Method for controlling mass spectrometer using reinforcement learning

Abstract

Embodiments herein relate to neural network control of mass spectrometry processes. A system may include a memory storing computer executable components and a processor executing the computer executable components. The computer executable component may include: an acquisition component that acquires data of a compound, the data defining a first mass spectrum of the compound; an evaluation component to generate a recommendation to perform a mass spectrometry action on the compound based on the data and employing a neural network trained on an input dataset including an acquisition metric and employing an association score associated with the acquisition metric; and an execution component that instructs, based on the recommendation, to perform the mass spectrometry action at the mass spectrometer and to obtain mass spectrometry results.

Description

Background Art

[0001] Scientific instruments used for material analysis can help determine the composition and properties of unknown components. In one or more examples, scientific instruments that provide mass spectrometry (MS) analysis or analyze the results of MS equipment can help identify unknown components by helping to provide or understand one or more spectra that compare mass calculations based on the mass-to-charge ratio of the molecules after detecting ionization. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Each embodiment 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. Each embodiment is illustrated in the various figures of the accompanying drawings by way of example and not limitation.

[0003] Figure 1 A block diagram of an example scientific instrument for performing operations according to one or more embodiments described herein is illustrated.

[0004] Figure 2 Illustrate the use of one or more embodiments described herein Figure 1 A flow chart of an example method of performing operations on a scientific instrument.

[0005] Figure 3 A graphical user interface (GUI) that can be used to perform one or more of the methods described herein is illustrated according to one or more embodiments described herein.

[0006] Figure 4 A block diagram of an example computing device that can perform one or more of the methods disclosed herein is illustrated according to one or more embodiments described herein.

[0007] Figure 5 A block diagram of an example non-limiting system that can facilitate reinforcement learning based mass spectrometry control according to one or more embodiments described herein is illustrated.

[0008] Figure 6 Illustrated is a block diagram of another example non-limiting system that can facilitate reinforcement learning based mass spectrometry control according to one or more embodiments described herein.

[0009] Figure 7 Illustrate that according to one or more embodiments described herein, Figure 6 An exemplary high-level process performed by a material analysis system.

[0010] Figure 8 An exemplary scoring matrix is ​​provided according to one or more embodiments described herein, which illustrates a scoring matrix composed of Figure 6 The material analysis system uses the input. Figure 8Also provided are example scoring keys for defining scores for an example scoring matrix according to one or more embodiments described herein.

[0011] Fig. 9 Illustrate that according to one or more embodiments described herein, Figure 5 A set of exemplary reward functions employed by a material analysis system.

[0012] Fig.10 Illustrate that according to one or more embodiments described herein, Figure 6 A flow chart of one or more processes performed by a materials analysis system.

[0013] Fig.11 Illustrate that according to one or more embodiments described herein, Figure 6 The material analysis system performs one or more processes Fig.10 Continuation of the flowchart.

[0014] Fig.12 Illustrate that according to one or more embodiments described herein, Figure 6 A flow chart of one or more processes performed by a materials analysis system.

[0015] Fig.13 Illustrate that according to one or more embodiments described herein, Figure 6 The material analysis system performs one or more processes Fig.12 Continuation of the flowchart.

[0016] Fig.14 Illustrate that according to one or more embodiments described herein, Figure 6 A flow chart of one or more processes performed by a materials analysis system.

[0017] Fig.15 Illustrate that according to one or more embodiments described herein, Figure 6 The material analysis system performs one or more processes Fig.14 Continuation of the flowchart.

[0018] Fig.16 A block diagram of an example scientific instrument system is illustrated in which one or more of the methods described herein may be performed, according to one or more embodiments described herein.

[0019] Fig.17 A block diagram illustrating an example operating environment in which implementations of the subject matter described herein may be incorporated.

[0020] Fig.18An example schematic block diagram illustrating a computing environment with which the subject matter described herein may interact and / or be at least partially implemented is illustrated. Summary of the invention

[0021] An overview is presented below to provide a basic understanding of one or more embodiments described herein. This overview is not intended to identify key or important elements and / or to delineate the scope of a particular embodiment or the scope of the claims. Its sole purpose is to present concepts in a simplified form as a preface to a more detailed description presented later. In one or more embodiments, the systems, computer-implemented methods, devices, and / or computer program products described herein can provide a process for training a neural network using reinforcement learning-based training and employing the neural network to control mass spectrometry-based experiments.

[0022] According to one embodiment, a system may include a memory storing a computer executable component and a processor executing the computer executable component stored in the memory. The computer executable component includes: an acquisition component that acquires data of a compound, the data defining a first mass spectrometry spectrum of the compound; an evaluation component that generates a recommendation to perform a mass spectrometry action on the compound based on the data and using a neural network trained on an input data set including an acquisition metric and using an association score associated with the acquisition metric; and an execution component that instructs to perform a mass spectrometry action at a mass spectrometer and obtain a mass spectrometry result based on the recommendation.

[0023] According to another embodiment, a computer-implemented method may include: comparing, by a system operatively coupled to a processor, first data of a compound to an input data set comprising one or more acquisition metrics of the compound and one or more association scores associated with the one or more acquisition metrics, and based on the comparison and based on the obtained metrics of interest associated with the compound and using a neural network trained on the input data set, generating, by the system, a first recommendation to perform a recommended mass spectrometry action on the compound, wherein the recommended mass spectrometry action comprises using a mass spectrometry device to obtain an acquisition of the compound.

[0024] According to yet another embodiment, a computer program product facilitating a process for reinforcement learning-based mass spectrometry control comprises a computer-readable storage medium having program instructions embodied therewith, and the program instructions are executable by a processor to cause the processor to generate, by the processor, an input data set comprising one or more acquisition metrics corresponding to a compound type and one or more association scores associated with the one or more acquisition metrics, wherein the one or more association scores define a probability that one or more thresholds corresponding to the one or more acquisition metrics will be satisfied by a mass spectrometry action performed on the compound type; train, by the processor, a neural network on the input data set; and generate, by the processor, a recommended mass spectrometry action for obtaining second data for a compound of the compound type by employing the neural network to compare first data for the compound with the input data set associated with the compound type. DETAILED DESCRIPTION

[0025] The following detailed description is only illustrative and is not intended to limit the application or utilization of the embodiments and / or embodiments. In addition, there is no intention to be constrained by any express or implied information presented in the previous summary of the invention or the specific implementation part. 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 cases, one or more embodiments can be practiced without these specific details.

[0026] 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, and / or the described operations may be omitted in additional embodiments.

[0027] Turning now to the subject of materials analysis and one or more embodiments described herein, when performing materials analysis using mass spectrometry (e.g., mass spectrometry experiments), the ultimate goal can be to determine the maximum amount of a compound (also referred to herein as a "precursor") within a given amount of time. The given amount of time can be defined as a request cycle until a result, such as where the time cost can be defined based on one or more of instrument depreciation, analyst costs, and / or reagent costs within that time period.

[0028] In general, a mass spectrometer is a device that can detect, identify, and quantify molecules within a sample based on the mass-to-charge ratio of the molecules after ionization. That is, a mass spectrometer can employ a variety of ionization methods and can be used in conjunction with different separation techniques.

[0029] Mass spectrometry (MS) experiments can be complex endeavors employing multiple acquisition metrics to tune. Significant work can be involved in optimizing methods and their acquisition metrics, which can be complicated by varying samples, batches, and experimental effects.

[0030] One existing method, a data dependent acquisition (DDA) experiment, involves acquiring an MS1 ​​spectrum, processing the MS1 spectrum, and continuing to acquire MSn spectra of the expected compound (where n is the total number of mass analysis stages, such as for MS2, MS3, etc.). In such cases, the isolation event can be a mass analysis stage. By way of further example, a DDA experiment can typically employ MS2 acquisition, and a TMT-DDA experiment can employ MS3 acquisition. When a tandem mass tag (TMT) technique is employed, the MSn spectrum can be used for relative quantification, or when a label-free (LF) strategy is used and where quantification is performed with the MS1 spectrum, it is used only for identification purposes, but is not limited thereto.

[0031] Such LF-DDA can achieve good identification and quantitative coverage, which may compromise reproducibility due to stochastic triggering of expected compounds and sensitivity due to the use of MS1 for quantification. TMT-DDA can have better throughput due to sample multiplexing, but has issues with reproducibility and coverage.

[0032] Another existing approach, data-independent acquisition (DIA) experiments, involves iteratively acquiring a set of MS / MS spectra spanning the expected precursor range. In some cases, DIA can achieve higher reproducibility and simplicity, at the expense of some sensitivity and specificity due to spectral multiplexing.

[0033] As used herein, "MS / MS" spectra refer to the first mass analysis performed through the isolation process, fragmentation events, and final mass analysis to generate a mass spectrum. It should be noted that if the process continues to isolation + fragmentation of the first stage fragments, an additional " / MS" can be added (e.g., MS / MS / MS). It should be noted that MS / MS=MS2 and MS / MS / MS=MS3.

[0034] A series of these acquisitions can span the precursor m / z space, which refers to the range of precursor mass-to-charge (m / z) values, i.e., the minimum and maximum values, that encompass all precursor m / z values. For example, for peptide analysis, the range of m / z 400 to 1000 or 400 to 1200 can encompass most of the compounds in the sample, and [400, 1000] can be selected as the precursor m / z space to be experimentally resolved.

[0035] Another existing method, targeted MSn (tMSn) experiments, can have higher sensitivity than other existing methods, but the number of compounds identified is minimal. Although under certain conditions (such as low sample amount and low total number of addressable compounds), the number of quantified compounds for tMSn experiments can approach and / or exceed other existing methods.

[0036] These types of optimizations can often be incorporated into automated workflows. However, these types of optimizations are often based on a user entity providing acquisition metrics and MS settings to allow for the maximum number of compounds acquired.

[0037] As used herein, "real-time" may refer to performing one or more optimizations and / or changes to an experiment during operation of one or more mass spectrometry devices (eg, in real time).

[0038] That is, the structure of the experiment and the gates and thresholds that control the transition from one state to another are still very clearly defined by the programmer, which can have benefits in terms of predictability, but this may not fully utilize the instrument capabilities or adapt to different experimental conditions. For example, the target ion quantity / spectrum or signal threshold that controls the MS to MS2 transition can vary depending on the chemical details of the LC gradient length, sample type and / or specific elution time. It may be the case that in one or more specific parts of the experiment, there is extra time dedicated to the analysis of specific compounds, while at other times in the experiment, instrument resources should be more evenly dispersed among several eluting compounds, such as due to specific details of eluting compounds from the chromatographic system. It is difficult or impossible to capture these details using existing optimization methods.

[0039] To account for one or more incapabilities or deficiencies of existing frameworks (e.g., existing material analysis frameworks employing spectral comparison), one or more embodiments are described herein that can specify high-level results and employ a neural network (NN) trained based on a reinforcement learning (RL-based) approach to achieve one or more of the high-level results. The RL-trained NN can automatically determine acquisition metrics and operational settings based on the context (e.g., m / z precursor space) resulting from each subsequent and / or further fragmentation acquisition to recommend and / or indicate subsequent acquisitions, and / or define such acquisitions.

[0040] That is, it may be desirable to control such experiments at a higher level than achieved with existing methods by alternatively setting one or more metrics of interest such as total acquisition time, number of compounds to be acquired, and / or type of compounds to be acquired, as opposed to controlling the experiment at a lower granularity level involving each acquisition metric and MS operating setting.

[0041] Thus, one or more embodiments herein may allow for evolving compound-specific acquisition metrics without explicitly programming acquisition metrics at each stage of the experiment (e.g., for each next acquisition, whether a subsequent acquisition or a further fragmentation acquisition, such as an MS / MS acquisition or an MS / MS / MS acquisition).

[0042] The discussion then turns to reinforcement learning (RL), which is an area of ​​optimization that can be used to instruct an agent (e.g., a system, device, processor, etc.) on actions when the environment changes and is uncertain and / or writing explicit instructions for the actions may be brittle and inflexible. Creating a program that allows a robot to stand up and run or clean the house are good examples of such situations where it is easy to explain what we want to happen, but difficult to write explicit instructions to achieve the goal under a wide range of circumstances.

[0043] Using conventional RL vocabulary, an agent interacts with an environment by performing actions. During training, the agent receives information about the state of the environment, chooses actions that update the environment, and receives a reward. This reward can be amortized over future states to help the system find actions that do not result in an immediate reward but pay off later. For example, in extreme cases, the agent may receive little or no reward (or penalty) until a set of many steps is over, which might be the case when training the agent to navigate to a goal, or when the agent experiences some kind of catastrophic event (falling into a hole). This process can be repeated in "episodes" consisting of one or more MS experiments (such as liquid chromatography MS experiments). Typically, hundreds or thousands of training episodes are required to train an effective agent for a particular task.

[0044] In one or more embodiments described herein, using the general RL framework described above, there is an opportunity to employ computer simulation optimization concepts to utilize the framework of reinforcement learning to not only optimize acquisition metrics and / or MS operation settings, but also to instruct the operation of one or more mass spectrometry devices in real time. For example, a general optimization scheme may include determining an experiment list with corresponding different acquisition metrics and MS operation settings for the experiment, employing a NN trained by RL, and performing the experiment in real life or in simulation to obtain corresponding corresponding experiment scores. The experiment score may take the form of multiple unique identification metrics, involving the number of compounds detected in the experiment and the quality of those detections (e.g., their signal-to-noise ratio S / N). Optimization may be a simple enumeration on a grid of experimental acquisition metrics, or a more advanced optimization using, for example, experimental design, genetic algorithms, or particle optimization.

[0045] The following discussion turns to a general discussion of one or more scientific instrument systems and related methods, computing devices, and computer-readable media disclosed herein. For example, in one or more embodiments, a system may include a memory storing computer executable components and a processor executing the computer executable components stored in the memory. The computer executable components include: an acquisition component that acquires data of a compound, the data defining a first mass spectrometry spectrum of the compound; an evaluation component that generates a recommendation for performing a mass spectrometry action on the compound based on the data, using a neural network trained on an input data set including one or more acquisition metrics and using one or more association scores associated with the one or more acquisition metrics; and an execution component that performs a mass spectrometry action at a mass spectrometer based on the recommendation and obtains a mass spectrometry result.

[0046] One or more embodiments disclosed herein can achieve improved performance relative to existing methods. For example, based on RL training and based on historical data on which the NN can be RL trained, a system employing a RL-trained NN as described herein can employ better compound-specific acquisition actions than can be pre-programmed by a management entity alone. That is, such a system can learn to recognize when resources should be expended during the current acquisition and during future recommended or future directed acquisition actions. As described above, there may be precursor environments where extended accumulation times may result in lower limits of detection, or there may be precursor environments where fast acquisition times may be required given the large number of compounds entering the mass spectrometer.

[0047] In addition, even based on different recommended acquisition actions, the embodiments described herein can also advantageously provide focusing / direction for multiple compounds simultaneously. That is, the embodiments described herein can employ hybrid optimization techniques to achieve optimal results based on selected high-level goals (such as analytical specificity, improved probability of accuracy, and / or improved number of compound detections).

[0048] Furthermore, the embodiments described herein can be adapted to different types of experiments, labeling techniques, and / or mass or cross-sectional analysis techniques. That is, depending on the level of sample description or applicable high-level experimental goals, the framework described herein can be adapted to fit the sample. For example, rewards, high-level goals, available acquisition actions, etc. can be input into the system and the NN, where the NN is updated to operate within the scope of the input.

[0049] Thus, embodiments disclosed herein provide improvements to scientific instrumentation technology (eg, improvements in computer technology to support such scientific instrumentation, among other improvements).

[0050] Various embodiments disclosed herein can improve existing methods by using reinforcement learning agents (e.g., NNs trained using RL) to achieve technical advantages of high accuracy and / or large number of identifications of material compounds. That is, for each step of a multi-step material analysis experiment using MS, the generation of output data can be achieved without the need for specific low-level control of acquisition metrics and / or operational settings.

[0051] Such technical advantages cannot be achieved through routine and existing methods, and all user entities of systems that include such embodiments can benefit from these advantages (e.g., by helping the user entities perform technical tasks, such as indicating one or more compounds through a guided human-machine NN interaction process).

[0052] Thus, the technical features of the embodiments disclosed herein (e.g., the ability to generate one or more MS results using RL-based control) are therefore clearly unconventional in the field of materials analysis, as are the combinations of features of the embodiments disclosed herein.

[0053] As further discussed herein, various aspects of the embodiments disclosed herein can improve the functionality of the computer itself. In addition, the computing and user interface features disclosed herein not only involve the collection and comparison of information, but also apply new analysis and expertise to change the computer analysis operations of material compounds. For example, based on the result output of the experiment, and based on one or more reward indicators corresponding to the result output, the system / NN combination can adopt an update process to strengthen the good recommendations of the NN. Therefore, the non-limiting system described herein, including the material analysis system and the NN, can be self-improving.

[0054] Thus, the present disclosure introduces functionality that neither existing computing devices nor humans can perform. Rather, such existing computing devices would instead require low-level control based on MS or MS corresponding experiments, such as including (but not limited to) specific control of acquisition metrics and operation settings for each stage of a multi-stage experiment. Given the time, energy, and human error involved, in addition to the lack of computerized reference using historical data, it is not practical to operate within the scope of existing methods.

[0055] Thus, embodiments of the present disclosure may serve any of a number of technical purposes, such as controlling a particular technical system or process; determining how to control a machine based on a measurement result; digital audio, image, or video enhancement or analysis; separation of material sources in a mixed signal; generating data for reliable and / or efficient transmission or storage; providing estimates and confidence intervals for material samples; or providing faster processing of sensor data. In particular, the present disclosure provides technical solutions to technical problems, including but not limited to: providing acquisition recommendations and / or indicating acquisitions, using historical data for acquisition recommendations, generating updated scores corresponding to one or more probabilities; and / or providing faster, more thorough, and / or more efficient processing of material compounds.

[0056] Thus, embodiments disclosed herein provide improvements to materials analysis technology (eg, improvements in computer technology to support materials analysis, among other improvements).

[0057] As used herein, the phrase "based on" should be understood to mean "based, at least in part, on" unless otherwise specified.

[0058] As used herein, the term "component" may refer to an atomic element, a molecular element, a phase of atomic or molecular elements, or a combination thereof.

[0059] As used herein, the terms "compound" and "precursor" are used interchangeably.

[0060] As used herein, the term "data" may include metadata.

[0061] As used herein, the terms "entity," "requesting entity," and "user entity" may refer to machines, devices, components, hardware, software, intelligent devices, parties, organizations, individuals, and / or humans.

[0062] 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 drawing elements. In the following description, for the purpose of explanation, numerous specific details are set forth in order 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.

[0063] In addition, it should be understood that the embodiments depicted in one or more of the figures described herein are for illustrative purposes only, and therefore the architecture of the embodiments is not limited to the systems, devices and / or components depicted therein, nor is it limited to any specific order, connection and / or coupling of the systems, devices and / or components depicted therein.

[0064] Turning now particularly to one or more of the accompanying drawings, and first to Figure 1 , illustrates a block diagram of a scientific instrument module 100 for performing material analysis operations using a NN trained with RL and for performing at least one or more aspects of training according to various embodiments described herein. The scientific instrument module 100 can be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument module 100 can be included in a single computing device or can be distributed across multiple computing devices that communicate with each other, as appropriate. This document refers to Figure 4 Examples of computing devices that may implement the scientific instrument support module 100, either alone or in combination, are discussed in conjunction with computing device 400 and are referenced herein. Fig.16 The scientific instrument system 1600 discusses an example of a system of interconnected computing devices, where the scientific instrument module 100 can be implemented across one or more computing devices.

[0065] The scientific instrument support module 100 may include a first logic 102, a second logic 104, a third logic 106, a fourth logic 108, and a fifth logic 110. As used herein, the term "logic" may include a device for performing a set of operations associated with logic. For example, any of the logic elements included in the module 100 may be implemented by one or more computing devices, which are programmed with instructions to enable one or more processing devices of the computing device to perform an associated set of operations. In a specific embodiment, the logic element may include one or more non-transient computer-readable media, which have instructions on them, which, when executed by one or more processing devices in one or more computing devices, enable 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, which together perform functions associated with the module. Different logic elements in the logic elements in the module may take the same form or may take different forms. For example, some logics in the module may be implemented by a programmed general-purpose processing device, while other logics 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 sets of instructions executed by one or more processing devices. A module may omit one or more logic elements depicted in the associated figures; for example, when the module is to 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 figures.

[0066] The first logic 102 may compare the input data of the compound with the acquisition metrics, historical data, and / or compound type data to generate a data set matrix including scores defining the probability of achieving one or more acquisition metrics and / or metrics of interest. That is, the first logic 102 may generate and / or transform the data for a recommendation to perform an acquisition action.

[0067] The second logic 104 may generate one or more scores for the aforementioned scoring. That is, the second logic 104 may help define (eg, including ranking) acquisition metrics and / or metrics of interest, focus on these metrics, and recommended acquisition actions may be based on these metrics.

[0068] The third logic 106 may include an evaluation performed by a RL trained neural network, such as based on any of the outputs of the first logic 102 or the second logic 104 .

[0069] The fourth logic 108 may include output of a recommended acquisition action or output of an instruction to perform an acquisition action.

[0070] The fifth logic 110 may include evaluating the performance of the NN based on the output of the fourth logic 108. That is, the fifth logic 110 may employ a reward system to reinforce outputs that meet and / or satisfy one or more acquisition metrics and / or metrics of interest.

[0071] As used herein, the acquisition metric can be, but is not limited to, probability of detection, signal strength, fit to predicted isotope clusters, mass accuracy, delta time between estimated current time and expected elution time, and / or whether the acquisition of the same precursor was acquired on the last iteration (e.g., step or stage) of the experiment. Another acquisition metric can be the identification of neighboring compounds, where the identification of one compound corresponds to an increase in the probability that another compound (such as a compound of interest) is present or will soon be present.

[0072] Differently, as used herein, a metric of interest may be a high level aspect such as the amount of precursor identified, the specificity of the precursor identified, the accuracy of precursor abundance measurement, and / or the speed of precursor identification, but is not limited thereto.

[0073] Figure 2 A flow chart illustrating a method 200 of performing operations of the scientific instrument module 100 according to various embodiments. Although reference may be made to specific embodiments disclosed herein (e.g., Figure 1 The scientific instrument module 100 discussed in this article is referenced Figure 3 GUI 300 discussed herein, reference Figure 4 The computing device 400 discussed herein and / or referenced herein Fig.16 The operation of method 200 is illustrated using the scientific instrument system 1600 discussed above, but method 200 can be used in any suitable setting to perform any suitable operation. Figure 2 The operations are each illustrated once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (eg, different operations performed may be performed in parallel where appropriate).

[0074] At 202, a first operation can be performed. For example, the first logic 102 of the module 100 can perform the first operation 202. The first operation 202 can include identifying one or more compounds for use in an experiment (eg, identifying one or more precursors therefrom).

[0075] As used herein, a precursor is a chemical compound, and the term may be used to refer to a compound that is identified from another compound.

[0076] At 204, a second operation may be performed. For example, the second logic 104 of the module 100 may perform the second operation 204. The second operation 204 may include assigning a score to the processing parameter and / or the metric of interest.

[0077] At 206, a third operation may be performed. For example, the third logic 106 of the module 100 may perform the third operation 206. The third operation 206 may include performing data evaluation using the RL trained neural network.

[0078] At 208, a fourth operation may be performed. For example, the fourth logic 108 of the module 100 may perform the fourth operation 208. The fourth operation 208 may include determining one or more recommended acquisition actions and / or instructing performance of one or more acquisition actions.

[0079] At 210, a fifth operation may be performed. For example, the fifth logic 110 of the module 100 may perform the fifth operation 210. The fifth operation 210 may include performing a performance evaluation of the NN based on the output from the fourth operation. The fifth operation 210 may include employing a reward system and updating data and / or functionality of the NN based on the reward system.

[0080] The scientific instrument methods disclosed herein may include interaction with a user entity (e.g., via the reference Fig.16 These interactions may include providing information to the user entity (e.g., about scientific instruments such as Fig.16 information about the operation of a scientific instrument 1610), information about a sample being analyzed or other test or measurement performed by the scientific instrument, information retrieved from a local or remote database, or other information) or providing the user entity with the option of inputting commands (e.g., controlling a scientific instrument such as Fig.16 In some embodiments, these interactions may be performed through 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, and a display device such as a computer program product, such as a computer program product, and a display device such as Figure 4 410) that provides output to a user entity and / or prompts the user entity to provide input (e.g., via a display device 410 discussed herein). Figure 4 Other I / O devices 412 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen). The scientific instrument system 1600 disclosed herein may include any GUI suitable for interacting with a user entity.

[0081] Next turn Figure 3 , depicts an example GUI 300 that can be used to perform some or all of the methods described herein according to various embodiments described herein. As described above, the GUI 300 can be provided in a scientific instrument system (e.g., Fig.16A computing device (e.g., a scientific instrument system 1600 discussed herein) Figure 4 The computing device 400 discussed herein may include a display device (eg, Figure 4 The user entity may use any suitable input device (e.g., the display device 410 discussed herein) to input the content of the input to the display device 410. Figure 4 Any input devices (including those discussed in other I / O devices 412 ) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation, etc.) interact with GUI 300 .

[0082] GUI 300 may include a data display area 302 , a data analysis area 304 , a scientific instrument control area 306 , and a settings area 308 . Figure 3 The specific number and arrangement of regions depicted in are merely illustrative, and any number and arrangement of regions including any desired characteristics thereof may be included in GUI 300 .

[0083] The data display area 302 may display data generated by a scientific instrument (e.g., Fig.16 For example, the data display area 302 can display one or more output results 682, which can include text, graphs, charts, matrices and / or spectra, but are not limited to these.

[0084] The data analysis area 304 can display the results of the data analysis (e.g., the results of analyzing the data illustrated in the data display area 302 and / or other data). For example, the data analysis area 304 can display one or more compound results 682 and / or one or more reward indicators 722. In one or more cases, the data analysis area 304 can display a list, flow chart, or other schematic diagram of the acquisition actions taken and / or recommended with respect to the experiment. In one or more embodiments, the data display area 302 and the data analysis area 304 can be combined in the GUI 300 (e.g., including the data output from the scientific instrument and some analysis of the data in a common graphic or area).

[0085] The scientific instrument control area 306 may include options that allow a user entity to control scientific instruments (e.g., Fig.18 For example, the scientific instrument control area 306 may include one or more controls for inputting one or more metrics of interest.

[0086] The settings region 308 may include options that allow the user to physically control the features and functions of the GUI 300 (and / or other GUIs), and / or to perform common computing operations with respect to the data display region 302 and the data analysis region 304 (e.g., saving data to a storage device (such as a storage device such as described herein). Figure 4 storage device 404 discussed above), transferring data to another user entity, marking data, etc.). For example, the settings area 308 may include one or more options to change the graphic representation (such as Fig.11 The color, fill, or format of icon 1150 and icon 1160).

[0087] As described above, the scientific instrument module 100 may be implemented by one or more computing devices. Therefore, the following discussion turns to Figure 4 , which illustrates a block diagram of a computing device 400 that can perform some or all of the scientific instrument methods disclosed herein, according to various embodiments. In one or more embodiments, the scientific instrument module 100 can be implemented by a single computing device 400 or multiple computing devices 400. In addition, as discussed below, the computing device 400 (or multiple computing devices 400) that implements the scientific instrument module 100 can be Fig.16 A portion of one or more of a scientific instrument 1610, a user local computing device 1620, a service local computing device 1630, or a remote computing device 1640.

[0088] Figure 4 The computing device 400 is illustrated as having multiple components, but any one or more of these components may be omitted or duplicated depending on the application and settings. As illustrated, these components may include one or more of a processor 402, a storage device 404, an interface device 406, a battery / power circuit 408, a display device 410, and other input / output (I / O) devices 412, as will be described below.

[0089] In one or more embodiments, one or more of the components included in computing device 400 may be attached to one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated onto a single system on a chip (SoC) (e.g., an SoC may include one or more processors 402 and one or more storage devices 404). Additionally, in one or more embodiments, computing device 400 may omit Figure 4In one or more embodiments, computing device 400 may include interface circuitry (not shown) for coupling to one or more 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, computing device 400 may omit display device 410, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which display device 410 may be coupled.

[0090] Computing device 400 may include processor 402 (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 registers and / or memory to convert the electronic data into other electronic data that can be stored in registers and / or memory. Processor 402 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.

[0091] The computing device 400 may include a storage device 404 (e.g., one or more storage devices). The storage device 404 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 networked drive, a cloud drive, or any combination of memory devices. In one or more embodiments, the storage device 404 may include a memory that shares a die with the processor 402. 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 one or more embodiments, the storage device 404 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., the processor 402), cause the computing device 400 to perform any appropriate method or portion of the methods disclosed herein.

[0092] The computing device 400 may include an interface device 406 (e.g., one or more interface devices 406). The interface device 406 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 400 and other computing devices. For example, the interface device 406 may include circuits for managing wireless communications for transmitting data to and from the computing device 400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can transmit data through a non-solid medium using modulated electromagnetic radiation. The term does not mean that the associated device does not contain any wires, but in one or more embodiments, the associated device may not contain any wires. The circuitry included in the interface device 406 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 family), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendments), Long Term Evolution (LTE) projects and any amendments, updates and / or revisions (e.g., Advanced LTE projects, Ultra Mobile Broadband (UMB) projects (also known as "3GPP2"), etc.). In one or more embodiments, the circuitry included in the interface device 406 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 one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with Enhanced Data GSM Evolution (EDGE), GSM The interface device 406 may be configured to operate in accordance with an EDGE Radio Access Network (GERAN), a Universal Terrestrial Radio Access Network (UTRAN), or an Evolved UTRAN (E-UTRAN). In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution Data Optimized (EV-DO) and its derivatives, and any other wireless protocols designated as 3G, 4G, 5G, etc. In one or more embodiments, the interface device 406 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or send wireless communications.

[0093] In one or more embodiments, interface device 406 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 406 may include a circuit supporting communication according to Ethernet technology. In one or more embodiments, interface device 406 may support wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first group of circuits of interface device 406 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 406 may be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In one or more embodiments, a first group of circuits of interface device 406 may be dedicated to wireless communication, and a second group of circuits of interface device 406 may be dedicated to wired communication.

[0094] Computing device 400 may include battery / power circuitry 408. Battery / power circuitry 408 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 400 to an energy source separate from computing device 400 (e.g., AC line power).

[0095] Computing device 400 may include display device 410 (eg, multiple display devices). Display device 410 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.

[0096] The computing device 400 may include other input / output (I / O) devices 412. For example, the other I / O devices 412 may include one or more audio output devices (e.g., speakers, headphones, earbuds, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a positioning device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 400 as is known in the art), 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, etc.), 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 barcode reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0097] The computing device 400 can 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, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0098] Next reference Figure 5 and Figure 6 , in one or more embodiments, Figure 5 and Figure 6 The non-limiting systems 500 and / or 600 illustrated in the drawings and / or the systems thereof may also include the computing environment (such as Fig.18 One or more computers and / or computing-based elements described in the computing environment 1800 shown in FIG. 10A . In one or more of the described embodiments, the computers and / or computing-based elements may be combined to implement the combined Figure 5 and / or Figure 6 and / or used in combination with one or more of the systems, devices, components, and / or computer-implemented operations illustrated and / or described in other figures described herein.

[0099] First turn Figure 5 , which illustrates a block diagram of an example, non-limiting system 500 that may include a material analysis system 502, a neural network (NN) 570, and an MS device 550. The material analysis system 502 may facilitate the use of an RL-trained NN to control the process of an MS-based experiment.

[0100] In one or more embodiments, the material analysis system 502 can be comprised, at least in part, of the computing device 400 .

[0101] In one or more embodiments, the material analysis system 502 may include, at least in part, the NN 570 and / or the MS device 550 .

[0102] Note that the material analysis system 502 is only briefly described in detail to provide Figure 6 The introduction of a more complex and / or extensive material analysis system 602 exemplified herein. That is, the following will be with respect to Figure 6 Non-limiting system 600 provides further details regarding processes that may be performed by one or more embodiments described herein.

[0103] Still reference Figure 5, the material analysis system 502 may include at least a memory 504, a bus 505, a processor 506, an acquisition component 510, an evaluation component 516, and an execution component 518. The processor 506 may be the same as, consist of, or be different from the processor 402. The memory 504 may be the same as, consist of, or be different from the storage device 404.

[0104] Using the above components, the material analysis system 502 can employ the RL-trained NN to control MS-based experiments to provide output information related to the compounds to be analyzed 562. That is, the material analysis system 502 can determine the acquisition metrics 810 (e.g., input data 560), historical data 542, and / or analysis thereof for one or more stages of the MS experiment. Figure 8 Based on these acquisition metrics 810 , operational settings, and / or historical data 542 , the material analysis system can define at least one recommended acquisition action to be taken to achieve the metric of interest corresponding to compound 562 .

[0105] The metrics of interest may be provided by a user entity and / or may be programmed into the experimental procedure employed by the material analysis system 502. The metrics of interest may include focusing on one or more of the number of compounds identified, identification speed, identification accuracy, and / or identification precision, but are not limited thereto.

[0106] In general, the collection component 510 can collect data (e.g., input data 560; also referred to herein as first data) of the compound 562. In one or more embodiments, the data can define a first mass spectrometer spectrum 566 of the compound 562. The data can include one or more compound parameters 564 (e.g., physical phase temperature range, physical properties, mechanical properties, chemical properties, etc., but not limited thereto).

[0107] Based on this data and using a neural network 570 trained on an input data set that includes one or more acquisition metrics and one or more associated scores associated with the one or more acquisition metrics, the evaluation component 516 can generate a recommendation to perform a mass spectrometry action (e.g., acquisition action 580) on the compound 562. It should be noted that the input data set is different from the input data 560, but the input data 560 can be used to populate, generate, update and / or transform the input data set. The input data set, such as an exemplary scoring matrix 800, will be described in more detail below with respect to the material analysis system 602.

[0108] Based on the recommendation to perform a mass spectrometry action (eg, acquisition action 580), execution component 518 can instruct that the mass spectrometry action be performed at a mass spectrometer (eg, MS device 550) and mass spectrometry results (eg, including compound results 582) can be obtained therefrom.

[0109] The acquisition component 510, the evaluation component 516, and the generation component 518 can be operatively coupled to the processor 506, which can be operatively coupled to the memory 504. The bus 505 can provide the operative coupling. The processor 506 can facilitate the execution of the acquisition component 510, the evaluation component 516, and the execution component 518. The acquisition component 510, the evaluation component 516, and the execution component 518 can be stored at the memory 504.

[0110] In general, the non-limiting system 500 may employ any suitable communication method (e.g., electronic, telecommunication, Internet, infrared, fiber optic, etc.) to provide communication between the material analysis system 502, database 540, NN 570, MS device 550, and / or any device associated with a user entity.

[0111] Next turn Figure 6 , illustrates a non-limiting system 600 that may include a material analysis system 602, a NN 670, a database 640, and an MS device 550. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted. Figure 5 The description of the embodiments can be applied to Figure 6 Similarly, regarding Figure 6 The description of the embodiments can be applied to Figure 5 implementation plan.

[0112] In general, the material analysis system 502 can facilitate employing RL-trained NNs to control the process of MS-based experiments.

[0113] In one or more embodiments, the material analysis system 602 can be comprised, at least in part, of the computing device 400 .

[0114] In one or more embodiments, the material analysis system 602 may include, at least in part, the NN 670 , the database 640 , and / or the MS device 650 .

[0115] NN 670 can be and / or can include any suitable artificial intelligence aspects, neurons, neuron layers, neuron clusters, etc. NN 670 can be initially trained using reinforcement learning by updating component 622 and / or by any external training system. For example, during the training of NN 670, NN 670 can receive information about the state of the environment, be requested to select an action to update the environment, and receive a reward. The reward can be amortized within the future state to help NN 670 find actions that do not result in immediate rewards but are paid later. For example, in extreme cases, NN 670 can receive little or no rewards (or penalties) until a group of many steps are completed, which may be the case of training agents to navigate to a target, or when NN 670 experiences some catastrophic event (falling into a hole). The process is repeated within a "segment" including one or more LC-MS experiments. Typically, hundreds or thousands of training segments can be used to train NN 670 for material analysis of a specific compound or compound type. As used herein, "type" can include various compounds, such as human tissue, mining gas, etc.

[0116] As used herein, an example of a NN 670 experiencing a catastrophic event may refer to using a metric to characterize the entire elution peak (e.g., 8-10 points are collected per LC peak). It may be difficult for the NN to determine completion until the entire peak is characterized, thus resulting in actions relative to the beginning or middle of the peak, resulting in low rewards. Instead, a reward amortization scheme may be employed over time, such as transferring the final reward backwards to previous actions.

[0117] In one or more embodiments, an array of two or more NNs 670 may be trained and employed, such as for different precursors. In one or more examples, such two or more NNs 670 may be aggregated (e.g., stacked) to form a super NN that may be used to more quickly and efficiently generate recommended acquisition actions 720, such as with respect to different precursors.

[0118] The database 640 may include historical data 542 in any suitable format, including but not limited to numbers, text, code, data, metadata, images, etc. The historical data 542 may include historical acquisition metrics 544, historical spectra 546, and / or historical acquisition actions 548. The historical acquisition metrics 544 may include, but are not limited to, the amount of ions required for analysis, the analysis temperature, and / or the analysis degradation parameters. The historical acquisition actions 548 may be those actions taken to achieve certain metrics of interest in past MS-based experiments or in previous acquisitions in the same experiment.

[0119] In one or more embodiments, the database 640 can be comprised, at least in part, of the material analysis system 602 .

[0120] MS device 650 may be any suitable MS device that can provide a material analysis output, such as one or more spectra.

[0121] One or more communications between one or more components of the non-limiting system 600 may be provided via wired and / or wireless means, including but not limited to using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Suitable wired or wireless technologies for supporting communications may include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), Enhanced General Packet Radio Service (Enhanced GPRS), Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or traditional telecommunication technologies, Session Initiation Protocol (SIP), RF4CE protocol, WirelessHART protocol, 6LoWPAN (Ipv6 over Low Power Wireless Area Network), Z-Wave, Advanced and / or Adaptive Networking Technology (ANT), Ultra-Wideband (UWB) standard protocol and / or other proprietary and / or non-proprietary communication protocols.

[0122] The material analysis system 602 can be integrated with a cloud computing environment such as Fig.17 The cloud computing environment 1700 is associated with (such as accessible via) the cloud computing environment.

[0123] The material analysis system 602 may include multiple components. These components may include a memory 604, a processor 606, a bus 605, a collection component 610, an analysis component 612, a metric component 614, an evaluation component 616, an execution component 618, a reward component 620, and / or an update component 622. Using these components, the material analysis system 602 may output one or more recommendations 720, one or more directed collection actions 680, and / or one or more compound results 682.

[0124] Next, the discussion turns to the processor 606, memory 604, and bus 605 of the material analysis system 602. For example, in one or more embodiments, the material analysis system 602 may include a processor 606 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or the like). In one or more embodiments, as described herein with or without reference to one or more figures of one or more embodiments, the components associated with the material analysis system 602 may include one or more computer and / or machine readable, writable, and / or executable components and / or instructions, which may be executed by the processor 606 to provide the performance of one or more processes defined by such components and / or instructions. In one or more embodiments, the processor 606 may include an acquisition component 610, an analysis component 612, a metric component 614, an evaluation component 616, an execution component 618, a reward component 620, and / or an update component 622.

[0125] In one or more embodiments, the material analysis system 602 may include a computer-readable memory 604 that may be operably connected to the processor 606. The memory 604 may store computer-executable instructions that, when executed by the processor 606, may cause the processor 606 and / or one or more other components of the material analysis system 602 (e.g., an acquisition component 610, an analysis component 612, a metric component 614, an evaluation component 616, an execution component 618, a reward component 620, and / or an update component 622) to perform one or more actions. In one or more embodiments, the memory 604 may store computer-executable components (e.g., an acquisition component 610, an analysis component 612, a metric component 614, an evaluation component 616, an execution component 618, a reward component 620, and / or an update component 622).

[0126] The material analysis system 602 and / or components thereof as described herein may be communicatively, electrically, operatively, optically, and / or otherwise coupled to each other via a bus 605. The bus 605 may include one or more of a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and / or another type of bus that may employ one or more bus architectures. One or more of these examples of the bus 605 may be employed.

[0127] In one or more embodiments, the material analysis system 602 can be coupled (e.g., communicatively, electrically, operatively, optically, and / or the like) to one or more external systems (e.g., an unillustrated electrical output generation system, one or more output targets, and / or output target controllers), sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or the like), such as via a network. In one or more embodiments, one or more of the components of the material analysis system 602 and / or the non-limiting system 600 can reside in the cloud and / or can reside locally in a local computing environment (e.g., at a specified location).

[0128] In addition to the processor 606 and / or memory 604 described above, the material analysis system 602 may also include one or more computer and / or machine readable, writable and / or executable components and / or instructions, which, when executed by the processor 606, may provide for the execution of one or more operations defined by such components and / or instructions.

[0129] Turning now to additional components of the materials analysis system 602 (e.g., a collection component 610, an analysis component 612, a metric component 614, an evaluation component 616, an execution component 618, a reward component 620, and / or an update component 622), generally speaking, the materials analysis system 602 can perform a set of processes that can be divided into three steps: initial collection and analysis, evaluation and recommendation, and reward and update.

[0130] Turning first to initial acquisition and analysis, details regarding acquisition component 610, analysis component 612, and metrics component 614 will be provided.

[0131] Turning first to the acquisition component 610, this component can generally acquire data (e.g., input data 660) of a compound 662 that defines a first mass spectrometer spectrum 665 of the compound 662. The input data 660 can also include one or more compound metrics 664 (e.g., physical phase temperature range, physical properties, mechanical properties, chemical properties, etc., but not limited thereto).

[0132] In one or more embodiments, the input data 660 may include data defining one or more initial acquisitions 703 that define and / or identify one or more starting compounds 662 (e.g., the starting compounds 662 that the non-limiting system 600 will use to begin an MS-based experiment).

[0133] In one or more embodiments, the acquisition component 610 can identify one or more compounds (e.g., at least a first compound 662 and a second compound 662) from the input data (e.g., from the spectrum 665). In one or more other embodiments, the initial compound 662 can alternatively be provided as an input to the non-limiting system 600.

[0134] Input data 660 may be provided in any suitable format (eg, text, code, numbers, data, metadata, but not limited thereto).

[0135] In one or more embodiments, acquisition component 610 can provide input data 660 as input to NN 670, and / or NN 670 can obtain input data 660 through one or more other suitable means.

[0136] The metric component 614 can obtain and provide one or more metrics of interest 668 related to the compound 662 or compound type to be analyzed as input to the NN by adopting the RL-based controlled MS-based experiment provided by one or more embodiments discussed herein. The metric of interest 668 can be a high-level goal to be achieved by the non-limiting system 600. In one or more embodiments, the metric of interest 668 is not so descriptive that it includes lower-level acquisition metrics and / or operational settings that more specifically define one or more stages of the MS-based experiment. For example, the metric of interest 668 can include instructions to achieve a specific number of identifications, achieve a maximum number of identifications, perform a selected number of acquisition actions, achieve a selected identification accuracy, perform rapid identification within the limits of the desired sample separation period (i.e., LC gradient length), use a specific experimental technique or method, use a specific MS device 650, etc., but is not limited to this.

[0137] Metrics of interest 668 may be provided by a user entity and / or may be obtained from input data 660 and / or historical data 642 (eg, historical compound metrics 644 , historical spectra 646 , and / or historical acquisition actions 648 ).

[0138] The analysis component 612 can generate an input data set based on the input data 660, the data on which the NN 670 has been trained, and / or the historical data 642 from the database 640. The input data set can be the basis of information on which the NN 670 can provide recommendations (e.g., when directed by the evaluation component 616).

[0139] For example, turning Figure 7 , in addition to still referring to Figure 6In addition, after the acquisition component 610 completes the step 702 of obtaining input data, the input data processing 704 can be performed by the analysis component 712. That is, the operation of the analysis process 714 by the analysis component 612 can generate information defining one or more relationships between one or more acquisition metrics 810 (e.g., the amount of ions necessary for analysis, analysis temperature, analysis degradation parameters, detection probability, signal intensity, predicted next signal intensity, fit to predicted isotope clusters, mass accuracy, estimated delta time between current time and expected elution time, and / or whether the acquisition was acquired for a precursor on the last iteration, but not limited thereto) and one or more associated scores 716.

[0140] That is, as a result of the analysis component 612's comparison and / or analysis of the input data 660, the historical data 642, and / or the data on which the NN 670 has been trained, the analysis component 612 can output comparisons between different aspects of these data with respect to each potential compound (e.g., precursor P = {p1, p2, ... p N})

[0141] It should be noted that the analysis process 714 can be based on the requested one or more metrics of interest 668 (eg, performed by the analysis component 612 within its boundaries).

[0142] As a result of the analysis process 714, the score assignment 706 can be performed by the analysis component 612. That is, for one or more acquisition metrics 810, for one or more potential compounds (e.g., for each acquisition metric for each potential compound), a score 716 can be assigned. That is, the score 716 can be assigned external to the NN 670.

[0143] In one or more embodiments, the collection metrics 810 and / or the ratings 716 may be determined by input from a user entity and / or may be obtained from the input data 660 and / or the historical data 642 .

[0144] In one or more embodiments, the score 716 can be generated at least in part by another NN, trained to generate a score that defines a relationship between a precursor and the acquisition metric 810. In one example, the additional NN can be employed to predict the next intensity point given a vector of previous intensities that can be used as input (either as initial input data 660 or as acquisition metric 810). In one example, the additional NN can be employed to estimate whether evidence is present in the input data 660 or data resulting from the acquisition action 680.

[0145] In one or more embodiments, a set of scores (pi ; {s1,s2,…s k =E nxk ) can be a number between 0 and 1 (and including 0 and 1) (e.g., [0,1]), where n is the number of precursors considered and k is the number of scores per precursor. Scores can take on any value, but most commonly they can be normalized to the range [0, 1] or [-1, 1], or regularized by dividing by the expected or measured mean and by the expected or measured standard deviation. In general, in RL parlance, a score is a description of the environment that, in conjunction with a reward feedback loop, enables the system to output a recommended action.

[0146] For example, now in addition to Figure 8 , precursor P1 is entering the mass spectrometer and has k scores associated with it, including, but not limited to, relatively high intensity, high next predicted intensity, and low delta time between the current elution time and the predicted elution time. The RL system previously determined that it is favorable to acquire a spectrum for P1, and this is represented in the last score having a value of 1. Other precursors in this example have scores that may indicate to the RL system a lower probability of their presence, making it unfavorable to acquire their spectra, such as lower intensity, poor mass accuracy, and a large delta between the current elution time and the predicted elution time.

[0147] Based on these inputs, the NN outputs an action a for each precursor i , which in some embodiments may take the value 0 to indicate no precursor collection, and the value 1 to indicate collection of the precursor spectrum. Alternatively, the action output may be a continuous variable between 0 and 1, such that 0 indicates no collection, and a value greater than 0 indicates that a certain amount of accumulation time (e.g., accumulation time a) is to be used. i xt max ) to collect the spectrum, where t max is the maximum accumulation time. A collection acquired with 1.0 may take longer than one acquired with 0.1, but may be of higher quality (e.g., higher precision).

[0148] In one or more embodiments, one or more association scores 716 may include and / or define one or more probabilities that one or more thresholds corresponding to one or more acquisition metrics (e.g., acquisition metrics 810) will be satisfied by a mass spectrometry action performed on a compound or compound type. As used herein, the term "satisfy" may refer to reaching or exceeding a threshold. As used herein, one or more (such as each) acquisition metrics 810 may be associated with a threshold. For example, referring to Figure 8 , thresholds may include thresholds for fit, signal strength, mass accuracy, detection probability, delta time, and the like.

[0149] Still reference Figure 8 Based on the scores 716 and the collection metrics 810, a data set matrix, such as the exemplary score matrix 800, can be generated by the analysis component 612. Figure 8 As illustrated, the matrix 800 includes a set of acquisition metrics 810 along a vertical side (eg, rows) and a set of precursors (eg, potential compounds to be acquired / identified) along a horizontal side (eg, columns).

[0150] Although in Figure 8 600, but the resulting data (e.g., acquisition metrics 810, scores 716, and relationships therebetween) may be stored in any suitable format for use by the NN 670. The resulting data may be stored at the memory 604 and / or at any other suitable location that may be communicatively coupled to the non-limiting system 600.

[0151] In one or more embodiments, the input data set (e.g., a data set matrix) can be pre-generated and the NN 670 can have been trained on the input data set. That is, the input data set can include one or more acquisition metrics (e.g., acquisition metrics 810) of compounds 662 and / or compound types, and one or more association scores 716 associated with the one or more acquisition metrics.

[0152] In one or more embodiments, the pre-generated or generated input data set can be updated, for example, by analysis component 612 (such as by indication from metric component 614) to take into account metrics of interest 668. That is, for example, as a result of metrics of interest 668, acquisition metrics 810 can be expanded, changed, and / or reduced (e.g., the list of acquisition metrics 810 at table 800 can be changed).

[0153] In one or more embodiments, the metric component 614 and / or the analysis component 612 can change, modify, update, and / or generate input data set dimensions based on one or more metrics of interest 668. For example, a user entity submitting a metric of interest 668 may desire to focus on a specific high-level goal defined by the metric of interest. For example, based on the goal, the score 716 can be updated, or the acquisition metrics 810 can be added or deleted, but are not limited to this. In another example, a precursor can be more or less important based on the metric of interest 668.

[0154] Based on the scores 716 (such as based on an aggregation or other calculation employing the scores 716), and guided by the one or more metrics of interest 668, the NN may perform NN processing 708, thereby producing one or more NN outputs 728. The NN outputs 728 may be in any suitable format, such as may be employed by the material analysis system 602. For example, the one or more NN outputs 728 may define a recommended acquisition action 720, which may itself be defined by and / or include one or more operational settings (e.g., the number of ions to be employed, exposure time, number of fragments, etc., but not limited thereto).

[0155] In other words, based on the score 716 guided by the one or more metrics of interest 668 (such as based on an aggregation or other calculation using the score 716), and based on the one or more NN outputs 728, the evaluation component 616 can generate one or more recommendations (e.g., one or more recommended acquisition actions 720). Typically, the recommended acquisition action can be and / or include a recommendation to perform one or more separation and / or mass spectrometry actions. The one or more separation and / or mass spectrometry actions can directly or indirectly result in the acquisition of a compound.

[0156] In one or more embodiments, the NN 670 may be limited to selecting from, or instructed to preferably select from, an action database 672 of available mass spectrometry actions that may be capable of being performed using one or more mass spectrometry devices and / or other separation devices communicatively coupled to the non-limiting system 600.

[0157] The action database 672 may be comprised of the material analysis system 602 and / or otherwise communicatively coupled to the material analysis system.

[0158] As used herein, any one or more of any one or more of the historical acquisition actions 648, the recommended acquisition actions 720, and / or the directed acquisition actions 680 may include the acquisition of compounds directly or indirectly obtained using a mass spectrometer or other separation device. Such actions 648 / 680 / 720 may include, but are not limited to, any one or more of the following: fragmentation acquisition, MSn acquisition, selection of a specific mass spectrometer, selection of a specific separation device, selection of device operating settings, selection of a time range for performing acquisition, selection of the amount of ions to be employed, selection of an ion application schedule and / or mode to be employed, selection of two or more acquisitions in parallel, selection of back-to-back acquisitions, and / or selection of reagents.

[0159] For example, in one or more embodiments, NN 670 may recommend using fragmentation-based acquisition.

[0160] As another example, in one or more embodiments, NN670 may recommend MS2 or MS3 acquisition.

[0161] For further examples, turn to Figure 8 , relative to an acquisition metric focused on the predicted next signal density, an acquisition action may be recommended based on an acquisition with a high score for precursor P1, also based on a high score for precursor P2, or based on two higher scoring acquisitions for both targets (e.g., P1 and P2).

[0162] In one or more embodiments, the output of the NN 670 / evaluation component 616 can be based on historical data 642 that defines the degradation of the potential precursor or initial compound 662 prior to performing the acquisition action 680. Thus, for example, such input can relate to operational settings for the number of ions accumulated for acquisition and / or accumulation time or isolation width.

[0163] The recommended acquisition action 720 may be defined by Equation 1.

[0164] Equation 1: A = {a i ∈[0,1]:1≤i≤n}, A is the number corresponding to each precursor p i A set of actions a enumerated in the range of variable i from precursors i=1 to n i , and where action can take values ​​in the range 0 to 1 (inclusive).

[0165] The recommended acquisition action 720 can be and / or can include a transformation of one or more NN outputs 728, and can therefore itself be defined by and / or include one or more operational settings (e.g., number of ions to accumulate, accumulation time, number of fragments, etc., but not limited to such).

[0166] The recommended acquisition actions 720 (and NN outputs 728) can satisfy one or more acquisition metrics and / or include one or more recommended operational settings. In one or more embodiments, multiple targets corresponding to compound 662, expected to be generated by fragments, and / or corresponding to one or more fragment compounds from a previous spectrum (e.g., spectrum 665) can be specified, and data for these targets will be obtained by performing the recommended acquisition actions 720.

[0167] The recommended acquisition actions 720 may include a recommendation for a fragmentation acquisition, which is a mass spectrum / mass spectrum (MS2) acquisition of the compound or a mass spectrum / mass spectrum / mass spectrum (MS3) acquisition of the compound.

[0168] The one or more recommended acquisition metrics and / or operational settings may define recommendations for an amount of resources (eg, time, number of ions, equipment type, specific equipment, etc.) for the recommended acquisition action 720 .

[0169] In one or more embodiments, NN 670 , and therefore evaluation component 616 , may additionally or alternatively output an instruction to perform acquisition action 680 .

[0170] In one or more embodiments, one or more types or specific collection actions 680 may be pre-approved (e.g., by a user entity or via a template used) to automatically execute without additional authorization. In one or more embodiments, one or more types or specific collection actions 680 may be indicated as requiring authorization for continued execution. In one or more embodiments, all collection actions 680 may be pre-approved.

[0171] In one or more embodiments, the NN 670, and therefore the evaluation component 616, can additionally or alternatively output two or more recommendations, two or more output indications, and / or a combination of two or more recommendations and output indications, at least partially in parallel with each other. In this way, the operation of the non-limiting system 600 can be scalable.

[0172] As a result of the output indication, or as a result of the approved recommendation 720, the execution component 618 can indicate the execution of a mass spectrometry action (e.g., acquisition action 680) at a mass spectrometer (e.g., MS device 650) and mass spectrometry results (e.g., including compound results 682) can be obtained therefrom. For example, the user entity can provide approval to the non-limiting system 600 using any suitable input and / or input device.

[0173] Additionally and / or alternatively, based on the execution of the acquisition act 680, the execution component 618 can obtain one or more data, outputs, results, and / or compound results 682 generated by the execution of the acquisition act 680. In one or more embodiments, the output of the acquisition act 680 can include generating one or more spectra that can be obtained by the execution component 618 and analyzed by the execution component 618 and / or the analysis component 612.

[0174] Referring now to the reward component 620, the component may generally perform a reward process 712 that may generate a reward indicator 722 derived from the recommendation 720 of the mass spectrometry action and / or derived from the result of the execution of the acquisition action 680. In other words, the reward component 620 may evaluate the recommended mass spectrometry action 720 / performed acquisition action 680 by generating the reward indicator 722. The reward indicator 722 may be based on the satisfaction, non-satisfaction, or partial satisfaction of at least one of the one or more acquisition metrics 810 of the input data set and / or based on the satisfaction, non-satisfaction, or partial satisfaction of one or more metrics of interest 668. That is, more generally, the reward process 712 may be used to enhance outputs (e.g., the recommended acquisition action 720 and the directed acquisition action 680) that conform to and / or satisfy one or more acquisition metrics 810 and / or the metrics of interest 668.

[0175] For example, the reward can be an output of a function associated with the NN as and / or providing a nonlinear function module, such as a state score or action. However, in one or more embodiments, additional NNs can be generated, trained, and employed to assist the reward component 620 in generating the reward indicator 722.

[0176] For a specific example, temporarily go to Fig. 9 , a table 900 of exemplary reward functions is illustrated. As illustrated, one or more reward indicators r generated by one or more reward functions may be employed with respect to any acquisition action and its results. As shown, for ease of understanding, each exemplary reward function has a situation associated with it to which the reward function may be applicable. Exemplary reward functions may include, but are not limited to, unique compound identification, completed peaks, experiments with identical result identifications, and / or any combination thereof.

[0177] For example, a simple reward function could include the sum of the number of unique peptide detections. As another example, the reward could depend on an acquisition metric, such as the signal-to-noise ratio (SNR) of the resulting spectrum. In yet another example, additional value could be assigned to acquiring multiple spectra that span the elution profile of a compound. This could be accomplished through a time-localized "peak completion" analysis, or through even higher levels of reward such as measurement accuracy over multiple experiments. Value could also be given to detecting the same peptide or other compound type, class, and / or group in each iteration of a set of MS-based experiments.

[0178] Based on the reward indicator 722 , whether positive or negative (eg, the term “negative” corresponding to a penalty), the update component 622 can instruct the NN 670 to self-update.

[0179] In one or more embodiments, the update process 714 may result in one or more updates 724 including updates to weights employed by the NN 670 and / or one or more adjustments to one or more acquisition metrics 810 based on the particular one or more reward indicators 722 generated.

[0180] In one or more embodiments, update component 622 can update the NN (e.g., update weights employed by the NN or update related acquisition metrics 810) based on a set of reward outputs (e.g., reward indicators 722) amortized over time and obtained by evaluation component 616 based on multiple generations (e.g., of recommendations 720).

[0181] Now turn back to Figure 7 For any MS-based experiment performed by the non-limiting system 600, one or more high-level processes 700 may be performed ( Figure 7 ) of one or more additional iterations. For example, after performing any recommendation process 710, the evaluation component 616 can determine whether additional recommendations 720 should be generated to satisfy the metric of interest 668 for the compound based on the mass spectrometry results (e.g., compound results 682) obtained from the recommendations 720. This determination can be made at any suitable frequency, depending on the number of parallel recommendations for the same compound or different compounds, or for compounds connected by subsequent fragments.

[0182] Note that the reward step 712 and subsequent update step 714 may be performed at any suitable frequency, such as after the recommendation process 710, after the outcome of the acquisition action 680 is determined (eg, by the execution component 618), and so forth.

[0183] In one or more embodiments, one or more input data sets may be pre-generated and / or one or more NNs 670 may be trained on compounds or compound types, such as to provide templates for use by one or more user entities.

[0184] For example, it is envisioned that the general method that can be applicable to different types of experiments, labeling techniques and / or quality or cross-sectional analysis techniques. Relative to each template, the sample should be understood well enough so that the simulation can match the experimental results, such as the number based on ID, signal-to-noise ratio (SNR), measurement accuracy, etc., but not limited to this. The template can include an indication (for example, as a metric of interest 668) of the real actual liquid phase fractionation separation using a chromatographic library, a gas phase fractionation separation method or a combination with traditional DIA or DDA analysis. DDA analysis with a narrow isolation window can be adopted, so that the actual MS / MS spectra of all compounds in the sample can be allowed to be generated at all times. In such cases, the simulation of the LC-MS spectrum may need to simply retrieve appropriate spectra from the chromatographic library. Alternatively, the prediction using available software tools can be used to generate MSn spectra and retention time order, and / or experimental information can be used to generate chemical noise and anchor the predicted retention time. In one or more embodiments, the simulation of the experiment can utilize data enhancement to prevent overfitting and make NN 670 experience one or more different states. Such data enhancement can take the form of random noise generated by the electrospray or detection process as well as chromatographic drift and / or autosampler sample delivery errors.

[0185] With respect to the general approach, as described above, multiple trained agents (e.g., NN 670) can be generated and trained to cover common use cases, such as parameter spaces including organisms, sample types, labeling techniques, separation columns, separation times, and / or analyzer types. For example, a user entity can specify compounds or compound types for human plasma labeled with TMT 10plex using a C18 column, a 30 minute gradient, and an ion trap analyzer. These specifications can be applied as metrics of interest 668 or as requested acquisition metrics 810 and / or operational settings.

[0186] In the example of a particular set of samples or compounds that are not fully represented by multiple trained agents, the setup may include gas phase fractionation characterization of the samples, followed by training of the agents, which may start with the most similar trained agents in a library of agents. The agents may be trained in real time, or evolved in a series of priming experiments, although it may be preferred, generally from the user entity's perspective, that the same agent is used to collect all samples in a set.

[0187] The disclosure next turns to an overview of one or more functions described above as being performed by the non-limiting system 600. That is, consider an LC-MS scenario in which there is a set of n possible precursors p (eg, potential precursors p) known or suspected to be present in a sample.

[0188] At time t, one or more MS or MSn acquisitions can be acquired (e.g., by acquisition component 610), and the spectra can be used as input to a processing facility of the agent (e.g., the processing facility of NN 670). For example, an MS1 ​​spectrum or a set of MS / MS spectra spanning the precursor m / z range can be acquired.

[0189] The system (e.g., analysis component 612) can perform analysis such as cross-correlation of these spectra relative to a stored set of spectra to estimate a reference retention time (e.g., of database 640) and / or perform a search relative to an experimental or computer library. Based on the analysis, each precursor can receive a vector of k scores (e.g., by analysis component 612). The scores can correspond to acquisition metrics 810, such as probability of detection from a database search, signal strength, fit to a predicted isotope cluster, mass accuracy, predicted next signal strength, delta time between estimated current time and expected elution time, and / or whether the precursor acquisition was acquired in the last iteration. The scores can include a history of the last m values ​​of each score.

[0190] An nxk scoring matrix for each precursor (e.g., exemplary scoring matrix 800) is one way to describe the environment, and data representing such a matrix can be input into neural network 670. The output of neural network 670 and evaluation component 616 can be a set of actions A, one for each of potential precursors p with values ​​in [0, 1], with keys 850 defining which values ​​are used for the neural network action output.

[0191] Via execution component 618, one or more instruments (e.g., MS device 650) can generate spectra for each of the potential precursors, which can take a certain amount of time. In effect, this changes the state of the environment as new compounds elute from the column and enter the mass spectrometer used (e.g., MS device 650).

[0192] Based on one or more results of one or more acquisition actions 680 , a reward indicator 722 can be calculated based on the state of the system and the analysis of the acquired spectra (e.g., by reward component 620 ). Reward indicator 722 can be used to update neural network 670 by update component 622 .

[0193] The next iteration of training and / or use of the material analysis system 602 can then begin by acquiring (e.g., via acquisition component 610) a new batch of exploratory spectra (e.g., DIA spectra). Additionally or alternatively, analysis of the triggered spectra can be used to update the description of the environment for such next experiment. This can result in one or more changes to the input data set (e.g., to one or more scores 716, acquisition metrics 810, and / or operational settings).

[0194] With respect to the above, it should be noted that even with a fairly large isolation window, collecting exploratory spectra in the form of DIA can also have one or more benefits in terms of data integrity. That is, even if a higher quality acquisition is never triggered, there can be at least some information for all precursors of interest. Alternatively, the exploratory spectra can include a set of DIA spectra that vary over time, or can include narrow isolation targeted MS / MS acquisitions for compounds predicted to elute at a specific time. That is, any type of acquisition action 680 can trigger another acquisition action 680 and / or another type of acquisition action 680. In fact, in one or more embodiments, the selection of exploratory acquisition can be part of a set of actions available to the system (e.g., evaluation component 616).

[0195] The triggered spectrum can take the form of an MS2 spectrum or an MS3 spectrum. For example, the set of actions available to the system (e.g., available to the evaluation component 616) can have a length of 2n, i.e., an option to acquire an MS2 spectrum and an option to acquire an MS3 spectrum. The system (e.g., the evaluation component 616) can determine which one or more acquisition actions 680 to recommend, indicate, and / or take based on the available information and the state of the neural network 670.

[0196] Thus, a benefit of one or more embodiments described herein can be that a non-limiting system can learn not only simple operations, such as which acquisitions to acquire based on the current environment (e.g., compound environment), but that such decisions can lead to methods that consider higher level metrics of interest 668, such as acquiring spectra at the top of an LC peak and / or focusing resources on compounds that may be quantifiable across multiple experiments.

[0197] As another overview of the above components and their functions, refer to Fig.10 and Fig.11 , illustrating one or more embodiments described herein (such as Figure 6 Flowchart of an example non-limiting method 1000 of a non-limiting system 600 of the present invention that can facilitate a process for reinforcement learning-based mass spectrometer control. Figure 6 The non-limiting system 600 of FIG. 600 is used to describe the non-limiting method 1000, but the non-limiting method 1000 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0198] At 1002, non-limiting method 1000 can include acquiring, by a system operatively coupled to a processor (e.g., acquisition component 610), data (e.g., input data 660) of a compound (e.g., compound 662) that defines a first mass spectrometer spectrum (e.g., spectrum 665) of the compound.

[0199] At 1004, non-limiting method 1000 can include obtaining, by a system (e.g., metric component 614), a metric of interest for a compound (e.g., metric of interest 668), wherein a recommendation to perform a mass spectrometry action (e.g., recommended acquisition action 720) is based at least in part on the metric of interest.

[0200] At 1006, non-limiting method 1000 can include generating, by a system (e.g., evaluation component 616), a recommendation to perform a mass spectrometry action on a compound (e.g., recommended acquisition action 720) based on the data and employing a neural network (e.g., NN 670) trained on an input data set including acquisition metrics (e.g., compound metrics 664, acquisition metrics 810, and / or historical compound metrics 644) and employing an associated score (e.g., score 716) associated with the acquisition metrics.

[0201] At 1008, non-limiting method 1000 can include specifying, by the system (eg, evaluation component 616), an amount of resources or operational settings (eg, time, energy, isolation width) to use for the mass spectrometry action.

[0202] At 1010, non-limiting method 1000 can include designating, by a system (eg, evaluation component 616), a compound or fragmented compound from the first mass spectrometry spectrum as a target for a mass spectrometry action.

[0203] At 1012, non-limiting method 1000 can include specifying, by the system (e.g., evaluation component 616), a plurality of targets corresponding to compounds to be fragmented from compounds, or a plurality of targets corresponding to one or more fragmented compounds from a first mass spectrometry spectrum, and obtaining additional data for the plurality of targets by performing a mass spectrometry action.

[0204] At 1014 , non-limiting method 1000 can include instructing, based on the recommendation, the system (eg, execution component 618 ) to perform a mass spectrometry action at a mass spectrometer (eg, MS device 650 ) and obtain a mass spectrometry result (eg, compound result 682 ).

[0205] At 1016 , the non-limiting method 1000 can include generating, by the system (eg, the reward component 620 ), a reward indicator (eg, the reward indicator 722 ) resulting from the recommendation of the mass spectrometry action or from the performance of the recommended mass spectrometry action.

[0206] At 1018 , the non-limiting method 1000 can include updating, by the system (eg, updating component 622 ), one or more weights employed by the NN or adjusting an acquisition metric (eg, acquisition metric 810 ) based on the reward indicator.

[0207] At 1020 , non-limiting method 1000 can include updating, by the system (eg, updating component 622 ), the neural network based on a set of reward indicators amortized over time and obtained based on multiple generations of multiple recommendations, including recommendations to perform mass spectrometry actions.

[0208] At 1022, the non-limiting method 1000 can include determining, by the system (e.g., the evaluation component 616), whether additional recommendations should be generated to satisfy the metric of interest for the compound based on the mass spectrometry results. If yes, the non-limiting method 1000 can return to step 1006. If no, the non-limiting method 1000 can continue to end.

[0209] As another overview of the above components and their functions, refer to Fig.12 and Fig.13 , illustrating one or more embodiments described herein (such as Figure 6 Flowchart of an example non-limiting method 1200 of a non-limiting system 600 of the present invention that can facilitate a process for reinforcement learning-based mass spectrometer control. Figure 6 The non-limiting method 1200 is described with reference to the non-limiting system 600, but the non-limiting method 1200 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0210] At 1202, non-limiting method 1200 can include identifying, by a system operatively coupled to a processor (e.g., acquisition component 610), a compound (e.g., compound 662) and a second compound (e.g., another compound 662) from a mass spectrometer spectrum (e.g., spectrum 665) defined by first data (e.g., input data 660).

[0211] At 1204, non-limiting method 1200 can include comparing, by a system (e.g., analysis component 612), first data for a compound (e.g., input data 660) to an input data set comprising one or more acquisition metrics for the compound (e.g., acquisition metrics 810 and / or historical data 642) and one or more associated scores (e.g., score 716) associated with the one or more acquisition metrics.

[0212] At 1206, non-limiting method 1200 can include generating, by the system (e.g., analysis component 612), a dataset matrix (e.g., exemplary score matrix 800) for compounds based on the input dataset and including one or more association scores. In one or more embodiments, the one or more association scores can define a probability that one or more thresholds corresponding to one or more acquisition metrics will be satisfied by one or more additional mass spectrometry actions performed on the compound.

[0213] At 1208 , the non-limiting method 1200 can include generating, by the system (eg, the evaluation component 616 ), a relevance score as a number between 0 and 1.

[0214] At 1210, non-limiting method 1200 can include generating, by a system (e.g., evaluation component 616), a first recommendation (e.g., recommended acquisition action 720) to perform a mass spectrometry action (e.g., acquisition action 680) on the compound based on the comparison and based on an obtained metric of interest associated with the compound (e.g., metric of interest 668), and employing a neural network (e.g., NN670) trained on an input data set.

[0215] At 1212 , non-limiting method 1200 can include generating, by a system (eg, evaluation component 616 ), a first recommendation based on historical data (eg, historical data 642 ) defining one or more results of acquisition of a compound resulting from mass spectrometry analysis of the compound.

[0216] At 1214, non-limiting method 1200 can include correlating, by the system (eg, updating component 622), the input data set with the metric of interest prior to employing the neural network to generate the recommended mass spectrometry action.

[0217] At 1215, non-limiting method 1200 can include generating, by the system (eg, evaluation component 616 and / or neural network 670), a first recommended mass spectrometry action comprising using a mass spectrometry device to directly or indirectly obtain an acquisition of the compound.

[0218] At 1216, non-limiting method 1200 can include selecting, by the system (eg, evaluation component 616 and / or neural network 670), a first recommended mass spectrometry action that includes acquisition of fragmentation of an acquisition compound.

[0219] At 1217, non-limiting method 1200 can include selecting, by the system (e.g., evaluation component 616 and / or neural network 670), a first recommended mass spectrometry action from an action database (e.g., action database 1272) of available mass spectrometry actions that can be performed using one or more mass spectrometry devices communicatively coupled to the system.

[0220] At 1218 , non-limiting method 1200 can include generating, by the system (eg, evaluation component 616 and / or neural network 670 ), in parallel, a first recommendation and a second recommendation for a second compound (eg, second recommendation action 720 ).

[0221] At 1220, non-limiting method 1200 may include evaluating, by the system (e.g., reward component 620), the recommended mass spectrometry action, thereby generating a reward indicator (e.g., reward indicator 722) earned by the system, wherein the reward indicator is used to update one or more weights employed by the neural network.

[0222] At 1222, the non-limiting method 1200 can include determining, by the system (e.g., the evaluation component 616), whether additional recommendations should be generated to satisfy the metric of interest for the compound based on the mass spectrometry results. If yes, the non-limiting method 1200 can return to step 1206. If no, the non-limiting method 1200 can continue to end.

[0223] As another overview of the above components and their functions, refer to Fig.14 and Fig.15 , illustrating one or more embodiments described herein (such as Figure 6 Flowchart of an example non-limiting method 1400 of a non-limiting system 600 of the present invention that can facilitate a process for mass spectrometer control based on reinforcement learning. Figure 6 The non-limiting method 1400 is described with reference to the non-limiting system 600, but the non-limiting method 1400 may also be applicable to other systems described herein, such as Figure 5 For the sake of brevity, repeated descriptions of similar elements and / or processes employed in corresponding embodiments are omitted.

[0224] At 1402, non-limiting method 1400 can include generating, by a system operatively coupled to a processor (e.g., analysis component 612), an input data set (e.g., exemplary scoring matrix 800) comprising one or more acquisition metrics corresponding to a compound type (e.g., acquisition metrics 810 and / or operational settings) and one or more associated scores (e.g., scores 716) associated with the one or more acquisition metrics.

[0225] At 1404, non-limiting method 1400 can generate, by a system (e.g., analysis component 612), an input data set comprising one or more association scores comprising a probability that one or more thresholds corresponding to one or more acquisition metrics will be satisfied by a mass spectrometry action performed on the compound type.

[0226] At 1406 , non-limiting method 1400 can include training, by a system (eg, updating component 622 ), a neural network (eg, neural network 670 ) on the input data set.

[0227] At 1408, non-limiting method 1400 can include generating, by a system (e.g., evaluation component 616), a recommended mass spectrometry action (e.g., recommended acquisition action 720) for obtaining second data (e.g., compound result 682) about a compound of the compound type (e.g., compound 662) by employing a neural network to compare first data about the compound (e.g., input data 660) to an input data set associated with the compound type (e.g., exemplary scoring matrix 800).

[0228] At 1410, non-limiting method 1400 can include generating, by a system (e.g., evaluation component 616), a recommended mass spectrometry action based on a metric of interest (e.g., metric of interest 668) for a compound type, the metric of interest provided by a user entity to customize functionality of the neural network.

[0229] 1412, non-limiting method 1400 can include specifying, by a system (e.g., evaluation component 616), an amount of resources (e.g., time, energy, isolation width) for a recommended mass spectrometry action based on historical data (e.g., historical data 642) defining acquisition of compounds resulting from mass spectrometry analysis of the compound type.

[0230] At 1414, non-limiting method 1400 can include designating, by a system (e.g., evaluation component 616), a compound or fragmented compound identified from a mass spectrometry spectrum (e.g., spectrum 665) as a target for a recommended mass spectrometry action, wherein the first data defines the mass spectrometry spectrum of the compound.

[0231] At 1416, the non-limiting method 1400 can include specifying, by the system (eg, the evaluation component 616), a set of two or more targets for which second data is obtained by performing the recommended mass spectrometry action.

[0232] At 1418, non-limiting method 1400 can include generating, by the system (e.g., evaluation component 616), a recommended mass spectrometry action comprising a fragmentation acquisition, which is a mass spectrometry / mass spectrometry (MS2) acquisition for the compound or a mass spectrometry / mass spectrometry / mass spectrometry (MS3) acquisition for the compound.

[0233] At 1420, the non-limiting method 1400 can include determining, by the system (e.g., the evaluation component 616), whether additional recommended mass spectrometry actions should be generated to satisfy the metric of interest for the compound based on the second data. If yes, the non-limiting method 1400 can return to step 1408. If no, the non-limiting method 1400 can continue to end.

[0234] Additional Invention Summary

[0235] For simplicity of explanation, the computer-implemented and non-computer-implemented methods provided herein are depicted and / or described as a series of actions. It should be understood that the present invention is not limited by the illustrated actions and / or action sequences, such as actions that can occur in one or more sequences and / or occur simultaneously, and occur together with other actions that are not presented and described herein. In addition, not all illustrated actions can be used to implement the computer-implemented and non-computer-implemented methods according to the subject matter. In addition, the computer-implemented and non-computer-implemented methods can alternatively be represented as a series of interrelated states via state diagrams or events. In addition, the computer-implemented methods described hereinafter and throughout this specification can be stored in a manufactured product, so that the computer-implemented methods are transported and transferred to a computer. The term "article" as used herein is intended to encompass a computer program that can be accessed from any computer-readable device or storage medium.

[0236] This paper has (and / or will further) described the interaction between system and / or equipment about one or more components.Such system and / or component can include those parts or subcomponents specified therein, one or more of the specified parts and / or subcomponents and / or additional components.Subcomponents can be implemented as components coupled to other components in a communication manner rather than being included in the parent component.One or more components and / or subcomponents can be combined into a single component that provides aggregation functionality.These components can interact with one or more other components, and for the sake of brevity, these other components are not specifically described in this article, but known to those skilled in the art.

[0237] In summary, one or more systems, computer program products, and / or computer-implemented methods provided herein relate to neural network control of mass spectrometry processes. A system may include a memory storing a computer executable component and a processor executing the computer executable component. The computer executable component may include: an acquisition component that acquires data of a compound, the data defining a first mass spectrometry spectrum of the compound; an evaluation component that generates a recommendation for performing a mass spectrometry action on the compound based on the data and using a neural network trained on an input data set including an acquisition metric and using an association score associated with the acquisition metric; and an execution component that instructs to perform the mass spectrometry action at a mass spectrometer and obtain a mass spectrometry result based on the recommendation.

[0238] One or more embodiments disclosed herein can achieve improved performance relative to existing methods. For example, based on RL training and based on historical data on which the NN can be RL trained, a system employing a RL trained NN as described herein can employ better compound specific acquisition actions than can be pre-programmed by a management entity alone. That is, such a system can learn to recognize when resources should be expended during the current acquisition and during future recommended or future directed acquisition actions. As described above, there may be precursor environments where extended acquisition times can elute larger amounts of compounds, or there may be precursor environments where fast acquisition times may be required given the large amounts of compounds entering the mass spectrometer.

[0239] In addition, even based on different recommended acquisition actions, the embodiments described herein can also advantageously provide focusing / orientation for multiple compounds simultaneously. That is, the embodiments described herein can employ hybrid optimization techniques to achieve optimal results based on selected high-level goals (such as analytical specificity, improved compound abundance estimation accuracy, and / or improved compound detection quantity).

[0240] Furthermore, the embodiments described herein can be adapted to different types of experiments, labeling techniques, and / or mass or cross-sectional analysis techniques. That is, depending on the level of sample description or applicable high-level experimental goals, the framework described herein can be adapted to fit the sample. For example, rewards, high-level goals, available acquisition actions, etc. can be input into the system and the NN, where the NN is updated to operate within the scope of the input.

[0241] Indeed, in view of one or more embodiments described herein, a practical application of one or more systems, computer-implemented methods, and / or computer program products described herein may be the ability to efficiently maximize the output of an MS-based experiment, whether the output goal corresponds to one or more of analytical specificity, increased probability of accuracy, and / or increased number of compound detections, etc. This application may be achieved without specifically setting all acquisition metrics and / or operational settings for an MS-based experiment. Instead, these acquisition metrics and / or operational settings, in addition to one or more acquisition recommendations, may be generated by a RL-trained NN and a corresponding material analysis system, as described herein. In these ways, one or more embodiments described herein may improve existing methods of MS-based experimental control.

[0242] These are useful and practical applications of computers, thus providing enhanced (e.g., improved and / or optimized) materials analysis and compound data output (e.g., output related to one or more compounds identified). In general, such computerized tools can bring specific and significant technical improvements in the field of materials analysis, and more specifically in materials analysis using separations and / or MS.

[0243] Furthermore, based on the disclosed teachings, one or more embodiments described herein can be employed in real-world systems. For example, as described above, one or more embodiments described herein can perform MS-based experiments based on RL-based methods for control of MS-based experiments, even when acquisition metrics and / or instrument operation settings are not explicitly input into a system as described herein. Thus, the embodiments disclosed herein provide improvements to scientific instrumentation technology (e.g., improvements in computer technology to support such scientific instruments, among other improvements).

[0244] Furthermore, the apparatus and / or methods described herein may be implemented in one or more domains to achieve a scaled synthetic spectral output. Indeed, use of the system as described herein may be scalable, such as where multiple high-level objectives (e.g., metrics of interest) may be facilitated and / or where multiple compounds may be the focus of current acquisition, at least at the same time as one another.

[0245] This paper has (and / or will further) described the interaction between system and / or equipment about one or more components.Such system and / or component can include those parts or subcomponents specified therein, one or more of the specified parts and / or subcomponents and / or additional components.Subcomponents can be implemented as components coupled to other components in a communication manner rather than being included in the parent component.One or more components and / or subcomponents can be combined into a single component that provides aggregation functionality.These components can interact with one or more other components, and for the sake of brevity, these other components are not specifically described in this article, but known to those skilled in the art.

[0246] One or more embodiments described herein may be inherently and / or unavoidably related to computer technology in one or more embodiments and cannot be implemented outside of a computing environment. For example, one or more processes performed by one or more embodiments described herein may provide program and / or program instruction execution more efficiently and more feasible than existing systems and / or technologies, such as with respect to material analysis based on RL-based control. Systems, computer-implemented methods, and / or computer program products that provide the performance of these processes have an important role in the field of material analysis (such as including separation and / or MS instrumentation) and cannot be implemented in a reasonable manner outside of a computing environment as well.

[0247] One or more embodiments described herein may employ hardware and / or software to solve problems that are highly technical, non-abstract, and cannot be performed by a human through a set of mental behaviors. For example, a human or even thousands of humans cannot automatically generate recommended acquisition actions based on an RL-trained method efficiently, accurately, and / or effectively, as one or more embodiments described herein can provide for this process. Furthermore, one or more of these processes cannot be performed by a human or a person holding a pen and paper, as performed by one or more embodiments described herein.

[0248] In one or more embodiments, one or more of the processes described herein can be performed by one or more special-purpose computers (e.g., special-purpose processing units, special-purpose classical computers, special-purpose quantum computers, special-purpose hybrid classical / quantum systems, and / or another type of special-purpose computer) to perform defined tasks associated with one or more of the above-mentioned technologies. One or more embodiments described herein and / or components thereof can be used to solve new problems arising from the advancement of the above-mentioned technologies, the adoption of quantum computing systems, cloud computing systems, computer architectures, and / or other technologies.

[0249] One or more embodiments described herein may be fully operable to perform one or more other functions (eg, fully powered on, fully executed, and / or another function) while also performing one or more of the one or more operations described herein.

[0250] To provide additional inventive context, a list of embodiments and their features is provided below.

[0251] A system comprises: a memory storing computer executable components; and a processor executing the computer executable components stored in the memory, wherein the computer executable components comprise: an acquisition component acquiring data of a compound, wherein the data defines a first mass spectrometry spectrum of the compound; an evaluation component generating a recommendation to perform a mass spectrometry action on the compound based on the data, using a neural network trained on an input data set including one or more acquisition metrics, and using one or more association scores associated with the one or more acquisition metrics; and an execution component instructing, based on the recommendation, to perform the mass spectrometry action at a mass spectrometer and obtain a mass spectrometry result.

[0252] The system of the preceding paragraph, wherein the assessment component further specifies an amount of resources to be used for the mass spectrometry action.

[0253] A system according to any preceding paragraph, wherein the evaluation component further designates the compound or fragmented compound from the first mass spectrometry spectrum as a target for the mass spectrometry action.

[0254] A system according to any of the preceding paragraphs, wherein the evaluation component further specifies multiple targets corresponding to the compound, which are to be fragmented from the compound, or multiple targets corresponding to one or more fragmented compounds from the first mass spectrometry spectrum, and additional data of the multiple targets are obtained by performing the mass spectrometry action.

[0255]

[00136] A system according to any preceding paragraph, further comprising: a metric component that obtains a metric of interest for the compound, wherein the recommendation to perform the mass spectrometry action is based at least in part on the metric of interest.

[0256] According to the system described in any of the preceding paragraphs, the system also includes: a reward component, which generates a reward indicator resulting from the recommendation of the mass spectrometry action or the execution of the recommended mass spectrometry action; and an update component, which updates one or more weights adopted by the NN or performs an adjustment to the acquisition metric based on the reward indicator.

[0257] A system according to any of the preceding paragraphs, the system further comprising: an updating component that updates the neural network based on a set of reward indicators amortized over time and obtained by the evaluation component based on multiple generations of recommendations, the multiple generations of recommendations including the recommendation to perform the mass spectrometry action.

[0258] A computer-implemented method, the computer-implemented method comprising: comparing, by a system operatively coupled to a processor, first data of a compound with an input data set, the input data set comprising one or more acquisition metrics of the compound and one or more association scores associated with the one or more acquisition metrics; and based on the comparison, and based on the obtained metrics of interest associated with the compound, and using a neural network trained on the input data set, generating, by the system, a first recommendation to perform a recommended mass spectrometry action on the compound, wherein the recommended mass spectrometry action comprises using a mass spectrometry device to obtain an acquisition of the compound.

[0259] According to the computer-implemented method described in the previous paragraph, the computer-implemented method also includes: identifying, by the system, the compound and a second compound from a mass spectrometry spectrum defined by the first data; and generating, by the system in parallel, the first recommendation and a second recommendation for the second compound.

[0260] A computer-implemented method according to any preceding paragraph, wherein the first recommendation is based on historical data defining one or more results of acquisition of the compound resulting from mass spectrometry or other separation analysis of the compound.

[0261] According to the computer-implemented method described in any of the preceding paragraphs, the computer-implemented method also includes: generating, by the system, a dataset matrix for the compound, the dataset matrix being based on the input dataset and including the one or more association scores, wherein the one or more association scores define the probability that one or more thresholds corresponding to the one or more acquisition metrics will be satisfied by one or more additional mass spectrometry actions performed on the compound.

[0262] According to the computer-implemented method described in any of the preceding paragraphs, the computer-implemented method also includes: evaluating the recommended mass spectrometry action by the system to generate a reward indicator obtained by the system, wherein the reward indicator is used to update one or more weights used by the neural network.

[0263] A computer-implemented method as described in any preceding paragraph, further comprising generating, by the system, a relevance score as a number between 0 and 1.

[0264] The computer-implemented method of any preceding paragraph, further comprising: correlating, by the system, the input data set with the metric of interest before employing the neural network to generate the recommended mass spectrometry action.

[0265] The computer-implemented method of any preceding paragraph, further comprising: selecting, by the system, a recommended mass spectrometry action, the recommended mass spectrometry action comprising acquiring a fragmentation acquisition of the compound.

[0266] According to the computer-implemented method described in any of the preceding paragraphs, the computer-implemented method also includes: the system selects the recommended mass spectrometry action of the neural network from an action database of available mass spectrometry actions, and the available mass spectrometry actions can be performed using one or more mass spectrometry devices communicatively coupled to the system.

[0267] A computer program product that facilitates a process for reinforcement learning-based mass spectrometry control, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, and the program instructions being executable by a processor to cause the processor to: generate, by the processor, an input data set comprising one or more acquisition metrics corresponding to a compound type and one or more association scores associated with the one or more acquisition metrics, wherein the one or more association scores define a probability that one or more thresholds corresponding to the one or more acquisition metrics are satisfied by a mass spectrometry action performed on the compound type; train, by the processor, a neural network on the input data set; and generate, by the processor, a recommended mass spectrometry action for obtaining second data about a compound by comparing, by employing the neural network, first data about the compound with the input data set associated with the compound type.

[0268] The computer program product of the preceding paragraph, wherein said generating of said recommended mass spectrometry actions is further based on a metric of interest for said compound type, said metric of interest being provided by a user entity to customize functionality of said neural network.

[0269] A computer program product according to any preceding paragraph, wherein the generating of the recommended mass spectrometry action further comprises specifying, by the processor, an amount of resources for the recommended mass spectrometry action based on historical data defining acquisition of the compound resulting from mass spectrometry analysis of the compound type.

[0270] A computer program product according to any preceding paragraph, wherein the first data defines a mass spectrometry spectrum for the compound, and wherein the generation of the recommended mass spectrometry action also includes designating, by the processor, the compound or fragmented compound from the mass spectrometry identification as a target for the recommended mass spectrometry action.

[0271] A computer program product according to any preceding paragraph, wherein said generating of said recommended mass spectrometry actions further comprises: specifying, by said system, a set of two or more targets for which said second data is obtained by executing said recommended mass spectrometry actions.

[0272] A computer program product according to any preceding paragraph, wherein the recommended mass spectrometry action comprises fragmentation acquisition, wherein the fragmentation acquisition is a mass spectrum / mass spectrum (MS2) acquisition for the compound or a mass spectrum / mass spectrum / mass spectrum (MS3) acquisition for the compound.

[0273] Scientific Instrument System Description

[0274] Next turn Fig.16 , provides a Figures 1 to 15 A detailed description of additional context for one or more embodiments described in . One or more computing devices implementing any of the scientific instrument modules or methods disclosed herein may be part of a scientific instrument system. Fig.16 A block diagram of an example scientific instrument system 1600 is illustrated, in accordance with various embodiments described herein, in which one or more of the scientific instrument methods or other methods disclosed herein may be performed. The scientific instrument modules and methods disclosed herein (e.g., Figure 1 The scientific instrument module 100 and Figure 2 The method 200) may be implemented by one or more of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 of the scientific instrument system 1600.

[0275] Any of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 may include any of the foregoing references. Figure 4 Any of the embodiments of computing device 400 discussed herein, and any of scientific instrument 1610, user local computing device 1620, service local computing device 1630, and / or remote computing device 1640 may employ any of the embodiments described herein. Figure 4 Any suitable form of one or more of the embodiments of computing device 400 discussed.

[0276] One or more of the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 may include a processing device 1602, a storage device 1604, and / or an interface device 1606. The processing device 1602 may take any suitable form, including reference Figure 4 The processor 402 discussed herein may be in the form of any processor. The processing device 1602 in different devices including the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630 and / or the remote computing device 1640 may take the same form or different forms. The storage device 1604 may take any suitable form, including reference Figure 4 The storage device 1604 may be in the form of any storage device discussed herein. The storage devices 1604 in different devices included in the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and / or the remote computing device 1640 may take the same form or different forms. The interface device 1606 may take any suitable form, including reference Figure 4 Any form of storage device in interface device 406 discussed herein. Interface devices 1606 in different devices included in scientific instrument 1610, user local computing device 1620, service local computing device 1630, and / or remote computing device 1640 may take the same form or different forms.

[0277] The scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and the remote computing device 1640 can communicate with other elements of the scientific instrument system 1600 via a communication path 1608. The communication path 1608 can communicatively couple the interface device 1606 of different elements of the elements of the scientific instrument system 1600, as shown, and can be a wired or wireless communication path (e.g., according to the present reference Figure 4 The interface device 406 of the computing device 400 may be any of the communication technologies discussed above). Fig.16The particular scientific instrument system 1600 depicted in FIG. 1 includes a communication path between each pair of devices in the scientific instrument 1610, the user local computing device 1620, the service local computing device 1630, and the remote computing device 1640, but such a “fully connected” implementation is merely illustrative, and in various embodiments, various communication paths in the communication paths 1608 may be omitted. For example, in one or more embodiments, the service local computing device 1630 may omit a direct communication path 1608 between its interface device 1606 and the interface device 1606 of the scientific instrument 1610, and may instead communicate with the scientific instrument 1610 via the communication path 1608 between the service local computing device 1630 and the user local computing device 1620 and / or the communication path 1608 between the user local computing device 1620 and the scientific instrument 1610.

[0278] Scientific instrument 1610 may include any suitable scientific instrument, such as a separation or MS instrument, or other instrument that facilitates analysis of materials.

[0279] The user-local computing device 1620 can be a computing device local to a user of the scientific instrument 1610 (e.g., according to any of the embodiments of the computing device 400 discussed herein). In one or more embodiments, the user-local computing device 1620 can also be local to the scientific instrument 1610, but this is not necessarily the case; for example, a user-local computing device 1620 associated with a home, office, or other building associated with a user entity can be remote from the scientific instrument 1610, but in communication with it so that the user entity can use the user-local computing device 1620 to control and / or access data from the scientific instrument 1610. In one or more embodiments, the user-local computing device 1620 can be a laptop, smartphone, or tablet device. In one or more embodiments, the user-local computing device 1620 can be a portable computing device. In one or more embodiments, the user-local computing device 1620 can be deployed in the field.

[0280] The service local computing device 1630 can be a local computing device of an entity that services the scientific instrument 1610 (e.g., according to any of the embodiments of the computing device 400 discussed herein). For example, the service local computing device 1630 can be a local device of the manufacturer of the scientific instrument 1610 or a third-party service company. In one or more embodiments, the service local computing device 1630 can communicate with the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (e.g., via a direct communication path 1608 or via multiple "indirect" communication paths 1608, as discussed above) to receive data about the operation of the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (e.g., self-test results of the scientific instrument 1610, calibration coefficients used by the scientific instrument 1610, measurements of sensors associated with the scientific instrument 1610, etc.). In one or more embodiments, the service local computing device 1630 can communicate with the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (e.g., via a direct communication path 1608 or via multiple "indirect" communication paths 1608, as described above) to send data to the scientific instrument 1610, the user local computing device 1620, and / or the remote computing device 1640 (e.g., to update programming instructions (such as firmware) in the scientific instrument 1610 to initiate the execution of a test or calibration sequence in the scientific instrument 1610, to update programming instructions (such as software) in the user local computing device 1620 or the remote computing device 1640, etc.). A user entity of the scientific instrument 1610 can communicate with the service local computing device 1630 using the scientific instrument 1610 or the user local computing device 1620 to report a problem with the scientific instrument 1610 or the user local computing device 1620, to request a technician visit to improve the operation of the scientific instrument 1610, to order consumables or replacement parts associated with the scientific instrument 1610, or for other purposes.

[0281] Remote computing device 1640 can be a computing device remote from scientific instrument 1610 and / or user local computing device 1620 (e.g., according to any of the embodiments of computing device 400 discussed herein). In one or more embodiments, remote computing device 1640 can be included in a data center or other large-scale server environment. In one or more embodiments, remote computing device 1640 can include network attached storage (e.g., as part of storage device 1604). Remote computing device 1640 can store data generated by scientific instrument 1610, perform analysis of data generated by scientific instrument 1610 (e.g., according to programmed instructions), facilitate communication between user local computing device 1620 and scientific instrument 1610, and / or facilitate communication between service local computing device 1630 and scientific instrument 1610.

[0282] In one or more embodiments, the Fig.16 One or more of the elements of the scientific instrument system 1600 illustrated in FIG. In addition, in one or more embodiments, there may be Fig.16 1600. For example, the scientific instrument system 1600 may include multiple user-local computing devices 1620 (e.g., different user-local computing devices 1620 associated with different user entities or located in different locations). In another example, the scientific instrument system 1600 may include multiple scientific instruments 1610, all of which communicate with the service local computing device 1630 and / or the remote computing device 1640; in such embodiments, the service local computing device 1630 may monitor these multiple scientific instruments 1610, and the service local computing device 1630 may cause updates or other information to be "broadcasted" to the multiple scientific instruments 1610 simultaneously. The different scientific instruments 1610 in the scientific instrument system 1600 may be close to each other (e.g., in the same room) or far away from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In one or more embodiments, the scientific instrument 1610 can be connected to an Internet of Things (IoT) stack that allows command and control of the scientific instrument 1610 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications can be accessed by a user entity operating a user-local computing device 1620 that communicates with the scientific instrument 1610 through an intermediary remote computing device 1640. In one or more embodiments, the scientific instrument 1610 can be sold by a manufacturer along with one or more associated user-local computing devices 1620 as part of a local scientific instrument computing unit 1612.

[0283] In one or more embodiments, the different scientific instruments 1610 included in the scientific instrument system 1600 can be different types of scientific instruments 1610; for example, one scientific instrument 1610 can be an EDS device, while another scientific instrument 1610 can be an analysis device that analyzes the results of the EDS device. In some such embodiments, the remote computing device 1640 and / or the user's local computing device 1620 can combine data from different types of scientific instruments 1610 included in the scientific instrument system 1600.

[0284] Example operating environment

[0285] Fig.1717 is a schematic block diagram of an operating environment 1700 with which the subject matter can interact. The operating environment 1700 includes one or more remote components 1710. The remote component 1710 can be hardware and / or software (e.g., a thread, a process, a computing device). In one or more embodiments, the remote component 1710 can be a distributed computer system connected to a local automatic extension component and / or a program using the resources of the distributed computer system via a communication framework 1740. The communication framework 1740 can include wired network devices, wireless network devices, mobile devices, wearable devices, radio access network devices, gateway devices, femtocell devices, servers, etc.

[0286] The operating environment 1700 also includes one or more local components 1720. The local components 1720 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the local components 1720 can include an automatic scaling component and / or a program that connects to a remote distributed computing system via a communication framework 1740 to communicate / use remote resources 1710 and 1720, etc.

[0287] One possible communication between the remote component 1710 and the local component 1720 may take the form of a data packet suitable for sending between two or more computer processes. Another possible communication between the remote component 1710 and the local component 1720 may take the form of circuit-switched data suitable for sending between two or more computer processes in a radio time slot. The operating environment 1700 includes a communication framework 1740, which can be used to facilitate communication between the remote component 1710 and the local component 1720, and may include an air interface, such as a UMTS network interface via an LTE network, etc. The remote component 1710 may be operably connected to one or more remote data repositories 1750, such as a hard drive, a solid-state drive, a subscriber identity module (SIM) card, an electronic SIM (eSIM), a device memory, etc., which may be used to store information on the remote component 1710 side of the communication framework 1740. Similarly, the local component 1720 may be operably connected to one or more local data repositories 1730, which may be used to store information on the local component 1720 side of the communication framework 1740.

[0288] Sample computing environment

[0289] To provide additional context for the various embodiments described herein, Fig.18The following discussion is intended to provide a brief, general description of a suitable computing environment 1800 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 and / or as a combination of hardware and software.

[0290] Generally, program modules include routines, programs, components, data structures, etc. that perform tasks or implement abstract data types. In addition, these methods 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 operatively coupled to one or more associated devices.

[0291] The embodiments illustrated 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.

[0292] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, the two terms being used differently in this article 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).

[0293] 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 tapes, magnetic disk storage devices or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that may be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage devices, memories, or computer-readable media herein exclude only the propagation of transient signals themselves as a modifier, and do not disclaim all rights to standard storage devices, memories, or computer-readable media that are not merely propagation of transient signals themselves.

[0294] 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.

[0295] 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 and not limitation, communication media includes wired media (such as a wired network or direct-wired connection) and wireless media (such as acoustic, RF, infrared and other wireless media).

[0296] Still refer to Fig.18 , an example computing environment 1800 in which one or more embodiments described herein may be implemented includes a computer 1802, which includes a processing unit 1804, a system memory 1806, and a system bus 1808. The system bus 1808 couples system components including, but not limited to, the system memory 1806 to the processing unit 1804. The processing unit 1804 may be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures may also be used as the processing unit 1804.

[0297] The system bus 1808 may be any of several types of bus structures and may further interconnect with a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1806 includes ROM 1810 and RAM 1812. A basic input / output system (BIOS) may be stored in a nonvolatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, wherein the BIOS contains the basic routines that help transfer information between elements within the computer 1802, such as during startup. The RAM 1812 may also include high-speed RAM, such as static RAM for caching data.

[0298] The computer 1802 also includes an internal hard disk drive (HDD) 1814 (e.g., EIDE, SATA), and may include one or more external storage devices 1816 (e.g., magnetic floppy disk drive (FDD) 1816, memory stick or flash drive reader, memory card reader, etc.). Although the internal HDD 1814 is illustrated as being located within the computer 1802, the internal HDD 1814 may also be configured for external use in a suitable chassis (not shown). In addition, although not shown in the computing environment 1800, a solid state drive (SSD) may be used in addition to or in place of the HDD 1814.

[0299] Other internal or external storage devices may include at least one other storage device 1820 having a storage medium 1822 (e.g., a solid-state storage device, a non-volatile memory device, and / or an optical drive that can read or write from a removable medium (such as a CD-ROM disk, a DVD, a BD, etc.). The external storage device 1816 can be facilitated by a network virtual machine. The HDD 1814, the external storage device 1816, and the storage device (e.g., drive) 1820 can be connected to the system bus 1808 via a HDD interface 1824, an external storage interface 1826, and a drive interface 1828, respectively.

[0300] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. For the computer 1802, 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 the corresponding type of storage device, other types of storage media that can be read by a computer (whether currently existing or developed in the future) 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.

[0301] A number of program modules may be stored in the drives and RAM 1812, including an operating system 1830, one or more application programs 1832, other program modules 1834, and program data 1836. All or portions of the operating system, application programs, modules, and / or data may also be cached in RAM 1812. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0302] Computer 1802 may optionally include emulation technology. For example, a virtual machine hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 1830, and the emulated hardware may optionally be different from the hardware environment of the operating system 1830. Fig.18 The illustrated hardware. In such embodiments, operating system 1830 may include one of a plurality of virtual machines (VMs) hosted at computer 1802. In addition, operating system 1830 may provide a runtime environment, such as a Java runtime environment or a .NET framework, for application 1832. A runtime environment is a consistent execution environment that allows application 1832 to run on any operating system that includes a runtime environment. Similarly, operating system 1830 may support containers, and application 1832 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.

[0303] In addition, the computer 1802 can 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 can be performed at any layer in the code execution stack of the computer 1802, for example, applied to the application execution level or the operating system (OS) kernel level, thereby achieving security of code execution at any level.

[0304] A user entity may enter commands and information into the computer 1802 through one or more wired / wireless input devices (e.g., a keyboard 1838, a touch screen 1840, and a pointing device such as a mouse 1842). 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 and / or a 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 1804 through an input device interface 1844, which may be coupled to the system bus 1808, 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.

[0305] A monitor 1846 or other type of display device may also be connected to the system bus 1808 via an interface, such as a video adapter 1848. In addition to the monitor 1846, computers typically include other peripheral output devices (not shown), such as speakers, printers, and the like.

[0306] The computer 1802 can operate in a network environment, using logical connections to one or more remote computers, such as a remote computer 1850, via wired and / or wireless communications. The remote computer 1850 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 1802, but only a memory / storage device 1852 is illustrated for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 1854 and / or a larger network, such as a wide area network (WAN) 1856. 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).

[0307] When used in a LAN networking environment, the computer 1802 can be connected to the local network 1854 through a wired and / or wireless communication network interface or adapter 1858. The adapter 1858 can facilitate wired or wireless communication with the LAN 1854, which can also include a wireless access point (AP) provided thereon for communicating with the adapter 1858 in a wireless mode.

[0308] When used in a WAN networking environment, the computer 1802 may include a modem 1860 or may be connected to a communication server on the WAN 1856 via other means for establishing communications over the WAN 1856 (such as over the Internet). The modem 1860 may be connected to the system bus 1808 via the input device interface 1844, which may be internal or external to a wired or wireless device, and may be a wired or wireless device. In a networked environment, program modules depicted relative to the computer 1802 or portions thereof may be stored in a remote memory / storage device 1852. The network connections shown are examples only, and other means of establishing a communications link between the computers may be used.

[0309] When used in a LAN or WAN networking environment, the computer 1802 can access a cloud storage system or other network-based storage system in addition to or as an alternative to the external storage device 1816 described above. Typically, the connection between the computer 1802 and the cloud storage system can be established through the LAN 1854 or WAN 1856, for example, by an adapter 1858 or a modem 1860, respectively. When the computer 1802 is connected to the associated cloud storage system, the external storage interface 1826 can manage the storage provided by the cloud storage system with the help of the adapter 1858 and / or the modem 1860, just as it manages other types of external storage. For example, the external storage interface 1826 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1802.

[0310] Computer 1802 may be operable to communicate with any wireless device or entity operatively disposed in wireless communication (e.g., printers, scanners, desktop and / or portable computers, portable data assistants, communication satellites, any equipment or location associated with a wirelessly detectable tag (e.g., kiosks, newsstands, store shelves, etc.), and telephones). This may include Wireless Fidelity (Wi-Fi) and Wireless technology. Therefore, the communication can be a defined structure like existing networks, or just an ad hoc communication between at least two devices.

[0311] Additional Information

[0312] The embodiments described herein may be directed to one or more of the systems, methods, devices and / or computer program products at any possible level of technical detail integration. A computer program product may include a computer-readable storage medium (or multiple media) having a computer-readable program instruction thereon, which is used to cause a processor to perform various aspects of one or more embodiments described herein. A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device and / 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: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punch card or a raised structure in a groove with instructions recorded thereon) and / or any suitable combination of the above. Computer-readable storage media as used herein should not be interpreted as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (for example, light pulses transmitted through fiber optic cables), and / or electrical signals sent through wires.

[0313] Computer-readable program instructions as described herein can be downloaded from computer-readable storage media to corresponding computing / processing equipment and / or downloaded to external computers or external storage devices via a network (e.g., the Internet, local area network, wide area network and / or wireless network). The network can include copper transmission cables, optical transmission optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and / 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 one or more embodiments described herein can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcodes, firmware instructions, state setting data, configuration data for integrated circuits and / or source code and / or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc.) and / or process programming languages ​​(such as "C" programming languages ​​and / or similar programming languages). Computer readable program instructions can be executed entirely on a computer, partially on a computer, executed as an independent software package, partially on a computer and / or partially on a remote computer or entirely on a remote computer and / or server. In the latter case, the remote computer can be connected to the computer and / or can be connected to an external computer (for example, using an Internet service provider through the Internet) through any type of network (including a local area network (LAN) and / or a wide area network (WAN)). In one or more embodiments, an electronic circuit (including, for example, a programmable logic circuit, a field programmable gate array (FPGA) and / or a programmable logic array (PLA)) can execute a computer readable program instruction to personalize the electronic circuit by utilizing the state information of the computer readable program instruction, so as to perform the various aspects of one or more embodiments described herein.

[0314] Reference is made to the flowchart illustrations and / or block diagrams of the methods, devices (systems) and computer program products according to one or more embodiments described herein to describe various aspects of one or more embodiments described herein. It should be understood that each frame in the flowchart illustration and / or block diagram and the combination of frames in the flowchart illustration and / 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 and / 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 can create a device for implementing the function / action specified in one or more frames of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can indicate that a computer, a programmable data processing device and / or other equipment operate in a particular manner, and a computer-readable storage medium storing instructions can include a manufactured product, which includes instructions for various aspects of the function / action specified in one or more frames of the flowchart and / or block diagram. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing devices, and / or other devices to cause a series of operational actions to be performed on the computer, other programmable devices, and / or other devices, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable devices, and / or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0315] The flow chart and block diagram in the figure show the possible architecture, function and / or operation of the system, computer-implemented method and / or computer program product according to one or more embodiments described herein. In this regard, each frame in the flow chart or block diagram can represent a module, segment and / or partial instruction, which includes one or more executable instructions for realizing the specified logical function. In one or more alternative embodiments, the function marked in the frame may not occur in the order marked in the figure. For example, two frames displayed continuously can be executed substantially at the same time, and / or can sometimes be executed in reverse order, depending on the function involved. It should also be noted that each frame in the block diagram and / or flow chart illustration, and / or the combination of frames in the block diagram and / or flow chart illustration can be realized by a dedicated hardware system, which can perform specified functions and / or actions, and / or perform one or more combinations of dedicated hardware and / or computer instructions.

[0316] Although the subject matter has been described above in the general context of computer executable instructions of computer program products running on computers and / or computers, it will be appreciated by those skilled in the art that one or more embodiments herein may also be implemented at least in part in parallel with one or more other program modules. In general, program modules include routines, programs, components and / or data structures that perform specific tasks and / or implement specific abstract data types. In addition, the aforementioned computer-implemented methods may be practiced with other computer system configurations, including single-processor and / or multi-processor computer systems, small computing devices, large computers, and computers, handheld computing devices (e.g., PDAs, phones) and / or based on microprocessors or programmable consumer and / or industrial electronic devices. The illustrated aspects may also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected via a communication network. However, one or more (if not all) aspects of one or more embodiments described herein may be practiced on a stand-alone computer. In a distributed computing environment, program modules may be located in both local memory storage devices and remote memory storage devices.

[0317] As used in this application, the terms "component", "system", "platform" and / or "interface" may refer to and / or may include computer-related entities or entities related to an operating machine with one or more specific functions. The entities described 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, a processor, an object, an executable program, a thread of execution, a program, and / or a computer running on a processor. As an example, both an application program and a server running on a server may be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In another example, corresponding components may be executed from various computer-readable media having various data structures stored thereon. These components may communicate via local and / or remote processes, such as according to a signal with one or more data packets (e.g., data from a component may interact with another component in a local system, a distributed system via a signal and / or interact with other systems across a network such as the Internet). As another example, a component may be a device having a specific functionality provided by a mechanical component operated by an electrical or electronic circuit, which is operated by software and / or firmware applications executed by a processor. In such cases, the processor may be internal and / or external to the device and may execute at least a portion of a software and / or firmware application. As yet another example, a component may be a device that provides a particular functionality through electronic components without mechanical components, where the electronic components may include a processor and / or other devices for executing software and / or firmware that at least partially imparts the functionality to the electronic components. In one aspect, the component may emulate the electronic component via, for example, a virtual machine within a cloud computing system.

[0318] 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 employs 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 employs A or B" is satisfied under any of the foregoing examples. In addition, unless otherwise specified or clear from the context to refer to a singular form, the articles "a" and "an" as used in this specification and the drawings should generally be interpreted to mean "one or more". As used herein, the terms "example" and / or "exemplary" are used to mean as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited to such examples. In addition, any aspect or design described herein as "example" and / 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.

[0319] As used in this specification, the term "processor" may refer to substantially any computational processing unit and / 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 / or 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, and / 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 / or gates based on molecules and quantum dots, in order to optimize space usage and / or enhance the performance of related equipment. The processor may be implemented as a combination of computational processing units.

[0320] In this document, 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 that includes a memory. The memory and / or memory components described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. By way of example and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, which may, for example, act as external cache memory. By way of illustration and not limitation, RAM may take 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 / or Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer-implemented methods described herein are intended to include, but are not limited to, these and / or any other suitable types of memory.

[0321] The foregoing includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components and / or computer-implemented methods for the purpose of describing one or more embodiments, but one of ordinary skill in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. In addition, with respect to the use of the terms "including," "having," "having," and the like in the detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive in a manner similar to the way the term "comprising" is interpreted when used as a transitional word in a claim.

[0322] The description of various embodiments may use the phrases "embodiment," "various embodiments," "one or more embodiments," and / or "some embodiments," each of which may refer to one or more of the same or different embodiments.

[0323] Descriptions of various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the embodiments described. The terms used herein are selected to best explain the principles of the embodiments, practical applications, and / or technical improvements over technologies on the market, and / or to enable others of ordinary skill in the art to understand the embodiments described herein.

Claims

1. A system, comprising: a memory storing computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components include: A collecting component, wherein the collecting component collects data of the compound, wherein the data defines a first mass spectrum of the compound; an evaluation component that generates a recommendation to perform a mass spectrometry action on the compound based on the data and employing a neural network trained on an input data set including acquisition metrics and employing a relevance score associated with the acquisition metrics; and An execution component is configured to instruct, based on the recommendation, to execute the mass spectrometry action at a mass spectrometer and obtain a mass spectrometry result.

2. The system of claim 1, wherein the assessment component further specifies an amount of resources to be used for the mass spectrometry action.

3. The system of claim 1, wherein the evaluation component further designates the compound or fragmented compound from the first mass spectrometry spectrum as a target for the mass spectrometry action.

4. A system according to claim 1, wherein the evaluation component further specifies multiple targets corresponding to the compound, which are to be fragmented from the compound, or multiple targets corresponding to one or more fragmented compounds from the first mass spectrometry spectrum, and additional data of the multiple targets are obtained by performing the mass spectrometry action.

5. The system according to claim 1, further comprising: A metric component that obtains a metric of interest for the compound, wherein the recommendation to perform the mass spectrometry action is based at least in part on the metric of interest.

6. The system according to claim 1, further comprising: a reward component that generates a reward indicator resulting from the recommendation of the mass spectrometry action or from the performance of the recommended mass spectrometry action; and An updating component that updates one or more weights employed by the NN or performs an adjustment to the acquisition metric based on the reward indicator.

7. The system according to claim 1, further comprising: An updating component that updates the neural network based on a set of reward indicators amortized over time and obtained by the evaluation component based on multiple generations of recommendations, the multiple generations of recommendations including the recommendation to perform the mass spectrometry action.

8. A computer-implemented method, the computer-implemented method comprising: comparing, by a system operatively coupled to a processor, first data for a compound to an input data set including one or more acquisition metrics for the compound and one or more relevance scores associated with the one or more acquisition metrics; as well as generating, by the system, a first recommendation to perform a recommended mass spectrometry action on the compound based on the comparison and based on the obtained metric of interest associated with the compound and using a neural network trained on the input data set, The recommended mass spectrometry action includes using a mass spectrometry device to obtain a sample of the compound.

9. The computer-implemented method of claim 8, further comprising: identifying, by the system, the compound and a second compound from a mass spectrometric spectrum defined by the first data; as well as The first recommendation and a second recommendation for the second compound are generated in parallel by the system.

10. The computer-implemented method of claim 8, wherein the first recommendation is based on historical data defining one or more results of acquisition of the compound resulting from mass spectrometry or other separation analysis of the compound.

11. The computer-implemented method of claim 8, further comprising: A dataset matrix for the compound is generated by the system, the dataset matrix being based on the input dataset and including the one or more association scores, wherein the one or more association scores define a probability that one or more thresholds corresponding to the one or more acquisition metrics will be satisfied by one or more additional mass spectrometry actions performed on the compound.

12. The computer-implemented method of claim 8, further comprising: The recommended mass spectrometry actions are evaluated by the system to generate a reward metric earned by the system, wherein the reward metric is used to update one or more weights employed by the neural network.

13. The computer-implemented method of claim 8, further comprising: A relevance score is generated by the system as a number between 0 and 1.

14. The computer-implemented method of claim 8, further comprising: Prior to employing the neural network to generate the recommended mass spectrometry action, the input data set is correlated by the system with the metric of interest.

15. The computer-implemented method of claim 8, further comprising: A recommended mass spectrometry action is selected by the system, the recommended mass spectrometry action comprising acquiring a fragmentation acquisition of the compound.

16. The computer-implemented method of claim 8, further comprising: The recommended mass spectrometry action of the neural network is selected by the system from an action database of available mass spectrometry actions executable using one or more mass spectrometry devices communicatively coupled to the system.

17. A computer program product facilitating a process for reinforcement learning based mass spectrometry control, the computer program product comprising a computer readable storage medium having program instructions embodied therewith and executable by a processor to cause the processor to: generating, by the processor, an input data set comprising one or more acquisition metrics corresponding to a compound type and one or more association scores associated with the one or more acquisition metrics, wherein the one or more relevance scores define a probability that one or more thresholds corresponding to the one or more acquisition metrics will be satisfied by a mass spectrometry action performed on the compound type; training, by the processor, a neural network on the input data set; as well as A recommended mass spectrometry action for obtaining second data about a compound is generated by the processor by employing the neural network to compare the first data about the compound to the input data set associated with the compound type.

18. The computer program product of claim 15, wherein the generation of the recommended mass spectrometry actions is further based on a metric of interest for the compound type, the metric of interest provided by a user entity to customize the functionality of the neural network.

19. The computer program product of claim 15, wherein the generation of the recommended mass spectrometry action further comprises specifying, by the processor, an amount of resources to be used for the recommended mass spectrometry action based on historical data defining acquisition of the compound resulting from mass spectrometry analysis of the compound type.

20. The computer program product of claim 15, wherein the recommended mass spectrometry action comprises fragmentation acquisition, wherein the fragmentation acquisition is a mass spectrum / mass spectrum (MS2) acquisition for the compound or a mass spectrum / mass spectrum / mass spectrum (MS3) acquisition for the compound.

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