Patent Description:
According to an aspect of the present invention, there is provided a computer-implemented method as claimed in claim <NUM>.

Exemplary embodiments relate to techniques for analyzing chromatography data and metadata across an enterprise or supply chain in order to identify possible compliance risks. Unless otherwise noted, it is contemplated that these embodiments may be used individually in order to achieve the advantages noted, or in any combination in order to achieve synergistic effects.

As used herein, a compliance risk refers to a circumstance or set of circumstances that do not comply with data integrity best practices, potentially violates regulatory or contractual requirements, are preconfigured situations in which an administrator has required record-keeping for audit purposes, or any other situations in which the process of acquiring or analyzing chromatography data potentially runs afoul of predetermined required conditions. Assessing compliance risks may be important for (e.g.) proactively assessing risks and correcting problematic issues before an audit is conducted by compliance authorities.

Recognizing compliance risks can be a difficult problem when analyzing one's own chromatography data, since it may not be clear when a set of circumstances does or does not constitute a compliance risk. It is even more difficult, however, when working with outside partners or other third parties (e.g., analyzing compliance risks across an enterprise or supply chain) because the third party's data and/or practices may not be made available for analysis. This is particularly common, for instance, in the pharmaceutical industry (where one company may rely on receiving pharmaceutical compounds from outside suppliers). In these situations, it may be necessary to rely on the third party to conduct their own compliance analysis, which may not be the most desirable outcome.

Exemplary embodiments provide visualization and advanced data science on information collected in an analytical data system. Embodiments identify correlations and patterns in chromatography metadata around areas of potential user error. Examples of such metadata include whether some chromatography injections were not processed, whether some injections were processed manually instead of programmatically or in accordance with pre-approved processes, whether some injections were aborted, manually integrated peaks, sign-off records, audit trail records, indicia of performance degradation in the analytical data system (for example, changes to injection data over time), and other information such as a user name of the user conducting the analysis, an instrument ID for the instrument used in the analysis, type of column or solvent used, an instrument location, a server location for a server used to process the data, and what administration privileges were assigned to the users having access to the data. Correlations between these data sources may point to compliance risk areas.

Metadata from the analytical system may be combined with other data sources such as laboratory balances, laboratory access records, and time of data acquisition for the purpose of performing data science for regulatory compliance. The metadata may also be combined with analytical data (e.g., LC data, LCMS data, and other laboratory information sources) to correlate an analytical outcome (such as but not limited to peak shape, concentration of analyte/impurity, retention time) with compliance artifacts. Supervised and/or unsupervised machine learning techniques may be used to combine these data source and learn correlations between them and compliance risks.

The results of these analyses may be displayed on a dashboard or map, allowing a user to visualize compliance risks across an entire enterprise or supply chain. Automatic notifications of compliance risks may be generated and presented on a user interface. A system may also use pattern recognition to provide insights around potential compliance risks that have not yet occurred.

These embodiments will be described in detail below with reference to the accompanying Figures.

For purposes of illustration, <FIG> is a schematic diagram of a system that may be used in connection with techniques herein. Although <FIG> depicts particular types of devices in a specific LCMS configuration, one of ordinary skill in the art will understand that different types of chromatographic devices (e.g., LC, MS, tandem MS, etc.) may also be used in connection with the present disclosure. In particular, it is contemplated that exemplary embodiments may be particularly well-suited to use with an LC system, especially when used without an accompanying MS apparatus. Exemplary embodiments may also be used in conjunction with other data sources than the ones depicted and described in detail herein, especially large-scale chromatography (such as the GE Akta system), NMR, IR, CE etc..

A sample <NUM> is injected into a liquid chromatograph <NUM> through an injector <NUM>. A pump <NUM> pumps the sample through a column <NUM> to separate the mixture into component parts according to retention time through the column.

The output from the column is input to a mass spectrometer <NUM> for analysis. Initially, the sample is desolved and ionized by a desolvation/ionization device <NUM>. Desolvation can be any technique for desolvation, including, for example, a heater, a gas, a heater in combination with a gas or other desolvation technique. Ionization can be by any ionization techniques, including for example, electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), matrix assisted laser desorption (MALDI) or other ionization technique. Ions resulting from the ionization are fed to a collision cell <NUM> by a voltage gradient being applied to an ion guide <NUM>. Collision cell <NUM> can be used to pass the ions (low-energy) or to fragment the ions (high-energy).

Different techniques (including one described in <CIT>, which is incorporated by reference herein) may be used in which an alternating voltage can be applied across the collision cell <NUM> to cause fragmentation. Spectra are collected for the precursors at low-energy (no collisions) and fragments at high-energy (results of collisions).

The output of collision cell <NUM> is input to a mass analyzer <NUM>. Mass analyzer <NUM> can be any mass analyzer, including quadrupole, time-of-flight (TOF), ion trap, magnetic sector mass analyzers as well as combinations thereof. A detector <NUM> detects ions emanating from mass analyzer <NUM>. Detector <NUM> can be integral with mass analyzer <NUM>. For example, in the case of a TOF mass analyzer, detector <NUM> can be a microchannel plate detector that counts intensity of ions, i.e., counts numbers of ions impinging it.

A raw data store <NUM> may provide permanent storage for storing the ion counts for analysis. For example, raw data store <NUM> can be an internal or external computer data storage device such as a disk, flash-based storage, and the like. An acquisition device <NUM> analyzes the stored data. Data can also be analyzed in real time without requiring storage in a storage medium <NUM>. In real time analysis, detector <NUM> passes data to be analyzed directly to computer <NUM> without first storing it to permanent storage.

Collision cell <NUM> performs fragmentation of the precursor ions. Fragmentation can be used to determine the primary sequence of a peptide and subsequently lead to the identity of the originating protein. Collision cell <NUM> includes a gas such as helium, argon, nitrogen, air, or methane. When a charged precursor interacts with gas atoms, the resulting collisions can fragment the precursor by breaking it up into resulting fragment ions.

Metadata describing various parameters related to data acquisition may be generated alongside the raw data. This information may include a configuration of the liquid chromatograph <NUM> or mass spectrometer <NUM> (or other chromatography apparatus that acquires the data), which may define a data type, temperatures (e.g., of the laboratory or LC system), and others discussed in more detail below. An identifier (e.g., a key) for a codec that is configured to decode the data may also be stored as part of the metadata and/or with the raw data. The metadata may be stored in a metadata catalog <NUM> in a document store <NUM>.

The acquisition device <NUM> may operate according to a workflow, providing visualizations of data to an analyst at each of the workflow steps and allowing the analyst to generate output data by performing processing specific to the workflow step. The workflow may be generated and retrieved via a client browser <NUM>. As the acquisition device <NUM> performs the steps of the workflow, it may read raw data from a stream of data located in the raw data store <NUM>. As the acquisition device <NUM> performs the steps of the workflow, it may generate processed data that is stored in a metadata catalog <NUM> in a document store <NUM>; alternatively or in addition, the processed data may be stored in a different location specified by a user of the acquisition device <NUM>. It may also generate audit records that may be stored in an audit log <NUM>.

The exemplary embodiments described herein may be performed at the client browser <NUM> and acquisition device <NUM>, among other locations. An example of a device suitable for use as an acquisition device <NUM> and/or client browser <NUM>, as well as various data storage devices, is depicted in <FIG>. Servers and other computer hardware can either be on a local network or using cloud technology.

<FIG> depicts an exemplary system suitable for use with exemplary embodiments. The system includes a chromatography data environment <NUM> configured to manage the acquisition and storage of chromatography data from chromatography data systems 204a, 204b, 204c,. The chromatography data systems 204a, 204b, 204c,. may upload their data to an integration platform <NUM> configured to store data to data lake <NUM> along with metadata such a timestamp indicating when the data was captured or uploaded to the data lake, identifying characteristics of the components of or of the configuration of the chromatography data system, format characteristics sufficient to identify the format that the data is in, and/or to extract data from the CDSes and standardize the data acquired by different instruments, different types of instruments, in different laboratories, etc. The data may be stored in a data lake <NUM> in an access group <NUM>. Different parent organizations (e.g., different companies performing chromatography experiments) may each control a different data lake <NUM>, and access to the data in the respective data lake <NUM> may be managed based on the access group <NUM>.

In some embodiments, third parties may be capable of requesting the right to review data in a given access group <NUM> from the parent organization that controls the access group <NUM>. This may allow the requesting organization to review the data for potential compliance issues. For example, the reviewing organization may apply one or more data science applications <NUM> to analyze the data and identify compliance issues. Examples of data science applications <NUM> include applications configured to consider, in isolation or in combination: whether some injections acquired in the chromatography data environment <NUM> were not processed (and or a number of unprocessed injections <NUM>); whether some injections were processed multiple times (multiple processing <NUM>); whether some of the data was subjected to manual integration <NUM>; whether some of the chromatography data runs were aborted (aborted runs <NUM>); and whether the data was subjected to partial sign off <NUM>, among other possibilities discussed in more detail below.

When compliance issues are identified by the data science applications <NUM>, the results may be displayed in a dashboard in a compliance graphical user interface. <FIG> depicts various compliance plots <NUM>, <NUM>, <NUM>. These plots may be filled in or colored with indicators showing the presence or absence of conditions, such as those noted above, that may be associated (individually or in combination) with a compliance risk. The compliance plots <NUM>, <NUM>, <NUM> may be displayed on the compliance graphical user interface. Alternatively or in addition, when compliance issues are identified, a notification may be generated and transmitted to a user responsible for monitoring compliance issues. The compliance issues could also trigger an automatic response back into the data system to pause or stop operation of the system. The compliance system can notify personnel or bodies outside of the normal ecosystem to potential risks, such as those risks outlined in a quality management system or regulatory standards.

<FIG> and <FIG> depict alternative implementations of dashboards suitable for use with exemplary embodiments.

The compliance issues may be identified using machine learning. Instead of categorical machine learning for predictive analytics, a method is proposed to use "Industry <NUM>" / TinyML techniques and a cascade architecture to facilitate the detection of bad data acquisition - faults in chromatography data systems. Using this method greatly simplifies the process of identifying a pool of "exemplar" data that is the basis of modern ML algorithms.

<FIG> depicts an exemplary training and visualization pipeline suitable for use with exemplary embodiments. In this pipeline, data processing elements are combined with data visualization elements. Multiple horizontal slices may be made across the pipeline at appropriate locations to separate processing steps into different processes/algorithm combinations.

<FIG> and <FIG> depict exemplary data explorers for visualizing operational and analytical data. Prior to visualizing the data, it may be pre-processed. This may be as simple as translating from one data format to another (e.g. from Empower data to CSV format for instance). However, at this stage there may be filtering (remove NaN or Null values) or even translation of categorical values to continuous for machine learning or human visualization. In addition at this stage further calculations across multiple continuous values may be performed for instance for MS there may be a calculated FOM (figure of merit) calculated using Resolution and Sensitivity (as well as statistical measures such as skew, kurtosis for instance) and FFT for spectrogram analysis followed by machine vision techniques for supervised learning techniques. At this point there may also be statistical profiling correlation calculations applied (such as provided by a standard library e.g. Pandas that reports correlation information for instance, as in <FIG>).

The data may then be visualized in a dashboard such as the one depicted in <FIG>. The data explorer of <FIG> allows for the visualization of two or more parameters. Using this dashboard, a user may identify outliers or regions of interest, which may be fed into a machine learning algorithm (e.g., kNN clustering). Outliers may be identified using a distance metric, and suitable hyperparameters used by the ML algorithm may include the number of clusters, the distance metric, etc..

<FIG> depicts an example of feature extraction. Feature extraction is the process by which the dimensionality of the data is reduced to something more readily understandable by human beings (or machine). The input data are the dimensions that we wish to reduce on, for instance the number of manual injections, number of times processed, number of sign offs, time between sign offs vs user can be clustered ← in this instance the correct number of clusters and distance metrics need to be determined either by user interactions and / or further processing (in this example above the UMAP manifold learning technique coupled to kNN and visualization of the results. Once these parameters are known these can be used online / inline to determine automatically when some data are anomalous or needs to be flagged automatically for further human attention (review by exception / anomaly detection).

Predictions may be made on large batches - that is, run through the whole of the data within a certain (long time frame) and flagged for follow up on any items requiring attention (or simply visualize a trend for example). Trending analysis over time of things like pump pressure, charge current being drawn, time take to process injections or time between injections per user may lead to insights in the data that may be readily identifiable by a human operator or if threshold is used for automatic flagging and communication to a human supervisor. (after collating / histogramming and looking at highest percentile e.g. the <NUM>% longest times taken to process or the <NUM>% shortest).

Predictions may be made in "real time" upon request. Anomalous behavior detection may be performed based on a number of collated input parameter observations on a particular activity. For instance, on a delete action (a trigger), present a group of specified input parameters to the model for a prediction (Flag for follow up or OK status automatically).

<FIG> brings these concepts and data explorers together into a visualization pipeline. The results of the visualization pipeline may be used to train a machine learning algorithm.

For instance, <FIG> depicts a machine learning pipeline suitable for use with exemplary embodiments. Of particular interest are the feature extraction and inference boxes (in this case Fast Fourier Transforms and a Machine Vision DL network) that process continuously streamed data. The data may include both low level sensor data and high-level analytical data coupled in a cascade architecture and combined to give a higher likelihood of anomalous events detection. One of ordinary skill in the art will recognize that <FIG> depicts one example of an architecture, although other implementations (e.g., using different types of machine learning or different types of data) are also applicable to the present disclosure.

<FIG> depicts exemplary compliance analysis logic <NUM> for storing data in, and retrieving data from, a chromatography data processing environment, and for processing the data to identify potential compliance issues, according to an exemplary embodiment. The compliance analysis logic <NUM> may be embodied as a computer-implemented method or as instructions stored on a non-transitory computer-readable storage medium and may be configured to cause a processor to perform the logical blocks included in <FIG>. In some embodiments, the compliance analysis logic <NUM> may be performed by a computing system configured to perform the logical blocks included in <FIG>.

Processing starts at start block <NUM>. At block <NUM>, a chromatography apparatus may acquire data. For instance, the chromatography apparatus may perform an experiment and output data in the form of a stream of measurements. The chromatography apparatus may store the measurements in a raw data store. At block <NUM>, the chromatography apparatus may generate metadata related to the experiment and may store the metadata in a metadata catalog distinct from the raw data store.

At block <NUM>, the system may train an AI/ML system to recognize a compliance issue. The AI/ML system may be trained by providing labeled training data, where the training data includes metadata, additional parameters, and/or analytical data, and is labeled with a flag indicating whether the data is associated with a compliance issue. By applying an AI/ML algorithm, a relationship between the data, metadata, and/or additional parameters and potential compliance issues can be learned.

In some embodiments, it may be simpler to identify when a compliance issue exists by examining the metadata and other parameters, as opposed to the analytical data. For example, the metadata may include an indicator of whether the experiment was associated with a manually-processed peak. As opposed to programmatically processing peaks according to known methods, a manually-processed peak may indicate that a user observed the chromatography data and opted to apply custom settings configured to yield a desired result (instead of a more objective result). The resulting analytical data may appear very similar to data generated by a compliant experiment, and so it may be difficult to learn when a compliance issue exists from the analytical data itself. However, when a compliance issue is identified based on the metadata and other parameters, it may then be possible to apply this understanding to label the analytical data and identify features in the analytical data (e.g., peak shape, tailing factors, column degradation profile, etc.) that may be indicative of compliance problems.

To that end, at block <NUM> the system may optionally train an AI/ML system (the same system as was trained in block <NUM>, or a different system) to correlate compliance problems to the analytical data.

Once trained, the AI/ML system(s) may then be used to analyze new chromatography data to determine whether compliance issues may exist in the new chromatography data. The new chromatography data may originate with the user/organization applying the compliance analysis, or with a third party (such as suppliers of the analyzing organization in a supply chain). To that end, it may be necessary for the current user/organization to request access rights to the third-party data in a data lake at block <NUM>. The third-party may provide limited access rights allowing the data to be analyzed for compliance purposes.

At block <NUM>, the local and/or third-party data may be analyzed using the trained AI/ML system(s) for compliance issues. Compliance issues may be identified based on one or more rules, such as a parameter value being toggled to true or exceeding a predefined threshold value. In some embodiments, compliance issues may be identified based on trends in the data (e.g., determining that a compliance issue does not exist, but if the data continues on its current trend, a compliance issue will exist within a predetermined time limit).

Any problematic conditions may be displayed, at block <NUM> and block <NUM>, in a compliance dashboard on a compliance user interface (see, e.g., <FIG>). If the system determines that a compliance issue is likely (e.g., the AI/ML system determines that a probability of a compliance issue is more than a predetermined threshold value), then the system may generate a notification or alert and transmit the notification/alert to a user responsible for monitoring compliance.

Processing may then proceed to done block <NUM> and terminate.

In order to learn associations between metadata and compliance issues (and/or between compliance issues and analytical data), artificial intelligence/machine learning (AI/ML) may be applied. To that end, <FIG> depicts an AI/ML environment <NUM> suitable for use with exemplary embodiments.

The AI/ML environment <NUM> may include an AI/ML System <NUM>, such as a computing device that applies an AI/ML algorithm to learn relationships between the above-noted protein parameters.

The AI/ML System <NUM> may make use of experimental data <NUM> returned by an experimental apparatus <NUM> as (or after) chromatography data is collected. In some cases, the experimental data <NUM> may include pre-existing experimental data from databases, libraries, repositories, etc. The experimental data <NUM> may be collocated with the AI/ML System <NUM> (e.g., stored in a Storage <NUM> of the AI/ML System <NUM>), may be remote from the AI/ML System <NUM> and accessed via a Network Interface <NUM>, or may be a combination of local and remote data.

In the Training Data <NUM>, the experimental data returned from experimental apparatuses may be supplemented by data learned by modeling and simulating chromatography data collection in software, and by parsing scientific and academic literature for information about the relationships.

As noted above, the AI/ML System <NUM> may include a Storage <NUM>, which may include a hard drive, solid state storage, and/or random-access memory. The storage may hold Training Data <NUM>, which may compare different data and metadata against a classification of whether a compliance issue exists. In one example, these Training Data <NUM> may include the metadata <NUM>, Analytical data <NUM> and/or other additional parameters <NUM>, although other properties may be measured depending on the application. The metadata <NUM> may include, among other information:.

The additional parameters <NUM> may include, among other information:.

The analytical data <NUM> may include unprocessed data from a chromatography apparatus and/or processed data.

Some embodiments may be used in conjunction with a machine learning model, such as a neural network, decision tree, support vector machine, etc. In such embodiments, the Training Data <NUM> may be applied to train a model <NUM>. Depending on the particular application, different types of models <NUM> may be suitable for use. For instance, in the depicted example, an artificial neural network (ANN) may be particularly well-suited to learning associations between metadata, analytical data, and compliance issues. Similarity and metric distance learning may also be well-suited to this particular type of task, although one of ordinary skill in the art will recognize that different types of models <NUM> may be used, depending on the designers goals, the resources available, the amount of input data available, etc. Other embodiments may use a model-less AI paradigm, in which case no model <NUM> is used.

Any suitable Training Algorithm <NUM> may be used to train the model <NUM>. Nonetheless, the example depicted in <FIG> may be particularly well-suited to a supervised training algorithm or reinforcement learning. For a supervised training algorithm, the AI/ML System <NUM> may apply the Metadata <NUM> and Additional parameters <NUM> as input data, to which a compliance flag <NUM> (indicating whether the data is associated with a compliance issue) may be mapped to learn associations between the inputs and compliance issues.

The Training Algorithm <NUM> may be applied using a Processor Circuit <NUM>, which may include suitable hardware processing resources that operate on the logic and structures in the Storage <NUM>. The Training Algorithm <NUM> and/or the development of the trained model <NUM> may be at least partially dependent on model Hyperparameters <NUM>; in exemplary embodiments, the model Hyperparameters <NUM> may be automatically selected based on Hyperparameter Optimization logic <NUM>, which may include any known hyperparameter optimization techniques as appropriate to the model <NUM> selected and the Training Algorithm <NUM> to be used.

Optionally, the model <NUM> may be re-trained over time, in order to accommodate new knowledge about proteins and new experiments performed.

In some embodiments, some of the Training Data <NUM> may be used to initially train the model <NUM>, and some may be held back as a validation subset. The portion of the Training Data <NUM> not including the validation subset may be used to train the model <NUM>, whereas the validation subset may be held back and used to test the trained model <NUM> to verify that the model <NUM> is able to generalize its predictions to new data.

As discussed above, the metadata <NUM> and additional parameters <NUM> may be used to learn when a compliance issue exists. Subsequently, the trained model <NUM> may be applied to the analytical data <NUM> to learn configurations in the analytical data <NUM> that signify that a compliance issues may exist. Accordingly, a second model <NUM> may optionally be trained.

Once the model <NUM> is trained, it may be applied (by the Processor Circuit <NUM>) to new input data. The new input data may include current metadata <NUM> and additional parameters <NUM>, and/or may include analytical data <NUM>. This input to the model <NUM> may be formatted according to a predefined input structure <NUM> mirroring the way that the Training Data <NUM> was provided to the model <NUM>. The model <NUM> may generate an output structure <NUM> which may be, for example, a prediction of whether a compliance issue exists, given the input data.

The above description pertains to a particular kind of AI/ML System <NUM>, which applies supervised learning techniques given available training data with input/result pairs. However, the present invention is not limited to use with a specific AI/ML paradigm, and other types of AI/ML techniques may be used. For example, in some embodiments the AI/ML System <NUM> may apply reinforcement learning, in which the AI/ML System <NUM> may learn a policy or set of rules defining which changes to analytical data <NUM>, metadata <NUM>, and/or additional parameters <NUM> affect compliance. Other AI/ML techniques, such as evolutionary algorithms, are also contemplated for use with exemplary embodiments.

<FIG> illustrates one example of a system architecture and data processing device that may be used to implement one or more illustrative aspects described herein in a standalone and/or networked environment. Various network nodes, such as the data server <NUM>, web server <NUM>, computer <NUM>, and laptop <NUM> may be interconnected via a wide area network <NUM> (WAN), such as the internet. Other networks may also or alternatively be used, including private intranets, corporate networks, LANs, metropolitan area networks (MANs) wireless networks, personal networks (PANs), and the like. Network <NUM> is for illustration purposes and may be replaced with fewer or additional computer networks. A local area network (LAN) may have one or more of any known LAN topology and may use one or more of a variety of different protocols, such as ethernet. Devices data server <NUM>, web server <NUM>, computer <NUM>, laptop <NUM> and other devices (not shown) may be connected to one or more of the networks via twisted pair wires, coaxial cable, fiber optics, radio waves or other communication media.

Computer software, hardware, and networks may be utilized in a variety of different system environments, including standalone, networked, remote-access (aka, remote desktop), virtualized, and/or cloud-based environments, among others.

The term "network" as used herein and depicted in the drawings refers not only to systems in which remote storage devices are coupled together via one or more communication paths, but also to stand-alone devices that may be coupled, from time to time, to such systems that have storage capability. Consequently, the term "network" includes not only a "physical network" but also a "content network," which is comprised of the data--attributable to a single entity--which resides across all physical networks.

The components may include data server <NUM>, web server <NUM>, and client computer <NUM>, laptop <NUM>. Data server <NUM> provides overall access, control and administration of databases and control software for performing one or more illustrative aspects described herein. Data server <NUM> may be connected to web server <NUM> through which users interact with and obtain data as requested. Alternatively, data server <NUM> may act as a web server itself and be directly connected to the internet. Data server <NUM> may be connected to web server <NUM> through the network <NUM> (e.g., the internet), via direct or indirect connection, or via some other network. Users may interact with the data server <NUM> using remote computer <NUM>, laptop <NUM>, e.g., using a web browser to connect to the data server <NUM> via one or more externally exposed web sites hosted by web server <NUM>. Client computer <NUM>, laptop <NUM> may be used in concert with data server <NUM> to access data stored therein or may be used for other purposes. For example, from client computer <NUM>, a user may access web server <NUM> using an internet browser, as is known in the art, or by executing a software application that communicates with web server <NUM> and/or data server <NUM> over a computer network (such as the internet).

Servers and applications may be combined on the same physical machines, and retain separate virtual or logical addresses, or may reside on separate physical machines. <FIG> illustrates just one example of a network architecture that may be used, and those of skill in the art will appreciate that the specific network architecture and data processing devices used may vary, and are secondary to the functionality that they provide, as further described herein. For example, services provided by web server <NUM> and data server <NUM> may be combined on a single server.

Each component data server <NUM>, web server <NUM>, computer <NUM>, laptop <NUM> may be any type of known computer, server, or data processing device. Data server <NUM>, e.g., may include a processor <NUM> controlling overall operation of the data server <NUM>. Data server <NUM> may further include RAM <NUM>, ROM <NUM>, network interface <NUM>, input/output interfaces <NUM> (e.g., keyboard, mouse, display, printer, etc.), and memory <NUM>. Input/output interfaces <NUM> may include a variety of interface units and drives for reading, writing, displaying, and/or printing data or files. Memory <NUM> may further store operating system software <NUM> for controlling overall operation of the data server <NUM>, control logic <NUM> for instructing data server <NUM> to perform aspects described herein, and other application software <NUM> providing secondary, support, and/or other functionality which may or may not be used in conjunction with aspects described herein. The control logic may also be referred to herein as the data server software control logic <NUM>. Functionality of the data server software may refer to operations or decisions made automatically based on rules coded into the control logic, made manually by a user providing input into the system, and/or a combination of automatic processing based on user input (e.g., queries, data updates, etc.).

Memory <NUM> may also store data used in performance of one or more aspects described herein, including a first database <NUM> and a second database <NUM>. In some embodiments, the first database may include the second database (e.g., as a separate table, report, etc.). That is, the information can be stored in a single database, or separated into different logical, virtual, or physical databases, depending on system design. Web server <NUM>, computer <NUM>, laptop <NUM> may have similar or different architecture as described with respect to data server <NUM>. Those of skill in the art will appreciate that the functionality of data server <NUM> (or web server <NUM>, computer <NUM>, laptop <NUM>) as described herein may be spread across multiple data processing devices, for example, to distribute processing load across multiple computers, to segregate transactions based on geographic location, user access level, quality of service (QoS), etc..

One or more aspects may be embodied in computer-usable or readable data and/or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices as described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. The modules may be written in a source code programming language that is subsequently compiled for execution or may be written in a scripting language such as (but not limited to) HTML or XML. The computer executable instructions may be stored on a computer readable medium such as a nonvolatile storage device. Any suitable computer readable storage media may be utilized, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, and/or any combination thereof. In addition, various transmission (non-storage) media representing data or events as described herein may be transferred between a source and a destination in the form of electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, and/or wireless transmission media (e.g., air and/or space). various aspects described herein may be embodied as a method, a data processing system, or a computer program product. Therefore, various functionalities may be embodied in whole or in part in software, firmware and/or hardware or hardware equivalents such as integrated circuits, field programmable gate arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects described herein, and such data structures are contemplated within the scope of computer executable instructions and computer-usable data described herein.

The components and features of the devices described above may be implemented using any combination of discrete circuitry, application specific integrated circuits (ASICs), logic gates and/or single chip architectures. Further, the features of the devices may be implemented using microcontrollers, programmable logic arrays and/or microprocessors or any combination of the foregoing where suitably appropriate. It is noted that hardware, firmware and/or software elements may be collectively or individually referred to herein as "logic" or "circuit.

It will be appreciated that the exemplary devices shown in the block diagrams described above may represent one functionally descriptive example of many potential implementations. Accordingly, division, omission or inclusion of block functions depicted in the accompanying figures does not infer that the hardware components, circuits, software and/or elements for implementing these functions would be necessarily be divided, omitted, or included in embodiments.

At least one computer-readable storage medium may include instructions that, when executed, cause a system to perform any of the computer-implemented methods described herein.

Some embodiments may be described using the expression "one embodiment" or "an embodiment" along with their derivatives. These terms mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Moreover, unless otherwise noted the features described above are recognized to be usable together in any combination. Thus, any features discussed separately may be employed in combination with each other unless it is noted that the features are incompatible with each other.

With general reference to notations and nomenclature used herein, the detailed descriptions herein may be presented in terms of program procedures executed on a computer or network of computers. These procedural descriptions and representations are used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art.

A procedure is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. These operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It proves convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be noted, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein, which form part of one or more embodiments. Rather, the operations are machine operations. Useful machines for performing operations of various embodiments include general purpose digital computers or similar devices.

The term "coupled," however, may also mean that two or more elements are not in direct contact with each other, but yet still cooperate or interact with each other.

Various embodiments also relate to apparatus or systems for performing these operations. This apparatus may be specially constructed for the required purpose or it may comprise a general-purpose computer as selectively activated or reconfigured by a computer program stored in the computer. The procedures presented herein are not inherently related to a particular computer or other apparatus. Various general-purpose machines may be used with programs written in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these machines will appear from the description given.

Claim 1:
A computer-implemented method comprising:
requesting input information relating to a chromatography experiment from a data lake, the input information comprising metadata (<NUM>) relating to how the chromatography experiment was performed;
applying machine learning to identify a relationship between the metadata (<NUM>) and whether a compliance issue exists with regards to the chromatography experiment;
applying the identified relationship to new chromatography input information, the new chromatography input information including a new configuration of metadata; and
flagging one or more parameters in the new configuration of metadata contributing to a compliance issue in a dashboard user interface.