Scientific experimental method creation

Abstract

The present invention provides methods and systems for scientific method creation. A method includes: establishing a data storage device including multiple linked data sets, the multiple linked data sets linking predetermined experimental methods, predetermined sample types, and predetermined analyses that define multiple experimental parameters; using the multiple linked data sets stored in the data storage device to train a model; inputting at least one of the group consisting of sample type and desired analysis to the trained model; and outputting a list of one or more recommended methods from the model based on the sample and the at least one of the desired analysis. The method may also include: receiving actual experimental results collected via one or more scientific instructions, which are associated with the execution of methods from the list; determining expected results of the executed methods; and verifying these actual experimental results based on the expected results.

Description

[0001] Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 588,067, filed on October 5, 2023, the entire contents of which are hereby incorporated by reference. Background Art

[0003] A scientific instrument may include a complex arrangement of movable parts, sensors, input and output ports, energy sources, and consumable parts. A laboratory system may include many instruments and many individuals that interact with different instruments. A laboratory system may support one or more scientific experiments based on the selection of one or more experimental parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The embodiments will be easily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, the same reference numerals designate the same structural elements. The embodiments are illustrated in the various figures of the accompanying drawings by way of example and not by way of limitation.

[0005] Figure 1 is a block diagram of an example scientific instrument support system in accordance with various embodiments.

[0006] Figure 2 is a block diagram of an example scientific instrument support module for performing laboratory management operations according to various embodiments.

[0007] Figure 3 is a diagram showing various embodiments including Figure 2 A block diagram of the functionality of the method creator logic in the support module.

[0008] Figure 4 is an example graphical user interface ("GUI") provided as part of the laboratory management methods disclosed herein, according to various embodiments.

[0009] Figure 5 is a block diagram of an example computing device that may perform some or all of the scientific instrument support methods disclosed herein, according to various embodiments.

[0010] Figure 6 is a block diagram of an example scientific instrument support system in which some or all of the scientific instrument support methods disclosed herein may be performed, according to various embodiments.

[0011] Figures 7 and 8 is a flow diagram of an example method of performing support operations according to various embodiments.

[0012] [TP386058USPRV1]

[0013] Fig. 9 is a schematic representation of various embodiments according to Figure 7 and Figure 8 Block diagram of the method. DETAILED DESCRIPTION

[0014] Scientific instrument support systems and related methods, computing devices, and computer-readable media are disclosed herein. For example, in some embodiments, a support system is provided to establish a cloud-based platform for one or more scientific instruments. The platform can identify what scientific instruments are connected, determine the availability of such instruments, manage the schedule of such instructions (through a predetermined system), track the projects used with these instruments, including, for example, the methods and associated results applied by each instrument for a specific type of sample, etc. For example, the platform can track and receive project information associated with a scientific instrument group (including one or more instruments) connected to the platform, including sample information, methods (experimental parameters) applied by these scientific instrument groups, and results of such methods (e.g., project results, timing information, etc.), including data collected in raw form, processed form, or a combination thereof via a scientific instrument group. For example, a networked instrument platform may include one or more data storage devices storing methods and associated information associated with networked instruments or interact with the one or more data storage devices.

[0015] Given such a connected network and data tracking and collection, the data can be used as described herein to create a method for analyzing a sample by one or more analytical instruments. For example, using machine learning techniques, the data collected within the network platform can be used to train a model to (1) recommend a method based on, for example, sample type, desired analysis (e.g., one or more compounds to be identified and / or quantified in the sample, characteristics of the sample, etc.), or a combination thereof (e.g., defining one or more experimental parameters of an experiment performed via one or more analytical instruments), and in some embodiments, (2) provide an evaluation of the resulting data relative to the predicted data to efficiently evaluate whether the resulting data is correct and further train the model and improve subsequent method recommendations. The embodiments described herein can provide one or more user interfaces as part of method creation. The user interface can provide a chat function, in which the user enters the sample type, the desired analysis (e.g., one or more compounds of interest), or a combination thereof, and a response (e.g., a list of one or more methods) is provided to the user and prompts for further selection or input to narrow down the initial recommendation provided via the chat interaction or filter options from the initial recommendation. In some embodiments, a model can be trained using linked datasets, where each dataset can associate a particular method (e.g., a set of experimental parameters) with a particular sample and analysis (e.g., one or more compounds of interest), where the model learns to recommend a method for a particular sample and / or desired analysis and also optionally predicts a method that can be used to evaluate a product produced via the method.

[0016] Feedback from users on the recommendations and feedback from the results can also be provided to the model as further training data.

[0017] The connected network can also be used to filter the methods recommended to the user to limit the recommendations to those methods that are used with instruments that are available (e.g., present in the laboratory, available for current use (e.g., based on predetermined data, consumable inventory, etc.), etc.). Similarly, in some embodiments, the network can be configured to provide one or more recommended methods that use available instruments, one or more recommended methods that use one or more recommended instruments (which may not be available in the relevant laboratory), or a combination thereof, and the user can specify (by setting one or more optimization settings or parameters, which are configured through, for example, one or more user interfaces). Laboratory managers can use recommended methods that are not optimal for available laboratory resources to make purchasing decisions for the laboratory (e.g., purchase instruments that are commonly used in the recommended methods).

[0018] Similarly, the methods and systems described herein can act as a method converter that can use methods created for one instrument vendor to efficiently build methods for a different vendor. For example, if an existing method for one vendor is associated with specific predicted results, these results can be used to build a similar method for a different vendor, such as, for example, by building an initial method from the existing method and interactively running the initial method on the vendor instrument (modifying between interactions) until the results from the method approximately match the expected results.

[0019] Thus, the methods and systems described herein simplify and reduce the time and resources required to configure projects (which can take days or months manually), which otherwise may limit the types of samples and analyses that a laboratory can perform. A connected (e.g., cloud-based) platform allows data collection and linking to create appropriate training data for such methods and systems. Thus, the methods and systems described herein increase productivity, reduce learning time, and reduce resource usage (human resources, laboratory resources, and computing resources).

[0020] Methods created via the support systems and associated functionality described herein may define "how" a sample will be analyzed and may include one or more experimental parameters, where these experimental parameters define, for example, the identification of one or more instruments, sample preparation requirements, instrument and environmental settings, regulatory factors, or a combination thereof. These experimental parameters may also define how the results of the method are analyzed and checked, such as, for example, by specifying calibrations to be used in data processing, what the expected results should be (within specified tolerances), what erroneous results look like, or a combination thereof. In some embodiments, these experimental parameters may include predicted results (these predicted results may include historical results or synthetic results), which may be compared to actual results from the method to determine the results [TP386058USPRV1]

[0021] As noted above, the comparison between the predicted results and the actual results can also be used as feedback to the model for further training purposes.

[0022] As noted above, the embodiments described herein achieve improved laboratory management and operation relative to conventional methods. Specifically, relative to conventional methods, the embodiments described herein reduce the time and resources required for the method for developing scientific experiments and data review, which improves the overall operation of scientific laboratories. For example, some embodiments disclosed herein provide various user interfaces and associated computing processes that are unconventional in the field of laboratory management and the development and data review of scientific experiments. The computing processes and user interface features disclosed herein not only relate to the collection and comparison of information, but also apply new analysis and technical means to create methods more efficiently and accurately than conventional methods. Therefore, the present disclosure introduces functions that both conventional computing devices and humans cannot perform.

[0023] Thus, embodiments of the present disclosure may serve any of a number of technical purposes, such as developing scientific experiments faster and more efficiently in terms of resource usage, providing data review of scientific experiment results to verify data correctness (which can limit or avoid reruns or other service issues), managing specific technical systems or processes; determining machine or instrument availability or status; optimizing load distribution in a computer network (e.g., by distributing projects and associated data processing across instruments included in one or more laboratories); providing faster processing of sensor data (e.g., by identifying optimized methods for processing various experimental parameters); or a combination thereof.

[0024] Thus, the embodiments disclosed herein provide improvements to the development of scientific experiments, as well as laboratory and instrument management and use, and associated technologies (e.g., improvements in computer technology to support scientific instruments, among other improvements).

[0025] In the following detailed description, reference is made to the accompanying drawings, which form a part of the detailed description, wherein the same reference numerals designate the same parts throughout, and in the accompanying drawings, practicable embodiments are shown by way of example. It should be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description should not be considered to have a limiting meaning.

[0026] The various operations may be described as a plurality of 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. Specifically, these operations may not be performed in the order presented. 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] [TP386058USPRV1]

[0028] For purposes of this disclosure, the phrases "A and / or B" and "A or B" mean (A), (B), or (A and B). For purposes of this disclosure, the phrases "A, B, and / or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Although some elements may be referred to in the singular (e.g., "processing device"), any appropriate element may be represented by multiple instances of the element, and vice versa. For example, a set of operations described as being performed by a processing device may be implemented by different ones of the operations being performed by different processing devices.

[0029] This specification uses the phrases "embodiment," "various embodiments," and "some embodiments," each of which may refer to one or more of the same or different embodiments. In addition, the terms "including," "comprising," "having," and the like as used with respect to the embodiments of the present disclosure are synonymous. When used to describe a range of sizes, the phrase "between X and Y" means a range that includes X and Y. As used herein, "device" may refer to any individual device, collection of devices, part of a device, or collection of parts of a device. The drawings are not necessarily drawn to scale.

[0030] Unless the context in which they are used clearly indicates otherwise, the articles "a", "an", and "the" should not be construed as meaning "one" or "only one". Instead, these articles should be construed as meaning "at least one" or "one or more". Similarly, when the terms "the" or "the" are used to refer to a noun previously introduced by the indefinite article "a" or "an", "the" and "the" mean "at least one" or "one or more" unless the usage clearly indicates otherwise.

[0031] It should also be understood that although some of the drawings illustrate hardware and software located in a particular device, these depictions are for illustrative purposes only. In some embodiments, the illustrated components may be combined or divided into separate software, firmware and / or hardware. For example, instead of being located in a single electronic processor and being executed by the single electronic processor, logic and processing may be distributed between multiple electronic processors. Regardless of how they are combined or divided, hardware and software components may be located on the same computing device or may be distributed between different computing devices connected by one or more networks or other suitable communication links.

[0032] Thus, in a claim, if an apparatus or system is claimed to include, for example, an electronic processor or other element configured in a certain manner, for example, to make a plurality of determinations, then the claim or claim element should be interpreted to mean one or more electronic processors (or other elements), any of which is configured as claimed, for example, to make some or all of the plurality of determinations. To reiterate, those electronic processors and processing may be distributed.

[0033] [TP386058USPRV1]

[0034] Figure 1 1 is a block diagram of a scientific instrument support system 100 (hereinafter referred to as support system 100) for performing scientific instrument support operations according to various embodiments. Figure 1As shown, the support system 100 includes multiple instrument personal computers (IPCs) 104 connected by a communication connection or network 120. One or more devices 108 are connected to each IPC 104. Device 108 is a physical entity that performs some functions of sample preparation and / or sample analysis. For example, device 108 can be a physical device with a serial number, a device that communicates with other external entities, and can include a processor, a memory, and firmware. Device 108 can include, for example, a sensor, a detector, an actuator, a spectrometer, a spectrogram, an oscilloscope, an electrometer, an interferometer, etc. IPC 104 may also include one or more instruments 112. Instrument 112 is a logical container that includes a collection of devices 108. For example, devices 108 work together to perform operations and produce results. The logical collection of devices 108 (e.g., instrument 112) prepares or analyzes inputs such as blood samples or other biological samples, semiconductors, chemical compounds, solutions, food and drug samples, etc. Each IPC 104 is connected to its corresponding device 108 and can be configured to communicate unidirectionally or bidirectionally with the connected device 108. Each IPC 104 may be connected to a device 108 via a wired or wireless communication medium and / or protocol. Each IPC 104 may send commands to a connected device 108, receive status signals from a device 108, receive measurements from a device 108, etc., as described in more detail below. The IPCs 104 and their corresponding connected devices 108 may be organized into a plurality of scientific instrument groups 118. For example, a scientific instrument group 118 may include a liquid chromatography ("LC") instrument IPC 104 and a mass spectrometry ("MS") IPC 104. Each scientific instrument group 118 includes at least one IPC 104. The IPCs 104, devices 108, and instruments 112 may be more generally referred to as service components. Instruments 112 may include, for example, liquid chromatography instruments, gas chromatography instruments, ion chromatography instruments, mass spectrometry instruments, trace element instruments (e.g., inductively coupled plasma mass spectrometry, inductively coupled plasma optical emission spectroscopy, atomic absorption, etc.), capillary electrophoresis instruments, spectroscopic instruments, absorptiometry instruments, scattered radiation instruments, emitted radiation instruments, conductimetric instruments, amperometric instruments, voltammetric instruments, electrogravimetric instruments, coulometric instruments, potentiometric instruments, etc. However, it should be understood that instruments 112 are not limited to those described herein, and other types of instruments are contemplated. For example, any type of instrument configured to follow or interact with a defined user interface may be supported and may be automatically detected (as described below) and managed within system 100. In addition, as described in more detail below, in some embodiments, system 100 is configured to enable a user to manually add an instrument for management within system 100.

[0035] The support system 100 includes a database 124, a server 128, and a user device 130 that are communicatively coupled to the plurality of IPCs 104 (and to each other) via a communication network 120. In other embodiments, the plurality of IPCs 104, the server 128, and the database 124 are connected via one or more dedicated wired connections [TP386058USPRV1]

[0036] or other forms of wired or wireless electronic communications. It should be understood that the support system 100 may include Figure 1 For example, support system 100 may include fewer or more IPCs 104, multiple servers 128, multiple databases 124, or a combination thereof. In some cases, the storage functionality of database 124 is provided by server 128, rather than including a separate database. The components of support system 100 may also be provided by Figure 1 Communicates with one or more intermediate devices not illustrated.

[0037] The server 128 acts as a "control hub" for multiple IPCs 104 and can establish a connection platform for the IPCs 104 and associated instruments 112 and devices 108. For example, the server 128 communicates with each of the IPCs 104 by transmitting commands to the IPCs 104 via a wired connection, a wireless connection, or a combination thereof to execute the methods described herein. Additionally, the server 128 receives measurements and / or manages the status of the devices 108 and instruments 112 from their corresponding IPCs 104 via a wired connection, a wireless connection, or a combination thereof. The database 124 stores data indicating the status of components within the support system 100. For example, the database 124 can store the current status of the devices 108 and instruments 112, the historical status of the devices 108 and instruments 112, the measurements recorded by the devices 108 and instruments 112, and the like. The database 124 may also store data defining methods, samples, and analyses associated with the support system 100, such as, for example, acquisition method data 301, processing method data 302, approved method data 303, vendor-specific method data 304, legacy method data 305, methods associated with a particular user or user group (e.g., company-focused method data 306), methods extracted or retired from various third-party systems (e.g., journals, research papers, etc.) (i.e., third-party data 307), or combinations thereof (see, e.g., Figure 3 ). The database 124 may also store one or more libraries (ie, library data 308), such as one or more spectral libraries, instrument geometry (eg, column geometry), chromatographic databases, etc. (see, e.g., Figure 3). Thus, in some embodiments, database 124 may be referred to as an analytical data store, and, as noted above, may include one data store device or multiple data store devices.

[0038] Figure 2 1 is a block diagram of a scientific instrument support module 200 for performing support operations according to various embodiments. The scientific instrument support module 200 may be implemented, for example, via the server 128. The scientific instrument support module 200 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument support module 200 may be included in a single computing device or may be distributed across multiple computing devices that communicate with each other as appropriate. Figure 5 The computing device 600 discusses an example of a computing device that can implement the scientific instrument support module 200 alone or in combination, and this document [TP386058USPRV1]

[0039] refer to Figure 6 The scientific instrument support system 700 of FIG. 7 discusses an example of a system of interconnected computing devices, wherein the scientific instrument support module 200 may be implemented across one or more of the computing devices.

[0040] like Figure 2As shown, the scientific instrument support module 200 may include instrument utilization logic 204, method creator logic 206 and data review logic 208. As used herein, the term "logic component" may include a device that performs a set of operations associated with the logic component. For example, any of the logic elements included in the support module 200 may be implemented by one or more computing devices, and the one or more computing devices are programmed with instructions that cause 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 the one or more non-transient computer-readable media, which cause the one or more computing devices to perform an associated set of operations when executed by one or more processing devices in one or more computing devices. 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 logic components in the module may be implemented by a programmed general-purpose processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). As another example, different ones of the logic elements in a module may be associated with different sets of instructions executed by one or more electronic processing devices. A module may not include all of the 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.

[0041] The instrument utilization logic 204 may be configured to obtain asset information associated with each IPC 104, such as, for example, a device 108, an instrument 112, or other component associated with each IPC 104, for each scientific instrument group in the plurality of scientific instrument groups 118. The asset information may include an identifier of the instrument, including a type identifier, a unique identifier, or any combination thereof. In various specific implementations, the asset information also includes a real-time data acquisition status, which may include a status that an operation is currently being performed by the device 108, the IPC 104, the instrument 112, or a combination thereof, previously performed by them, or pending execution. The asset information may also include device details associated with each device 108, such as, for example, a device name, a device serial number, a device model, a device firmware version, a device warranty expiration date, a device warranty status, a device service provider, a service status, a contract expiration date, or any combination thereof. The asset information may also include other status logs associated with the device 108, the IPC 104, and / or the instrument 112, such as, for example, an error log or an operation history.

[0042] [TP386058USPRV1]

[0043] The instrument utilization logic 204 may also be configured to obtain reservation information for each scientific instrument group in the plurality of scientific instrument groups 118. The reservation information includes scheduling data associated with the planned availability of each scientific instrument group 118. The reservation information may include, for example, a reservation start date and time, a reservation end date and time, a reservation assignee (e.g., a scientist or technician in the laboratory who owns the reservation), etc. The reservation information may also include a project identifier associated with the reservation. The asset and reservation information obtained via the instrument utilization logic 204 may be stored in the database 124.

[0044] The method creator logic 206 may be configured to receive data associated with a sample of interest (e.g., information defining the type of sample of interest (e.g., soil, a particular plant, a biological sample, etc.), a desired analysis (e.g., one or more compounds of interest), or a combination thereof, and generate a list of one or more recommended methods for performing a scientific experiment on the sample. The desired analysis may specify one or more chemical components and / or one or more biological components (i.e., compounds of interest) to identify a characteristic of the sample (e.g., pH, concentration, mass percentage, mole percentage, volume percentage, or ratio of the sample), density, hardness, boiling point, melting point, volatility, flammability, toxicity, reactivity, half-life, or a combination thereof in the sample (and optionally its amount). In some embodiments, the desired analysis is optional, and the systems and methods described herein may be configured to output a recommended method as part of an open or non-targeted screening or analysis of the sample. Similarly, in some embodiments, the sample type is optional, and the systems and methods described herein may be configured to output a recommended method for one or more specified compounds of interest without requiring a sample type input.

[0045] Each method included in the generated list may include one or more experimental parameters. The experimental parameters may specify parameters associated with the sample, the instrument used to analyze the sample, the processing of the analysis results, etc. For example, the experimental parameters may include preparation parameters, liquid dispersion parameters, solid dispersion parameters, one or more solvent parameters, one or more concentration parameters, one or more ratios, volume percentages, mass percentages, molar percentages, temperatures, pressures, times, column types or parameters, or combinations thereof.

[0046] In some embodiments, the method creator logic 206 includes and applies a machine learning model (e.g., one or more neural networks) that is trained to generate a list of one or more recommended methods based on the input sample type, the desired analysis, or a combination thereof. Such a machine learning model can be trained using data stored on the database 124. For example, the machine learning model can utilize information about previous methods applied by instruments on the platform, methods provided by third parties (e.g., approved methods, vendor-specific methods, third-party data extracted from external sources such as journals and white papers [TP386058USPRV1]

[0047] Specifically, Figure 3 As shown, logic 206 can use acquisition method data 301, processing method data 302, approval method data 303, supplier-specific method data 304, old-style method data 305, company-focused method data 306, third-party method data 307, library data 308 or any combination thereof to recommend one or more methods, and in some embodiments, all or a subset of the data can be used to train the model to generate such recommendations. Information for training the model can associate a specific method (including various experimental parameters) with a specific type of sample and a specific type of analysis (i.e., linked data sets). Therefore, using such data as training data allows the machine learning model to receive sample type, expected analysis or a combination thereof as input and output one or more recommended methods once trained. For example, the model can be configured to output a list of methods (the list of methods can be ranked based on suitability to the input via, for example, confidence scores) and can use further interactions with the user to narrow the list and create methods for samples of interest. This interaction can be used as further feedback to the machine learning model, which allows the model to provide improved recommendations during subsequent use.

[0048] The data stored in the database 124 and used for training the model can take various forms, including, for example, text data, binary data, raw files, analysis result data, etc., and can be compiled from various sources and linked in various ways for training purposes. For example, the data for training the model can include multiple data sets, wherein each data set links samples or sample types, one or more compounds of interest, and experimental methods. Such data can be compiled by the system from various sources. For example, the historical method implemented on the platform can be stored (for example, as acquisition method data 301, treatment method data 302, approval method data 303, old-style method data 305, company focus method data 306, or a combination thereof) and can represent a linked data set, wherein the stored data executed about the history of the method identifies the experimental parameters of the method and can be stored or linked to the sample data managed via the platform and the data results also managed by the platform. Therefore, the connected platform creates a data repository, wherein the linked data set is stored or can be established from the stored data collected and managed by the platform.

[0049] Alternatively or in addition, one or more linked data sets may be created from other sources and using other types of data. For example, library data 308 may include a library of spectra of various compounds of interest and associated experimental parameters, and this data may be combined with historical data representing methods applied to samples to create a linked data set for training purposes. Similarly, library data 308 may include a database of compounds of interest and various identifiers or names of such compounds, and this data may be used by the system to transform or link historical data, other library data, third-party data, etc. to a particular compound of interest. Library data 308 may also include a database of instrument geometries [TP386058USPRV1]

[0050] Can be used to establish experimental parameters (e.g., instrument settings) for a particular method. As another example, third-party method data 307 may include experimental logs or other text-based descriptions of experiments, and the system can be configured to parse these experimental logs and text-based descriptions and establish linked data sets (e.g., from the text-based descriptions and / or combined with other data from other sources). Thus, data used to train a model as described herein can be obtained from one or more sources (e.g., various databases) and can take various forms and can be linked to data from other sources to create training data.

[0051] Alternatively or in addition, the method creator logic 206 may be configured to access the stored method data and retrieve applicable method data based on one or more static rules applied by the method creator logic 206 (e.g., matching the user input with the historical method data accessible to the method creator logic 206). This configuration may similarly provide a list of recommended methods to the user, but in some embodiments, the list of recommended methods may be limited to the available stored methods and therefore, unlike the machine learning model, it may not be possible to recommend methods for inputs generated to match the input data. In some embodiments, the method creator logic 206 may use a combination of rules and machine learning to provide recommended methods. For example, in some embodiments, the method creator logic 206 may be configured to initially generate a list of recommended methods from historical data via one or more rules, and the list may be further refined based on the user's interaction with the list of recommended methods using the functionality of machine learning (e.g., chat-based interaction). Other combinations are also possible.

[0052] Regardless of how the stored data is used to generate one or more recommended methods, the method creator logic 206 may also take into account the stored asset and reservation information. Figure 3 As shown, the method creator logic 206 can use data managed by the instrument utilization logic 204 (which data can be provided from the instrument utilization logic 204 or accessed from a data storage device in which the instrument utilization logic 204 stores data) to recommend methods that are suitable for instruments available to the user for processing samples (e.g., the types of instruments included in the laboratory, the status or availability of such instruments, the health or maintenance status of the instruments (e.g., the level of available consumables, etc.)). Similarly, the method creator logic 206 can use the instrument information managed by the instrument utilization logic 204 to recommend one or more methods that are optimized based on the available instruments. For example, the method creator logic 206 can use the instrument information to recommend a method associated with the shortest time frame for completing a scientific experiment, a method that allows the experiment to be performed fastest based on the availability of one or more instruments 112, a method that uses the least number of instruments or equipment to complete a scientific experiment, a method that is the least cost-intensive operation to perform a scientific experiment, or a combination thereof. Thus, the method creator logic 206 uses the availability of data associated with the connected platform devices to not only recommend methods for processing a particular [TP386058USPRV1]

[0053] methods for a sample of interest, but also considers which instruments and equipment are actually available to process that sample, which further reduces processing time and inefficient use of computational and instrumental resources.

[0054] This optimization can be realized by one or more stored rules applied via method creator logic 206. In addition, in some embodiments, as a part of development method, a user can specify one or more optimization rules to be applied. For example, as a part of these optimization rules, a user can specify whether they only want to receive a recommended method that can be implemented using available instruments, whether the method can be implemented using available instruments, or a combination thereof (for example, by opening and closing or otherwise switching one or more optimization rules). Providing a recommended method for use with available instruments improves efficiency because the user will not waste time considering methods that may not be realized in the case of current laboratory resources. However, providing a recommended method that is not limited to available laboratory resources can allow users (for example, laboratory managers) to assess the type of laboratory resources that may be needed to adapt to current needs or understand how available laboratory resources can limit the efficiency, accuracy, etc. of the laboratory. This data can be used to make purchase recommendations and overall laboratory management.

[0055] In addition, in some embodiments, in addition to outputting the developed method (e.g., a list of one or more recommended methods with associated experimental parameters), the method creator logic 206 is also configured to automatically generate and issue one or more reservations for booking one or more instruments, equipment, etc. associated with a specific developed method. Therefore, the method creator logic 206 can be configured to automatically reserve resources for the developed method. The method creator logic 206 can reserve such resources in response to receiving a selection of a specific developed method from a user. Alternatively, the method creator logic 206 can reserve resources for one or more developed methods in response to limiting the number of available methods. In some embodiments, the method creator logic 206 can send a reservation to the instrument utilization logic 204 or a separate logic configured to manage the schedule of instruments, devices, etc.

[0056] The data review logic 208 may be configured to assist the system and / or user in analyzing the results of a method (e.g., developed via the method creator logic 206). For example, the method creator logic 206 and / or the data review logic 208 may be configured to generate expected results associated with the method, and the method creator logic 206 and / or the data review logic 208 may be configured to compare actual results of the method with the expected results, such as, for example, to identify problems or errors with the method, sample, etc. The expected results may include historical results data (e.g., stored in the database 124) in raw or processed form (e.g., averages) associated with methods similar to the method used to process the sample. Alternatively or in addition, the expected results may include predicted (synthesized) results of the method, which may be generated using data pairs utilizing the method and associated data results (which, again, may be stored in the database [TP386058USPRV1] as described above).

[0057] 124) to generate a machine learning model trained. In various specific implementations, the data output from the data review logic 208 can be provided in a user interface, and such data can include expected results of the method, actual results, comparisons of these expected results and actual results, or a combination thereof.

[0058] In some implementations, the output from the data review logic 208 may be fed back to the method creator logic 206. For example, the output from the data review logic 208 may be used as additional feedback or training data for the machine learning model implemented via the method creator logic 206. Alternatively or in addition, the method creator logic 206 may use the feedback to create a modified method. For example, based on the output from the data review logic 208, the method creator logic 206 may develop a new method that can generate results that better match the expected results. The method creator logic 206 may develop new methods based on the data from the data review logic 208, such as, for example, by developing new methods that use alternative instruments 112 to reduce operating costs, reduce the time frame for completing one or more scientific experiments, prioritize the order in which one or more scientific experiments will be performed before various other scientific experiments in any given working day, or a combination thereof. Data associated with the rerun of the method may be fed back to the model as training data to further improve the recommendations output by the model.

[0059] Similarly, the method creator logic 206 may develop a new method based on data from the data review logic 208 that modifies one or more experimental parameters of the initial method to better calibrate the developed method for the expected results. As noted above, in some embodiments, the method creator logic 206 may be configured as a method translator that may convert a method associated with one vendor (e.g., one instrument vendor) for use with a different vendor, such as by comparing actual results to expected results and iteratively modifying the initially developed method until the actual results match the expected results.

[0060] Figure 3 is a block diagram illustrating the functionality of the method creator logic 206 according to some embodiments. Figure 3 , and as generally described above, the method creator logic 206 is communicatively connected to one or more data storage devices that store, for example, acquisition method data 301, processing method data 302, approved method data 303, vendor-specific method data 304, legacy method data 305, company-focused method data 306, third-party method data 307, library data 308, data review logic 208, or a combination thereof. As described above, the method creator logic 206 can use the accessible data to recommend one or more methods for processing a particular type of sample (which can be input by a user in one or more user interfaces). For example, the method creator logic 206 can use the data to identify a stored method that is suitable for a particular sample and / or can use the data to train one or more machine learning models to respond to the input data (e.g., [TP386058USPRV1]

[0061] The method creator logic 206 may also communicate with the instrument utilization logic 204 (and / or data managed by the logic 204) to recommend one or more methods based on instrument availability and / or optimization. In addition, in some embodiments, the method creator logic 206 may issue a reservation for an instrument via communication with the instrument utilization logic 204. In addition, as Figure 3 As shown, method creator logic 206 can communicate with data review logic 208 to compare actual method results with expected results, and optionally use the comparison to modify the originally developed method (e.g., recommend a different method, recommend adjustments to the originally developed method), or a combination thereof. It should be understood that the functions described herein as being performed by logic 204, 206, and 208 can be distributed in various configurations.

[0062] The scientific instrument support methods disclosed herein may include interaction with a human user (e.g., via the referenced Figure 6 These interactions may include providing information to the user (e.g., about scientific instruments such as Figure 6 In some embodiments, these interactions may be performed via a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device such as a display device (e.g., a graphical user interface such as a display device) and a display device (e.g., a graphical user interface such as a display device) that may be used to display information about the operation of the scientific instrument 710, information about samples analyzed by the scientific instrument or other tests or measurements performed by the scientific instrument, information retrieved from a local or remote database, or other information). Figure 5 610) that provides output to a user and / or prompts a user to provide input (e.g., via a display device 610 discussed herein). Figure 5 Other I / O devices 612 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen. The scientific instrument support system disclosed herein may include any suitable GUI for interacting with a user.

[0063] Figure 4 An example GUI 500 is depicted that can be used to perform some or all of the supporting methods disclosed herein according to various embodiments. As described above, the GUI 500 can be provided in a scientific instrument support system (e.g., as described herein with reference to Figure 6 A computing device (e.g., a scientific instrument support system 700 as discussed herein) Figure 5 The computing device 600 discussed herein) may include a display device (eg, Figure 5 610) and the user may use any suitable input device (e.g., Figure 5 Any of the input devices discussed in other I / O devices 612 ) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button actuation, etc.) interact with GUI 500 .

[0064] GUI 500 may include a data display area 502 , a data analysis area 504 , a scientific instrument management area 506 , and a settings area 508 . Figure 4 The specific number and arrangement of regions depicted in are merely exemplary [TP386058USPRV1], and any number and arrangement of regions (including any desired features) may be included in GUI 500.

[0065] The data display area 502 may display data generated by a scientific instrument (e.g., Figure 6 Data generated by the scientific instrument 710 discussed.

[0066] Data analysis area 504 can display the results of the data analysis (e.g., the results of analyzing the data illustrated in data display area 502 and / or other data). In some embodiments, data display area 502 and data analysis area 504 can be combined in GUI 500 (e.g., to include data output from a scientific instrument and some analysis of the data in a common graph or area).

[0067] The scientific instrument management area 506 may include a user-friendly interface that allows the user to manage scientific instruments (e.g., Figure 6 Options for scientific instruments 710) discussed.

[0068] Settings area 508 may include options that allow a user to manage features and functions of GUI 500 (and / or other GUIs) and / or perform common computing operations with respect to data display area 502 and data analysis area 504 (e.g., save data to a storage device (such as a computer program product described herein)). Figure 5 The settings area 508 may include sorting options, grouping options, filtering options, and / or a search bar for the scheduler user interface.

[0069] As noted above, the scientific instrument support module 200 may be implemented by one or more computing devices. Figure 5 6 is a block diagram of a computing device 600 that can perform some or all of the scientific instrument support methods disclosed herein, according to various embodiments. In some embodiments, the scientific instrument support module 200 can be implemented by a single computing device 600 or by multiple computing devices 600. In addition, as discussed below, the computing device 600 (or multiple computing devices 600) that implements the scientific instrument support module 200 can be Figure 6 A portion of one or more of a scientific instrument 710, a user local computing device 720, a service local computing device 730, or a remote computing device 740.

[0070] Figure 5 The computing device 600 is illustrated as having multiple components, but any one or more of these components may be omitted or repeated depending on suitability for the application and setting. In some embodiments, some or all of the components included in the computing device 600 may be attached to one or more motherboards and encapsulated in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be manufactured on a single system on a chip (SoC) (e.g., the SoC may include one or more electronic processing devices 602 and one or more storage devices 604). Additionally, in various embodiments, the computing device 600 may not include Figure 5One or more of the illustrated components, but may include any suitable interface for use [TP386058USPRV1]

[0071] The computing device 600 may include a display device 610, but may include a display device interface circuit (e.g., a connector and a driver circuit) to which the display device 610 may be coupled.

[0072] The computing device 600 may include an electronic processing device 602 (e.g., one or more processing devices). As used herein, the term "electronic processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory to transform the electronic data into other electronic data that can be stored in registers and / or memory. The electronic processing device 602 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 electronic processing devices.

[0073] The computing device 600 may include a storage device 604 (e.g., one or more storage devices). The storage device 604 may include one or more memory devices, such as a random access memory (RAM) (e.g., a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive bridge RAM (CBRAM) device), a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In some embodiments, the storage device 604 may include a memory that shares a die with the electronic processing device 602. In such embodiments, the memory may be used as a cache memory and may include, for example, an embedded dynamic random access memory (eDRAM) or a spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the storage device 604 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more electronic processing devices (e.g., the electronic processing device 602), cause the computing device 600 to perform any appropriate method or portion of the methods disclosed herein.

[0074] The computing device 600 may include an interface device 606 (e.g., one or more interface devices 606). The interface device 606 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 600 and other computing devices. For example, the interface device 606 may include circuitry for managing wireless communications for transmitting data to and from the computing device 600. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc. that can communicate data through a non-solid medium using modulated electromagnetic radiation. The term [TP386058USPRV1]

[0075] The term "interface device 606" does not imply that the associated device does not include any wires, although in some embodiments, no wires may be included. The circuitry included in the interface device 606 for managing wireless communications may implement any of a plurality of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 82.11 series), IEEE 82.16 standards (e.g., IEEE 82.16--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 some embodiments, the circuitry included in the interface device 606 for managing wireless communications may operate according to a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some embodiments, the circuitry included in the interface device 606 for managing wireless communications may operate in accordance with Enhanced Data GSM Evolution (EDGE), GSM Evolution Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuitry included in the interface device 606 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, and higher versions, etc. In some embodiments, the interface device 606 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or send wireless communications.

[0076] In some embodiments, interface device 606 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 606 may include a circuit supporting communication according to Ethernet technology. In some embodiments, interface device 606 may support both 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 606 may be dedicated to shorter range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 606 may be dedicated to longer range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In some embodiments, a first group of circuits of interface device 606 may be dedicated to wireless communication, and a second group of circuits of interface device 606 may be dedicated to wired communication.

[0077] Computing device 600 may include battery / power circuitry 608. Battery / power circuitry 608 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 600 to an energy source separate from computing device 600 (e.g., AC line power).

[0078] [TP386058USPRV1]

[0079] Computing device 600 may include display device 610 (eg, multiple display devices). Display device 610 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.

[0080] The computing device 600 may include other input / output (I / O) devices 612. The other I / O devices 612 may include, for example, 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 600 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.

[0081] The computing device 600 may have any suitable form factor suitable for its applications and settings, such as a handheld or mobile computing device (e.g., a cellular 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.

[0082] One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. Figure 6 is a block diagram of an example scientific instrument support system 700 in which some or all of the scientific instrument support methods disclosed herein may be performed, according to various embodiments. The scientific instrument support modules and methods disclosed herein (e.g., Figure 2 Scientific Instrument Support Module 200 and Figure 7 and Figure 8 Processes 900 and 1000 ) may be implemented by one or more of the scientific instrument 710 , the user local computing device 720 , the service local computing device 730 , or the remote computing device 740 of the scientific instrument support system 700 .

[0083] Any of the scientific instrument 710, the user local computing device 720, the service local computing device 730, or the remote computing device 740 may include any of the devices described herein. Figure 5 Any of the embodiments of computing device 600 discussed herein, and any of scientific instrument 710, user local computing device 720, service local computing device 730, or remote computing device 740 may adopt any of the embodiments described herein. Figure 5 Any suitable implementation form of the implementation of computing device 600 discussed above.

[0084] The scientific instrument 710, the user local computing device 720, the service local computing device 730 or the remote computing device 740 may each include an electronic processing device 702, a storage device 704 and an interface device [TP386058USPRV1]

[0085] 706. The electronic processing device 702 may take any suitable form, including any suitable form as described herein. Figure 5 The electronic processing device 602 discussed above may be in the form of any electronic processing device, and the electronic processing device 702 in different devices including the scientific instrument 710, the user local computing device 720, the service local computing device 730, or the remote computing device 740 may take the same form or different forms. The storage device 704 may take any suitable form, including those described herein. Figure 5The storage device 604 discussed above may be in the form of any storage device, and the storage device 604 in different devices including the scientific instrument 710, the user local computing device 720, the service local computing device 730, or the remote computing device 740 may take the same form or different forms. The interface device 706 may take any suitable form, including those described herein. Figure 5 The form of any of the interface devices 606 discussed, and the interface devices 706 included in different devices of the scientific instrument 710, the user local computing device 720, the service local computing device 730, or the remote computing device 740 may take the same form or different forms.

[0086] The scientific instrument 710, the user local computing device 720, the service local computing device 730, and the remote computing device 740 can communicate with other elements of the scientific instrument support system 700 via a communication path 708. The communication path 708 can communicatively couple the interface device 706 (as shown) of different elements of the scientific instrument support system 700, and can be a wired or wireless communication path (e.g., according to the present reference Figure 5 The interface device 606 of the computing device 600 may be any communication technology discussed above). Figure 6 The specific scientific instrument support system 700 depicted in FIG. 7 includes a communication path between each pair of devices in the scientific instrument 710, the user local computing device 720, the service local computing device 730, and the remote computing device 740, but this specific implementation of "fully connected" is merely illustrative, and in various embodiments, various communication paths in the communication paths 708 may not exist. For example, in some embodiments, the service local computing device 730 may not have a direct communication path 708 between its interface device 706 and the interface device 706 of the scientific instrument 710, but may communicate with the scientific instrument 710 via the communication path 708 between the service local computing device 730 and the user local computing device 720 and the communication path 708 between the user local computing device 720 and the scientific instrument 710.

[0087] Scientific instrument 710 may include any suitable scientific instrument, such as those described above with respect to Figure 1 Scientific instrument described in 112.

[0088] The user-local computing device 720 can be a computing device local to the user of the scientific instrument 710 (e.g., according to any of the embodiments of the computing device 600 discussed herein). In some embodiments, the user-local computing device 720 can also be local to the scientific instrument 710, but this is not necessarily the case; for example, a user-local computing device in the user's home or office [TP386058USPRV1]

[0089] 720 may be remote from the scientific instrument 710, but in communication with the scientific instrument, such that a user may use the user-local computing device 720 to manage and / or access data from the scientific instrument 710. In some embodiments, the user-local computing device 720 may be a laptop, a smart phone, or a tablet device. In some embodiments, the user-local computing device 720 may be a portable computing device.

[0090] The service local computing device 730 can be a computing device local to the entity serving the scientific instrument 710 (e.g., according to any of the embodiments of the computing device 600 discussed herein). For example, the service local computing device 730 can be a device local to the manufacturer of the scientific instrument 710 or a third-party service company. In some embodiments, the service local computing device 730 can communicate with the scientific instrument 710, the user local computing device 720, and / or the remote computing device 740 (e.g., via a direct communication path 708 or via multiple "indirect" communication paths 708, as described above) to receive data about the operation of the scientific instrument 710, the user local computing device 720, and / or the remote computing device 740 (e.g., self-test results of the scientific instrument 710, calibration coefficients used by the scientific instrument 710, measurement results of sensors associated with the scientific instrument 710, etc.). In some embodiments, the service local computing device 730 may communicate with the scientific instrument 710, the user local computing device 720, and / or the remote computing device 740 (e.g., via a direct communication path 708 or via multiple "indirect" communication paths 708, as described above) to send data to the scientific instrument 710, the user local computing device 720, and / or the remote computing device 740 (e.g., to update programmed instructions (such as firmware) in the scientific instrument 710 to initiate execution of a test or calibration sequence in the scientific instrument 710, to update programmed instructions (such as software) in the user local computing device 720 or the remote computing device 740, etc.). A user of the scientific instrument 710 may utilize the scientific instrument 710 or the user local computing device 720 to communicate with the service local computing device 730 to report problems with the scientific instrument 710 or the user local computing device 720, to request a visit by a technician to improve the operation of the scientific instrument 710, to order consumables or replacement parts associated with the scientific instrument 710, or for other purposes.

[0091] The remote computing device 740 may be a computing device remote from the scientific instrument 710 and / or the user local computing device 720 (e.g., according to any of the embodiments of the computing device 600 discussed herein). In some embodiments, the remote computing device 740 may be included in a data center or other large-scale server environment. In some embodiments, the remote computing device 740 may include a network attached storage device (e.g., as part of the storage device 704). The remote computing device 740 may store data generated by the scientific instrument 710, perform analysis of data generated by the scientific instrument 710 (e.g., according to programmed instructions), facilitate communication between the user local computing device 720 and the scientific instrument 710, and / or facilitate communication between the service local computing device 730 and the scientific instrument 710 [TP386058USPRV1]. The remote computing device may perform some or all of the functions of the server 128 (e.g., supporting the server-based platform 300).

[0092] In some embodiments, Figure 6 One or more of the elements of the scientific instrument support system 700 illustrated in FIG. 7 may not be present. In addition, in some embodiments, Figure 6 Multiple of the various elements of the elements of the scientific instrument support system 700 may exist. For example, the scientific instrument support system 700 may include multiple user local computing devices 720 (e.g., different user local computing devices 720 associated with different users or located in different locations). As another example, the scientific instrument support system 700 may include multiple scientific instruments 710, all of which communicate with the service local computing device 730 and / or the remote computing device 740; in such an embodiment, the service local computing device 730 may monitor these multiple scientific instruments 710, and the service local computing device 730 may cause updates or other information to be "broadcasted" to multiple scientific instruments 710 at the same time. Different scientific instruments 710 in the scientific instrument support system 700 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 some embodiments, the scientific instruments 710 may be connected to an Internet of Things (IoT) stack that allows the scientific instruments 710 to be managed through a web-based application, a virtual or augmented reality application, a mobile application, and / or a desktop application. Any of these applications may be accessed by a user operating a user-local computing device 720 that communicates with the scientific instrument 710 through an intermediary remote computing device 740. In some embodiments, the scientific instrument 710 may be sold by a manufacturer along with one or more associated user-local computing devices 720 as part of a local scientific instrument computing unit 712.

[0093] In some embodiments, different scientific instruments in the scientific instruments 710 included in the scientific instrument support system 700 may be different types of scientific instruments 710; for example, one scientific instrument 710 may be a mass spectrometer, while another scientific instrument 710 may be a liquid chromatography instrument. In some such embodiments, the remote computing device 740 and / or the user's local computing device 720 may combine data from different types of scientific instruments 710 included in the scientific instrument support system 700.

[0094] Figure 7 900 is a flow chart of a method 900 for performing support operations according to various embodiments. Although the operations of method 900 may refer to specific embodiments disclosed herein (e.g., Figure 2 The scientific instrument support module 200 (eg, the method creator logic 206) discussed herein is described with reference to Figure 4 GUI 500 discussed herein, reference Figure 5 The computing device 600 discussed herein and / or referenced herein Figure 6 1 ) is described with reference to the scientific instrument support system 700 discussed above, but the method 900 may be used in any suitable setting to perform any suitable support operation. Figure 7 The operations are each instantiated once in a specific order, but [TP386058USPRV1]

[0095] Operations may be reordered and / or repeated as necessary and appropriate (eg, different operations may be performed in parallel where appropriate).

[0096] At 902, method 900 includes establishing a data storage device that links the experimental method to the data set of samples and analyses. As described above, in some embodiments, the data can be stored from a connected instrument platform (see, e.g., Figure 1) manages information to establish a data storage device, and at least some of the data may include historical methods and associated sample types and analysis descriptions. The data storage device may be stored in one or more databases or other data storage device devices. As also described above, data can be obtained from various sources and can be in various forms. In addition, the system (e.g., method creator logic 206) can be configured to create a collection of linked data based on data received from one or more sources. As noted above, in some embodiments, each data set links a sample (sample type), one or more compounds of interest and methods, and the system can be configured to create these data sets based on historical data, journals or other text-based reports or publications (e.g., from a third party), libraries (e.g., spectral libraries, instrument geometry libraries, compound libraries or databases, etc.), established methods (e.g., as established by a company, as established by a supplier, as established according to an accreditation process or entity, etc.), and in many cases, links can be established between data obtained from different sources and potentially in different formats.

[0097] At 904, method 900 includes training a model using data (e.g., a data set) from a data storage device. The model may include various neural networks or other types of machine learning models, and training the model may include training the model to output one or more recommendation methods based on the input sample type, the desired analysis, or a combination thereof.

[0098] At 906, method 900 includes inputting sample type, desired analysis, or a combination thereof into a trained model. The input may be received from a user interface. As noted above, in some embodiments, only sample type or only desired analysis (e.g., one or more compounds of interest) is input. However, in other embodiments, the input includes a combination of these information fragments. In addition, in some embodiments, additional inputs are provided, such as, for example, priority information (e.g., how fast the result is needed), instrument optimization settings (e.g., whether the recommended method should be limited to those recommended methods associated with available instruments, or whether the recommended method should not be limited or filtered based on instrument availability), etc.

[0099] At 908, method 900 includes generating, using the trained model, a list of one or more recommended methods for analyzing the sample type based on the input sample type, the desired analysis, or a combination thereof. As described above, the model is trained to output one or more recommendations based on the provided input [TP386058USPRV1]

[0100] Method. As part of 908, method 900 may also include validating the generated list, such as, for example, by checking the recommended methods for feasibility. The check may be performed by comparing the recommended methods from the list with a method database, which may include approved methods, historical methods, etc. If the system cannot match the recommended method with a method from the database (e.g., as an identical match or a close match, where a predetermined number of parameters match or fall within various thresholds), the method may be removed from the list. Similarly, the validation method may include validating one or more experimental parameters included in the recommended method (e.g., by applying various rules) and removing from the list the recommended methods that include one or more parameters that did not pass such validation. In some embodiments, the validation results (e.g., whether the method was removed and what method was removed) may be provided to the model as feedback (i.e., as additional training information) to improve subsequent recommendations.

[0101] As noted above, in some embodiments, the list of recommended methods may be optimized for available scientific instrument resources, which may be collected and managed via instrument utilization logic 904. For example, in some embodiments, the method creator logic 206 may create an initial list of recommended methods, and modify the list by obtaining information about available scientific instruments (e.g., instruments associated with a particular laboratory or set of laboratories) and modifying the list of one or more recommended methods based on the information obtained. Modifying the list may include removing methods, modifying methods, modifying rankings or scores within the list, or a combination thereof. For example, as noted above, modifying the list may include removing methods based on what type of instrument is available for use (e.g., methods using a particular type of instrument that is not included in the laboratory or is included in the laboratory but is scheduled for the next month may be removed from the list, methods associated with a particular speed may be removed when there is no available scientific instrument that can provide such speed, etc.). The list of recommended methods may be output via a user interface.

[0102] At 910, method 900 includes outputting (e.g., within a user interface) a list of one or more recommended (and validated) methods for analyzing the sample type. As described in further detail below, the outputted list may include various rankings or scores and may allow a user to select a method from the list for execution (e.g., with or without modifications). Optionally, at 912, method 900 includes refining the list of recommended methods. In some embodiments, the refinement is performed via interaction with a user (e.g., within a user interface) and may be configured as a chat-based interaction, wherein one or more prompts or questions may be presented to the user along with the list of recommended methods, wherein responses to the prompts / questions may be processed by logic 206 to refine the list. For example, if the desired analysis was not initially input to the model, the user interface may prompt the user to input the desired analysis or may ask the user whether a specific analysis is desired (e.g., starting with the most common or least common analysis presented in the list). Similarly, the user [TP386058USPRV1]

[0103] The interface may prompt the user to specify one or more compounds of interest (e.g., specific pesticides or pesticide lists), specific regulatory requirements to be applied, desired timing, etc. The response to this prompt may be used by logic 206 to further refine the list of recommended methods, wherein the refinement may include removing the method from the list, modifying the method included in the list, changing the ranking or score of the method in the list, or a combination thereof. In some embodiments, the refinement may continue until only a single method is retained or until the user selects one or more methods from the list for execution. The refinement process may prompt the user to input, which may be applied as a filter to the recommended method or used to rank the list or narrow the list to the initial method to be implemented. For example, in some embodiments, the recommended method may be associated with a ranking or confidence score, which may be generated by a model and / or set or modified by a user. For example, in some embodiments, the method may be output by a model with an associated confidence score, which may be used to rank the method, and in some embodiments, the user may be able to modify the score. In some embodiments, the information collected during the refinement process may be provided to the model as feedback to improve subsequent method creation.

[0104] Optionally, at 914, method 900 includes issuing a reservation for one or more instruments to implement one of the recommended methods (e.g., a method selected by a user). The reservation information may not only indicate the type of instrument being reserved and when such reservation is requested, but may also include one or more of the experimental parameters associated with the selected method. In this regard, method creation is further optimized by automatically setting various parameters of the method at the instrument, which makes more efficient use of resources and prevents potential human errors.

[0105] Figure 8 1 is a flow chart of a method 1000 for performing support operations according to various embodiments. Although the operations of method 1000 may refer to specific embodiments disclosed herein (e.g., Figure 2 The scientific instrument support module 200 discussed herein (eg, the method creator logic 206, the data review logic 208, or a combination thereof), Figure 4 GUI500 discussed herein, reference Figure 5 The computing device 600 discussed herein and / or referenced herein Figure 6 1 ) is described with reference to the scientific instrument support system 700 discussed above, but the method 900 may be used in any suitable setting to perform any suitable support operation. Figure 8 The operations are each illustrated once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (e.g., different operations may be performed in parallel where appropriate). In some embodiments, method 1000 may be performed after performing method 900 (e.g., for a selected recommended method as developed according to method creator logic 206). However, in other embodiments, method 1000 may be performed regardless of how the method being performed is developed.

[0106] [TP386058USPRV1]

[0107] At 1002, method 1000 includes receiving an actual result associated with the executed method. As noted above, the executed method may be one of the recommended methods developed according to method 900, such as, for example, a method selected from a list by a user, and including any modifications made to the selected method by the user (e.g., via a user interface as part of selecting the method from the outputted list). The actual result may be raw data or processed data, and may represent data collected via one or more sensors of an instrument used to execute the method.

[0108] At 1004, method 1000 includes determining the expected results of the method being performed. As noted above, the expected results may include historical (actual) results generated using methods similar to (matching) the method being performed. In other embodiments, the expected results may be predicted (synthesized results), which may be generated via one or more models trained using data that associates the experimental method with the results. The expected results may be in the same format (e.g., original form, processed form) as the actual results, or may be processed into the same format as the actual results after being received.

[0109] At 1006, method 1000 includes verifying the actual results based on the expected results. The verification may include comparing the actual results to the expected results and identifying any differences (e.g., differences that exceed a (configurable) threshold). Various forms of data analysis may be performed on the actual results and the expected results to identify whether there are any problems or errors with the performed method such that it is necessary to perform the method again (e.g., with new samples, with new parameters, as a new / different method), or a combination thereof.

[0110] At 1008, method 1000 includes providing verification results as further training data to the model. For example, as noted above, in some embodiments, the verification results can be used as part of a method converter, which is configured to convert a known method for a known supplier into a similar method for a new supplier, and the verification results can be part of an iterative process for developing a method for a new supplier. Similarly, in some embodiments, the verification results can be output by applying the verification results to the executed method to create a modified method that can be automatically implemented (e.g., in response to the verification results meeting specific standards). Automatically implementing the modified method may include issuing a new reservation for executing the modified method. Such rerunning of the method (e.g., automatically executed or manually executed by the user) can be used as feedback to the model to improve subsequent method recommendations. In some embodiments, the verification results (or parts thereof) can also be provided to the user via one or more user interfaces.

[0111] Fig. 9 is a block diagram schematically illustrating an example application of methods 900 and 1000 using an example sample type (eg, orange). Fig. 9 As shown, in the example scenario, the method creator logic 206 receives the input sample type and can output a list of recommended methods for the sample type (e.g., methods for analyzing orange samples). As noted above, the method creator logic 206 can be based on the available instruments 112 and / or [TP386058USPRV1]

[0112] Other optimization criteria (some of which may be specified as input) are used to optimize (e.g., refine) the list of recommended methods (see block 1106). Fig. 9 As shown, the user may specify that the sample of interest is an orange sample and that the sample analysis has a high priority (see block 1102). The logic 206 may use this input to limit and / or rank the list of recommended methods based on the speed characteristics of the methods (e.g., only listing those methods that can be completed within the next one to two days).

[0113] The method creator logic 206 may also refine the list of recommended methods based on the desired analysis (see block 1104), which may be an initial input or a response to a prompt associated with providing the initial list. Fig. 9 In the example of , the desired analysis may be the identification of one or more pesticides according to particular regulations (eg, European regulations).

[0114] like Fig. 9 As shown, after a method is selected and executed, the actual results of the method can be processed (e.g., by data review logic 208, such as by comparing with expected results) to confirm the results, and the results of the analysis can be fed back as training data for the model created by the method (see box 1108). In addition, the analysis results can be used to analyze the results in an optimized manner to provide fast and intelligent data processing, where relevant data is summarized or otherwise presented to the user for focused and efficient review. As noted above, in some embodiments, one or more machine learning techniques can be used to perform such review.

[0115] The result of verification can be used for various purposes (e.g., various analyses and / or associated user interfaces) and is not limited to verifying the performance of a particular method. For example, the result can be used to track and control instrument utilization (e.g., identify instruments that provide high return on investment, arrange maintenance and plan downtime, identify what instruments are not running, etc.). Similarly, the result can be used to track reanalysis (e.g., re-execution of the method) and keep the low percentage of reanalysis of the implemented method. For example, keeping the analysis correct during the initial implementation of the method reduces costs and leads to more efficient use of laboratory resources. Therefore, tracking analysis results can be used to provide a system overview or laboratory dashboard, which can identify the cause or reason for reanalysis that can be corrected or solved. In addition, as shown in some example user interfaces in the example user interfaces provided herein, verification results can be used to provide efficient and simplified user review in one or more user interfaces. For example, anomalies can be identified to allow analysis time to be spent on data analysis rather than on bottlenecks associated with extracting or obtaining data. Similarly, by optimizing the recommended method based on available instruments, instruments can be used more efficiently, and various dashboards can be provided to (e.g., in real time) track idle instruments, how long it takes for specific user review matches, etc. All this information not only improves the management and use of laboratory resources (e.g.,

[0116] It improves the ease of use of the laboratory for analyzing samples and increases confidence in the results for inexperienced laboratory users), and provides a high-level view or dashboard of the laboratory or portions thereof that allows various laboratory managers, schedulers, etc. to identify and correct problems.

[0117] The following paragraphs provide various examples of the embodiments disclosed herein. For example, according to the above, for example, in the invention summary section and / or reference Figures 1 to 9 An example disclosed in any one of the figures or any combination of some or all of the figures, provides a computer-implemented method for scientific instrument method creation. The method includes: establishing a data storage device including a plurality of linked data sets, each of which links a predetermined experimental method, a predetermined sample type, and a predetermined analysis that define a plurality of experimental parameters; using machine learning techniques to train a model using the plurality of linked data sets stored in the data storage device; inputting at least one selected from the group consisting of sample type and desired analysis to the trained model; and outputting a list of one or more recommended methods from the model based on the sample type and the at least one of the desired analysis. The method may also include: receiving actual experimental results collected via one or more scientific instructions, the actual experimental results being associated with the execution of a method from the list of one or more recommended methods; determining an expected result of the executed method; and verifying the actual experimental results based on the expected result.

[0118] In some examples of the above methods, establishing the data storage device includes obtaining data from at least one selected from the group consisting of a database of approved methods, a database of historical methods, a database of vendor-specific methods, a spectral library, an instrument geometry library, a compound database, and a database of research materials.

[0119] In some examples of any of the methods above, establishing the data storage device includes obtaining data in multiple formats, the multiple formats including at least two selected from the group consisting of text data, binary data, and raw analytical data.

[0120] In some instances of any of the methods described above, the desired analysis includes one or more compounds of interest.

[0121] In some examples of any of the above methods, outputting the list of one or more recommended methods for analyzing the sample type includes: obtaining information identifying a set of available scientific instruments, and modifying the list of one or more recommended methods based on the set of available scientific instruments. In some examples, obtaining the information identifying the set of available scientific instruments includes obtaining a type of each instrument in the set of available scientific instruments, and wherein modifying the list of one or more recommended methods includes selecting from the set of one or more recommended methods based on the type of each instrument in the set of available scientific instruments.

[0122] In some embodiments, the method of claim 1 further comprises obtaining information identifying the set of available scientific instruments, wherein the method of claim 1 further comprises obtaining a state of each instrument in the set of available scientific instruments, and wherein modifying the list of one or more recommended methods comprises removing methods from the list of one or more recommended methods based on the state of at least one instrument in the set of available scientific instruments. In some embodiments, the method of claim 1 further comprises obtaining information identifying the set of available scientific instruments, wherein the method of claim 1 further comprises obtaining a state of each instrument in the set of available scientific instruments, and wherein modifying the list of one or more recommended methods comprises removing methods from the list of one or more recommended methods based on the state of at least one instrument in the set of available scientific instruments. In some embodiments, the method of claim 1 further comprises obtaining information identifying the set of available scientific instruments, wherein the method of claim 1 further comprises obtaining a state of each instrument in the set of available scientific instruments, and wherein modifying the list of one or more recommended methods comprises removing methods from the list of one or more recommended methods based on the speed of at least one instrument in the set of available scientific instruments.

[0123] In some examples of any of the methods above, the method further includes issuing a reservation for one or more instruments to perform the method from the list of one or more recommended methods.

[0124] In some examples of any of the above methods, the method further includes verifying a method included in the list of the one or more recommended methods. In some examples, verifying the method included in the list of the one or more recommended methods includes comparing the method to methods stored in a method database, and removing the method from the list of the one or more recommended methods in response to the method not matching one of the methods stored in the database. In these or other examples, verifying the method included in the list of the one or more recommended methods includes comparing at least one experimental parameter of the method to a threshold value, and removing the method from the list of the one or more recommended methods in response to the experimental parameter not satisfying the threshold value.

[0125] In some examples of any of the above methods, the method further comprises refining the list of recommended methods. In some examples, refining the list of recommended methods comprises providing a prompt for input to a user and refining the list of recommended methods based on a response to the prompt.

[0126] In some examples of any of the methods described above, the expected result includes at least one selected from the group consisting of historical results and predicted composite results.

[0127] In some examples of any of the methods above, the method also includes providing the results of the verification to the model as training data.

[0128] In some examples of any of the methods above, the method also includes providing results of a rerun of the method to the model as training data.

[0129] [TP386058USPRV1]

[0130] In some examples of any of the methods above, the one or more experimental parameters include at least one selected from the group consisting of matrix preparation, solid dispersion preparation, liquid dispersion preparation, time, temperature, pressure, concentration, and column type.

[0131] In some examples of any of the above methods, the method further comprises providing user feedback about the list of recommended methods to the model as training data. In some examples, the user feedback comprises scores of the methods included in the list of recommended methods.

[0132] According to the above, for example in the Summary section and / or reference Figures 1 to 9 Another example disclosed in any one of the figures or any combination of some or all of the figures, provides a system for creating a scientific instrument method. An example system includes: a non-transitory computer-readable medium storing a data storage device, the data storage device including a plurality of linked data sets, each of the linked data sets linking a predetermined experimental method, a predetermined sample type, and a predetermined analysis that define a plurality of experimental parameters; and at least one electronic processing device. The at least one electronic processor is used (e.g., configured to): train a model using the plurality of linked data sets stored in the data storage device using machine learning techniques; input at least one selected from the group consisting of sample type and desired analysis to the trained model; and output a list of one or more recommended methods from the model based on the sample type and the at least one of the desired analysis. In some examples, the at least one electronic processing device is also used to: receive actual experimental results collected via one or more scientific instructions, the actual experimental results being associated with the execution of a method from the list of one or more recommended methods; determine the expected results of the executed method; and verify the actual experimental results based on the expected results.

[0133] In some examples of the above systems, the desired analysis includes one or more compounds of interest.

[0134] In some examples of any of the above systems, to output the list of one or more recommended methods from the model, the electronic processing device is configured to: obtain information identifying a set of available scientific instruments, and modify the list of one or more recommended methods based on the set of available scientific instruments. In these examples or other examples, to obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain a type of each instrument in the set of available scientific instruments. In these examples or other examples, to modify the list of one or more recommended methods, the electronic processing device is configured to remove methods from the list of one or more recommended methods based on the type of each instrument in the set of available scientific instruments. In these examples or other examples, to obtain [TP386058USPRV1] identifying the set of available scientific instruments

[0135] The electronic processing device is configured to obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain the status of each instrument in the set of available scientific instruments. In these examples or other examples, to modify the list of one or more recommended methods, the electronic processing device is configured to remove methods from the list of one or more recommended methods based on the status of at least one instrument in the set of available scientific instruments. In these examples or other examples, to obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain the speed of each instrument in the set of available scientific instruments. In these examples or other examples, to modify the list of one or more recommended methods, the electronic processing device is configured to remove methods from the list of one or more recommended methods based on the speed of at least one instrument in the set of available scientific instruments.

[0136] In some examples of any of the systems above, the electronic processing device is further configured to issue a reservation for one or more instruments to perform the method from the list of one or more recommended methods.

[0137] In some examples of any of the above systems, the electronic processing device is further configured to validate the method by comparing a method included in the list of one or more recommended methods with methods stored in a method database and removing the method from the list of one or more recommended methods in response to the method not matching one of the methods stored in the database. In these or other examples, the electronic processing device is further configured to validate the method by comparing at least one experimental parameter of the method included in the list of one or more recommended methods with a threshold value and removing the method from the list of one or more recommended methods in response to the experimental parameter not satisfying the threshold value.

[0138] In some examples of any of the systems above, the electronic processing device is further configured to refine the list of recommended methods by providing a prompt to a user for input and refining the list of recommended methods based on a response to the prompt.

[0139] In some examples of any of the systems described above, the expected outcome includes at least one selected from the group consisting of historical outcomes and predicted composite outcomes.

[0140] In some examples of any of the systems above, the electronic processing device is further configured to provide the results of the verification to the model as training data.

[0141] In some examples of any of the systems above, the electronic processing device is further configured to provide results of reruns of the method to the model as training data.

[0142] [TP386058USPRV1]

[0143] In some examples of any of the systems above, the plurality of experimental parameters includes at least one selected from the group consisting of matrix preparation, solid dispersion preparation, liquid dispersion preparation, time, temperature, pressure, concentration, and column type.

[0144] In some examples of any of the above systems, the electronic processing device is configured to provide the model with user feedback about the list of recommended methods as training data. In these or other examples, the user feedback includes scores for methods included in the list of recommended methods.

[0145] According to the above, for example in the Summary section and / or reference Figures 1 to 9Another example disclosed in any one of the figures or any combination of some or all of the figures, provides a non-transitory computer-readable medium storing instructions, which perform a set of functions when executed by one or more electronic processing devices. The set of functions includes: establishing a data storage device including a plurality of linked data sets, each of which links a predetermined experimental method, a predetermined sample type, and a predetermined analysis that define a plurality of experimental parameters; using machine learning techniques to train a model using the plurality of linked data sets stored in the data storage device; inputting at least one selected from the group consisting of sample type and desired analysis to the trained model; and outputting a list of one or more recommended methods from the model based on the sample type and the at least one of the desired analysis. In some examples, the set of functions also includes: receiving actual experimental results collected via one or more scientific instructions, the actual experimental results being associated with the execution of a method from the list of one or more recommended methods; determining an expected result of the executed method; and verifying the actual experimental results based on the expected result.

[0146] In some examples of the non-transitory computer-readable medium described above, establishing the data storage device includes obtaining data from at least one of the group consisting of a database of approved methods, a database of historical methods, a database of vendor-specific methods, a spectral library, an instrument geometry library, a compound database, and a database of research materials.

[0147] In some examples of any of the non-transitory computer-readable media above, establishing the data storage device includes obtaining data in multiple formats, the multiple formats including at least two selected from the group consisting of text data, binary data, and raw analytical data.

[0148] According to the above, for example in the Summary section and / or reference Figures 1 to 9 Another example disclosed in any one or any combination of some or all of the figures in the present invention provides a computer-implemented method for creating a scientific instrument method. The method includes: establishing a data storage device including a plurality of linked data sets, each of the linked data sets linking and defining a plurality of experimental parameters [TP386058USPRV1]

[0149] a plurality of predetermined experimental methods, predetermined sample types, and predetermined analyses; and using machine learning techniques to train a model using the plurality of linked data sets stored in the data storage device, the model being configured when being trained to receive at least one selected from the group consisting of a sample type and a desired analysis, and outputting a list of one or more recommended methods based on the sample type and the at least one of the desired analyses.

[0150] According to the above, for example in the Summary section and reference Figures 1 to 9 In another example disclosed in any one of the figures or any combination of some or all of the figures, a computer-implemented method for scientific instrument method creation is provided. The method includes: inputting at least one selected from the group consisting of sample type and desired analysis to a model trained using linked data, the linked data linking a predetermined experimental method defining a plurality of experimental parameters, a predetermined sample type, and a predetermined analysis; and outputting a list of one or more recommended methods from the model based on the sample type or the desired analysis.

[0151] In some examples of any of the above methods, the method also includes: receiving actual experimental results collected via one or more scientific instructions, the actual experimental results being associated with the execution of a method from the list of one or more recommended methods; determining expected results of the executed method; and verifying the actual experimental results based on the expected results.

Claims

1. A system for creating a scientific instrument method, the system comprising: a non-transitory computer readable medium storing a data storage device including a plurality of linked data sets, each of the linked data sets linking a predetermined experimental method, a predetermined sample type, and a predetermined analysis defining a plurality of experimental parameters; and An electronic processing device, the electronic processing device being configured to: training a model using the plurality of linked data sets stored in the data storage device using machine learning techniques; inputting to the trained model at least one selected from the group consisting of sample type and desired analysis; outputting a list of one or more recommended methods from the model based on the at least one of the sample type and the desired analysis; receiving actual experimental results collected via one or more scientific instructions, the actual experimental results associated with the performance of a method from the list of one or more recommended methods; Determine the expected results of the methods implemented; as well as The actual experimental results are verified based on the expected results.

2. The system of claim 1, wherein the desired analysis includes one or more compounds of interest.

3. The system according to claim 1, wherein: To output the list of one or more recommended methods from the model, the electronic processing device is configured to: obtain information identifying the collection of available scientific instruments, and The list of one or more recommended methods is modified based on the set of available scientific instruments.

4. The system according to claim 3, wherein: To obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain a type of each instrument in the set of available scientific instruments, and wherein to modify the list of one or more recommended methods, the electronic processing device is configured to modify the list of one or more recommended methods based on the available scientific instruments. The method is removed from the list of one or more recommended methods for each instrument of the type in the instrument set.

5. The system according to claim 3, wherein: In order to obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain a status of each instrument in the set of available scientific instruments, and wherein, in order to modify the list of one or more recommended methods, the electronic processing device is configured to remove methods from the list of one or more recommended methods based on the status of at least one instrument in the set of available scientific instruments.

6. The system according to claim 3, wherein: In order to obtain the information identifying the set of available scientific instruments, the electronic processing device is configured to obtain a speed of each instrument in the set of available scientific instruments, and wherein, in order to modify the list of one or more recommended methods, the electronic processing device is configured to remove methods from the list of one or more recommended methods based on the speed of at least one instrument in the set of available scientific instruments.

7. The system of claim 1, wherein the electronic processing device is further configured to issue a reservation for one or more instruments to perform the method from the list of one or more recommended methods.

8. The system of claim 1 , wherein the electronic processing device is further configured to verify the method by comparing methods included in the list of the one or more recommended methods with methods stored in a method database and removing the method from the list of the one or more recommended methods in response to the method not matching one of the methods stored in the database.

9. A system according to claim 1, wherein the electronic processing device is further configured to verify the method by comparing at least one experimental parameter of the method included in the list of one or more recommended methods with a threshold value, and removing the method from the list of one or more recommended methods in response to the experimental parameter not satisfying the threshold value.

10. The system of claim 1, wherein the electronic processing device is further configured to refine the list of recommended methods by providing a prompt to a user for input and refining the list of recommended methods based on a response to the prompt.

11. The system of claim 1, wherein the expected result comprises at least one selected from the group consisting of historical results and predicted composite results.

12. The system of claim 1, wherein the electronic processing device is further configured to provide the results of the verification to the model as training data.

13. The system of claim 1, wherein the electronic processing device is further configured to provide results of reruns of the method to the model as training data.

14. The system of claim 1, wherein the plurality of experimental parameters comprises at least one selected from the group consisting of matrix preparation, solid dispersion preparation, liquid dispersion preparation, time, temperature, pressure, concentration, and column type.

15. The system of claim 1, wherein the electronic processing device is configured to provide user feedback regarding the list of recommended methods as training data to the model.

16. The system of claim 14, wherein the user feedback comprises scores of methods included in the list of recommended methods.

17. A non-transitory computer readable medium storing instructions that, when executed by one or more electronic processing devices, perform a set of functions comprising: establishing a data storage device comprising a plurality of linked data sets, each of the linked data sets linking a predetermined experimental method, a predetermined sample type, and a predetermined analysis defining a plurality of experimental parameters; training a model using the plurality of linked data sets stored in the data storage device using machine learning techniques; inputting to the trained model at least one selected from the group consisting of sample type and desired analysis; outputting a list of one or more recommended methods from the model based on the at least one of the sample type and the desired analysis; receiving actual experimental results collected via one or more scientific instructions, the actual experimental results associated with the performance of a method from the list of one or more recommended methods; Determine the expected results of the methods implemented; as well as The actual experimental results are verified based on the expected results.

18. The non-transitory computer readable medium of claim 17, wherein establishing the data storage device comprises selecting from a database of approved methods, a database of historical methods, a database of [TP386058USPRV1] The data is obtained from at least one of the group consisting of a database of vendor-specific methods, a spectral library, an instrument geometry library, a compound database, and a database of research materials.

19. The non-transitory computer readable medium of claim 17, wherein establishing the data storage device comprises obtaining data in a plurality of formats, the plurality of formats comprising at least two selected from the group consisting of text data, binary data, and raw analytical data.

20. A computer-implemented method for scientific instrument method creation, the method comprising: establishing a data storage device comprising a plurality of linked data sets, each of the linked data sets linking a predetermined experimental method, a predetermined sample type, and a predetermined analysis defining a plurality of experimental parameters; as well as A model is trained using machine learning techniques using the multiple linked data sets stored in the data storage device, and the model, when trained, is configured to receive at least one selected from the group consisting of a sample type and a desired analysis, and output a list of one or more recommended methods based on the sample type and the at least one of the desired analysis.

Images