to one or more embodiments described herein is shown.

[0021] Figures 14 to 16 A flow chart is shown of an example non-limiting computer-implemented method for facilitating smart maintenance according to one or more embodiments described herein.

[0022] Fig.17 An example non-limiting block diagram of a graphical user interface that can be used to implement some or all of the methods or techniques disclosed herein is shown according to various embodiments described herein.

[0023] Fig.18 An example non-limiting block diagram of a computing device that can execute some or all of the methods or techniques disclosed herein is shown according to various embodiments described herein.

[0024] Fig.19 An example non-limiting block diagram of a scientific instrument support system in which some or all of the methods or techniques disclosed herein may be performed, according to various embodiments described herein.

[0025] Fig. 20 A block diagram is shown of an example non-limiting operating environment in which one or more embodiments described herein may be facilitated.

[0026] Fig.21 An example networking environment is shown that is operable to perform various implementations described herein. DETAILED DESCRIPTION

[0027] The following detailed description is illustrative only and is not intended to limit the embodiments and / or the application or uses of the embodiments. In addition, there is no intention to be bound by any express or implied information presented in the previous background or summary sections or the detailed description section.

[0028] One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are used throughout to refer to the same elements. In the following description, for the purpose of explanation, many specific details are set forth to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various circumstances, one or more embodiments may be practiced without these specific details.

[0029] [TP386185CNSEC1]

[0030] Various operations may be described as multiple discrete actions or operations in a manner that is most helpful for understanding the subject matter disclosed herein. However, the described order should not be interpreted as implying that these operations must rely on the order. In particular, these operations may be performed in an order different from the order of presentation. The described operations may be performed in an order different from the described embodiments. Various additional operations may be performed, or the described operations may be omitted in additional embodiments.

[0031] Although some elements may be referred to in singular form (e.g., "processing device"), any appropriate element may be represented by multiple instances of the element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented as different ones of the operations being performed by different processing devices. As used herein, the phrase "based on" should be understood to mean "based at least in part on," unless otherwise specified.

[0032] The scientific instrument may be any suitable computerized device capable of capturing electronic measurements in a scientific, laboratory, research, or clinical operating context or environment. As a non-limiting example, the scientific instrument may be a mass spectrometer coupled to a chromatograph, which may help determine the chemical composition or composition of an unknown sample. As another non-limiting example, the scientific instrument may be a scanning electron microscope, which may help determine the surface topography of a microscopic or nanoscale sample.

[0033] In any case, a scientific instrument may include a complex arrangement of actuatable components (e.g., ion sources, ion lenses, heaters, coolers, fluid valves, fluid pumps, circuit switches), sensors (e.g., ion detectors, voltmeters, thermistors, potentiometers, manometers), or consumables (e.g., carrier fluids, calibrants, filters). Any part of such a complex arrangement may gradually or suddenly degrade over the life of the scientific instrument (e.g., as the scientific instrument experiences wear). Such degradation may adversely affect the accuracy and reliability of the scientific instrument in analyzing samples, which may be considered undesirable.

[0034] Therefore, systems or techniques that can improve such degradation may be considered desirable.

[0035] Various embodiments described herein can address such degradation.One or more embodiments described herein can include a system, computer-implemented method, apparatus, or computer program product that can facilitate intelligent maintenance of scientific instruments.

[0036] In particular, the scientific instrument may be able to determine, via any embedded counters or sensors, whether its component hardware is operating properly, within defined tolerances, or otherwise as expected. If the scientific instrument determines that one or more of its component hardware is not operating properly, the scientific instrument may provide a notification or warning to a user or technician indicating that it is necessary to perform a scientific [TP386185CNSEC1]

[0037] Based on reading or viewing such notifications, a user or technician can perform maintenance tasks on the scientific instrument accordingly.

[0038] However, as the inventors of the various embodiments described herein recognize, the prior art has various shortcomings in the arrangement of maintenance tasks. In particular, when the prior art is implemented, the scientific instrument notifies the user or technician that it is necessary to perform maintenance, and the user or technician manually performs the appropriate maintenance tasks on the scientific instrument at any time or date desired by the user or technician. It would be of great benefit if the user or technician immediately performs the maintenance tasks when realizing that it is necessary to perform maintenance. But the workload of the user or technician may make such immediate maintenance impractical or infeasible. On the contrary, when realizing that it is necessary to perform maintenance, the user or technician often manually arranges a future time or date for manually performing the maintenance task. Since the prior art relies on manually arranging maintenance tasks, such prior art is prone to excessive delays or direct skipping of maintenance tasks (for example, the user or technician may arrange a time or date too far away for a given maintenance task; the user or technician may completely forget or deliberately choose not to arrange a time or date for a given maintenance task). Therefore, when the prior art is implemented, the scientific instrument can be considered to be more likely to experience unsolvable or irreparable degradation or damage.

[0039] Various embodiments described herein can improve this technical problem via intelligent maintenance of scientific instruments. In particular, various embodiments described herein can relate to scientific instruments determining whether it is necessary to perform a specific maintenance task. In response to determining that it is necessary to perform a specific maintenance task, the scientific instrument can predict a future time or date that will be suitable, appropriate, or otherwise convenient for the execution of the specific maintenance task. As described herein, in various cases, the scientific instrument can facilitate such determination via machine learning based on the operating history of the scientific instrument or based on any other suitable information related to the specific maintenance task. In this way, a specific maintenance task can be automatically scheduled at a predicted time or date without relying on a manual prompt from a user or technician (for example, this can solve the situation where a user or technician forgets, chooses not to, or is otherwise unable to manually schedule a specific maintenance task). In addition, in various cases, the scientific instrument can automatically perform any suitable hardware adjustments (e.g., temperature adjustments, fluid flow adjustments, electrical power adjustments) shortly before the predicted time or date in order to prepare for the execution of the maintenance task. In this way, time and effort of a user or technician may be saved (e.g., the user's or technician's time or effort may be spent actually performing a particular maintenance task at a predicted time or date, rather than preparing the scientific instrument for such performance).

[0040] [TP386185CNSEC1]

[0041] Therefore, various embodiments described herein may be considered to enable scientific instruments to automatically perform scheduling and preparation for maintenance. Such embodiments may therefore be considered to improve, deepen or otherwise enhance the intelligence or wisdom capabilities of scientific instruments.

[0042] Various embodiments described herein may be considered to be a computerized tool (e.g., any suitable combination of computer executable hardware or computer executable software) that may be electronically installed on a scientific instrument or otherwise installed on a scientific instrument and may facilitate intelligent maintenance of the scientific instrument. In various aspects, such a computerized tool may include accessing a component, determining a component, arranging a component, or preparing a component.

[0043] In some embodiments, scientific instrument may include a mass spectrometer equipped with a chromatograph. That is, scientific instrument may include a mass spectrometer coupled to a chromatograph. In various fields, a mass spectrometer may include any suitable composition hardware. As some non-limiting examples, a mass spectrometer may include any suitable ion beam emitter (e.g., electronic ion generator, chemical ion generator, matrix-assisted laser desorption ion generator, electrospray ion generator, atmospheric pressure photoionizer, inductively coupled plasma ion generator), any suitable mass analyzer (e.g., quadrupole mass analyzer, ion trap mass analyzer, time-of-flight mass analyzer, sector mass analyzer), any suitable ion detector (e.g., electron multiplier detector, microchannel plate detector, image charge detector, Faraday cup detector) or any suitable ion optical device (e.g., ion focusing lens, ion guide, ion deflector). Similarly, in various cases, a chromatograph may include any suitable composition hardware (e.g., gas chromatography hardware, liquid chromatography hardware, ion chromatography hardware). As some non-limiting examples, the chromatograph may include any suitable sample injector (e.g., an injection syringe, an infusion syringe, a vaporizer, a nebulizer), any suitable chromatographic column (e.g., including any suitable absorbent packaging material or stationary phase membrane), any suitable column oven or heater, or any suitable carrier fluid flow device (e.g., a fluid valve, a fluid pump). In various cases, given any sample, the sample can be injected into the chromatograph, the sample can be separated into various components by the chromatograph, and the various components can be ionized and subsequently analyzed by a mass spectrometer (e.g., a mass spectrometer can record the relative abundance of the sample ions as a function of mass-to-charge ratio).

[0044] In various embodiments, the access component of the computerized tool can electronically access the mass spectrometer equipped with the chromatograph. That is, the access component can be electronically connected or communicated with the mass spectrometer equipped with the chromatograph so that other components of the computerized tool can communicate with the mass spectrometer equipped with the chromatograph in an electronic manner.

[0045] Interact electronically (e.g., sending electronic commands to, reading electronic signals from, a mass spectrometer equipped with a chromatograph).

[0046] In various embodiments, a determination component of a computerized tool can electronically determine whether it is necessary to perform a maintenance task on a mass spectrometer equipped with a chromatograph. In various aspects, the determination component can facilitate the determination by enabling status awareness.

[0047] As non-limiting examples, a chromatograph-equipped mass spectrometer may have one or more electronic counters or one or more readback sensors embedded therein. In various aspects, the electronic counter may be any suitable electronic device or logic component that can count, record, or otherwise track any suitable quantity, such as the total or cumulative number of occurrences of a defined event associated with a scientific instrument with respect to a reference value (e.g., a counter may count or record the total number of samples that have been analyzed by a chromatograph-equipped mass spectrometer since the chromatograph-equipped mass spectrometer was last serviced; a counter may count or record the total number of injections that have been performed by a syringe of a chromatograph-equipped mass spectrometer since the syringe's liner was last replaced). In various cases, the readback sensor may be any suitable electronic device that can sense, measure, or otherwise generate an electronic output signal in response to any suitable physical stimulus (e.g., a temperature sensor, a voltage sensor, a pressure sensor, a humidity sensor, a chemical sensor, an ion detector). In any case, the determination component may record any data currently or currently indicated by the electronic counter or by the readback sensor, and such recorded data may be considered to represent the current or current health status of the chromatograph-equipped mass spectrometer. In various aspects, the determination component can compare this current or present health status to any suitable thresholds. If the current or present health status meets these thresholds, the determination component can conclude that it is currently or currently not necessary to perform a maintenance task on the mass spectrometer equipped with the chromatograph. Conversely, if the current or present health status fails to meet any of these thresholds, the determination component can conclude that it is currently or currently necessary to perform a corresponding maintenance task on the mass spectrometer equipped with the chromatograph.

[0048] In various embodiments, a scheduling component of a computerized tool can, in response to determining that it is necessary to perform a maintenance task, electronically predict a future time or date that would be appropriate to perform a maintenance task on a mass spectrometer equipped with a chromatograph. In various aspects, the scheduling component can facilitate such prediction via machine learning based on an operating history of the mass spectrometer equipped with a chromatograph and based on any suitable metadata related to the maintenance task.

[0049] In various aspects, the operation history can be any suitable electronic data that represents, indicates, or otherwise communicates any suitable information about when a mass spectrometer equipped with a chromatograph was operated in the past. As a non-limiting example, in various cases, the operation history can include corresponding to the mass spectrometer equipped with a chromatograph [TP386185CNSEC1]

[0050] The operation history may include an electronic record of the time or date in the past that the mass spectrometer equipped with the chromatograph is being run, running, or otherwise analyzing a sample. As another non-limiting example, in various cases, the operation history may include a graphical user interface (GUI) interaction log corresponding to the mass spectrometer equipped with the chromatograph. In various cases, the GUI interaction log may be an electronic record of the time or date in the past that the user or technician interacts (e.g., clicks) with the GUI of the mass spectrometer equipped with the chromatograph. It should be noted that any time or date indicated in the operation history can be specified at any suitable granularity level (e.g., year, month, week, day, hour, minute, or second).

[0051] In various aspects, metadata may be any suitable electronic data representing, indicating, or otherwise communicating any suitable property, characteristic, or attribute of a maintenance task. As a non-limiting example, metadata may include a task duration. In various cases, the task duration may be any suitable electronic data indicating how long the maintenance task is expected to take to complete. As another non-limiting example, metadata may include a task priority. In various cases, the task priority may be any suitable electronic data indicating the importance or criticality of the maintenance task to the scientific instrument.

[0052] In various aspects, the arrangement component can store, maintain, control, or otherwise access the machine learning model. In various cases, the machine learning model can exhibit any suitable internal architecture, such as a deep learning neural network internal architecture. For example, the machine learning model may include any suitable number and any suitable type of layers (e.g., an input layer, one or more hidden layers, an output layer, any of which can be a convolutional layer, a dense layer, a nonlinear layer, a pooling layer, a batch normalization layer, or a padding layer). As another example, the machine learning model may include any suitable number of neurons in various layers (e.g., different layers may have the same or different numbers of neurons). As another example, the machine learning model may include any suitable activation function (e.g., softmax, sigmoid, hyperbolic tangent, rectified linear unit) in various neurons (e.g., different neurons may have the same or different activation functions). As another example, the machine learning model may include any suitable inter-neuron connection or inter-layer connection (e.g., forward connection, skip connection, recurrent connection).

[0053] In various cases, the scheduling component can execute the machine learning model on the operation history and metadata, and such execution can cause the machine learning model to determine the future time or date. More specifically, the scheduling component can concatenate the operation history and metadata together, the scheduling component can feed such concatenation to an input layer of the machine learning model, such concatenation can complete a forward pass through one or more hidden layers of the machine learning model, and the output layer of the machine learning model can calculate or operate on the future time or date based on the activation map from the one or more hidden layers.

[0054] [TP386185CNSEC1]

[0055] In various aspects, a future time or date may be considered to be any time or date in the future that a machine learning model has inferred will be appropriate, suitable, or convenient to perform a maintenance task on a mass spectrometer equipped with a chromatograph. In particular, a future time or date may be considered to be a time or date inferred by a machine learning model that will be consistent with the metadata, will be inconsistent with the runtime log, and will be consistent with the GUI interaction log. That is, the machine learning model may be considered to have inferred that: the runtime log indicates that the mass spectrometer equipped with a chromatograph is unlikely to run or analyze samples at a future time or date; the runtime log indicates that the mass spectrometer equipped with a chromatograph is unlikely to start running or analyzing samples after the future time or date but within the duration of the maintenance task; the GUI interaction log indicates that a user or technician may interact with the mass spectrometer equipped with a chromatograph at a future time or date and is therefore actually present; and given the priority of the maintenance task, the future time or date is not too far away.

[0056] In some aspects, the machine learning model may not only predict a future time or date, but may also predict multiple possible times or dates, any one of which would be suitable, appropriate, or convenient for performing the maintenance task. In this case, a user or technician may select or pick the future time or date from the multiple possible times or dates.

[0057] Note that in order to make the future time or date actually suitable, appropriate or convenient to perform the maintenance task, the machine learning model can first be trained. In various aspects, such training can be facilitated in any suitable manner (e.g., supervised training, unsupervised training, reinforcement learning).

[0058] In any case, the scheduling component can predict the future time or date via machine learning, and the scheduling component can accordingly schedule the maintenance tasks to be performed on the mass spectrometer equipped with the chromatograph at the future time or date. In fact, in some aspects, the scheduling component can visually present an electronic notification indicating that the maintenance tasks will be performed at the future time or date on the electronic display of the scientific instrument, or transmit these electronic notifications to any suitable computing device. In some cases, such electronic notifications can be repeated so as to repeatedly remind the scheduling of the maintenance tasks to be performed at the future time or date. In other aspects, the scheduling component can mark the maintenance tasks to be performed at the future time or date in any suitable electronic calendar of the user or technician.

[0059] In this way, the scheduling component can automatically schedule or plan a future time or date for performing a maintenance task on a mass spectrometer equipped with a chromatograph. Therefore, such scheduling can be considered to be isolated or protected from errors caused by manual scheduling, which may otherwise be submitted by a user or technician. In other words, such automatic scheduling can be considered to prevent: a user or technician may forget to manually schedule a maintenance task; a user or technician may intentionally choose to [TP386185CNSEC1]

[0060] or a user or technician might inadvertently manually schedule a maintenance task for a time or date that is too far in the future.

[0061] In various embodiments, the preparation component of the computerized tool can electronically initiate, execute, or otherwise perform any suitable hardware adjustments on a mass spectrometer equipped with a chromatograph to prepare for performing a maintenance task. More specifically, the mass spectrometer equipped with a chromatograph can include any suitable actuatable hardware, such as a temperature control device (e.g., a column oven, an injector housing, an ion source heater), a fluid flow control device (e.g., a carrier fluid valve, a carrier fluid pump, a vacuum pump), or an electrical control device (e.g., an ion beam emitter circuit switch, a power supply). In various aspects, the maintenance task can be mapped to or otherwise associated with one or more specific adjustments to any of such actuatable hardware (e.g., full or partial activation, full or partial deactivation), wherein such one or more specific adjustments can be considered to place the chromatograph-equipped mass spectrometer in an operating state suitable for the maintenance task (e.g., if the maintenance task requires a column of the chromatograph-equipped mass spectrometer to be at a specific temperature, the one or more specific adjustments can include heating or cooling the column to the specific temperature; if the maintenance task requires an ion beam emitter of the chromatograph-equipped mass spectrometer to be at a specific voltage, the one or more specific adjustments can include powering on or off the ion beam emitter to the specific voltage). In various cases, in response to determining that the remaining time until the future time or date is less than any suitable threshold amount of time (e.g., no matter how long the one or more specific adjustments are expected to take), the preparation component can transmit any suitable electronic control signal to the chromatograph-equipped mass spectrometer to perform or implement the one or more specific adjustments. In this way, the preparation component can be considered to automatically prepare the chromatograph-equipped mass spectrometer to perform the maintenance task.

[0062] Thus, various embodiments of the computerized tools described herein can automatically detect when a mass spectrometer equipped with a chromatograph needs to perform a maintenance task, automatically schedule the maintenance to be performed at an appropriate or convenient future time or date, and automatically prepare the mass spectrometer equipped with a chromatograph to perform the maintenance task before the future time or date. Thus, such embodiments can be considered to enrich or otherwise enhance the smart or intelligent functionality of a mass spectrometer equipped with a chromatograph.

[0063] The various embodiments described herein can be used to solve problems that are highly technical in nature (e.g., facilitating intelligent maintenance of scientific instruments), non-abstract, and cannot be performed by humans as a set of mental behaviors using hardware or software. In addition, some of the processes performed can be performed by dedicated computers (e.g., a mass spectrometer including an ion detector, a deep learning neural network including internal parameters such as convolution kernels) to perform limited actions related to scientific instruments.

[0064] [TP386185CNSEC1]

[0065] For example, such defined actions may include: determining, by a scientific instrument including a mass spectrometer equipped with a chromatograph, whether it is necessary to perform a maintenance task on the mass spectrometer equipped with a chromatograph based on a failure of an electronic counter or readback sensor associated with the mass spectrometer equipped with a chromatograph to meet a threshold; scheduling, by the scientific instrument, a time or date for performing the maintenance task in response to determining that it is necessary to perform the maintenance task, wherein the time or date may be predicted by a machine learning model based on an operating history of the mass spectrometer equipped with a chromatograph or metadata related to the maintenance task; and preparing, by the scientific instrument, to perform the maintenance task by adjusting actuatable hardware of the mass spectrometer equipped with a chromatograph before the time or date.

[0066] Such defined behaviors are inherently computerized. In fact, scientific instruments such as chromatographs and mass spectrometers are highly technical computerized devices that include specific computerized hardware (e.g., temperature sensors, pressure sensors, voltage sensors, ion beam emitters, ion focusing lenses, mass analyzers, ion detectors). Without computers, scientific instruments and the operations performed by them cannot be implemented in any reasonable or feasible way by human thinking or human use of pen and paper. Similarly, machine learning models (e.g., deep learning neural networks) are also inherently computerized constructs that cannot be trained or executed in any reasonable or feasible way by human thinking or human use of pen and paper without computers.

[0067] In addition, the various embodiments described herein can incorporate various teachings related to intelligent maintenance of scientific instruments into practical applications. As described above, a scientific instrument can determine whether a given maintenance task should be performed on it. When implementing the prior art, the scientific instrument notifies the user or technician that a given maintenance task should be performed, the user or technician manually selects any time or date they want to perform the maintenance task, and the user or technician then manually performs the maintenance task at that time or date. This reliance on manual scheduling can lead to situations where maintenance tasks are performed at non-optimal times (for example, given the severity or criticality of the maintenance task, the user or technician may mistakenly schedule a time or date that is too far away; maintenance is usually performed regularly (for example, the user or technician may forget or simply choose not to schedule a maintenance task), which can lead to unresolved or unrepaired degradation of the constituent hardware of the scientific instrument.

[0068] Various embodiments described herein may help to improve such unaddressed or unrepaired degradation via intelligent maintenance. In particular, various embodiments described herein may involve: automatically detecting (e.g., via an electronic counter or readback sensor) whether it is necessary to perform a maintenance task on a scientific instrument; in response to detecting that it is necessary to perform a maintenance task, automatically identifying (e.g., via machine learning) a future time or date when it would be appropriate or convenient to perform the maintenance task; automatically scheduling the maintenance task to be performed at a future time or date; automatically adjusting the scientific instrument's operability prior to the future time or date;

[0069] Actuating hardware (e.g., heaters, coolers, fluid valves, fluid pumps) in preparation for performing maintenance tasks. Thus, such embodiments can help reduce the likelihood of a situation where a scientific instrument analyzes a sample with degraded or damaged component hardware (e.g., can help avoid situations where a user or technician forgets to schedule a maintenance task; can help avoid situations where a user or technician schedules a maintenance task at an inappropriate or unduly delayed time). For at least these reasons, the various embodiments described herein can be considered to be concrete, tangible technical improvements in the field of scientific instrumentation. Therefore, the various embodiments described herein certainly qualify as useful and practical applications of computers.

[0070] In addition, various embodiments described herein can control real-world tangible devices based on the disclosed teachings. For example, various embodiments described herein can electronically activate, deactivate, or otherwise actuate real-world hardware (e.g., ion beam emitters, ion focusing lenses, carrier fluid valves / pumps) of real-world scientific instruments (e.g., chromatographs, mass spectrometers, autosamplers).

[0071] Figure 1 An example non-limiting block diagram of a scientific instrument module 102 is shown in accordance with various embodiments described herein.

[0072] In various embodiments, the scientific instrument module 102 may be implemented by circuitry (e.g., including electrical or optical components) such as a programmed computing device. The logical components of the scientific instrument module 102 may be included in a single computing device, or may be distributed across multiple computing devices that communicate with each other as appropriate. Fig.18 and Fig. 20 Examples of computing devices that may implement the scientific instrument module 102, either alone or in combination, are discussed and referenced. Fig.19 and Fig.21 Examples of systems or networks of interconnected computing devices that may implement the scientific instrument module 102 across one or more of the computing devices are discussed.

[0073] The scientific instrument module 102 may include a first logic component 104, a second logic component 106, and a third logic component 108. As used herein, the term "logic component" may include a device that performs a set of operations associated with the logic. For example, any of the logic elements included in the scientific instrument module 102 may be implemented by one or more computing devices that are programmed with instructions to cause one or more processing devices of the computing devices to perform an associated set of operations. In a particular embodiment, the logic element may include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices of the one or more computing devices, cause the one or more computing devices to perform an associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements that together perform a function associated with the module. Logic elements in a module [TP386185CNSEC1]

[0074] Different logic elements in a module may take the same form or may take different forms. For example, some logic components in a module may be implemented by a programmed general-purpose processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different instruction sets executed by one or more processing devices. A module may omit one or more logic elements shown in an associated figure; for example, when a module will perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logic elements depicted in the associated figure.

[0075] In various embodiments, there may be a scientific instrument corresponding to the scientific instrument module 102. In various aspects, the scientific instrument can be any suitable computerized device that can electronically measure some scientifically relevant, clinically relevant or research-related characteristics, properties or attributes of an analytical sample (e.g., a known or unknown mixture, compound or set of substances). As a non-limiting example, the scientific instrument can be a mass spectrometer operably coupled to a chromatograph. In this case, the scientific instrument can measure or determine the ion spectrum (e.g., relative ion abundance as a function of mass-to-charge ratio) of the analytical sample. As another non-limiting example, the scientific instrument can be a scanning electron microscope. In this case, the scientific instrument can measure or determine the surface morphology of the analytical sample.

[0076] In various embodiments, the first logic component 104 can determine whether the scientific instrument currently or currently requires a maintenance task. In various aspects, the first logic component 104 can facilitate such a determination in any suitable manner.

[0077] As a non-limiting example, in some cases, the first logic component 104 can facilitate such a determination by utilizing any suitable electronic sensor embedded in or otherwise integrated with the scientific instrument. For example, the scientific instrument may include a temperature, pressure, voltage, current, humidity, or strain sensor that can measure the current or present temperature, pressure, voltage, current, moisture content, or strain of the scientific instrument. In some cases, if the current or present temperature, pressure, voltage, current, moisture content, or strain of the scientific instrument meets any suitable threshold corresponding to the maintenance task, the first logic component 104 can conclude or determine that the scientific instrument does not currently or currently require a maintenance task. On the other hand, if the current or present temperature, pressure, voltage, current, moisture content, or strain of the scientific instrument fails to meet the threshold, the first logic component 104 can instead conclude or determine that the scientific instrument currently or currently requires a maintenance task.

[0078] As another non-limiting example, in various aspects, the first logic component 104 can facilitate such determination by utilizing any suitable electronic counter embedded in or otherwise integrated with the scientific instrument. For example, the scientific instrument may include a device that tracks or records any information associated with the scientific instrument.

[0079] The first logic component 104 may also include a counter for the total number of times any suitable defined event has occurred (e.g., tracking or recording the total number of sample injections since the last syringe liner replacement). In some cases, if the current or current count of the counter meets any suitable threshold corresponding to the maintenance task, the first logic component 104 may conclude or determine that the scientific instrument does not currently or currently require a maintenance task. On the other hand, if the current or current count fails to meet the threshold, the first logic component 104 may instead conclude or determine that the scientific instrument currently or currently requires a maintenance task.

[0080] In various embodiments, in response to the first logic component 104 determining that the scientific instrument does not currently or currently require the maintenance task to be performed, the second logic component 106 may refrain from performing any actions or operations. However, in various aspects, in response to the first logic component 104 determining that the scientific instrument does not currently or currently require the maintenance task to be performed, the second logic component 106 may schedule the maintenance task to be performed on the scientific instrument at an automatically predicted time or date that is convenient or appropriate for the maintenance task.

[0081] In particular, in various aspects, the second logic component 106 can access the operation history of the scientific instrument and metadata associated with the maintenance task. In various aspects, the operation history can indicate or communicate past times or dates when the scientific instrument is analyzing samples, and other past times or dates when the scientific instrument did not analyze samples. In addition, in various cases, the operation history can indicate or communicate past times or dates when a user or technician clicks or otherwise interacts with the GUI (e.g., touch screen) of the scientific instrument, and other past times or dates when the user or technician did not click or otherwise interact with the GUI. In various cases, the metadata can indicate or communicate any suitable attributes or characteristics of the maintenance task. In particular, the metadata can indicate the expected duration of the maintenance task or the priority of the maintenance task. In any case, the second logic component 106 can execute a trained machine learning model (e.g., a deep learning neural network) on the operational history and on the metadata, and such execution can cause the trained machine learning model to calculate as an output a future time or date that will be convenient or otherwise suitable for performing the maintenance task (e.g., a future time or date when a user or technician may interact with the GUI, a future time or date when the scientific instrument may not analyze a sample, a future time or date that will not be unduly delayed given the priority of the scientific instrument, and a future time or date that allows sufficient time to complete the maintenance task).

[0082] In various embodiments, the third logic component 108 can facilitate any suitable automatic hardware adjustments of the scientific instrument before the automatically predicted time or date to prepare for the performance of the maintenance task. In particular, the scientific instrument may include any suitable actuatable hardware (e.g., temperature control equipment, fluid flow control equipment, high voltage power control equipment), and the performance of the maintenance task may require [TP386185CNSEC1]

[0083] or otherwise dependent upon various components of such actuatable hardware being in a particular operating state or mode. In various aspects, the third logic component 108 may instruct, command, or control such various actuatable hardware to enter such a particular operating state or mode in response to the remaining time before the automatically predicted time or date is less than any suitable threshold amount of time (e.g., when the automatically predicted time or date is less than a threshold margin, the third logic component 108 may increase or decrease one or more internal temperatures, one or more fluid flow rates, or one or more voltage levels of the scientific instrument). In various cases, such hardware adjustments may be considered to prepare or prepare the scientific instrument for performing maintenance tasks.

[0084] Thus, the scientific instrument module 102 may facilitate smart maintenance of scientific instruments.

[0085] Figure 2is an example non-limiting flowchart of a computer-implemented method 200 according to various embodiments described herein. The operations of the computer-implemented method 200 may be used in any suitable setting to perform any suitable operations (e.g., may be performed by a computer program). Figure 1 , Fig.17 , Fig.18 , Fig.19 , Fig. 20 and Fig.21 Executed or used in conjunction with any of the various modules, computing devices, or graphical user interfaces described herein). Figure 2 In the drawings, operations are each shown once in a particular order, but the operations may be reordered or repeated as needed and appropriate (eg, different operations may be performed in parallel where appropriate).

[0086] In various aspects, act 202 can include performing a first operation to determine whether it is necessary to perform a maintenance task on the scientific instrument. In various cases, first logic component 104 can perform or otherwise facilitate act 202 .

[0087] In various cases, action 204 can include performing a second operation in response to or otherwise based on determining that it is necessary to perform a maintenance task, the second operation predicting a future time or date for performing the maintenance task via machine learning. In various cases, second logic component 106 can perform or otherwise facilitate action 204.

[0088] In various aspects, action 206 may include, in response to the remaining time until the future time or date being less than the threshold amount of time, performing a third operation of adjusting a hardware actuator of the scientific instrument in preparation for performing the maintenance task. In various cases, third logic component 108 may perform or otherwise facilitate action 206.

[0089] Thus, the computer-implemented method 200 may facilitate intelligent maintenance of scientific instruments.

[0090] The various embodiments described herein may be implemented to facilitate intelligent maintenance of any suitable scientific instrument. As a specific and non-limiting example, the various embodiments described herein may be implemented to facilitate intelligent maintenance of a scientific instrument including a mass spectrometer equipped with a chromatograph. Figure 3 To Figure [TP386185CNSEC1]

[0091] 16 describes non-limiting aspects of how various embodiments may be applied to a mass spectrometer equipped with a chromatograph.

[0092] Figure 3A block diagram of an example non-limiting scientific instrument that can facilitate smart maintenance according to one or more embodiments described herein is shown. As shown, a scientific instrument 302 can include a mass spectrometer 304 equipped with a chromatograph.

[0093] In various aspects, as its name implies, chromatograph-equipped mass spectrometer 304 can include a mass spectrometer operably coupled to a chromatograph.

[0094] In various embodiments, the chromatograph of the mass spectrometer 304 equipped with a chromatograph can be any suitable chromatograph, such as a gas chromatograph or a liquid chromatograph. In various aspects, the chromatograph may include any suitable component hardware for separating the analysis sample into two or more components. As a non-limiting example, the component hardware may include a syringe, an oven heating column, and a carrier fluid valve or pump. In various aspects, the carrier fluid valve or pump may allow the carrier fluid (e.g., an inert gas or a water-organic solvent mixture) to flow through the chromatograph. In various cases, the syringe may inject the analysis sample (e.g., a compound to be measured or analyzed) into the flowing carrier fluid. In various cases, the injected analysis sample may be carried by the flowing carrier fluid through the oven-heated column, which may include any suitable absorbent packaging material or stationary phase membrane. In various aspects, different components (e.g., different elements or molecules) of the analysis sample may interact differently or uniquely with the absorbent packaging material or the stationary phase membrane, thereby causing different components of the analysis sample to have different flow rates through the oven heating column. Due to such different flow rates, different components may be considered to be physically separated from each other.

[0095] In various aspects, the mass spectrometer of the mass spectrometer 304 equipped with a chromatograph can be any suitable mass spectrometer. In various cases, the mass spectrometer may include any suitable component hardware for measuring the ion spectrum of the analysis sample. As a non-limiting example, the component hardware may include an ion beam emitter, ion optical equipment, a mass analyzer and an ion detector. In various cases, the ion beam emitter can receive the component of the analysis sample from the chromatograph, and the component can be ionized into an ion beam. The ion beam emitter can promote this via any suitable ionization technology, such as electronic ionization, chemical ionization, matrix-assisted laser desorption ionization, electrospray ionization, photoionization or inductively coupled plasma ionization, any of which can be implemented in a vacuum or at atmospheric pressure. In various aspects, the ion optical equipment can guide or manipulate the ion beam generated by the ion beam emitter to pass through the mass analyzer and arrive at the ion detector. The non-limiting example of this ion optical equipment may include an ion focusing lens, an ion guide or an ion deflector. In various cases [TP386185CNSEC1]

[0096] In some embodiments, the mass analyzer can separate or classify any ions present in the ion beam according to the mass-to-charge ratio. Non-limiting examples of mass analyzers can include a quadrupole mass analyzer, a time-of-flight mass analyzer, a magnetic field sector mass analyzer, an electrostatic sector mass analyzer, a quadrupole ion trap mass analyzer, or an ion cyclotron resonance mass analyzer. In various cases, the ion detector can electronically detect or measure the relative abundance of any ions that strike it. Non-limiting examples of ion detectors can include an electron multiplier ion detector or a Faraday cup ion detector.

[0097] In any case, given an analytical sample, the mass spectrometer 304 equipped with a chromatograph can produce an ion spectrum that plots the relative abundance of various ions in the analytical sample versus the mass-to-charge ratio.

[0098] In various embodiments, the scientific instrument 302 can include a maintenance system 306. In various cases, the maintenance system 306 can facilitate intelligent maintenance of the scientific instrument 302.

[0099] In various aspects, the maintenance system 306 may include a processor 308 (e.g., a computer processing unit, a microprocessor) and a non-transitory computer-readable memory 310 that is operatively or operatively or communicatively connected or coupled to the processor 308. The non-transitory computer-readable memory 310 may store computer-executable instructions that, when executed by the processor 308, may cause the processor 308 or other components of the maintenance system 306 (e.g., an access component 312, a determination component 314, a scheduling component 316, a preparation component 318) to perform one or more actions. In various embodiments, the non-transitory computer-readable memory 310 may store the computer-executable components (e.g., an access component 312, a determination component 314, a scheduling component 316, a preparation component 318), and the processor 308 may execute the computer-executable components.

[0100] In various embodiments, the maintenance system 306 may include an access component 312. In various aspects, the access component 312 can communicate electronically or otherwise interact electronically with the mass spectrometer 304 equipped with a chromatograph (e.g., transmitting an electronic signal to the mass spectrometer equipped with a chromatograph, receiving an electronic signal from the mass spectrometer equipped with a chromatograph). Therefore, any other component of the maintenance system 306 can communicate or interact with the mass spectrometer 304 equipped with a chromatograph through or via the access component 312 (e.g., the access component 312 can act as an intermediary between any other component of the maintenance system 306 and the mass spectrometer 304 equipped with a chromatograph). However, this is merely a non-limiting example. In other cases, the access component 312 can be omitted, and any other component of the maintenance system 306 can communicate or interact directly with the mass spectrometer 304 equipped with a chromatograph.

[0101] In various embodiments, maintenance system 306 can include determination component 314. In various aspects, determination component 314 can electronically determine or detect when a given maintenance task needs to be performed for chromatograph-equipped mass spectrometer 304, as described herein.

[0102] [TP386185CNSEC1]

[0103] In various embodiments, the maintenance system 306 can include a scheduling component 316. In various cases, the scheduling component 316 can electronically schedule a future time or date to perform a maintenance task on the chromatograph-equipped mass spectrometer 304 via machine learning, as described herein.

[0104] In various embodiments, maintenance system 306 can include preparation component 318. In various cases, preparation component 318 can electronically prepare chromatograph-equipped mass spectrometer 304 to perform maintenance tasks by actuating or otherwise adjusting constituent hardware of chromatograph-equipped mass spectrometer 304, as described herein.

[0105] Figure 4 A block diagram of an example non-limiting scientific instrument including a maintenance map and maintenance tasks that may facilitate smart maintenance according to one or more embodiments described herein is shown.

[0106] In various embodiments, the determination component 314 can electronically determine whether it is necessary to perform a maintenance task 404 on the chromatograph-equipped mass spectrometer 304 based on the maintenance map 402 , based on one or more electronic counters 406 , or based on one or more readback sensors 408 .

[0107] In various aspects, maintenance task 404 may be any suitable hardware repair task or consumable replenishment task that may be manually performed on chromatograph-equipped mass spectrometer 304 by a user or technician of chromatograph-equipped mass spectrometer 304 . As some non-limiting examples, the maintenance tasks 404 may be: replacement of an autosampler syringe of a mass spectrometer 304 equipped with a chromatograph; replacement of a solvent wash of an autosampler of a mass spectrometer 304 equipped with a chromatograph; removal of solvent waste from an autosampler of a mass spectrometer 304 equipped with a chromatograph; replacement of a carrier fluid filter of a mass spectrometer 304 equipped with a chromatograph; replacement of a non-carrier fluid filter of a mass spectrometer 304 equipped with a chromatograph; replacement of a diaphragm of a mass spectrometer 304 equipped with a chromatograph; cleaning of an inlet of a mass spectrometer 304 equipped with a chromatograph; replacement of a gold seal of a mass spectrometer 304 equipped with a chromatograph; replacement of a pre-column tube or a post-column tube of a mass spectrometer 304 equipped with a chromatograph; trimming a column of a mass spectrometer 304 equipped with a chromatograph; replacement of a column of a mass spectrometer 304 equipped with a chromatograph; Replacement of the fluid cylinder of the mass spectrometer 304 equipped with a chromatograph; repair of liquid leaks in the mass spectrometer 304 equipped with a chromatograph; cleaning of the ion beam emitter of the mass spectrometer 304 equipped with a chromatograph; replacement of the calibrant of the mass spectrometer 304 equipped with a chromatograph; cleaning of the ion optical equipment of the mass spectrometer 304 equipped with a chromatograph; repair of the electrically floating ion focusing lens of the mass spectrometer 304 equipped with a chromatograph; repair of the electron emitter or receiver of the mass spectrometer 304 equipped with a chromatograph; replacement of the electron multiplier of the mass spectrometer 304 equipped with a chromatograph; replacement of the oil or lubricant of the mass spectrometer 304 equipped with a chromatograph; repair of the diaphragm pump or vortex pump of the mass spectrometer 304 equipped with a chromatograph; repair, replacement or lubrication of the turbine pump oil bearing of the mass spectrometer 304 equipped with a chromatograph; or replacement of the collision fluid cylinder of the mass spectrometer 304 equipped with a chromatograph.

[0108] [TP386185CNSEC1]

[0109] In various aspects, as shown, the mass spectrometer 304 equipped with a chromatograph may include one or more electronic counters 406. In various cases, one or more electronic counters 406 may include any suitable number of electronic counters. In various cases, the electronic counter may be any suitable hardware or software that can count, record, add up or otherwise track the total number of occurrences of any suitable limiting event belonging to or involving the operation of the mass spectrometer 304 equipped with a chromatograph (e.g., as measured relative to any suitable reference). As a non-limiting example, the electronic counter may record the number of total samples that have been run or analyzed by the mass spectrometer 304 equipped with a chromatograph since the mass spectrometer 304 equipped with a chromatograph has been recently serviced or maintained. As another non-limiting example, the electronic counter may record how many injections have been performed by the injector of the mass spectrometer 304 equipped with a chromatograph since the mass spectrometer 304 equipped with a chromatograph has been recently serviced or maintained. As another non-limiting example, the electronic counter may record how many total operating hours the chromatograph-equipped mass spectrometer 304 has accumulated since the chromatograph-equipped mass spectrometer 304 last received service or maintenance. As yet another non-limiting example, the electronic counter may record how many total volumes or masses of carrier fluid have flowed through the chromatograph-equipped mass spectrometer 304 since the chromatograph-equipped mass spectrometer 304 last received service or maintenance. As yet another non-limiting example, the electronic counter may record how many total pass-to-block or block-to-pass transitions the ion focus lens of the chromatograph-equipped mass spectrometer 304 has undergone since the chromatograph-equipped mass spectrometer 304 last received service or maintenance.

[0110] In various cases, as shown, the mass spectrometer 304 equipped with a chromatograph may include one or more readback sensors 408. In various cases, the one or more readback sensors 408 may include any suitable number of readback sensors. In various aspects, the readback sensor may be any suitable hardware or software that can generate an electronic output signal in response to exposure to any suitable physical stimulus associated with the mass spectrometer 304 equipped with a chromatograph. As a non-limiting example, the readback sensor may be any suitable temperature sensor (e.g., a thermocouple, a thermistor) that can electronically measure the internal temperature or internal temperature difference of the mass spectrometer 304 equipped with a chromatograph. As another non-limiting example, the readback sensor may be any suitable pressure sensor (e.g., a resistive, capacitive, piezoelectric, or optical pressure gauge) that can electronically measure the internal pressure or internal pressure difference of the mass spectrometer 304 equipped with a chromatograph. As another non-limiting example, the readback sensor may be any suitable voltage sensor (e.g., a resistive or capacitive voltmeter) that can electronically measure the internal voltage or internal voltage difference of the mass spectrometer 304 equipped with a chromatograph. As yet another non-limiting example, the readback sensor may be any suitable humidity sensor (e.g., resistive, capacitive, or [TP386185CNSEC1]) that can electronically measure the internal humidity or internal humidity difference of the mass spectrometer 304 equipped with the chromatograph.

[0111] Thermal conductivity hygrometer). As yet another non-limiting example, the readback sensor may be any suitable motion or deflection sensor (e.g., accelerometer, potentiometer, tachometer, strain gauge) that can electronically measure the internal motion or deflection of the mass spectrometer equipped with a chromatograph 304. As another non-limiting example, the readback sensor may be any suitable fluid flow sensor (e.g., Coriolis mass flowmeter, ultrasonic flowmeter, electromagnetic flowmeter) that can electronically measure the internal fluid flow rate of the mass spectrometer equipped with a chromatograph 304. Note that in some cases, the ion detector of the mass spectrometer equipped with a chromatograph 304 may be considered a readback sensor.

[0112] In any case, the one or more electronic counters 406 and the one or more readback sensors 408 may collectively be considered to measure the current or present operating state of the chromatograph-equipped mass spectrometer 304. In other words, any data captured or represented by the one or more electronic counters 406 and the one or more readback sensors 408 at any given point in time may be considered to represent the health state of the chromatograph-equipped mass spectrometer 304 at that given point in time. Thus, the chromatograph-equipped mass spectrometer 304 may be considered to understand its own health or operating state and how that health or operating state changes over time.

[0113] In various embodiments, the maintenance map 402 can be any suitable electronic data that can list various maintenance tasks that may be associated with the chromatograph-equipped mass spectrometer 304. In addition, the maintenance map 402 can list, for each of those different maintenance tasks, a corresponding health or operating status condition that can be considered a trigger indicating that it is necessary to perform such a maintenance task on the chromatograph-equipped mass spectrometer 304. Thus, the determination component 314 can determine whether it is necessary to perform a maintenance task 404 on the chromatograph-equipped mass spectrometer 304 by comparing the operating status measured by the one or more electronic counters 406 and by the one or more readback sensors 408 to the maintenance map 402. Figure 5 Various non-limiting aspects are described.

[0114] Figure 5 An example non-limiting block diagram of a maintenance map 402 is shown in accordance with one or more embodiments described herein.

[0115] In various aspects, as shown, the maintenance map 402 may include a maintenance task set 504. In various cases, the maintenance task set 504 may include n tasks, for any suitable positive integer n: maintenance task 504(1) to maintenance task 504(n). In various cases, each maintenance task in the maintenance task set 504 may be any suitable unique or different maintenance task that can be performed on the mass spectrometer 304 equipped with a chromatograph. Therefore, the maintenance task sets 504 can be different from each other. As a non-limiting example, the maintenance task 504(1) may be to replace the syringe liner of the mass spectrometer 304 equipped with a chromatograph, while the maintenance task 504(n) may be to clean the ion transfer tube of the mass spectrometer 304 equipped with a chromatograph.

[0116] [TP386185CNSEC1]

[0117] In various cases, as shown, maintenance map 402 may also include state condition set 502. In various cases, state condition set 502 may correspond to maintenance task set 504, respectively. Thus, since maintenance task set 504 may include n tasks, state condition set 502 may include n conditions: state condition 502(1) through state condition 502(n). In various aspects, each state condition in state condition set 502 may be considered to represent or indicate a health or operational state that will necessitate the execution of a corresponding one of maintenance tasks in maintenance task set 504.

[0118] As a non-limiting example, the status condition 502(1) may correspond to the maintenance task 504(1). Thus, the status condition 502(1) may be considered to represent or otherwise indicate some particular health or operating state, the occurrence of which would indicate or indicate that it is necessary, required, or recommended to perform the maintenance task 504(1) on the chromatograph-equipped mass spectrometer 304. Equivalently, the status condition 502(1) may represent or indicate any suitable counter or readback signal threshold or tolerance, the failure of which to be met by the chromatograph-equipped mass spectrometer 304 would indicate or indicate that it is necessary, required, or recommended to perform the maintenance task 504(1) on the chromatograph-equipped mass spectrometer 304. In other words, the status condition 502(1) may be considered to represent or depict some particular hardware problem that may potentially or possibly affect the chromatograph-equipped mass spectrometer 304, and the maintenance task 504(1) may be considered to be a solution or remedy to the particular hardware problem.

[0119] As another non-limiting example, the state condition 502(n) may correspond to a maintenance task 504(n). Thus, the state condition 502(n) may be considered to represent or otherwise indicate some particular health or operating state, the occurrence of which would indicate or indicate that it is necessary, required, or recommended to perform the maintenance task 504(n) on the chromatograph-equipped mass spectrometer 304. Equivalently, the state condition 502(n) may represent or indicate any suitable counter or readback signal threshold or tolerance, the failure of which by the chromatograph-equipped mass spectrometer 304 to meet which would indicate or indicate that it is necessary, required, or recommended to perform the maintenance task 504(n) on the chromatograph-equipped mass spectrometer 304. In other words, the state condition 502(n) may be considered to represent or otherwise depict some defined hardware problem that may potentially or possibly affect the chromatograph-equipped mass spectrometer 304, and the maintenance task 504(n) may be considered to be a solution or remedy to the defined hardware problem.

[0120] In various aspects, as mentioned above, the one or more electronic counters 406 and the one or more readback sensors 408 can collectively measure the current or present health or operating state of the mass spectrometer 304 equipped with the chromatograph. In various cases, the determination component 314 can compare the current or present health or operating state to the state condition set 502. If the determination component 314 does not identify the current or present health or operating state, the determination component 314 can compare the current or present health or operating state to the state condition set 502.

[0121] If the determination component 314 instead identifies a status condition triggered by the current or current health or operating state (e.g., a threshold or tolerance thereof is not satisfied), the determination component 314 can conclude or determine that the mass spectrometer equipped with a chromatograph 304 does not currently or currently require any maintenance task. However, if the determination component 314 instead identifies a status condition triggered by the current or current health or operating state (e.g., a threshold or tolerance thereof is not satisfied), the determination component 314 can conclude or determine that the mass spectrometer equipped with a chromatograph 304 currently or currently requires any maintenance task corresponding to the status condition, and such maintenance task can be considered as the maintenance task 404.

[0122] In this manner, the determination component 314 can determine whether it is necessary to perform a maintenance task 404 on the chromatograph-equipped mass spectrometer 304 .

[0123] Figure 6 A block diagram of an example non-limiting scientific instrument including a maintenance time or date predicted by a machine learning model that can facilitate smart maintenance according to one or more embodiments described herein is shown.

[0124] In various embodiments, the scheduling component 316 can electronically store, electronically maintain, or otherwise access from any suitable electronic source (e.g., from the chromatograph-equipped mass spectrometer 304 itself) an operational history 602 associated with the chromatograph-equipped mass spectrometer 304. In various aspects, the operational history 602 can be any suitable electronic data that can indicate or otherwise represent previous times or dates when the chromatograph-equipped mass spectrometer 304 was or was not operating.

[0125] In various cases, the scheduling component 316 can electronically store, electronically maintain, or otherwise access task metadata 604 associated with the maintenance task 404 from any suitable electronic source (e.g., from the maintenance map 402). In various cases, the task metadata 604 can be any suitable electronic data that can indicate or otherwise represent any suitable attributes or characteristics of the maintenance task 404.

[0126] In various aspects, the arrangement component 316 can electronically store, electronically maintain, electronically control, or otherwise electronically access the machine learning model 606 from any suitable electronic source. In various aspects, the machine learning model 606 can be any suitable artificial neural network that can have or otherwise exhibit any suitable internal architecture. For example, the machine learning model 606 can have an input layer, one or more hidden layers, and an output layer. In various cases, any of such layers can be coupled together by any suitable inter-neuron connection or inter-layer connection, such as a forward connection, a skip connection, or a recurrent connection. In addition, in various cases, any of such layers can be any suitable type of neural network layer with any suitable learnable or trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer can be a convolutional layer, and the learnable or trainable parameters of the convolutional layer can be a convolution kernel. As another [TP386185CNSEC1]

[0127] For example, any of such input layer, one or more hidden layers, or output layer can be a dense layer, whose learnable or trainable parameters can be a weight matrix or a bias value. As another example, any of such input layer, one or more hidden layers, or output layer can be a batch normalization layer, whose learnable or trainable parameters can be a shift factor or a scale factor. In addition, in various cases, any of such layers can be a neural network layer of any suitable type with any suitable fixed or non-trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer can be a nonlinear layer, a padding layer, a pooling layer, or a cascade layer.

[0128] In any case, the scheduling component 316 can electronically execute the machine learning model 606 on the operation history 602 or on the task metadata 604, and such execution can cause the machine learning model 606 to predict as an output a time / date 608. In various aspects, the time / date 608 can be considered to be some future time or date (e.g., as identified in years, months, weeks, days, hours, minutes, or seconds) that the machine learning model 606 has inferred or determined will be appropriate to perform the maintenance task 404 on the chromatograph-equipped mass spectrometer 304. Figures 7 and 8 Various non-limiting aspects are described.

[0129] Figures 7 and 8 An example non-limiting block diagram illustrating how a machine learning model 606 may predict a time / date 608 is shown according to one or more embodiments described herein.

[0130] First, consider Figure 7In various embodiments, as shown, the operation history 602 may include a runtime log 702 and a graphical user interface interaction log 704 (hereinafter referred to as "GUI interaction log 704").

[0131] In various aspects, the operation time log 702 can be any suitable electronic data indicating when the chromatograph-equipped mass spectrometer 304 was run or operated in the past. As a non-limiting example, the operation time log 702 can indicate previous or past times or dates when the chromatograph-equipped mass spectrometer 304 analyzed a sample, and the operation time log 702 can further indicate previous or past times or dates when the chromatograph-equipped mass spectrometer 304 did not analyze a sample. In other words, the operation time log 702 can be considered an electronic record or ledger showing when the chromatograph-equipped mass spectrometer 304 was run or not run in the past.

[0132] In various cases, the GUI interaction log 704 may be any suitable electronic data indicating when the GUI (e.g., a touch screen, or a computer keyboard or keypad coupled to an electronic display) of the chromatograph-equipped mass spectrometer 304 was operated in the past. As a non-limiting example, the GUI interaction log 704 may indicate a previous or past time or date when a user or technician interacted with (e.g., was clicked on) the GUI of the chromatograph-equipped mass spectrometer 304, and the GUI interaction log 704 may further indicate that the user or technician did not interact with the GUI of the chromatograph-equipped mass spectrometer 304.

[0133] Because interacting with (e.g., clicking on) the GUI of the chromatograph-equipped mass spectrometer 304 may require physical presence at the chromatograph-equipped mass spectrometer 304, the GUI interaction log 704 may be considered an electronic record or ledger showing whether a user or technician was physically present at the chromatograph-equipped mass spectrometer 304.

[0134] In various aspects, the task metadata 604 may include a task priority 706. In various cases, the task priority 706 may be any suitable electronic data that may indicate or otherwise represent the importance, criticality, or urgency of the maintenance task 404 relative to the mass spectrometer 304 equipped with the chromatograph. After all, some maintenance tasks (e.g., repairing an electrically disconnected ion focus lens) may be considered more urgent than other maintenance tasks (e.g., cleaning an ion beam emitter). In some cases, the task priority 706 may be any suitable scalar whose magnitude may indicate a level of importance, criticality, or urgency of the maintenance task 404 (e.g., a higher magnitude may indicate a higher importance, criticality, or urgency; a lower magnitude may indicate a lower importance, criticality, or urgency).

[0135] In various cases, task metadata 604 may include task duration 708. In various cases, task duration 708 may be any suitable length of time (eg, measured in hours, minutes, or seconds) expected to be consumed by performing maintenance task 404 on chromatograph-equipped mass spectrometer 304.

[0136] In various embodiments, as described above, the scheduling component 316 can execute the machine learning model 606 on the operation history 602 and on the task metadata 604, and such execution can produce the time / date 608. More specifically, the scheduling component 316 can concatenate the operation history 602 and the task metadata 604 together. In various aspects, the scheduling component 316 can feed such concatenation to the input layer of the machine learning model 606. In various cases, such concatenation can complete a forward pass through one or more hidden layers of the machine learning model 606. In various cases, the output layer of the machine learning model 606 can calculate or compute the time / date 608 based on the activation map or feature map produced by the one or more hidden layers.

[0137] In any case, time / date 608 can be any suitable electronic data indicating a future time or date that machine learning model 606 infers will be suitable, appropriate, or otherwise convenient to perform maintenance task 404 on chromatograph-equipped mass spectrometer 304 .

[0138] In particular, it may be convenient or otherwise appropriate to perform maintenance tasks 404 when the mass spectrometer 304 equipped with the chromatograph is not analyzing samples (e.g., maintenance cannot be performed when samples are running). Thus, in some aspects, the machine learning model 606 may be considered to infer the running time [TP386185CNSEC1]

[0139] Log 702 indicates that it is unlikely that mass spectrometer 304 equipped with a chromatograph will analyze a sample at time / date 608. In other words, machine learning model 606 can be considered to extrapolate run time log 702 into the future, and time / date 608 can be a future time or date at which, according to this extrapolation, it is unlikely that mass spectrometer 304 equipped with a chromatograph will run a sample.

[0140] Furthermore, performance of maintenance task 404 may be convenient or otherwise appropriate at a time when a user or technician is interacting with the GUI of chromatograph-equipped mass spectrometer 304 (e.g., maintenance cannot be performed without the user or technician being physically present, and the GUI interaction may suggest that the user or technician is physically present at chromatograph-equipped mass spectrometer 304). Thus, in some cases, machine learning model 606 may be considered to infer that GUI interaction log 704 indicates that the GUI of chromatograph-equipped mass spectrometer 304 may be interacted with at time / date 608. In other words, machine learning model 606 may be considered to extrapolate GUI interaction log 704 into the future, and time / date 608 may be some future time or date at which, according to such extrapolation, the user or technician may be physically present at chromatograph-equipped mass spectrometer 304.

[0141] In addition, the appropriate timing for performing the maintenance task 404 may depend on the task priority 706. In fact, the more important, critical, or urgent the maintenance task 404 is, the earlier or as soon as the maintenance task 404 should be performed on the mass spectrometer 304 equipped with a chromatograph. Conversely, the less important, critical, or urgent the maintenance task 404 is, the later or more delayed the performance of the maintenance task 404 may be. Therefore, since the machine learning model 606 can receive the task priority 706 as an input, the time / date 608 can be considered to be based on the task priority 706 (e.g., considering that the task priority is appropriate).

[0142] In addition, the appropriate timing for the execution of the maintenance task 404 may depend on the task duration 708. In fact, as described above, since the time / date 608 may be based on the run time log 702, the time / date 608 may be a future time or date when the mass spectrometer 304 equipped with the chromatograph is unlikely to run a sample. However, not every future time or date when the mass spectrometer 304 equipped with the chromatograph is unlikely to run a sample may be appropriate or convenient for performing the maintenance task 404. After all, the execution of the maintenance task 404 may consume some non-zero amount of time, and the mass spectrometer 304 equipped with the chromatograph cannot or should not run a sample within the non-zero amount of time. Therefore, since the time / date 608 may be based on both the run time log 702 and the task duration 708, the time / date 608 may be a future time or date when the mass spectrometer 304 equipped with the chromatograph is unlikely to run a sample; and thereafter, the mass spectrometer 304 equipped with the chromatograph is unlikely to start running a sample at least within the task duration 708.

[0143] [TP386185CNSEC1]

[0144] Note that in various embodiments, the machine learning model 606 may be configured to predict the time / date 608 based on any other suitable data that may supplement or complete the operation history 602 or the task metadata 604. In practice, such supplemental data may be concatenated with the operation history 602 and the task metadata 604, and such concatenation may complete a forward pass through the machine learning model 606, as described above. As a non-limiting example, such supplemental data may include one or more user scheduling preferences 710. In various cases, the one or more user scheduling preferences 710 may include any suitable number of user scheduling preferences. In various aspects, the user scheduling preferences may be any suitable electronic data indicating a time or date when a user prefers to perform maintenance on the mass spectrometer 304 equipped with a chromatograph or other times or dates when a user prefers not to perform maintenance on the mass spectrometer 304 equipped with a chromatograph. Therefore, in some cases, the time / date 608 may be consistent with one or more user scheduling preferences 710.

[0145] like Figure 7 As shown, in some embodiments, machine learning model 606 can directly predict time / date 608. However, in other embodiments, machine learning model 606 can instead predict multiple possible times / dates, each of which would be suitable, appropriate, or convenient for performing maintenance task 404 on mass spectrometer 304 equipped with a chromatograph, and a user or technician can select time / date 608 from such multiple times / dates. Figure 8 Various non-limiting aspects are described.

[0146] like Figure 8 As shown, in some aspects, the machine learning model 606 can predict multiple times / dates 802 based on the operation history 602 and the task metadata 604. That is, the scheduling component 316 can concatenate the operation history 602 and the task metadata 604 (along with any other supplementary or complementary data, such as one or more user scheduling preferences 710), the scheduling component 316 can feed this concatenation to the input layer of the machine learning model 606, this concatenation can complete a forward pass through one or more hidden layers of the machine learning model 606, and the output layer of the machine learning model 606 can calculate or operate on the multiple times / dates 802 based on the activation map or feature map produced by the one or more hidden layers.

[0147] In any case, the plurality of times / dates 802 may include m times / dates, for any suitable positive integer m: time / date 802(1) to time / date 802(m). In various aspects, each of the plurality of times / dates 802 may be a unique or different future time or date inferred by the machine learning model 606 to be suitable, appropriate, or otherwise convenient for performing the maintenance task 404 on the mass spectrometer 304 equipped with a chromatograph. In various cases, a user or technician may manually select any of the plurality of times / dates 802 (e.g., via a GUI), and such selected time / date may be considered as time / date 608. In some cases, a user or technician may not be able to manually select any of the plurality of times / dates 802. In such a case, the scheduling component 316 itself may select any of the plurality of times / dates 802, and such selected time / date [TP386185CNSEC1]

[0148] 802 from the plurality of times / dates 608. In the event that the scheduling component 316 itself selects the time / date 802 from the plurality of times / dates 608, the scheduling component 316 may facilitate such selection in any suitable manner. As a non-limiting example, the scheduling component 316 may randomly select the time / date 802 from the plurality of times / dates 608. As another non-limiting example, the scheduling component 316 may select any of the plurality of times / dates 608 that is the earliest or least postponed as the time / date 802. As yet another non-limiting example, the scheduling component 316 may select the latest or most postponed time / date from the plurality of times / dates 802 as the time / date 608.

[0149] In any case, in order to make the time / date 608 accurate (e.g., as is practical or appropriate given the operational history 602 or task metadata 604), the machine learning model 606 may first undergo training of any suitable type or paradigm. As a non-limiting example, the machine learning model 606 may undergo supervised training. Figures 9 and 10 Non-limiting aspects are described.

[0150] Fig. 9 An example non-limiting block diagram of a training data set 902 is shown according to one or more embodiments described herein.

[0151] In various aspects, training data set 902 may include a set of training operation histories 904. In various cases, training operation history set 904 may include q histories, for any suitable positive integer q: training operation history 904(1) to training operation history 904(q). In various cases, each training operation history in training operation history set 904 may be electronic data having the same format, size, or dimension as operation history 602. Thus, training operation history 904(1) may indicate or represent the first runtime log and the first GUI interaction log of any mass spectrometer equipped with a chromatograph, and training operation history 904(q) may indicate or represent the qth runtime log and the qth GUI interaction log of any other mass spectrometer equipped with a chromatograph.

[0152] In various aspects, the training data set 902 may include a training task metadata set 906. In various cases, the training task metadata set 906 may correspond to the training operation history set 904, respectively. Therefore, since the training operation history set 904 may include q histories, the training task metadata set 906 may include q metadata indicators: training task metadata 906(1) to training task metadata 906(q). In various cases, each of the training task metadata set 906 may be any task metadata (e.g., electronic data having the same format, size, or dimension as 604) that is known or believed to correspond to a corresponding one of the training operation histories in the training operation history set 904. For example, the training task metadata 906(1) may indicate or represent a first task priority and a first task duration corresponding to any maintenance task that may be performed on any mass spectrometer equipped with a chromatograph corresponding to the training operation history 904(1). Similarly, the training task metadata 906[TP386185CNSEC1]

[0153] (q) may indicate or represent a qth task priority and a qth task duration corresponding to any maintenance task that can be performed on any chromatograph-equipped mass spectrometer corresponding to the training operation history 904 (q).

[0154] In various aspects, the training data set 902 may include a set of reference truth annotations 908. In various cases, the set of reference truth annotations 908 may correspond to the training operation history set 904 and the training task metadata set 906, respectively. Thus, the set of reference truth annotations 908 may include q annotations: reference truth annotation 908(1) to reference truth annotation 908(q). In various cases, each of the reference truth annotations in the set of reference truth annotations 908 may indicate a future time or date (or multiple future times or dates) that is known or believed to be correct or accurate for the corresponding historical metadata pair formed by the training operation history set 904 and the training task metadata set 906. For example, the reference truth annotation 908(1) may represent a future time or date that is known or believed to be convenient, appropriate, or otherwise suitable for performing any maintenance task corresponding to the training task metadata 906(1) on any chromatograph-equipped mass spectrometer corresponding to the training operation history 904(1). As another example, the baseline truth annotation 908(q) may represent a future time or date that is known or deemed convenient, appropriate, or otherwise suitable for performing any maintenance task corresponding to the training task metadata 906(q) on any chromatograph-equipped mass spectrometer corresponding to the training operation history 904(q).

[0155] Now, consider Fig.10 . Fig.10 An example non-limiting block diagram illustrating how a machine learning model 606 may be trained is shown according to one or more embodiments described herein.

[0156] In various embodiments, before starting training, whatever trainable internal parameters (e.g., weight matrix, bias values, convolution kernels) the machine learning model 606 has can be initialized in any suitable manner (e.g., random initialization).

[0157] In various aspects, any training operation history, corresponding training task metadata, and corresponding ground truth annotations may be selected from training dataset 902. These may be referred to as training operation history 1002, training task metadata 1004, and ground truth annotations 1006, respectively.

[0158] In various cases, the machine learning model 606 may be executed on the training operation history 1002 and on the training task metadata 1004. In various cases, this may cause the machine learning model 606 to produce an output 1008. More specifically, the training operation history 1002 and the training task metadata 1004 may be concatenated together (and any supplementary data, as appropriate). In various aspects, this concatenation may be fed to the input layer of the machine learning model 606, and this concatenation may be completed through the machine learning [TP386185CNSEC1]

[0159] A forward pass of one or more hidden layers of the model 606, and an output layer of the machine learning model 606 may compute or calculate output 1008 based on activation maps or feature maps produced by the one or more hidden layers.

[0160] Note that in various cases, the format, size, or dimensionality of the output 1008 may be controlled or otherwise determined by the number, arrangement, or size of neurons or other internal parameters (e.g., convolution kernels) included in or otherwise constituting the output layer (or any other suitable layer) of the machine learning model 606. Thus, the output 1008 may be forced to have any desired format, size, or dimensionality by adding, removing, or otherwise adjusting neurons or other internal parameters in the output layer (or any other layer) of the machine learning model 606.

[0161] In any case, output 1008 may be considered an inferred or predicted future time or date that machine learning model 606 deems appropriate, proper, or convenient for performing any maintenance task represented by training task metadata 1004 on the mass spectrometer equipped with a chromatograph represented by training operation history 1002. Conversely, reference ground truth annotation 1006 may be considered a correct or accurate future time or date that is known or deemed appropriate, proper, or convenient for performing any maintenance task represented by training task metadata 1004 on any mass spectrometer equipped with a chromatograph represented by training operation history 1002. Note that if machine learning model 606 has not been trained to date or has undergone very little training, output 1008 may be highly inaccurate (e.g., output 1008 may differ significantly from reference ground truth annotation 1006).

[0162] In various aspects, a loss 1010 can be calculated between the ground truth annotation 1006 and the output 1008. In various cases, the loss 1010 can be equal to or otherwise based on any suitable loss, error, or objective function (e.g., mean absolute error, mean squared error, cross entropy error). In various cases, the trainable internal parameters of the machine learning model 606 can be incrementally updated by performing back propagation driven by the loss 1010 (e.g., stochastic gradient descent).

[0163] In various cases, the above training process can be repeated for any suitable number of training operation histories and training task metadata (e.g., for all training operation histories and training task metadata in training data set 902). This can ultimately result in iterative optimization of the trainable internal parameters of machine learning model 606 to accurately predict future maintenance times / dates based on the input operation histories and input task metadata. In various aspects, any suitable training batch size or any suitable training termination criteria can be implemented in such training.

[0164] Although the disclosure herein mainly describes how to train the machine learning model 606 in a supervised manner, this is only a non-limiting example. In various other embodiments, the machine learning model 606 may be replaced by [TP386185CNSEC1]

[0165] Alternatively, the method may be trained in any other suitable manner (e.g., via unsupervised learning, via reinforcement learning).

[0166] In any case, the scheduling component 316 can generate or predict a time / date 608 at which it is appropriate or convenient to perform the maintenance task 404 on the chromatograph-equipped mass spectrometer 304. Thus, in various aspects, the scheduling component 316 can electronically schedule the maintenance task 404 to be performed on the chromatograph-equipped mass spectrometer 304 at the time / date 608. In some cases, such scheduling can involve electronically editing or updating any suitable electronic calendar associated with the chromatograph-equipped mass spectrometer 304 or associated with a user or technician. In other cases, such scheduling can involve electronically presenting (e.g., on a GUI) any suitable notification or message indicating that the maintenance task 404 will be performed at the time / date 608. In some cases, the scheduling component 316 can repeatedly present such a notification or message as the time / date 608 approaches or gets closer to continuously remind the user or technician that the maintenance task 404 has been scheduled at the time / date 608.

[0167] Fig.11 A block diagram of an example non-limiting scientific instrument including hardware adjustments that can facilitate smart maintenance according to one or more embodiments described herein is shown.

[0168] In various embodiments, the preparation component 318 can electronically make or perform one or more hardware adjustments 1102 on the chromatograph-equipped mass spectrometer 304 prior to the time / date 608 in preparation for performing the maintenance task 404. More specifically, the chromatograph-equipped mass spectrometer 304 can include actuatable hardware 1104. The actuatable hardware 1104 can be any suitable piece or pieces of component hardware that make up the chromatograph-equipped mass spectrometer 304 and can be electronically actuated, electronically activated, electronically deactivated, or otherwise controllably adjusted via one or more electronic control signals. In various aspects, the one or more hardware adjustments 1102 can be any suitable controlled actuation of the actuatable hardware 1104 that will prepare or otherwise prepare the chromatograph-equipped mass spectrometer 304 for the maintenance task 404. Fig.12 Non-limiting aspects are described.

[0169] Fig.12 An example non-limiting block diagram illustrating how one or more hardware adjustments 1102 may be performed in preparation for a maintenance task 404 is shown in accordance with one or more embodiments described herein.

[0170] In various aspects, as shown, the actuatable hardware 1104 may include one or more temperature heaters or coolers 1202 of the mass spectrometer equipped with a chromatograph 304. In various cases, the one or more temperature heaters / coolers 1202 may be any suitable device capable of controllably increasing (e.g., heating) or controllably decreasing (e.g., cooling) one or more internal temperatures of the mass spectrometer equipped with a chromatograph 304. As a non-limiting example, an oven of the mass spectrometer equipped with a chromatograph 304 may be considered to belong to the one or more temperature heaters / coolers 1202 because the oven may controllably [TP386185CNSEC1]

[0171] Heating a column of the chromatograph-equipped mass spectrometer 304. As another non-limiting example, a heater of an ion beam emitter of the chromatograph-equipped mass spectrometer 304 may be considered to belong to the one or more temperature heaters / coolers 1202, as such a heater may controllably heat the ion beam emitter.

[0172] In various cases, the one or more hardware adjustments 1102 may include adjusting one or more temperature heaters / coolers 1202 in response to determining that the remaining time until the time / date 608 is less than any suitable threshold amount of time to controllably increase or controllably decrease one or more internal temperatures of the chromatograph-equipped mass spectrometer 304. In various aspects, such controlled temperature increases or decreases may be considered to place the chromatograph-equipped mass spectrometer 304 in an operating state or mode suitable or otherwise applicable for the maintenance task 404. For example, assume that the maintenance task 404 may be performed on the chromatograph-equipped mass spectrometer 304 only when the internal temperature of the chromatograph-equipped mass spectrometer 304 is at a certain particular value (e.g., at a room temperature value, at a certain non-room temperature value). In this case, the one or more hardware adjustments 1102 may include controllably heating or cooling the chromatograph-equipped mass spectrometer 304 to the specific value (e.g., causing the internal temperature of the chromatograph-equipped mass spectrometer 304 to transition from an operating value to the specific value; causing the internal temperature of the chromatograph-equipped mass spectrometer 304 to transition from an idle value to the specific value). Thus, in various cases, the one or more hardware adjustments 1102 may be considered to pre-adjust the one or more temperature heaters / coolers 1202 so as to cause the internal temperature of the chromatograph-equipped mass spectrometer 304 to reach any value required or appropriate for performing the maintenance task 404.

[0173] In various aspects, as shown, the actuatable hardware 1104 may include one or more fluid flow valves or pumps 1204 of the chromatograph-equipped mass spectrometer 304. In various cases, the one or more fluid flow valves / pumps 1204 may be any suitable device that can controllably increase or controllably decrease one or more fluid flow rates within the chromatograph-equipped mass spectrometer 304 or fill or evacuate one or more fluid chambers within the chromatograph-equipped mass spectrometer 304. As a non-limiting example, any valve, pump, tank, or outlet that can store or direct a carrier fluid or calibrant of the chromatograph-equipped mass spectrometer 304 may be considered to belong to the one or more fluid flow valves / pumps 1204. As another non-limiting example, any vacuum pump, vacuum chamber, or vacuum exhaust port of the chromatograph-equipped mass spectrometer 304 may be considered to belong to the one or more fluid flow valves / pumps 1204.

[0174] In various cases, the one or more hardware adjustments 1102 may include, in response to determining that the remaining time until the time / date 608 is less than any suitable threshold amount of time, adjusting one or more fluid flow valves / pumps 1204 to controllably increase or controllably decrease one or more internal fluid flow rates or one or more internal fluid levels of the mass spectrometer 304 equipped with a chromatograph. In various aspects, such controlled increase or decrease in fluid flow rate or fluid level may be considered to be a controllable increase or decrease in the mass spectrometer 304 equipped with a chromatograph.

[0175] The mass spectrometer 304 is placed in an operating state or operating mode suitable or otherwise applicable to the maintenance task 404. For example, assume that the maintenance task 404 can be performed on the mass spectrometer equipped with a chromatograph 304 only when the internal fluid flow rate or fluid level of the mass spectrometer equipped with a chromatograph 304 is at a certain specific value (e.g., at a maximum value, at a minimum value, at a certain intermediate value, or completely closed or cut off). In this case, the one or more hardware adjustments 1102 may include controllably pumping or throttling the internal fluid flow rate or fluid level of the mass spectrometer equipped with a chromatograph 304 to the specific value. Therefore, the one or more hardware adjustments 1102 can be considered to pre-adjust the one or more fluid flow valves / pumps 1204 so that the internal fluid flow rate or fluid level of the mass spectrometer equipped with a chromatograph 304 presents any value required or suitable for the performance of the maintenance task 404.

[0176] In various aspects, as shown, the actuatable hardware 1104 may include one or more circuit switches 1206 of the chromatograph-equipped mass spectrometer 304. In various cases, the one or more circuit switches 1206 may be any suitable device that can controllably increase or controllably decrease one or more voltage levels or current levels within the chromatograph-equipped mass spectrometer 304. As a non-limiting example, a transistor switch that controls how much voltage or current flows to an ion focus lens of the chromatograph-equipped mass spectrometer 304 may be considered to belong to the one or more circuit switches 1206. As another non-limiting example, a transistor switch that controls how much voltage or current flows to an electron multiplier of the chromatograph-equipped mass spectrometer 304 may be considered to belong to the one or more circuit switches 1206.

[0177] In various cases, the one or more hardware adjustments 1102 may include, in response to determining that the remaining time until the time / date 608 is less than any suitable threshold amount of time, adjusting one or more circuit switches 1206 to controllably increase or controllably decrease one or more internal voltage or current levels of the chromatograph-equipped mass spectrometer 304. In various aspects, such a controlled increase or decrease in voltage or current may be considered to place the chromatograph-equipped mass spectrometer 304 in an operating state or mode suitable or otherwise applicable to the maintenance task 404. For example, assume that the maintenance task 404 can be performed on the chromatograph-equipped mass spectrometer 304 only when a given voltage or current level of the chromatograph-equipped mass spectrometer 304 is zero. In this case, the one or more hardware adjustments 1102 may include controllably opening an appropriate circuit switch of the chromatograph-equipped mass spectrometer 304 so as to cause the given voltage or current level to drop to zero. Thus, one or more hardware adjustments 1102 may be considered to pre-adjust one or more circuit switches 1206 so as to cause the voltage or current of the chromatograph-equipped mass spectrometer 304 to assume any value required or appropriate for performance of the maintenance task 404 .

[0178] [TP386185CNSEC1]

[0179] In this manner, when the remaining time before the time / date 608 arrives is less than any suitable threshold amount of time, the preparation component 318 can prepare or prepare the mass spectrometer equipped with a chromatograph 304 by performing one or more hardware adjustments 1102. Note that in various aspects, the threshold amount of time that triggers the one or more hardware adjustments 1102 can depend on the maintenance task 404 (e.g., different maintenance tasks can have different preparation steps that can consume different amounts of time). In any case, the preparation component 318 can be considered to automatically prepare or prepare the mass spectrometer equipped with a chromatograph 304 to perform the maintenance task 404. This can help any user or technician save time and effort in performing the maintenance task 404.

[0180] Fig.13 A block diagram of an example non-limiting scientific instrument including auxiliary components that can facilitate smart maintenance according to one or more embodiments described herein is shown.

[0181] In various embodiments, the maintenance system 306 may include an auxiliary component 1302. In various aspects, the auxiliary component 1302 may electronically assist a user or technician during the performance of the maintenance task 404. As a non-limiting example, the auxiliary component 1302 may visually present (e.g., on a GUI) at the time / date 608 any suitable instructions (e.g., a video graphical tutorial) that may explain how to properly perform the maintenance task 404 on the mass spectrometer 304 equipped with a chromatograph. Thus, the user or technician may watch or read the instructions while performing the maintenance task 404. This facilitates the correct or appropriate performance of the maintenance task 404.

[0182] Figures 14 to 16 A flow chart of an example non-limiting computer-implemented method 1400 that can facilitate smart maintenance according to one or more embodiments described herein is shown. In various cases, the maintenance system 306 can facilitate the computer-implemented method 1400 .

[0183] First, consider Fig.14 In various embodiments, act 1402 may include measuring electron counts (e.g., via 406) or reading back signals (e.g., via 408) associated with a mass spectrometer equipped with a chromatograph by a scientific instrument (e.g., via 314 mounted on 302) including the mass spectrometer equipped with a chromatograph (e.g., 304).

[0184] In various aspects, act 1404 may include determining, by the scientific instrument (e.g., via 314 mounted on 302), whether the electron count or readback signal fails to meet a threshold (e.g., any of 502). If not (e.g., if the threshold is not met), the computer-implemented method 1400 may return to act 1402. If yes (e.g., if the threshold is not met), the computer-implemented method 1400 may proceed to act 1406.

[0185] In various cases, act 1406 may include identifying, by a scientific instrument (eg, via 314 installed on 302 ), a maintenance task (eg, 404 ) corresponding to a threshold.

[0186] [TP386185CNSEC1]

[0187] In various cases, act 1408 may include accessing, by a scientific instrument (eg, via 316 mounted on 302), an operational history (eg, 602) of a mass spectrometer equipped with a chromatograph or metadata regarding a maintenance task (eg, 604).

[0188] In various aspects, action 1410 may include predicting, by a scientific instrument (e.g., via 316 installed on 302) and via executing a machine learning model (e.g., 606) on operational history or metadata, a time or date (e.g., 608) that would be appropriate to perform a maintenance task on a mass spectrometer equipped with a chromatograph.

[0189] Now, consider Fig.15 In various aspects, act 1412 may include scheduling, by the scientific instrument (eg, via 316 mounted on 302), a maintenance task to be performed at the time or date.

[0190] In various cases, act 1414 may include determining, by the scientific instrument (e.g., via 318 installed on 302), whether the remaining time until the time or date arrives is less than a threshold amount of time. If no (e.g., if the remaining time until the time or date arrives exceeds the threshold amount of time), the computer-implemented method 1400 may return to act 1414. If yes (e.g., if the remaining time until the time or date arrives is less than the threshold amount of time), the computer-implemented method 1400 may return to act 1416.

[0191] In various cases, action 1416 may include the scientific instrument (e.g., via 318 mounted on 302), adjusting the internal temperature (e.g., via 1202), fluid flow rate (e.g., via 1204), or voltage (e.g., via 1206) of a mass spectrometer equipped with a chromatograph in preparation for performing the maintenance task.

[0192] In various aspects, act 1418 can include presenting, by the scientific instrument (eg, via 316 mounted on 302) and on an electronic display of a mass spectrometer equipped with a chromatograph, a reminder that a maintenance task is scheduled for a particular time or date.

[0193] Now, consider Fig.16 In various cases, act 1420 may include determining, by a scientific instrument (e.g., via 1302 installed on 302), whether the time or date has arrived. If not, computer-implemented method 1400 may return to act 1420. If so, computer-implemented method 1400 may proceed to act 1422 instead.

[0194] In various cases, act 1422 may include presenting, by the scientific instrument (eg, via 1302 mounted on 302 ), instructions on an electronic display of a chromatograph-equipped mass spectrometer that explain how to perform the maintenance task.

[0195] The scientific instrument systems, methods, or techniques disclosed herein may include interaction with a human user (e.g., via a Fig.19The user local computing device 1920 discussed). These interactions can [TP386185CNSEC1]

[0196] Includes providing information to users (e.g., about scientific instruments such as Fig.19 information about the operation of a scientific instrument such as a scientific instrument 1910, information about samples being analyzed or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information) or provide the user with the option of inputting commands (e.g., controlling a scientific instrument such as Fig.19 In some embodiments, these interactions may be performed via a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device such as a display device such as a computer program product such as a computer program product, or a display device such as a computer program product, ... such as a computer program product, or a display device such as a computer program product, such as a computer program product, such as a computer program product, such as a computer program product, and a display device such as a computer program product, such as a computer program product Fig.18 1810) that provides output to a user and / or prompts a user to provide input (e.g., via a display device 1810 discussed herein). Fig.18 Other I / O devices 1812 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen). The scientific instrument systems, methods, or techniques disclosed herein may include any GUI suitable for interacting with a user.

[0197] Fig.17 An example graphical user interface 1700 (hereinafter referred to as "GUI 1700") that can be used to perform some or all of the supporting methods or techniques disclosed herein according to various embodiments is depicted. In various aspects, GUI 1700 can be provided in a scientific instrument support system (e.g., as described herein with reference to Fig.19 A computing device (e.g., a scientific instrument support system 1900 discussed herein) Fig.18 Any suitable electronic display (e.g., the computing device 1800 discussed herein) Fig.18 The user or technician may use any suitable input device (e.g., the display device 1810 discussed herein) to input the content of the display device 1810. Fig.18 The GUI 1700 can be interacted with by any of the other I / O devices 1812 discussed above and input technologies (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation).

[0198] GUI 1700 may include a data display area 1702 , a data analysis area 1704 , a scientific instrument control area 1706 , and a settings area 1708 . Fig.17 The specific number and arrangement of regions shown in is illustrative only, and any number and arrangement of regions (including any desired features) may be included in other embodiments of GUI 1700.

[0199] Data display area 1702 may display data generated by a scientific instrument (e.g., Fig.19 For example, the data display area 1702 may display any data measured by one or more electronic counters 406 or by one or more readback sensors 408.

[0200] The data analysis area 1704 can display any suitable data analysis results (e.g., the results of analyzing the data shown in the data display area 1702 or other data). For example, the data analysis area 1704 can display any electronic notification generated by the scheduling component 316 or by the auxiliary component 1302. In some embodiments, the data display area 1702 and the data analysis area 1704 can be displayed in the GUI 1700 [TP386185CNSEC1]

[0201] Combined in a common graph or area (e.g., including data output from a scientific instrument and some analysis of the data in a common graph or area).

[0202] The scientific instrument control area 1706 may include options that allow a user or technician to control the scientific instrument (e.g., Fig.19 For example, the scientific instrument control area 1706 may include configurable parameters for controlling the operation of such a scientific instrument (e.g., configurable parameters for controlling voltage or current of the scientific instrument, configurable parameters for controlling the internal temperature of the scientific instrument, or configurable parameters for controlling the flow rate of a fluid of the scientific instrument).

[0203] The settings area 1708 may include options that allow the user or technician to control any features or functions of the GUI 1700 (or other GUIs) or to perform common computing operations with respect to the data display area 1702 and the data analysis area 1704 (e.g., saving data on a storage device such as the one described herein). Fig.18 The storage device 1804 discussed, sending data to another user, marking the data).

[0204] As described above, the scientific instrument module 102 may be implemented by one or more computing devices. Fig.18 1 is a block diagram of a computing device 1800 that can perform some or all of the scientific instrument support methods or techniques disclosed herein, according to various embodiments. In some embodiments, the scientific instrument module 102 can be implemented by a single instance of the computing device 1800 or multiple instances of the computing device 1800. In addition, as described below, the computing device 1800 (or multiple instances thereof) that implements the scientific instrument module 102 can be Fig.19Part of one or more of a scientific instrument 1910, a user local computing device 1920, a service local computing device 1930, or a remote computing device 1940.

[0205] Computing device 1800 is shown as having multiple components, but any one or more of these components may be omitted or duplicated depending on the application and setting. In some embodiments, some or all of the components included in computing device 1800 may be attached to one or more motherboards and encapsulated in a housing (e.g., including plastic, metal, or other materials). In some embodiments, some of these components may be manufactured on a single system on a chip (SoC) (e.g., the SoC may include one or more instances of processing device 1802 and one or more instances of storage device 1804). Additionally, in various embodiments, computing device 1800 may omit the processor. Fig.18 The computing device 1800 may omit one or more of the components shown, but may include interface circuitry (not shown) for coupling to one or more omitted components using any suitable interface (e.g., a universal serial bus (USB) interface, a high-definition multimedia interface (HDMI) interface, a controller area network (CAN) interface, a serial peripheral interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, the computing device 1800 may omit the display device 1810, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which the display device 1810 may be coupled.

[0206] [TP386185CNSEC1]

[0207] Computing device 1800 may include processing device 1802 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from a register or memory to convert the electronic data into other electronic data that can be stored in the register or memory. Processing device 1802 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0208] The computing device 1800 may include a storage device 1804 (e.g., one or more storage devices). The storage device 1804 may include one or more memory devices, such as a random access memory (RAM) (e.g., a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive bridge RAM (CBRAM) device), a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In some embodiments, the storage device 1804 may include a memory that shares a die with the processing device 1802. In such embodiments, the memory may be used as a cache memory and may include, for example, an embedded dynamic random access memory (eDRAM) or a spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the storage device 1804 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., processing device 1802), cause the computing device 1800 to perform any appropriate method or portion of the methods disclosed herein.

[0209] The computing device 1800 may include an interface device 1806 (e.g., one or more instances of the interface device 1806). The interface device 1806 may include one or more communication chips, connectors, or other hardware and software to manage communications between the computing device 1800 and other computing devices. For example, the interface device 1806 may include circuitry for managing wireless communications for transmitting data to and from the computing device 1800. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, and communication channels that can transmit data through a non-solid medium using modulated electromagnetic radiation. The term does not imply that the associated device does not contain any wires, although in some embodiments it may not contain any wires. The circuitry included in the interface device 1806 for managing wireless communications may implement any of a variety of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendments), Long Term Evolution (LTE) projects, and [TP386185CNSEC1]

[0210] The interface device 1806 may include any amendments, updates, and / or revisions (e.g., the LTE Advanced project, the Ultra Mobile Broadband (UMB) project (also known as "3GPP2"). In some embodiments, the circuitry included in the interface device 1806 for managing wireless communications may operate in accordance with a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some embodiments, the circuitry included in the interface device 1806 for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM Evolution (EDGE), or GSM Evolution (EDGE). In some embodiments, the interface device 1806 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or transmit wireless communications.

[0211] In some embodiments, interface device 1806 may include a circuit for managing wired communication, such as electrical communication protocol, optical communication protocol or any other suitable communication protocol. For example, interface device 1806 may include a circuit supporting communication according to Ethernet technology. In some embodiments, interface device 1806 may support both wireless and wired communication, or may support multiple wired communication protocols or multiple wireless communication protocols. For example, a first group of circuits of interface device 1806 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 1806 may be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, a first group of circuits of interface device 1806 may be dedicated to wireless communication, and a second group of circuits of interface device 1806 may be dedicated to wired communication.

[0212] Computing device 1800 may include battery / power circuitry 1808. Battery / power circuitry 1808 may include one or more energy storage devices (e.g., batteries or capacitors) or circuitry for coupling components of computing device 1800 to an energy source separate from computing device 1800 (e.g., AC line power).

[0213] Computing device 1800 may include display device 1810 (eg, multiple display devices). Display device 1810 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light emitting diode display, or a flat panel display.

[0214] The computing device 1800 may include other input / output (I / O) devices 1812. For example, the other I / O devices 1812 may include one or more audio output devices (e.g., speakers, headphones, earbuds, [TP386185CNSEC1]

[0215] siren), one or more audio input devices (e.g., a microphone or microphone array), a positioning device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 1800), an audio codec, a video codec, a printer, a sensor (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, an accelerometer, a gyroscope), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, a stylus, a trackball, or a touchpad, a bar code reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0216] The computing device 1800 may have any suitable form factor suitable for its applications and settings, such as a handheld or mobile computing device (e.g., a cell phone, a smart phone, a mobile Internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer), a desktop computing device, or a server computing device or other networked computing component.

[0217] One or more computing devices implementing any scientific instrument module, method, or technique disclosed herein may be part of a scientific instrument support system. Fig.19 1 is a block diagram of an example scientific instrument support system 1900 in which some or all of the scientific instrument support methods disclosed herein may be performed, according to various embodiments. The scientific instrument modules, methods, or techniques disclosed herein (e.g., scientific instrument module 102, computer-implemented method 200, maintenance system 306, computer-implemented method 1400) may be implemented by one or more scientific instruments 1910, user local computing devices 1920, service local computing devices 1930, or remote computing devices 1940 of the scientific instrument support system 1900.

[0218] Any of scientific instrument 1910, user local computing device 1920, service local computing device 1930, or remote computing device 1940 may include any implementation of computing device 1800, and any of scientific instrument 1910, user local computing device 1920, service local computing device 1930, or remote computing device 1940 may take the form of any appropriate implementation of the implementation of computing device 1800.

[0219] The scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, or the remote computing device 1940 may each include a processing device 1902, a storage device 1904, and an interface device 1906. The processing device 1902 may take any suitable form, including any form of the processing device 1802, and the processing devices 1902 included in different devices in the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, or the remote computing device 1940 may take the same form or different forms. The storage device 1904 may take any suitable form, including any form of the storage device 1804, and the storage devices included in different devices in the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, or the remote computing device 1940 may be the same form or different forms.

[0220] The interface device 1906 may be in any suitable form, including any form of the interface device 1806, and the interface devices 1906 included in different devices in the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, or the remote computing device 1940 may be in the same form or in different forms.

[0221] The scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and the remote computing device 1940 may communicate with other elements of the scientific instrument support system 1900 via a communication path 1908. The communication path 1908 may communicatively couple an interface device 1906 of different ones of the elements of the scientific instrument support system 1900, as shown, and may be a wired or wireless communication path (e.g., according to any of the communication techniques discussed herein with reference to the interface device 1806). Fig.19The particular scientific instrument support system 1900 depicted in FIG. 1 includes a communication path between each pair of devices in the scientific instrument 1910, the user local computing device 1920, the service local computing device 1930, and the remote computing device 1940, but this “fully connected” implementation is merely illustrative, and in various embodiments, various communication paths in the communication paths 1908 may not exist. For example, in some embodiments, the service local computing device 1930 may lack a direct communication path 1908 between its interface device 1906 and the interface device 1906 of the scientific instrument 1910, and may communicate with the scientific instrument 1910 via the communication path 1908 between the service local computing device 1930 and the user local computing device 1920, and the communication path 1908 between the user local computing device 1920 and the scientific instrument 1910.

[0222] Scientific instrument 1910 may include any suitable scientific instrument, such as scientific instrument 302 .

[0223] The user-local computing device 1920 can be a computing device local to a user of the scientific instrument 1910 (e.g., according to any embodiment of the computing device 1800). In some embodiments, the user-local computing device 1920 can also be local to the scientific instrument 1910, but this is not necessarily the case; for example, a user-local computing device 1920 in a user's home or office can be remote from the scientific instrument 1910, but in communication with the scientific instrument so that the user can use the user-local computing device 1920 to control or access data from the scientific instrument 1910. In some embodiments, the user-local computing device 1920 can be a laptop, smartphone, or tablet device. In some embodiments, the user-local computing device 1920 can be a portable computing device.

[0224] The service local computing device 1930 can be a computing device local to the entity serving the scientific instrument 1910 (e.g., according to any embodiment of the computing device 1800). For example, the service local computing device 1930 can be a device local to the manufacturer of the scientific instrument 1910 or a third-party service company. In some embodiments, the service local computing device 1930 can communicate with the scientific instrument 1910, the user local computing device 1920, or the remote computing device 1940 (e.g., via the direct communication path 1908 or [TP386185CNSEC1]

[0225] The service local computing device 1930 may communicate with the scientific instrument 1910, the user local computing device 1920, or the remote computing device 1940 (e.g., via a plurality of “indirect” communication paths 1908, as described above) to receive data regarding the operation of the scientific instrument 1910, the user local computing device 1920, or the remote computing device 1940 (e.g., self-test results of the scientific instrument 1910, calibration coefficients used by the scientific instrument 1910, measurements of sensors associated with the scientific instrument 1910). In some embodiments, the service local computing device 1930 may communicate with the scientific instrument 1910, the user local computing device 1920, or the remote computing device 1940 (e.g., via a direct communication path 1908 or via a plurality of “indirect” communication paths 1908, as described above) to transmit data to the scientific instrument 1910, the user local computing device 1920, or the remote computing device 1940 (e.g., to update programming instructions (such as firmware) in the scientific instrument 1910 to initiate execution of a test or calibration sequence in the scientific instrument 1910, to update programming instructions (such as software) in the user local computing device 1920 or the remote computing device 1940). A user of scientific instrument 1910 may utilize scientific instrument 1910 or user local computing device 1920 to communicate with service local computing device 1930 to report problems with scientific instrument 1910 or user local computing device 1920, request a technician visit to improve the operation of scientific instrument 1910, order consumables or replacement parts associated with scientific instrument 1910, or for other purposes.

[0226] Remote computing device 1940 may be a computing device remote from scientific instrument 1910 or user local computing device 1920 (e.g., according to any of the embodiments of computing device 1800 discussed herein). In some embodiments, remote computing device 1940 may be included in a data center or other large-scale server environment. In some embodiments, remote computing device 1940 may include network attached storage (e.g., as part of storage device 1904). Remote computing device 1940 may store data generated by scientific instrument 1910, perform analysis of data generated by scientific instrument 1910 (e.g., according to programmed instructions), facilitate communication between user local computing device 1920 and scientific instrument 1910, or facilitate communication between service local computing device 1930 and scientific instrument 1910.

[0227] In some embodiments, the Fig.19 One or more elements of the scientific instrument support system 1900 shown in FIG. In addition, in some embodiments, there may be Fig.19The scientific instrument support system 1900 may include multiple elements of the various elements of the scientific instrument support system 1900. For example, the scientific instrument support system 1900 may include multiple user local computing devices 1920 (e.g., different user local computing devices 1920 associated with different users or located in different locations). In another example, the scientific instrument support system 1900 may include multiple scientific instruments 1910, all of which communicate with a service local computing device 1930 and / or a remote computing device 1940; in such an embodiment, the service local computing device 1930 may monitor these multiple scientific instruments 1910, and the service local computing device 1930 may cause updates or other information to be "broadcasted" to multiple scientific instruments 1910 simultaneously. Scientific Instrument Support System [TP386185CNSEC1]

[0228] The different scientific instruments 1910 in 1900 may be near each other (e.g., in the same room) or far from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some embodiments, the scientific instruments 1910 may be connected to an Internet of Things (IoT) stack that allows the scientific instruments 1910 to be commanded and controlled through web-based applications, virtual or augmented reality applications, mobile applications, or desktop applications. Any of these applications may be accessed by a user operating a user-local computing device 1920 that communicates with the scientific instruments 1910 through an intermediate remote computing device 1940. In some embodiments, the scientific instruments 1910 may be sold by a manufacturer along with one or more associated user-local computing devices 1920 as part of a local scientific instrument computing unit 1912.

[0229] In some embodiments, the different scientific instruments 1910 included in the scientific instrument support system 1900 may be different types of scientific instruments 1910; for example, one scientific instrument 1910 may be a mass spectrometer, while another scientific instrument 1910 may be a chromatograph or an autosampler. In some such embodiments, the remote computing device 1940 or the user's local computing device 1920 may combine data from different types of scientific instruments 1910 included in the scientific instrument support system 1900.

[0230] In various cases, the machine learning algorithm or model may be implemented in any suitable manner to facilitate any suitable aspect described herein. In order to facilitate some of the above machine learning aspects of various embodiments, consider the following discussion of artificial intelligence (AI). Various embodiments described herein may employ artificial intelligence to facilitate automation of one or more features or functionality. These components may employ various AI-based schemes to perform various embodiments / examples disclosed herein. In order to provide or assist in the numerous determinations described herein (e.g., determination, ascertainment, inference, calculation, prediction, prognosis, estimation, derivation, forecast, detection, calculation), the components described herein may examine all or a subset of the data to which they are granted access rights, and may provide a set of observations such as those captured via events or data to infer or determine the state of a system or environment. For example, determination may be employed to identify a specific context or action, or a probability distribution of a state may be generated. These determinations may be probabilistic; that is, a probability distribution of a state of interest is calculated based on consideration of data and events. Determination may also refer to the techniques employed to compose a higher-level event from a set of events or data.

[0231] This determination can result in constructing new events or actions from a set of observed events or stored event data, regardless of whether the events are closely related in time and whether the events and data come from one or several event and data sources. The components disclosed herein can employ various classifications (explicit training (e.g., via training data) as well as implicit training (e.g., via observed behaviors, preferences, [TP386185CNSEC1]

[0232] Historical information, receiving external information, etc.)) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) are related to performing automatic or determined actions related to the claimed subject matter. Thus, the classification scheme or system can be used to automatically learn and perform multiple functions, actions or determinations.

[0233] The classifier can take the input attribute vector z=(z1,z2,z3,z4,z n) is mapped to the confidence that the input belongs to a certain class, such as f(z) = confidence(class). This classification can use probabilistic or statistical-based analysis (for example, taking into account the utility and cost of the analysis) to determine the actions to be automatically performed. Support vector machine (SVM) can be an example of a classifier that can be used. SVM operates by finding a hypersurface in the space of possible inputs, where the hypersurface attempts to separate triggering criteria from non-triggering events. Intuitively, this makes the classification correct for test data that is close to but not the same as the training data. Other directed and non-directed model classification methods include, for example, naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or probabilistic classification models that provide different independent patterns, any of which can be used. The classification used in this article also includes statistical regression for developing priority models.

[0234] To provide additional context for the various embodiments described herein, Fig. 20 The following discussion is intended to provide a brief, general description of a suitable computing environment 2000 in which various embodiments of the embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in conjunction with other program modules or as a combination of hardware and software.

[0235] Generally, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, those skilled in the art will appreciate that the methods of the present invention can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can be operably coupled to one or more associated devices.

[0236] The embodiments shown herein can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0237] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, or communication media, the two terms are used differently in this article, such as [TP386185CNSEC1]

[0238] As shown below. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information (e.g., computer-readable or machine-readable instructions, program modules, structured data, or unstructured data).

[0239] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable media herein should be understood to exclude only the propagation of transient signals themselves as a modifier, and do not disclaim all rights to standard storage, memory, or computer-readable media that are not merely propagation of transient signals themselves.

[0240] Computer-readable storage media may be accessed by one or more local or remote computing devices, such as via access requests, queries, or other data retrieval protocols, for various operations regarding the information stored by the media.

[0241] Communication media typically includes computer readable instructions, data structures, program modules, or other structured or unstructured data in a data signal (such as a modulated data signal, such as a carrier wave or other transport mechanism), and includes any information delivery or transmission media. The term "modulated data signal" or signal refers to a signal whose one or more characteristics are set or changed so as to encode information in one or more signals. By way of example, but not limitation, communication media include wired media (such as a wired network or direct-wired connection) and wireless media (such as acoustic, RF, infrared and other wireless media).

[0242] Reference again Fig. 20 , an example environment 2000 for implementing various embodiments of aspects described herein includes a computer 2002, which includes a processing unit 2004, a system memory 2006, and a system bus 2008. The system bus 2008 couples system components including, but not limited to, the system memory 2006 to the processing unit 2004. The processing unit 2004 can be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 2004.

[0243] The system bus 2008 may be any of several types of bus structures, and may further utilize any of a variety of commercially available bus architectures in conjunction with a memory bus (with or without [TP386185CNSEC1]

[0244] The computer 2002 may be interconnected with a memory controller, a peripheral bus, and a local bus. The system memory 2006 includes a ROM 2010 and a RAM 2012. A basic input / output system (BIOS) may be stored in a nonvolatile memory such as a ROM, an erasable programmable read-only memory (EPROM), or an EEPROM, wherein the BIOS contains basic routines such as those that help transfer information between elements within the computer 2002 during startup. The RAM 2012 may also include a high-speed RAM such as a static RAM for caching data.

[0245] The computer 2002 also includes an internal hard disk drive (HDD) 2014 (e.g., EIDE, SATA), one or more external storage devices 2016 (e.g., magnetic floppy disk drive (FDD) 2016, memory stick or flash drive reader, memory card reader, etc.), and a drive 2020, such as a solid state drive, optical drive, which can read or write from a disk 2022 (such as a CD-ROM disk, DVD, BD, etc.). Alternatively, in the case of a solid state drive, the disk 2022 will not be included unless it is separated. Although the internal HDD 2014 is shown as being located within the computer 2002, the internal HDD 2014 can also be configured for external use in a suitable chassis (not shown). In addition, although not shown in the environment 2000, a solid state drive (SSD) can be used to supplement or replace the HDD 2014. HDD 2014, external storage device 2016, and drive 2020 can be connected to system bus 2008 via HDD interface 2024, external storage interface 2026, and drive interface 2028, respectively. Interface 2024 for external drive specific implementations may include at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are also within the contemplation of the embodiments described herein.

[0246] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. For the computer 2002, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to corresponding types of storage devices, it should be understood by those skilled in the art that other types of storage media (whether currently existing or developed in the future) that can be read by a computer can also be used in the example operating environment, and further, any such storage media can contain computer-executable instructions for performing the methods described herein.

[0247] A number of program modules may be stored in the drives and RAM 2012, including an operating system 2030, one or more applications 2032, other program modules 2034, and program data 2036. All or portions of the operating system, applications, modules, or data may also be cached in RAM 2012. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0248] Computer 2002 may optionally include emulation technology. For example, a virtual machine hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 2030, and the emulated hardware may optionally be different from the hardware environment of the operating system 2030. Fig. 20 . In this embodiment, operating system 2030 may include one of multiple virtual machines (VMs) hosted at computer 2002. In addition, operating system 2030 may provide a runtime environment, such as a Java runtime environment or a .NET framework, for application 2032. A runtime environment is a consistent execution environment that allows application 2032 to run on any operating system that includes a runtime environment. Similarly, operating system 2030 may support containers, and application 2032 may be in the form of containers, which are lightweight, independent, executable software packages that include, for example, application code, runtime, system tools, system libraries, and settings.

[0249] In addition, the computer 2002 may be equipped with a security module, such as a trusted processing module (TPM). For example, using a TPM, the boot component hashes the next boot component in time and waits for the result to match the security value before loading the next boot component. This process may be performed at any layer in the code execution stack of the computer 2002, for example, applied to the application execution level or the operating system (OS) kernel level, thereby achieving security of code execution at any level.

[0250] A user may enter commands and information into the computer 2002 through one or more wired / wireless input devices (e.g., a keyboard 2038, a touch screen 2040, and a pointing device such as a mouse 2042). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote control, a joystick, a virtual reality controller or virtual reality headset, a game controller, a stylus, an image input device (e.g., a camera), a gesture sensor input device, a visual movement sensor input device, an emotion or facial detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are typically connected to the processing unit 2004 through an input device interface 2044 that may be coupled to the system bus 2008, but may also be connected through other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a Interfaces, etc.

[0251] A monitor 2046 or other type of display device may also be connected to the system bus 2008 via an interface, such as a video adapter 2048. In addition to the monitor 2046, computers typically include other peripheral output devices (not shown), such as speakers, printers, and the like.

[0252] The computer 2002 can operate in a network environment, using logical connections to one or more remote computers, such as remote computer 2050, through wired or wireless communications. The remote computer 2050 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, and typically includes many or all of the elements described relative to the computer 2002, but only a memory / storage device 2052 is shown for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 2054 or a larger network, such as a wide area network (WAN) 2056. Such LAN and WAN networking environments are common in offices and companies and facilitate the establishment of enterprise-wide computer networks (e.g., intranets), all of which can be connected to a global communication network (e.g., the Internet).

[0253] When used in a LAN networking environment, the computer 2002 can be connected to a local network 2054 through a wired or wireless communication network interface or adapter 2058. The adapter 2058 can facilitate wired or wireless communication with the LAN 2054, which can also include a wireless access point (AP) disposed thereon for communicating with the adapter 2058 in a wireless mode.

[0254] When used in a WAN networking environment, the computer 2002 may include a modem 2060 or may be connected to a communication server on the WAN 2056 via other means to establish communications via the WAN 2056, such as via the Internet. The modem 2060 may be internal or external, wired or wireless, and may be connected to the system bus 2008 via the input device interface 2044. In a network environment, program modules shown relative to the computer 2002 or portions thereof may be stored in the remote memory / storage device 2052. It should be understood that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

[0255] When used in a LAN or WAN networking environment, the computer 2002 can access a cloud storage system or other network-based storage system in addition to or as an alternative to the external storage device 2016 described above, such as, but not limited to, a network virtual machine that provides one or more aspects of storage or processing of information. Generally speaking, the connection between the computer 2002 and the cloud storage system can be established through the LAN 2054 or WAN 2056, for example, through an adapter 2058 or a modem 2060, respectively. When the computer 2002 is connected to the associated cloud storage system, the external storage interface 2026 can manage the storage provided by the cloud storage system with the help of the adapter 2058 or the modem 2060, just as it manages other types of external storage. For example, the external storage interface 2026 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 2002.

[0256] The computer 2002 is operable to communicate with any wireless device or entity that operates in a wireless communication manner (e.g., printers, scanners, desktop or portable computers, portable data assistants, communication satellites, any device or location associated with a wirelessly detectable tag (e.g., information kiosks, newsstands, store shelves, etc.), and telephones). This may include Wireless Fidelity (Wi-Fi) and None [TP386185CNSEC1]

[0257] Therefore, the communication can be a predefined structure like a traditional network or an ad hoc communication between at least two devices.

[0258] Fig.212100 is a schematic block diagram of an example computing environment 2100 with which the disclosed subject matter can interact. The example computing environment 2100 includes one or more clients 2110. The client 2110 can be hardware or software (e.g., a thread, a process, a computing device). The example computing environment 2100 also includes one or more servers 2130. The server 2130 can also be hardware or software (e.g., a thread, a process, a computing device). For example, the server 2130 can accommodate threads to perform conversions by adopting one or more embodiments described herein. A possible communication between the client 2110 and the server 2130 can take the form of data packets suitable for transmission between two or more computer processes. The example computing environment 2100 includes a communication framework 2150 that can be used to facilitate communication between the client 2110 and the server 2130. The client 2110 is operably connected to one or more client data repositories 2120 that can be used to store information local to the client 2110. Similarly, the server 2130 is operably connected to one or more server data repositories 2140 that can be used to store information local to the server 2130.

[0259] Various embodiments can be systems, methods, devices or computer program products at any possible level of technical detail integration. Computer program products may include computer-readable storage media (or media) with computer-readable program instructions for making the processor execute various aspects of various embodiments. Computer-readable storage media can be tangible devices that can retain and store instructions for use by instruction execution devices. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media may also include the following: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disc read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punched cards with instructions recorded on them or raised structures in grooves) and any suitable combination of the above. Computer-readable storage media as used herein should not be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses transmitted through fiber optic cables), or electrical signals transmitted through wires.

[0260] [TP386185CNSEC1]

[0261] Computer-readable program instructions as described herein can be downloaded to corresponding computing / processing equipment from computer-readable storage media or downloaded to external computers or external storage devices via a network (e.g., the Internet, local area network, wide area network or wireless network). The network may include copper transmission cables, optical transmission optical fibers, wireless transmission, routers, firewalls, switches, gateway computers or edge servers. Network adapter cards or network interfaces in each computing / processing equipment receive computer-readable program instructions from the network, and forward computer-readable program instructions to be stored in the computer-readable storage media in the corresponding computing / processing equipment. Computer-readable program instructions for performing the operation of various embodiments can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcodes, firmware instructions, state setting data, configuration data of integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(e.g., Smalltalk, C++, etc.) and process programming languages ​​(e.g., "C" programming languages ​​or similar programming languages). Computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider through the Internet). In some embodiments, the electronic circuit includes, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA), and the electronic circuit can be personalized by utilizing the state information of the computer readable program instructions, thereby executing the computer readable program instructions, to perform various aspects.

[0262] Various aspects are described herein with reference to flowchart illustrations or block diagrams of methods, devices (systems) and computer program products according to various embodiments. It should be understood that each box in the flowchart illustration or block diagram and the combination of boxes in the flowchart illustration or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the function / action specified in one or more boxes of the flowchart or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide a computer, a programmable data processing device or other device to run in a specific manner, and the computer-readable storage medium storing instructions includes a manufactured product, which includes instructions for implementing various aspects of the function / action specified in one or more boxes of the flowchart or block diagram. Computer-readable program instructions can also be added [TP386185CNSEC1]

[0263] The instructions are loaded onto a computer, other programmable data processing device or other device to perform a series of operational actions on the computer, other programmable device or other device, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable device or other device implement the functions / actions specified in one or more boxes of the flowchart or block diagram.

[0264] The flow chart and block diagram in the figure show the possible implementation architecture, functionality and operation of the system, method and computer program product according to various embodiments.In this regard, each frame in the flow chart or block diagram can represent a module, segment or partial instruction, which contains one or more executable instructions for implementing a specified logical function.In some alternative specific implementations, the function marked in the frame may not occur in the order marked in the figure.For example, two blocks displayed continuously can actually be performed substantially at the same time, or sometimes can be performed in reverse order, depending on the function involved.It should also be noted that each frame in the block diagram or flow chart and the combination of frames in the block diagram or flow chart can be realized by a dedicated hardware system that performs a specified function or action or performs a combination of dedicated hardware and computer instructions.

[0265] Although the subject matter has been described above in the general context of computer executable instructions of a computer program product running on a computer, it will be appreciated by those skilled in the art that the present disclosure may also or may be implemented in combination with other program modules. Typically, a program module includes routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, it will be appreciated by those skilled in the art that various aspects may be practiced using other computer system configurations, including single-processor or multi-processor computer systems, small computing devices, large computers, and computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer or industrial electronics, etc. The various aspects shown may also be practiced in a distributed computing environment, where tasks are performed by a remote processing device connected via a communication network. However, some aspects of the present disclosure (if not all aspects) may be practiced on a stand-alone computer. In a distributed computing environment, a program module may be located in both a local memory storage device and a remote memory storage device.

[0266] The terms "component", "system", "platform", "interface", etc. used in this application may refer to or may include computer-related entities or entities related to an operating machine with one or more specific functions. The entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable program, a thread of execution, a program, or a computer. As an example, an application running on a server and a server may both be components. One or more components may reside in an executable file.

[0267] In another example, the components can be executed from various computer-readable media with various data structures stored thereon. These components can be communicated via local or remote processes (for example, data from a component can interact with another component in a local system, a distributed system via a signal or interact with other systems via a network such as the Internet) according to a signal with one or more data packets. As another example, the component can be a device with a specific functionality provided by a mechanical part operated by an electrical or electronic circuit, and the electrical or electronic circuit is operated by a software or firmware application executed by a processor. In this case, the processor can be inside or outside the device, and can execute at least a part of the software or firmware application. As another example, the component can be a device that provides specific functionality by an electronic component without a mechanical part, wherein the electronic component may include a processor or other devices for executing software or firmware that at least partially gives the electronic component functionality. In one aspect, the component can simulate the electronic component via a virtual machine such as in a cloud computing system.

[0268] In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" is intended to mean any natural inclusive arrangement. That is, if X employs A; X employs B; or X employs both A and B, then "X uses A or B" is satisfied under any of the foregoing examples. As used herein, the term "and / or" is intended to have the same meaning as "or". In addition, unless otherwise specified or clear from the context that it involves a singular form, the article "one" as used in should generally be interpreted to mean "one or more". As used herein, the term "example" or "exemplary" is used to mean as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. In addition, any aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0269] The content disclosed herein describes non-limiting examples. For ease of description or explanation, the various parts disclosed herein use the terms "each," "each," or "all" when discussing various examples. The usage of terms such as "each," "each," or "all" is not restrictive. In other words, when the content disclosed herein provides a description of "each," "all," or "all" applicable to a particular object or component, it should be understood that this is only a non-limiting example, and it should also be understood that in various other examples, such descriptions can be applicable to descriptions of "each," "all," or "all" less than the particular object or component.

[0270] [TP386185CNSEC1]

[0271] As used in this specification, the term "processor" may refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof designed to perform the functions described herein. In addition, the processor may utilize nanoscale architectures, such as (but not limited to) transistors, switches, and gates based on molecules and quantum dots to optimize space usage or enhance the performance of user devices. The processor may also be implemented as a combination of computing processing units. In the present disclosure, terms such as "repository", "storage device", "data repository", "data storage device", "database", and substantially any other information storage component related to the operation and functionality of the component are used to refer to a "memory component", an entity embodied in a "memory", or a component including a memory. It should be understood that the memory and / or memory component can be a volatile memory or a non-volatile memory, or can include both volatile memory and non-volatile memory. By way of illustration and not limitation, the non-volatile memory may include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable ROM (EEPROM), a flash memory, or a non-volatile random access memory (RAM) (e.g., a ferroelectric RAM (FeRAM)). The volatile memory may include a RAM, which may, for example, act as an external cache memory. As an example and not limitation, RAM takes a variety of forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems disclosed herein or the computer-implemented methods are intended to include, but are not limited to, these and any other suitable types of memory.

[0272] The foregoing includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing the present disclosure, but many further combinations and permutations of the present disclosure are possible. In addition, regarding the use of the terms "including", "having", "having", etc. in the detailed description, claims, appendices, and drawings [TP386185CNSEC1]

[0273] As used herein, these terms are intended to be inclusive in a manner similar to the way the term "comprise" is interpreted when used as a transition word in a claim.

[0274] Descriptions of various embodiments have been presented for illustrative purposes, but are not intended to be exhaustive or limited to the embodiments disclosed herein. Many modifications and variations are apparent without departing from the scope and spirit of the embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or technical improvements over technologies on the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

[0275] Various non-limiting aspects are described in the following examples.

[0276] Embodiment 1: A scientific instrument may include a mass spectrometer equipped with a chromatograph and a processor that can execute computer executable components stored in a non-transitory computer readable memory. In various aspects, the computer executable components may include a determination component that can determine whether it is necessary to perform a maintenance task on the mass spectrometer equipped with a chromatograph based on an electronic counter or readback sensor associated with the mass spectrometer equipped with a chromatograph failing to meet a threshold. In various cases, the computer executable components may include a scheduling component that can schedule a time or date for performing the maintenance task in response to determining that it is necessary to perform the maintenance task, wherein the time or date can be predicted by a machine learning model based on an operating history of the mass spectrometer equipped with a chromatograph.

[0277] Embodiment 2: A scientific instrument may implement any of the preceding embodiments, wherein the operational history may include previous times or dates when a sample was run by a mass spectrometer equipped with a chromatograph.

[0278] Embodiment 3: A scientific instrument may implement any of the preceding embodiments, wherein the operational history may include previous times or dates at which a technician interacted with a graphical user interface of a mass spectrometer equipped with a chromatograph.

[0279] Embodiment 4: A scientific instrument may implement any of the preceding embodiments, wherein the machine learning model further predicts the time or date based on one or more scheduling preferences of the technician.

[0280] Embodiment 5: A scientific instrument may implement any of the preceding embodiments, wherein the machine learning model further predicts the time or date based on a priority of the maintenance task.

[0281] Embodiment 6: A scientific instrument may implement any of the preceding embodiments, wherein the machine learning model may further predict the time or date based on an expected duration of the maintenance task.

[0282] Embodiment 7: A scientific instrument that can implement any of the preceding embodiments, wherein the machine learning model can receive an operating history as an input, wherein the machine learning model can generate as an output multiple possible times or dates for performing a maintenance task, and wherein the scheduling component can receive input for selecting the time or date from the multiple possible times or dates from a graphical user interface of a mass spectrometer equipped with a chromatograph.

[0283] [TP386185CNSEC1]

[0284] Embodiment 8: A scientific instrument may implement any of the preceding embodiments, wherein the computer executable components may include a preparation component that prepares to perform a maintenance task by adjusting actuatable hardware of a mass spectrometer equipped with a chromatograph before the time or date.

[0285] In various embodiments, any one or more combinations of Examples 1 to 8 may be implemented.

[0286] Embodiment 9: A computer-implemented method may include scheduling, by a scientific instrument including a mass spectrometer equipped with a chromatograph, a time or date for performing a maintenance task on the mass spectrometer equipped with a chromatograph. In various aspects, the computer-implemented method may include identifying, by the scientific instrument, a hardware actuator of the mass spectrometer equipped with a chromatograph that is associated with the maintenance task. In various cases, the computer-implemented method may include adjusting, by the scientific instrument, the hardware actuator in preparation for performing the maintenance task in response to less than a threshold amount of time remaining before the time or date.

[0287] Embodiment 10: A computer-implemented method may be implemented in any of the preceding embodiments, wherein the hardware actuator may be a temperature heater or a temperature cooler of a mass spectrometer equipped with a chromatograph.

[0288] Embodiment 11: A computer-implemented method may be implemented in any of the preceding embodiments, wherein the adjusting may cause an internal temperature of a mass spectrometer equipped with a chromatograph to transition from an operating temperature value to a room temperature value during a threshold amount of time.

[0289] Embodiment 12: A computer-implemented method may be implemented in any of the preceding embodiments, wherein the adjusting may cause an internal temperature of a mass spectrometer equipped with a chromatograph to transition from an operating temperature value to a non-room temperature value during a threshold amount of time.

[0290] Embodiment 13: A computer-implemented method may be implemented as in any of the preceding embodiments, wherein the hardware actuator may be a fluid valve or a fluid pump of a mass spectrometer equipped with a chromatograph.

[0291] Embodiment 14: A computer-implemented method of any preceding embodiment may be implemented, wherein the adjusting causes a vacuum of a mass spectrometer equipped with a chromatograph to be evacuated during a threshold amount of time.

[0292] Embodiment 15: A computer-implemented method may be implemented as in any of the preceding embodiments, wherein the hardware actuator may be a circuit switch of a mass spectrometer equipped with a chromatograph.

[0293] Embodiment 16: A computer-implemented method may be implemented in any of the preceding embodiments, wherein the adjustment may disconnect a circuit of a mass spectrometer equipped with a chromatograph during a threshold amount of time.

[0294] Embodiment 17: A computer-implemented method may be implemented in accordance with any of the preceding embodiments, wherein the threshold amount of time may depend on a maintenance task.

[0295] In various embodiments, any one or more combinations of Examples 9 to 17 may be implemented.

[0296] Embodiment 18: A computer program product for facilitating intelligent maintenance of scientific instruments may include a non-transitory computer readable memory containing program instructions. In various aspects, the program instructions [TP386185CNSEC1]

[0297] The instructions may be executable by the processor to cause the processor to: predict a time or date for performing a maintenance task on the mass spectrometer via executing a machine learning model on a log of graphical user interface interactions experienced by the mass spectrometer. In various cases, the program instructions may also be executable to cause the processor to prepare the mass spectrometer for the maintenance task by changing an internal temperature, a fluid flow rate, or a voltage of the mass spectrometer in response to less than a threshold amount of time remaining before the time or date arrives.

[0298] Embodiment 19: A computer program product may implement any of the preceding embodiments, wherein the program instructions are further executable to cause a processor to visually present instructions on an electronic display of the mass spectrometer and at that time or date explaining how to perform a maintenance task.

[0299] Embodiment 20: A computer program product may implement any of the preceding embodiments, wherein the program instructions are further executable to cause a processor to visually present a reminder on an electronic display of the mass spectrometer and prior to the time or date that a maintenance task is to be performed at the time or date.

[0300] In various embodiments, any one or more combinations of Examples 18 to 20 may be implemented.

[0301] In various embodiments, any one or more combinations of Examples 1 to 20 may be implemented.

Claims

1. A scientific instrument, comprising: a mass spectrometer equipped with a chromatograph; and a processor that executes computer-executable components stored in a non-transitory computer-readable memory, wherein the computer-executable components include: a determining component that determines whether it is necessary to perform a maintenance task on the chromatograph-equipped mass spectrometer based on a failure of an electronic counter or a readback sensor associated with the chromatograph-equipped mass spectrometer to meet a threshold; and A scheduling component that schedules a time or date for performing the maintenance task in response to determining that it is necessary to perform the maintenance task, wherein the time or date is predicted by a machine learning model based on an operating history of the chromatograph-equipped mass spectrometer.

2. The scientific instrument of claim 1, wherein the operational history comprises previous times or dates at which the mass spectrometer equipped with a chromatograph ran samples.

3. The scientific instrument of claim 1, wherein the operational history includes previous times or dates at which a technician interacted with a graphical user interface of the chromatograph-equipped mass spectrometer.

4. The scientific instrument of claim 1 , wherein the machine learning model further predicts the time or date based on one or more scheduling preferences of a technician.

5. The scientific instrument of claim 1 , wherein the machine learning model further predicts the time or date based on a priority of the maintenance task.

6. The scientific instrument of claim 1 , wherein the machine learning model further predicts the time or date based on an expected duration of the maintenance task.

7. The scientific instrument of claim 1 , wherein the machine learning model receives the operational history as input, wherein the machine learning model produces as output a plurality of possible times or dates for performing the maintenance task, and wherein the scheduling component: Input is received from a graphical user interface of the chromatograph-equipped mass spectrometer selecting the time or date from the plurality of possible times or dates.

8. The scientific instrument of claim 1 , wherein the computer executable components comprise: [TP386185CNSEC1] A preparation component prepares to perform the maintenance task by adjusting actuatable hardware of the chromatograph-equipped mass spectrometer before the time or date.

9. A computer-implemented method comprising: a time or date scheduled by a scientific instrument including a mass spectrometer equipped with a chromatograph for performing a maintenance task on the mass spectrometer equipped with a chromatograph; identifying, by the scientific instrument, a hardware actuator of the chromatograph-equipped mass spectrometer associated with the maintenance task; as well as The hardware actuator is adjusted, by the scientific instrument, in response to less than a threshold amount of time remaining before the time or date to prepare for the maintenance task.

10. The computer-implemented method of claim 9, wherein the hardware actuator is a temperature heater or a temperature cooler of the chromatograph-equipped mass spectrometer.

11. The computer-implemented method of claim 10, wherein the adjusting causes an internal temperature of the chromatograph-equipped mass spectrometer to transition from an operating temperature value to a room temperature value during the threshold amount of time.

12. The computer-implemented method of claim 10, wherein the adjusting causes an internal temperature of the chromatograph-equipped mass spectrometer to transition from an operating temperature value to a non-room temperature value during the threshold amount of time.

13. The computer-implemented method of claim 9, wherein the hardware actuator is a fluid valve or a fluid pump of the chromatograph-equipped mass spectrometer.

14. The computer-implemented method of claim 13, wherein the adjusting causes a vacuum of the chromatograph-equipped mass spectrometer to be evacuated during the threshold amount of time.

15. The computer-implemented method of claim 9, wherein the hardware actuator is a circuit switch of the chromatograph-equipped mass spectrometer.

16. The computer-implemented method of claim 15, wherein the adjusting disconnects a circuit of the chromatograph-equipped mass spectrometer during the threshold amount of time.

17. The computer implemented method of claim 9, wherein the threshold amount of time is dependent on the maintenance task.

18. A computer program product for facilitating intelligent maintenance of a scientific instrument, the computer program product comprising a non-transitory computer readable memory having program instructions embodied therein, the program instructions executable by a processor to cause the processor to: [TP386185CNSEC1] predicting a time or date for performing a maintenance task on a mass spectrometer via executing a machine learning model on a log of graphical user interface interactions experienced by the mass spectrometer; and The mass spectrometer is prepared for the maintenance task by changing an internal temperature, a fluid flow rate, or a voltage of the mass spectrometer in response to less than a threshold amount of time remaining before the time or date.

19. The computer program product of claim 18, wherein the program instructions are further executable to cause the processor to: Instructions explaining how to perform the maintenance task are visually presented on an electronic display of the mass spectrometer and at the time or date.

20. The computer program product of claim 18, wherein the program instructions are further executable to cause the processor to: A reminder is visually presented on an electronic display of the mass spectrometer and prior to the time or date that the maintenance task is to be performed at the time or date.

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