Predictive models for biological experiments

Abstract

Provided herein are systems, methods, computer-readable media, and techniques for monitoring a biological assay, including: (A) obtaining assay data for the biological assay, wherein the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay; (B) analyzing the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data; and (C) generating, based at least in part on the analyzing in (B), a trajectory for the biological assay.

Description

PREDICTIVE MODELS FOR BIOLOGICAL EXPERIMENTSCROSS REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 673,540, filed July 19, 2024, which is incorporated herein by reference in its entirety.BACKGROUND

[0002] A biological assay may be an experiment relating to life sciences. A biological assay can be quantal or quantitative, direct, or indirect. In one example, a biological assay may include analytical methods of determining the potency or effect of a substance by its effect on living animals or plants (e.g., in vivo), or on living cells or tissues (e.g., in vitro). In another example, a biological assay may be used to detect biological hazards or to give an assessment of the quality of a mixture. Biological assays may be important parts of pharmaceutical (e.g., development and launching of new drugs, vitamins, supplements, etc.), medical, agricultural science, environmental, etc. sciences.SUMMARY

[0003] The present disclosure pertains to the field of biological assays and more specifically systems, methods, computer-readable media, and techniques for monitoring biological assays using a trajectory model trained on historical assay data. The accurate and timely monitoring of biological assays is critical in numerous applications, from diagnostic testing to drug discovery. However, conventional methods often lack the capacity to effectively analyze and predict assay outcomes due to inherent complexities in biological data, and variations in the service delivery or parties involved in the assays. This disclosure introduces an approach that employs machine learning algorithms for analyzing assay data and generating a trajectory for the assay, thereby improving the efficiency, accuracy, and predictive capability of biological assay monitoring.

[0004] Disclosed herein is a computer system for monitoring a biological assay, comprising: one or more processors; and one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to: (A) obtain assay data for the biological assay, wherein the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay; (B) analyze the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data; and (C) generate, based at least in part on the analyzing in (B), a trajectory for the biological assay.

[0005] In some embodiments, at least one party comprises one or both of a supplier or a customer. In some embodiments, the service data comprises data measured by one or more scientific instruments from the biological assay. In some embodiments, the service data corresponds to at least one biological subject of the biological assay. In some embodiments, the trajectory model comprises a machine learning model. In some embodiments, the machine learning model was trained via operations comprising: obtaining the historical assay data for the historical biological assay comprising a plurality of actual trajectories for the historical biological assays each corresponding to an actual assay data set; classifying the historical assay data into a plurality of subsets of historical assay data that each correspond to a different actual trajectory of the plurality of trajectories or range of different actual trajectories of the plurality of trajectories; and generating the machine learning model using the plurality of subsets of historical assay data. In some embodiments, the machine learning model comprises a regression model, a decision tree, a random forest, or a neural network. In some embodiments, the trajectory comprises predicted time data.

[0006] In some embodiments, the predicted time data comprises a predicted date of completion of the biological assay. In some embodiments, the predicted time data comprises a predicted time until the completion of the biological assay. In some embodiments, the trajectory comprises predicted invoice data. In some embodiments, the predicted invoice data comprises a predicted total invoice realization following the completion of the biological assay. In some embodiments, the predicted invoice data comprises a predicted invoice realization for an increment. In some embodiments, the increment is a day, a week, or a month. In some embodiments, the biological assay comprises one or more of: a cell-based assay, a genomics or proteomics assay, an imaging assay, a clinical laboratory assay, a drug discovery assay, a metabolism or pharmacokinetics assay, or an immunology assay. In some embodiments, the computer-executable instructions, when executed, further cause the one or more processors to perform operations (A)-(C) with updated assay data for the biological assay once per interval. In some embodiments, the interval is a day, a week, or a month. In some embodiments, the computer-executable instructions, when executed, cause the one or more processors to perform the operations (A)-(C) with the updated assay data for the biological assay once per the interval until the completion of the biological assay.

[0007] Disclosed herein is a method for monitoring a biological assay, comprising: (A) obtaining assay data for the biological assay, wherein the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay; (B) analyzing the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historicalbiological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data; and (C) generating, based at least in part on the analyzing in (B), a trajectory for the biological assay.

[0008] Disclosed herein are one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by at least one processor, cause the at least one processor to: (A) obtain assay data for the biological assay, wherein the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay; (B) analyze the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data; and (C) generate, based at least in part on the analyzing in (B), a trajectory for the biological assay.

[0009] The technologies disclosed herein provide electronic data processing and is applicable to numerous forms of data. Therefore, a person skilled in the art, in light of the disclosure provided herein, will recognize that the technologies are readily applied to many fields.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0011] FIG. 1 depicts an example of an algorithm retraining process flowchart;

[0012] FIG. 2 depicts an example of a revenue recognition process flow chart;

[0013] FIG. 3 depicts an example of a graph of a purchase order;

[0014] FIG. 4 depicts an example of a system where suppliers report assay progress to a server that processes the data and communicates the output to the customer;

[0015] FIG. 5 shows an example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface; and

[0016] FIG. 6 depicts an example of a flowchart illustrating a method for monitoring a biological assay.DETAILED DESCRIPTION OF THE DISCLOSURE

[0017] The systems, the methods, the computer-readable media, and the techniques disclosed herein are configured for monitoring biological assays using a trajectory model. Traditionalmethods employed in biological assay monitoring often encounter difficulties in providing accurate and timely analysis due to inherent complexities in biological data.

[0018] The systems, the methods, the computer-readable media, and the techniques disclosed herein may provide The systems, the methods, the computer-readable media, and the techniques disclosed herein may be capable of obtaining and analyzing assay data. In some embodiments, The systems, the methods, the computer-readable media, and the techniques disclosed herein may employ a trajectory model, trained on historical assay data. Furthermore, The systems, the methods, the computer-readable media, and the techniques disclosed herein may generate a trajectory for the assay, allowing for more efficient and accurate monitoring of biological assays. The systems, the methods, the computer-readable media, and the techniques disclosed herein may be applied to in range of biological assays, including cell-based assays, genomics or proteomics assays, imaging assays, clinical laboratory assays, drug discovery assays, metabolism or pharmacokinetics assays, or immunology assays. Furthermore, the systems, the methods, the computer-readable media, and the techniques disclosed herein may be applied to other projects including experiments outside life sciences (e.g., engineering, physics, chemistry, computer sciences, psychology, etc.) or to projects outside of experiments (e.g., construction projects, financial projects, consulting projects, legal projects, business projects, etc.).

[0019] The systems, the methods, the computer-readable media, and the techniques the methods, the computer-readable media, and the techniques disclosed in may comprise computerexecutable instructions offering significant enhancements in efficiency, precision, and predictive capability in the biological assay monitoring field.

[0020] In one aspect, disclosed herein are computer system for monitoring a biological assay. In some embodiments, the computer systems may comprise one or more processors. In some cases, the computer systems may comprise one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to (A) obtain assay data for the biological assay. In some cases, the assay data may comprise an identifier for at least one party corresponding to the biological assay. In some cases, the assay data may comprise service data corresponding to the biological assay.

[0021] In some cases, the computer systems may comprise one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to (B) analyze the assay data for the biological assay with a trajectory model. In some cases, the trajectory model is trained using historical assay data for a plurality of historical biological assays. For example, the plurality of historical biological assay may each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data.

[0022] In some cases, the computer systems may comprise one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to (C) generate, based at least in part on the analyzing in (B), a trajectory for the biological assay.

[0023] In some embodiments, at least one party comprises one or both of a supplier or a customer. In some embodiments, the service data comprises data measured by one or more scientific instruments from the biological assay. In some embodiments, the service data corresponds to at least one biological subject of the biological assay. In some embodiments, the trajectory model comprises a machine learning model. In some embodiments, the machine learning model was trained via operations comprising: obtaining the historical assay data for the historical biological assay comprising a plurality of actual trajectories for the historical biological assays each corresponding to an actual assay data set. In some cases, the machine learning model was trained via operations further comprising classifying the historical assay data into a plurality of subsets of historical assay data that each correspond to a different actual trajectory of the plurality of trajectories or range of different actual trajectories of the plurality of trajectories. In some embodiments, the machine learning model was trained via operations further comprising generating the machine learning model using the plurality of subsets of historical assay data. In some embodiments, the machine learning model comprises a regression model, a decision tree, a random forest, or a neural network. In some embodiments, the trajectory comprises predicted time data.

[0024] In some embodiments, the predicted time data comprises a predicted date of completion of the biological assay. In some embodiments, the predicted time data comprises a predicted time until the completion of the biological assay. In some embodiments, the trajectory comprises predicted invoice data. In some embodiments, the predicted invoice data comprises a predicted total invoice realization following the completion of the biological assay.

[0025] In some embodiments, the predicted invoice data comprises a predicted invoice realization for an increment. In some cases, the increment comprises about 0 days to about 730 days. In some cases, the increment comprises about 0 days to about 7 days, about 0 days to about 14 days, about 0 days to about 21 days, about 0 days to about 28 days, about 0 days to about 30 days, about 0 days to about 31 days, about 0 days to about 60 days, about 0 days to about 90 days, about 0 days to about 180 days, about 0 days to about 365 days, about 0 days to about 730 days, about 7 days to about 14 days, about 7 days to about 21 days, about 7 days to about 28 days, about 7 days to about 30 days, about 7 days to about 31 days, about 7 days to about 60 days, about 7 days to about 90 days, about 7 days to about 180 days, about 7 days to about 365 days, about 7 days to about 730 days, about 14 days to about 21 days, about 14 days to about 28 days, about 14 days to about 30 days, about 14 days to about 31 days, about 14 daysto about 60 days, about 14 days to about 90 days, about 14 days to about 180 days, about 14 days to about 365 days, about 14 days to about 730 days, about 21 days to about 28 days, about 21 days to about 30 days, about 21 days to about 31 days, about 21 days to about 60 days, about 21 days to about 90 days, about 21 days to about 180 days, about 21 days to about 365 days, about 21 days to about 730 days, about 28 days to about 30 days, about 28 days to about 31 days, about 28 days to about 60 days, about 28 days to about 90 days, about 28 days to about 180 days, about 28 days to about 365 days, about 28 days to about 730 days, about 30 days to about 31 days, about 30 days to about 60 days, about 30 days to about 90 days, about 30 days to about 180 days, about 30 days to about 365 days, about 30 days to about 730 days, about 31 days to about 60 days, about 31 days to about 90 days, about 31 days to about 180 days, about 31 days to about 365 days, about 31 days to about 730 days, about 60 days to about 90 days, about 60 days to about 180 days, about 60 days to about 365 days, about 60 days to about 730 days, about 90 days to about 180 days, about 90 days to about 365 days, about 90 days to about 730 days, about 180 days to about 365 days, about 180 days to about 730 days, or about 365 days to about 730 days. In some cases, the increment comprises about 0 days, about 7 days, about 14 days, about 21 days, about 28 days, about 30 days, about 31 days, about 60 days, about 90 days, about 180 days, about 365 days, or about 730 days. In some cases, the increment comprises at least about 0 days, about 7 days, about 14 days, about 21 days, about 28 days, about 30 days, about 31 days, about 60 days, about 90 days, about 180 days, or about 365 days. In some cases, the increment comprises at most about 7 days, about 14 days, about 21 days, about 28 days, about 30 days, about 31 days, about 60 days, about 90 days, about 180 days, about 365 days, or about 730 days.

[0026] In some embodiments, the biological assay comprises one or more of: a cell-based assay, a genomics or proteomics assay, an imaging assay, a clinical laboratory assay, a drug discovery assay, a metabolism or pharmacokinetics assay, or an immunology assay. In some embodiments, the computer-executable instructions, when executed, further cause the one or more processors to perform operations (A) with updated assay data for the biological assay once per interval. In some cases, the computer-executable instructions, when executed, further cause the one or more processors to perform operations (B) with updated assay data for the biological assay once per interval. In some cases, the computer-executable instructions, when executed, further cause the one or more processors to perform operations (C) with updated assay data for the biological assay once per interval.

[0027] In some embodiments, the interval is a day, a week, or a month. In some embodiments, the computer-executable instructions, when executed, cause the one or more processors to perform the operations (A) with the updated assay data for the biological assay once per theinterval until the completion of the biological assay. In some cases, the computer-executable instructions, when executed, cause the one or more processors to perform the operations (B) with the updated assay data for the biological assay once per the interval until the completion of the biological assay. In some cases, the computer-executable instructions, when executed, cause the one or more processors to perform the operations (C) with the updated assay data for the biological assay once per the interval until the completion of the biological assay.

[0028] In another aspect, disclosed herein is a method for monitoring a biological assay, comprising: (A) obtaining assay data for the biological assay. In some cases, the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay.

[0029] In some cases, the method for monitoring a biological assay further comprises (B) analyzing the assay data for the biological assay with a trajectory model. In some cases, the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data.

[0030] In some cases, the method for monitoring a biological assay further comprises (C) generating, based at least in part on the analyzing in (B), a trajectory for the biological assay.

[0031] In another aspect disclosed herein are one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by at least one processor, cause the at least one processor to (A) obtain assay data for the biological assay. In some cases, the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay.

[0032] In another aspect disclosed herein are one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by at least one processor, cause the at least one processor to (B) analyze the assay data for the biological assay with a trajectory model. In some cases, the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service data.

[0033] In another aspect disclosed herein are one or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by at least one processor, cause the at least one processor to (C) generate, based at least in part on the analyzing in (B), a trajectory for the biological assay.Examples of Application to Biological Assays

[0034] The systems, the methods, the computer-readable media, and the techniques disclosed herein may provide predictive models configured specifically for monitoring and managingbiological assays. In some embodiments, the predictive models disclosed herein may offer comprehensive, dynamic, and robust mechanism for effective tracking, prediction, and management of outcomes in a variety of biological assays. In some embodiments, the predictive models described herein are equipped to handle the complexities of tracking and predicting the completion dates and corresponding outcomes of various biological assays based on an array of parameters.

[0035] In some cases, the array of parameters may comprise the type of assay being performed, which could include genomics, proteomics, cell-based, or imaging assays. In some cases, the array of parameters may comprise the specific bio-reagents involved, such as types of cells, antibodies, or enzymes. In some cases, the array of parameters may comprise the environmental conditions under which the assay is conducted, including temperature, humidity, and CO2 levels. In some cases, the array of parameters may comprise biological variables, like gene expression levels, protein concentrations, or cell proliferation rates. In some cases, the array of parameters may comprise time points for data collection, particularly in time-series experiments. In some cases, the array of parameters may comprise data outputs, which could range from fluorescence intensities to absorbance values or images. In some cases, the array of parameters may comprise the experimental design elements, such as control conditions, replication strategies, or randomization protocols. In some cases, the array of parameters may comprise resource allocation details, including cost, labor, and equipment usage. In some cases, the array of parameters may comprise statistical parameters like statistical power, effect sizes, and variability. In some cases, the array of parameters may comprise safety and ethical considerations, such as biohazard levels and animal use protocols. In some cases, the array of parameters may comprise compliance with standards or guidelines, which could include Good Laboratory Practice (GLP) or Good Clinical Practice (GCP). In some cases, the array of parameters may comprise data analysis methods, including statistical tests and data normalization strategies. In some cases, the array of parameters may comprise quality control measures such as reagent validation and use of negative / positive controls. In some cases, the array of parameters may comprise the experiment duration and significant milestones. In some cases, the array of parameters may comprise technical parameters, like instrument settings and calibration details. In some cases, the array of parameters may comprise biological responses or endpoints, such as cell death or gene expression changes. In some cases, the array of parameters may comprise the turnaround time from the initiation to the completion of the assay. In some cases, the array of parameters may comprise regular update intervals for monitoring the progress of the assay. In some cases, the array of parameters may comprise data storage and management factors, including the database used and data security measures. In some embodiments, a user(e.g., individual, organization, etc.) may leverage the predictive models described herein to predict the completion date of a biological assay and the corresponding results. In some cases, the predictive models may integrate machine learning techniques to facilitate these predictions. In some cases, the predictive models may apply an advanced trajectory -based approach to track the progress of an assay from initiation to completion.

[0036] In some embodiments, the predictive models may be configured to recalibrate predictions. In some cases, beyond the initially predicted completion date, the predictive model may utilize historical data from prior assays to recalibrate predictions for the ongoing assay over the ensuing period. In some cases, the ensuing period may comprise between about 1 day to about 90 days, or more. In some cases, the recalibration feature may assist in managing potential fluctuations in assay outcome predictions. In some cases, the recalibration feature may mitigate the risks associated with premature termination of assays.

[0037] In some cases, the predictive models disclosed herein may be configured for instantaneous amendments to previously predicted outcomes when an assay is flagged. In some cases, the predictive models disclosed herein may be configured to not make instantaneous amendments to previously predicted outcomes when an assay is flagged, wherein the instantaneous amendments may give rise to unnecessary variances.

[0038] In some embodiments, the predictive models disclosed herein may be configured to utilize historical assay data. In some cases, the model may utilize historical assay data wherein the assay comprises a maximum amount of time past the deadline of about 90 days. In some cases, the model may utilize historical assay data wherein the assay comprises a minimum amount of time past the deadline of about 90 days.

[0039] In some cases, the predictive models are configured to utilize additional data be introduced within 90-days past the deadline. In some cases, the predictive models are configured to recalibrate the assay trajectory. For example, the predictive models may adapt the prediction strategy to align with the revised trajectory by the end of the 90 days. In some cases, if pivotal data is received after the 90 days, the predictive models may be configured to be accommodative to carry out an immediate adjustment to the assay trajectory.Examples of Biological Assay Operation Workflows

[0040] The systems, the methods, the computer-readable media, and the techniques disclosed herein provide predictive models that may be configured to streamline the monitoring and management of biological assays.

[0041] In some embodiments, following this process, the user organization may officially accept the SOW. In some cases, the user accepting the SOW may result in the order being automatically forwarded to the selected service provider. In some cases, the intermediary teammay transform the SOW into an order and a corresponding project order on the platform. For example, upon the issuance of the order, the service provider may be signaled to commence work. In further examples, the issued order may serve as an input for the Biological Assay Recognition System, initiating the primary predictions for the assay's completion time and expected outcomes.

[0042] In some cases, subsequent to this process, the user organization may formally accept the SOW, resulting in an automatic forwarding of the order to the chosen service provider. In some cases, the intermediary team may then convert the SOW into a purchase order (PO) and a corresponding sales order (SO) on the platform. For example, upon issuance of the PO / SO, the service provider may be notified to begin work. In further examples, the issued PO / SO may then serve as an input for the biological assay recognition model, initiating the first predictions for the assay's completion time and anticipated results.

[0043] In some cases, once the service provider has completed a portion of the assay and is prepared to provide an update to the customer, they may access the order on the platform and submit an update statement, such as a progress report, which corresponds to the order. In certain examples, the update statement may then be reviewed and approved by a designated team before being transferred from the platform to a recording system. Concurrently, the submission of the update statement may automatically trigger the generation of a corresponding statement, such as a progress summary, in the recording system. For example, the statement may be subject to daily review for accuracy and any potential implications, with the status of its approval consistently monitored and tracked.

[0044] In some cases, terms (e.g., such as completion timelines) for biological assays may vary depending on the user. In certain examples, service providers and service requestors fulfill the assay (e.g., complete the service) within a specified timeframe, such as 75 days. For example, an assay is considered completed once the project has achieved the stipulated objectives. In some cases, an assay is considered completed once the project has been fully conducted and results have been delivered to the user. In further examples, an inactive order is defined by a lack of recent activity, and 90 days after the initial model time prediction. In even further examples, at this point, when an order becomes inactive, the model sets the recognition equal to the projected and actual outcomes and does not estimate any further results to be realized in future periods.

[0045] In some embodiments, the predictive models may be configured to process related components. In some cases, the predictive models may be configured to process invoices, reconcile billing discrepancies, handle tax implications associated with the assays, and monitor the transactions. In some cases, the predictive models can also manage payment terms and ensure timely transactions, often within a specific timeframe such as 75 days. Furthermore, thepredictive models are configured to adjust financial records based on the progress and status of the assays.Examples of Biological Assay Recognition Model Configuration

[0046] the systems, the methods, the computer-readable media, and the techniques disclosed herein may provide configurable Biological Assay Recognition Models (BARM). In some embodiments, the BARM may comprise machine learning capabilities. In some cases, the BARM may utilize historical assay data to generate future predictions. In some cases, BARM may receive an organization's historical assay data. For example, BARM may leverage an organization's historical assay data spanning several years, to formulate these predictions. In some cases, the accuracy of the predictions produced by BARM may increase with the availability of a larger quantity and higher quality of historical data. For example, BARM may consider a variety of factors that may impact the predictions. In further examples, the variety of factors may comprise the specific type of biological assay being conducted, the particular service provider involved, the specific customer, among other relevant variables. In some cases, the BARM may be constructed as an ensemble model using a machine learning methodology, which integrates a specific algorithm, such as an open-source gradient boosting method, introduced recently in the field of data science.

[0047] In some embodiments, BARS may encompass a range of features that account for numerous factors. In some cases, the features may comprise predictions for the duration of the assay. In some cases, the predictions may span from initiation to full recognition. In some cases, the predictions may consider one or more of the following elements: the service provider's estimated duration of the assay, cost, the average historical duration of similar assays, and the service provider's historical error margin in duration prediction. In some cases, the predictions may consider one or more of the following elements: historical reserve amount, depicted by the percentage of the invoice value to the purchase order value, influenced by the historical payment percentage associated with the specific service provider, assay type, and organization. In some cases, BARS may consider one or more of the following elements: adjustments for the cost of the order, normalized to a standard currency, the cost and count of milestones, the quantity of prior orders placed by a user or organization, and the provider's estimated time to complete the assay. For example, interactions between these variables and other features may also be considered by BARS.

[0048] In some embodiments, the accuracy of BARS may be evaluated using a statistical measure such as the R-squared metric. In some cases, the R-squared metric provides a representation of the ratio of error that can be explained by the model's features over the total error in the predictions. In some cases, BARS' accuracy may then be compared with baselinemetrics. For example, the baseline metrics may comprise the service provider's estimated turnaround time, and the average historical percentage invoiced by the service provider, organization, and type of assay.

[0049] In some embodiments, BARS utilizes a gradient-boosted random forest approach. In some cases, the gradient-boosted random forest approach comprises a flexible machine learning technique that can accommodate variables not normally distributed or lacking a linear relationship. In some cases, the gradient-boosted random forest approach technique enables BARS to select optimal binary splits of variables instead of relying solely on a linear trend for predictions. For example, the gradient-boosted random forest approach may account for complex interactions between features. In further examples, the gradient-boosted random forest approach enhances BARS' ability to generate more accurate predictions.Examples of Biological Assay Recognition Model Validation Processes

[0050] the systems, the methods, the computer-readable media, and the techniques disclosed herein may further comprise a Biological Assay Recognition Models (BARM) validation process.

[0051] In some embodiments, the validation process may comprise recognition calculations daily, with the resulting data retained within the system. In some cases, to ensure the accuracy and completeness of the data, true-up events are periodically recorded in the organization's general ledger system. In some cases, this may occur at regular intervals, such as monthly or quarterly, to maintain up-to-date and accurate records.

[0052] In some embodiments, the validation process may comprise model completeness testing as a vital part of the process. BARS may function based on the transactional information stored on the platform. The process may involve confirming that the data inputted into the model, related to the assays, aligns accurately with the back-end data. The initial step in this process may involve extracting relevant data from the organization's record-keeping system and associating all relevant assay data with their corresponding orders. As an example, each entry recorded in the organization's record-keeping system may be checked to ensure its inclusion in the recognition model.

[0053] In some cases, if an order for a biological assay is not reflected in the BARS, further investigation may be necessary to ascertain the reason. In some cases, the reason may be that the assay has been designated as 'Obsolete' on the platform, yet it still retains a balance related to conducted procedures or expended laboratory resources. For example, these situations may call for a detailed review of the communication threads related to the assay order to understand why it was marked as 'Obsolete' while still retaining a balance. In further examples, if an investigation reveals that the assay was prematurely terminated but the associated procedures orresources were approved, the status of the assay order may be revised from 'Obsolete' to 'Complete', thereby incorporating it into the BARS. In even further examples, if the assay order was correctly designated as 'Obsolete', any associated procedures or resources may be reconciled in the accounting system and the assay order may remain 'Obsolete', excluding it from the BARS.

[0054] In some cases, an order for a biological assay may be not included in BARS. For example, there may be incomplete assay data on the platform. In some cases, data from the record-keeping system may be transferred to the back-end platform to reconcile any inconsistencies. For example, an order for a biological assay may have a net-zero effect within a period due to errors associated with incorrect labeling of assay entities. Consequently, such orders for biological assays may not necessitate inclusion in BARS.

[0055] In some cases, during the reconciliation phase, the invoiced and recorded amounts for each biological assay order may be compared against the stated cost for each order in the recordkeeping system. In some cases, the reconciliation phase may facilitate the detection of any unforeseen discrepancies in the recording of assay costs, thereby prompting necessary corrective measures. For example, if any biological assay orders are overcharged or undercharged by a variance of 2%, considered an acceptable level of precision, they may be subject to review. Following this, suitable corrective actions may be recommended.

[0056] There may be several factors leading to unexpected variations in the resource allocation or utilization on biological assay orders. For example, there could be a situation where a service provider may require additional resources for the assay, but due to contractual or other restrictions, the organization may not be able to redistribute those resources from the customer. This could result in the need for additional resources, subsequently impacting the overall resource utilization for the conduct of the assay.

[0057] In another potential example, a resource cap may be set for certain assays. In such examples, specific customers may have a predetermined resource limit beyond which the organization has contractually agreed to cap or waive the resource allocation for the assay. If an assay order accurately specifies a 0% resource allocation increase, but an increase is incorrectly levied as a separate line item on the order, it may result in no additional resources being allocated to that assay.

[0058] Moreover, errors related to resource allocation, such as examples of over-allocation or under-allocation of resources to customers, may be identified by the administrator , who may then propose corrective actions. Optimal solutions are typically determined on a case-by-case basis.

[0059] Finally, discrepancies in resource allocation timelines may emerge due to delays between when resources are provided and when they are utilized by the customer, causing the periods to overlap across different months. In such examples, the administrator may identify these cases and propose a timeline adjustment, either on the customer or service provider side of the transaction.Examples of Testing the Model Output

[0060] the systems, the methods, the computer-readable media, and the techniques disclosed herein may also encompass a process for testing the output of the Biological Assay Recognition Models (BARM).

[0061] In some embodiments, subsequent to model completeness testing and reconciliation to the general ledger, the next phase may involve evaluating the output of the predictive model. In some cases, this phase aims to ensure the correct inclusion of all assay orders in the predictive model for the specified period. In some cases, the completeness of the model may be confirmed by ensuring alignment between the data in the general ledger and the assay management platform, and reconciling the current period general ledger data with the platform activity.

[0062] In some cases, to ensure the predictions generated by the model are reasonable, an administrator may verify that for all completed assay orders, the cumulative resource allocation, net resources, and cost of resources correspond with the cumulative invoice and bill totals. In some cases, it may also be confirmed that the aggregate balance for these completed orders across all balance sheet accounts is zero.

[0063] For all "Work in Progress" assay orders, the administrator may compare the recorded resources against the stated transaction fee to identify any orders where the predictive model yields results deviating from expectations, specifically a variance of 2% or more. Such orders may be flagged for review and action by a relevant authority such as a software engineering manager. Any inconsistencies identified within the system may be rectified, leading to the predictive model being rerun with updated parameters and all reconciliation tasks being carried out anew.

[0064] Further checks may include reviewing the highest and lowest resource transactions to ensure their accuracy and reasonableness. Additionally, it may be confirmed that all assay orders included in the model correspond with platform revenue by reviewing the category of services provided.

[0065] Upon completion of all these steps, a review may be conducted with the administrator. During this review, the administrator may disclose all proposed adjustments which are subsequently reviewed, approved, and implemented.Examples of Adjustments to the General Ledger Based on BARM Results

[0066] The systems, the methods, the computer-readable media, and the techniques disclosed herein may also incorporate a process for adjusting the general ledger based on the Biological Assay Recognition Models (BARM) results.

[0067] In some cases, based on the results generated by the predictive BARM, the organization may calculate the necessary adjustments to be recorded in the record-keeping system. In some cases, this ensures that amounts and balances related to resources in the general ledger align with the predictions made by the model. For example, a journal entry may be manually calculated using the model data, and summarized in a reconciliation file. In further examples, an administrator may confirm that all debits and credits tie out to the model and the amounts currently in the record-keeping system. In even further examples, the journal entry, recorded in the original measurement unit of each assay order group, may be uploaded, and reconciled collectively with the assay orders, with each type of measurement unit handled separately.

[0068] An average conversion rate, corresponding to the period of assay recording, may be applied. Conversion rates may be obtained from a reliable external source. The summary file, which provides the consolidated balances for all accounts for the period, may then be compared with the data in the record-keeping system after the journal entries have been input. The administrator may upload the journal entry to the record-keeping system, where it may be subjected to a final review and approval by the head of accounting before being officially posted.Examples of Reserve Models

[0069] The systems, the methods, the computer-readable media, and the techniques disclosed herein may provide a resource allocation model for biological assays. In some cases, the resource allocation model may be configured to receive a multitude of inputs. In some cases, the multitude of inputs may comprise metrics such as the average resource allocation per assay, the typical number of assays a provider completes, the total cost of the assay order in a standard currency, and how much a provider typically invoices for resources. For example, the resource allocation also factor in typical invoiced amounts by a provider within a specific assay category, the total cost of the order in local currency units, the expected turnaround time, and the assay category. In further examples, the provider's name, the customer's organization name, any associated costs, and whether the order is for a product or a service may also be considered. In even further examples, the resource allocation model may consider the number of assays conducted by the provider, tax category, average cost per line item, days since the last invoice at the halfway point, and the invoiced percentage at the halfway mark.Examples of Turnaround Time Models

[0070] The systems, the methods, the computer-readable media, and the techniques disclosed herein may provide a turnaround time model for biological assays. In some cases, the turnaround time model may be configured to receive a plurality of inputs. In some cases, the plurality of inputs may comprise metrics such as whether the provider typically overestimates or underestimates assay duration, the average assay duration overall, and the provider's average assay duration. For example, factors like the provider's usual assay duration within a specific category, whether the order falls within the top 10% of costs, the provider's estimated assay duration, the max and min estimated durations for the assay's completion, and the month and year of submission may also be considered. In further examples, factors such as the provider's name, tax category, commission rate for the order, the number of line items in the Purchase Order, the sum of quantities in line items, and the average unit price may be included. In even further examples,, the model may also consider the average assay duration in that provider's country, the number of days since the last invoice at the halfway point, and the invoiced percentage at that point.

[0071] FIG. 1 depicts algorithm retraining process flow chart. In FIG. 1, the algorithm retraining process (ARP) 100 comprises a sequence order. In some cases, the ARP comprises start retraining 105. In some cases, the ARP comprises periodic algorithm training 110. In some cases, the ARP comprises train and test with closed projects 115. In some cases, the ARP comprises determining whether the algorithm was improved 120. In some cases, the ARP comprises determining the algorithm was improved 135. In some cases, the ARP comprises updating the algorithm. For example, the ARP may comprise ending retraining 145. In some cases, the ARP comprises the algorithm was not improved 130. In some cases periodic algorithm training 110, training and testing with closed projects 115, determining , the ARP comprises continuing with current algorithm 140. For example, the ARP may comprise ending retraining 145. As illustrated in FIG. 1, the Algorithm Retraining Process (ARP) 100 demonstrates a sequence of operations, which include starting retraining 105, conducting whether the algorithm was improved 120, and updating the algorithm 135 if it was improved. If the algorithm was not improved 130, the process continues with the current algorithm 140, followed by ending retraining 145.

[0072] In some cases, the order of operations of the algorithm retraining process (ARP) 100 can vary based on several factors such as the specific requirements of the project, the performance of the algorithm, and the availability of new data for training. As such, the depicted sequence order is one possible pathway, but other sequences are also feasible depending on the needs of the algorithm retraining process. Flexibility in the order of operations allows for theprocess to dynamically adapt to ongoing circumstances and optimize algorithm performance over time.

[0073] FIG. 2 depicts a revenue recognition process flow chart. In FIG. 2, the revenue recognition process (RRP) comprises a sequence order. In some cases, the RRP comprises starting the RRP 205. In some cases, the RRP comprises a nightly check 210. In some cases, the RRP comprises added information 215. In some cases, the RRP comprises new PO 220. In some cases, the RRP comprises overdue 225. In some cases, the RRP comprises change order 230. In some cases, the RRP comprises creating predictions 235. In some cases, the RRP comprises loop through open POs 240. In some cases, the RRP comprises calculating incremental revenue changes 245. In some cases, the RRP comprises updating database 255. In some cases, the RRP comprises determining whether final late predictions 260. In some cases, the RRP comprises determining there are no final late productions 250. For example, the RRP comprises looping through open POs 240. In some cases, the RRP comprises determining there are final late predictions. In some cases, the RRP comprises matching recognition revenue to invoiced amount 265. In some cases, the RRP comprises marking project as closed 270. In some cases, the RRP comprises ending the RRP 275.

[0074] As depicted in FIG. 2, the Revenue Recognition Process (RRP) 200 comprises a sequence of operations. The sequence may begin with starting the RRP 205, followed by a nightly check 210. The process may then handle added information 215, manage new Purchase Orders (POs) 220, attend to overdue items 225, and process change orders 230. The process may also involve creating predictions 235, looping through open POs 240, and calculating incremental revenue changes 245. Further sequence operations may include updating the database 255, determining whether there are final late predictions 260, or in examples when there are no final late predictions 250, looping through open POs 240 may be reiterated. If there are final late predictions, the process may then involve matching recognition revenue to invoiced amount 265, marking the project as closed 270, and ending the RRP 275.

[0075] In some embodiments, the sequence of Revenue Recognition Process (RRP) 200 operations may vary depending on varied factors such as the specific requirements of the revenue recognition process, the timing of added information or POs, and the status of existing POs. In some cases,, the sequence order illustrated is one possible pathway, but alternative sequences may also be feasible to meet the needs of dynamic revenue recognition examples. In some cases, the flexibility in the sequence of operations allows for the revenue recognition process 200 to adapt to changing circumstances and optimize revenue management over time.

[0076] FIG. 3 depicts a graph of a purchase order. In FIG. 3, the x-axis 301 comprises the date. In FIG. 3, the y-axis 302 comprises an amount of money. Furthermore, in FIG. 3, line 303comprises a net cumulative revenue. For example, as depicted in FIG. 3, the net cumulative revenue on “3 / 12 / 2024” is about $22,898. Furthermore, in FIG. 3, line 304 comprises a gross revenue cumulative. For example, as depicted in FIG. 3, the gross cumulative revenue on “3 / 12 / 2024” is about $352,278. Furthermore, in FIG. 3, line 305 comprises a billed cumulative revenue. For example, as depicted in FIG. 3, the billed cumulative revenue on “3 / 12 / 2024” is about $355,788 Furthermore, in FIG. 3, line 306 comprises a revenue. For example, as depicted in FIG. 3, the revenue on “3 / 12 / 2024” is about $395,906. In further examples, the graph 300 can chart one or more of the following: day, po(s), po amount, po amount (cumulative), invoice(s), invoiced, invoiced (cumulative), bill(s), billed, billed (cumulative), revenue, revenue (cumulative), gross revenue, gross revenue (cumulative), net revenue, net revenue (cumulative), cogs, unbilled cogs, prepaid cogs, unbilled revenue, deferred revenue, prediction amount, prediction date, prediction name, and prediction type.

[0077] In this case, Table 1 is providing an overview of various financial and transactional data related to business operations over a given time period, with each day representing a new entry or data point.Table 1: Daily Financial and Operational Data Overview with Cumulative Totals and Predictions

[0078] FIG. 4 illustrates an example system 450 configured for suppliers (e.g., contractors) to report their assay progress from a supplier device 430 to a server 420. In some cases, the server 420 processes the assay progress and relays the output to the customer via network 440. In some cases, the system 450 comprises a customer device 410, which features an input / output function 412, and a user application 412. In some cases, the customer device 410 is connected to a network 440. For example, the network 440 may be linked to the server 420. In further examples, the server 420 may be equipped with a model 422 and data storage 424. In even further examples, the server 420 further maintains a connection to the supplier device 430 via the network 440. For example, the supplier device 430 may comprise an input / output 432 and a user application 434.

[0079] In one example, for, a pharmaceutical supplier is working on an assay, a laboratory procedure measuring the biochemical activity of a sample. In some cases, as the supplier progresses with the assay, they input updates about their progress into their device (supplier device 430) using the user application 434. In some cases, the updates may include details about completed steps, time taken, issues encountered, and so forth. For example, the input / output function 432 of the supplier's device 430 may facilitate the sending of this data via network 440 to server 420. In further examples, the server 420 may be equipped with a model 422, which can be an algorithm or a set of rules that processes the incoming data from the supplier. For example, the processed data may involve interpreting the raw progress updates into a coherent status report or predicting the assay's completion time based on the provided updates. In further examples, simultaneously, the server 420 stores the original updates and the processed data in its data storage 424. In even further examples, the data storage 424 may be used for record-keeping, future reference, or improving the model's accuracy by learning from past data. Following the processing, the server 420 sends the output - the processed status report or prediction - back to the customer. For example, the transmission may be made via network 440 to the customer device 410. In further examples, the customer may view the received output through their userapplication 412 on their device. In further examples, the application 412 may present a user- friendly interface where the customer can review the assay's progress, understand potential issues, or anticipate the completion time. In even further examples, the input / output function 412 of the customer's device allows them to interact with the received data, such as by sending feedback to the supplier or the server. For example, the interconnected system 450 may be configured for real-time communication between the supplier and the customer, facilitated by server-side processing. In further examples, the system 450 enhances transparency, predictability, and responsiveness in examples where suppliers need to report progress to customers.

[0080] FIG. 6 depicts an example of a flowchart illustrating a method for monitoring a biological assay.

[0081] The method 600 may be implemented using one or more systems (e.g., hardware or software) described herein. The method 600 may implement one or more techniques described herein. At a high level, the method 600 may include obtaining assay data for the biological assay, wherein the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay (block 605); analyzing the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service date (block 610); and generating, based at least in part on the analyzing in (block 610), a trajectory for the biological assay (block 615).

[0082] The method 600 may be implemented using systems that may be the same as or similar to the systems of the environment 440 of FIG. 4, or the computer system 500 of FIG. 5. One or more operations in the method 600B may be performed on a repeating or iterative basis, for example, at some time interval or frequency.

[0083] In some cases, the method 600 may begin with obtaining assay data for the biological assay. In some cases, the assay data comprises an identifier for at least one party corresponding to the biological assay and service data corresponding to the biological assay at the block 605). In other examples, the assay data may comprise the type of assay performed, the start and expected completion dates of the assay, the specific biological parameters measured, the resources utilized in conducting the assay, the specific methods and protocols followed, the intermediate and final results obtained, the raw data from instruments, the analytical and statistical results derived from the raw data, any discrepancies or anomalies identified, as well as any notes or observations recorded during the assay.

[0084] In one example, the method 600 may comprise obtaining assay data for the biological assay. In some cases, the obtaining assay data for the biological assay may comprise gathering relevant information about a biological assay. In some cases, the biological assay may be a laboratory procedure configured to measure the biological or biochemical activity of a sample, with the collected data encompassing a variety of parameters or results tied to the assay. In some cases, an identifier for at least one party may comprise a unique code or name that differentiates a particular entity, such as an individual or organization, associated with the assay. For example, if a healthcare company (Party A) contracts a lab (Party B) for a DNA sequencing assay, an identifier for Party A would be obtained accordingly. In some cases, additionally, the method 600 may comprise the acquisition of service data corresponding to the biological assay. In some cases, the acquisition of service data corresponding to the biological assay may comprise specific aspects of the service provided in relation to the assay, including the type of assay being performed, the procedures being followed, the estimated timeline, and the associated costs. In further examples, the acquisition of service data corresponding to the biological assay may comprise the acquisition of comprehensive data that details both the parties involved and the specifics of the service related to the biological assay.

[0085] In some cases, the method 600 may include analyzing the assay data for the biological assay with a trajectory model. In some cases, the model may be configured to predict future outcomes or trends based on the provided data. For example, the model may have been trained using historical assay data. In further examples, the historical data may comprise a wide range of previous biological assays. In even further examples, each of these past assays is associated with one or both of two factors: a party (such as an individual or organization) corresponding to the identified party from the current assay data, or a service that correlates with the current service date. For example, the model may use past data to analyze the current assay data in a predictive manner. In even further examples, the model may generate insightful findings for future actions or decisions. In some cases, the model may analyze the assay data with a trajectory model. In some cases, analyzing the assay data with a trajectory model may comprise the utilization of a statistical or computational model that projects the likely course or evolution of the assay based on the input data. For example, the trajectory model may be trained with historical assay data. In further examples, the historical assay data may comprise includes data from previous biological assays that have been conducted. In even further examples, the historical assay data may assist the model to learn from past trends, patterns, and outcomes. For example, consider an example where a research lab is running an immunological assay. In such a case, the trajectory model might use historical data from past assays involving similar types of cells, reagents, or techniques, considering the specific party (e.g., the lab) and service (e.g., the type ofimmunological assay). In further examples, the model may then use this information to predict the likely progression of the current assay, such as how the cells might react, how long the assay might take, or what the final readings might be. In even further examples, the projection forms a trajectory for the biological assay, providing a roadmap of the assay's potential evolution over time. In even further examples, the trajectory allows for the anticipatory management of the assay and assists in making informed decisions, like adjusting procedures or anticipating results.

[0086] In some cases, the method 600 may include analyzing the assay data for the biological assay with a trajectory model, wherein the trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to the at least one party or a service corresponding to the service date (block 610). In some cases, the model may analyze the acquired assay data by using a trajectory model. In some cases, the model, essentially a predictive tool, has been trained using historical assay data collected from numerous previous biological assays. For example, the distinguishing factor of each historical assay incorporated into the model may be that it either corresponds to the identified party involved in the current assay or it is associated with the same type of service conducted on the present service date.

[0087] In one example, a pharmaceutical company (Party A) regularly carries out toxicity assays (a service). In some cases, the trajectory model may have been trained using historical data from several toxicity assays conducted by Party A in the past. In some cases, when a new toxicity assay is performed by Party A, the assay data is input into this model. Utilizing what it has learned from Party A's previous assays, the model analyzes the new data to predict the likely course or results of the current assay, such as the timeframe of the assay or potential toxic effects. In further examples, this provides valuable insights for Party A, helping them anticipate results and make informed decisions regarding the ongoing assay.

[0088] In some cases, the method 600 may comprise generating a trajectory for the biological assay under investigation, based at least in part on the analysis conducted in block 610. In some cases, generating a "trajectory" typically refers to the creation of a predictive pathway or projection that maps the possible progression or outcome of the biological assay over a period of time. For example, the trajectory may be influenced by the analysis of the assay data via the trajectory model, as described in block 610. In further examples, if the biological assay is a longterm cell culture experiment, the generated trajectory may provide a prediction of cell growth rates over time, potential times when the growth might plateau, or examples when the cells might undergo significant changes. In even further examples, the trajectory can be a valuable tool in managing the assay as it unfolds, allowing for timely interventions or adjustments based on the predicted outcomes. For example, the trajectory may enable the researchers or the teamconducting the assay to be proactive and make data-driven decisions, thereby improving the efficiency and effectiveness of the assay process.Examples of Computing Systems

[0089] Referring to FIG. 5, a block diagram is shown depicting an example machine that includes a computer system 500 (e.g., a processing or computing system) within which a set of instructions can execute for causing a device to perform or execute any one or more of the aspects or methodologies for static code scheduling of the present disclosure. The components in FIG. 5 are examples and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components with particular implementations.

[0090] Computer system 500 may include one or more processors 501, a memory 503, and a storage 508 that communicate with each other, and with other components, via a bus 540. The bus 540 may also link a display 532, one or more input devices 533 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 534, one or more storage devices 535, and various tangible storage media 536. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 540. For example, the various tangible storage media 536 can interface with the bus 540 via storage medium interface 526. Computer system 500 may have any suitable physical form, including one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[0091] Computer system 500 includes one or more processor(s) 507 (e.g., central processing units (CPUs), general purpose graphics processing units (GPGPUs), or quantum processing units (QPUs)) that carry out functions. Processor(s) 501 optionally contains a cache memory unit 502 for temporary local storage of instructions, data, or computer addresses. Processor(s) 501 are configured to assist in execution of computer readable instructions. Computer system 500 may provide functionality for the components depicted in FIG. 5 as a result of the processor(s) 501 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 503, storage 508, storage devices 535, or storage medium 536. The computer-readable media may store software that implements particular operations, and processor(s) 501 may execute the software. Memory 503 may read the software from one or more other computer-readable media (such as mass storage device(s) 535, 536) or from one or more other sources through a suitable interface, such as network interface 520. The software may cause processor(s) 501 to carry out one or more processes or one or more operations of one or more processes described or illustrated herein. Carrying out such processesor operations may include defining data structures stored in memory 503 and modifying the data structures as directed by the software.

[0092] The memory 503 may include various components (e.g., machine readable media) including, , a random access memory component (e.g., RAM 504) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 505), and any combinations thereof. ROM 505 may act to communicate data and instructions unidirectionally to processor(s) 501, and RAM 504 may act to communicate data and instructions bidirectionally with processor(s) 501. ROM 505 and RAM 504 may include any suitable tangible computer- readable media described below. In one example, a basic input / output system 506 (BIOS), including basic routines that help to transfer information between elements within computer system 500, such as during start-up, may be stored in the memory 503.

[0093] Fixed storage 508 is connected bidirectionally to processor(s) 501, optionally through storage control unit 507. Fixed storage 508 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 508 may be used to store operating system 509, executable(s) 510, data 511, applications 512 (application programs), and the like. Storage 508 can also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 508 may, in appropriate cases, be incorporated as virtual memory in memory 503.

[0094] In one example, storage device(s) 535 may be removably interfaced with computer system 500 (e.g., via an external port connector (not shown)) via a storage device interface 525. Particularly, storage device(s) 535 and an associated machine-readable medium may provide non-volatile or volatile storage of machine-readable instructions, data structures, program modules, or other data for the computer system 500. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 535. In another example, software may reside, completely or partially, within processor(s) 501.

[0095] Bus 540 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 540 may be any of several types of bus structures including a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[0096] Computer system 500 may also include an input device 533. In one example, a user of computer system 500 may enter commands or other information into computer system 500 via input device(s) 533. Examples of an input device(s) 533 include an alpha-numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some cases, the input device is a Kinect, Leap Motion, or the like. Input device(s) 533 may be interfaced to bus 540 via any of a variety of input interfaces 523 (e.g., input interface 523) including serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0097] In some cases, when computer system 500 is connected to network 530, computer system 500 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 530. Communications to and from computer system 500 may be sent through network interface 520. For example, network interface 520 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 530, and computer system 500 may store the incoming communications in memory 503 for processing. Computer system 500 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 503 and communicated to network 530 from network interface 520. Processor(s) 501 may access these communication packets stored in memory 503 for processing.

[0098] Examples of the network interface 520 include a network interface card, a modem, and any combination thereof. Examples of a network 530 or network segment 530 include a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus, or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 530, may employ a wired or a wireless mode of communication. In general, any network topology may be used.

[0099] Information and data can be displayed through a display 532. Examples of a display 532 include a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passivematrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 532 can interface to the processor(s) 501, memory 503,and fixed storage 508, as well as other devices, such as input device(s) 533, via the bus 540. The display 532 is linked to the bus 540 via a video interface 522, and transport of data between the display 532 and the bus 540 can be controlled via the graphics control 521. In some cases, the display is a video projector. In some cases, the display is a head-mounted display (HMD) such as a VR headset. In further cases, suitable VR headsets include, by way of example, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further cases, the display is a combination of devices such as those disclosed herein.

[0100] In addition to a display 532, computer system 500 may include one or more other peripheral output devices 534 including, , an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 540 via an output interface 524. Examples of an output interface 524 include a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0101] In addition or as an alternative, computer system 500 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more operations of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0102] Various illustrative logical blocks, modules, circuits, and algorithm operations described in connection with the examples disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and operations have been described above generally in terms of their functionality.

[0103] The various illustrative logical blocks, modules, and circuits described in connection with the examples disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof configured to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., acombination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0104] The operations of a method or algorithm described in connection with the examples disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0105] In accordance with the description herein, suitable computing devices include, by way of example, server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Select televisions, video players, and digital music players with optional computer network connectivity may be suitable for use in the system described herein. Suitable tablet computers, in various cases, include those with booklet, slate, and convertible configurations.

[0106] In some cases, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Suitable server operating systems may include, by way of example, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Suitable personal computer operating systems may include, by way of example, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some cases, the operating system is provided by cloud computing. Suitable mobile smartphone operating systems may include, by way of example, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.

[0107] In some cases, the systems, the methods, the computer-readable media, and the techniques disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of anoptionally networked computing device. In further cases, a computer readable storage medium is a tangible component of a computing device. In still further cases, a computer readable storage medium is optionally removable from a computing device. In some cases, a computer readable storage medium includes, by way of example, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.

[0108] In some cases, the systems, the methods, the computer-readable media, and the techniques disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, that perform particular tasks or implement particular abstract data types. A computer program may be written in various versions of various languages.

[0109] The functionality of the computer readable instructions may be combined or distributed in numerous ways across various environments. In some cases, a computer program comprises one sequence of instructions. In some cases, a computer program comprises a plurality of sequences of instructions. In some cases, a computer program is provided from one location. In some cases, a computer program is provided from a plurality of locations. In some cases, a computer program includes one or more software modules. In some cases, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.

[0110] In some cases, a computer program includes a web application. A web application, in various cases, may utilize one or more software frameworks and one or more database systems. In some cases, a web application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some cases, a web application utilizes one or more database systems including, by way of example, relational, non-relational, object oriented, associative, XML, and document oriented database systems. In further cases, suitable relational database systems include, by way of example, Microsoft® SQL Server, mySQL™, and Oracle®. A web application, in some cases, may be written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database querylanguages, or combinations thereof. In some cases, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML). In some cases, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some cases, a web application is written to some extent in a client-side scripting language such as Asynchronous JavaScript and XML (AJAX), Flash® ActionScript, JavaScript, or Silverlight®. In some cases, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tel, Smalltalk, WebDNA®, or Groovy. In some cases, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some cases, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some cases, a web application includes a media player element. In some cases, a media player element utilizes one or more of many suitable multimedia technologies including, by way of example, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.[OHl] In some cases, a computer program includes a mobile application provided to a mobile computing device. In some cases, the mobile application is provided to a mobile computing device at the time it is manufactured. In other cases, the mobile application is provided to a mobile computing device via the computer network described herein.

[0112] In view of the disclosure provided herein, a mobile application may be created using hardware, languages, and development environments known to the art. In some cases, mobile applications are written in several languages. Suitable programming languages may include, by way of example, C, C++, C#, Objective-C, Java™, JavaScript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML / HTML with or without CSS, or combinations thereof.

[0113] Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of example, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of example, Lazarus, MobiFlex, MoSync, and PhoneGap. Also, mobile device manufacturers distribute software developer kits including, by way of example, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

[0114] Several commercial forums may be available for distribution of mobile applications including, by way of example, Apple® App Store, Google® Play, Chrome WebStore,BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, and Samsung® Apps.

[0115] In some cases, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Standalone applications may be compiled. A compiler may be a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of example, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some cases, a computer program includes one or more executable complied applications.

[0116] In some cases, the computer program includes a web browser plug-in (e.g., extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Web browser plug-ins may include Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. In some cases, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some cases, the toolbar comprises one or more explorer bars, tool bands, or desk bands.

[0117] Several plug-in frameworks may be available that enable development of plug-ins in various programming languages, including, by way of example, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

[0118] Web browsers (also called Internet browsers) are software applications, configured for use with network-connected computing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of example, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some cases, the web browser is a mobile web browser. Mobile web browsers (also called microbrowsers, mini-browsers, and wireless browsers) are configured for use on mobile computing devices including, by way of example, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of example, Google® Android®browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

[0119] In some cases, the systems, the methods, the computer-readable media, and the techniques disclosed herein include software, server, or database modules, or use of the same. Software modules may be created by techniques using machines, software, and languages. The software modules disclosed herein are implemented in a multitude of ways. In some cases, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In some cases, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In some cases, the one or more software modules comprise, by way of example, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some cases, software modules are in one computer program or application. In some cases, software modules are in more than one computer program or application. In some cases, software modules are hosted on one machine. In some cases, software modules are hosted on more than one machine. In some cases, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some cases, software modules are hosted on one or more machines in one location. In some cases, software modules are hosted on one or more machines in more than one location.

[0120] In some cases, the systems, the methods, the computer-readable media, and the techniques disclosed herein include one or more databases, or use of the same. In some cases, various databases may be suitable for storage and retrieval of one or more of (i) wearable data, (ii) responses to health queries, (iii) geographic data, etc., one or more of which may be historical, present, or future data or information. In some cases, suitable databases include, by way of example, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, XML databases, document oriented databases, and graph databases. Further examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some cases, a database is Internet-based. In further cases, a database is web-based. In still further cases, a database is cloud computing-based. In a particular case, a database is a distributed database. In other cases, a database is based on one or more local computer storage devices.Certain Definitions and Additional Considerations

[0121] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present subject matter belongs.

[0122] As used in this specification and the appended claims, the terms “artificial intelligence,” “artificial intelligence techniques,” “artificial intelligence operation,” and “artificial intelligence algorithm” generally refer to any system or computational procedure that may take one or more actions to enhance or maximize a chance of achieving a goal. The term “artificial intelligence” may include “generative modeling,” “machine learning” (ML), or “reinforcement learning” (RL).

[0123] As used in this specification and the appended claims, the terms “machine learning,” “machine learning techniques,” “machine learning operation,” and “machine learning model” generally refer to any system or analytical or statistical procedure that may progressively improve computer performance of a task.

[0124] As used in this specification and the appended claims, “acute illness” may generally refer to an abnormal or detrimental condition of the body that may develop rapidly and last for an abbreviated period, including infectious diseases. The term “acute illness” may be used interchangeably herein with “acute condition” or “acute health condition.”

[0125] As used in this specification and the appended claims, “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in further embodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0126] As used in this specification and the appended claims, when the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0127] As used in this specification and the appended claims, when the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies toeach of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0128] As used in this specification, “or” is intended to mean an “inclusive or” or what is also known as a “logical OR,” wherein when used as a logic statement, the expression “A or B” is true if either A or B is true, or if both A and B are true, and when used as a list of elements, the expression “A, B or C” is intended to include all combinations of the elements recited in the expression, for example, any of the elements selected from the group consisting of A, B, C, (A, B), (A, C), (B, C), and (A, B, C); and so on if additional elements are listed. As such, any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0129] As used in this specification and the appended claims, the indefinite articles “a” or “an,” and the corresponding associated definite articles “the” or “the,” are each intended to mean one or more unless otherwise stated, implied, or physically impossible. Yet further, it should be understood that the expressions “at least one of A and B, etc.,” “at least one of A or B, etc.,” “selected from A and B, etc.” and “selected from A or B, etc.” are each intended to mean either any recited element individually or any combination of two or more elements, for example, any of the elements from the group consisting of “A,” “B,” and “A AND B together,” etc.

[0130] As used in this specification and the appended claims “about” or “approximately” may mean within an acceptable error range for the value, which will depend in part on how the value is measured or determined, e.g., the limitations of the measurement system. For example, “about” may mean within 1 or more than 1 standard deviation, per the practice in the art. Alternatively, “about” may mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Where values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value may be assumed.

[0131] As used herein, a “data unit” refers to any amount of electronic data or information. Examples of data units include electronic files, communications, documents, images, videos, contracts, other forms of data, and components thereof. In some cases, a single electronic file (e.g. a document, email, image, etc) is composed of multiple component data units. Thus, a block in a blockchain may contain one or more data units that make up a portion of one or more electronic files.

[0132] As used herein, “verification” refers to confirmation that the data or data unit being verified is unaltered. For example, a data unit can be verified by comparing its hash value to the hash value of the original data unit that was stored on a blockchain. Identical hash values wouldindicate that the data unit being verified is unaltered from the original data unit on the blockchain.

[0133] As used herein, “certification” refers to providing a certificate or other formal signifier of the integrity of a data or data unit (e.g. an electronic health record). For example, once a data unit has been verified, a certificate formally attesting to the accuracy and integrity of the data unit may be issued and delivered to a user and / or third party requesting certification.

[0134] As used herein, "request" refers to the initial inquiry made by a customer to obtain a quote. For example, when a customer is interested in a product or service, they initiate a request asking for the pricing details, which is known as a quote.

[0135] As used herein, "SOW" (Statement of Work) refers to a formal document that details the specific tasks, deliverables, and timeline of a service or project.

[0136] As used herein, "Quote" denotes a preliminary estimate of costs, intended to provide the customer with an understanding of the financial requirements of a proposed service or product.

[0137] As used herein, "Purchase Order (PO)" refers to an official agreement between Scientist.com and the supplier that sets forth the wholesale price of a service or product.

[0138] As used herein, "Sales Order (SO)" denotes the transactional agreement between the customer and Scientist.com, signifying the retail price of the service or product.

[0139] As used herein, "Vendor Bill" refers to the invoice given by the supplier to Scientist.com for services or goods supplied.

[0140] As used herein, "Customer Invoice" signifies the invoice issued by Scientist.com to the customer, detailing the cost of goods or services sold.

[0141] As used herein, "Vendor Credit" refers to a credit memo from the vendor to Scientist.com, typically granted when goods are returned or in the case of overbilling.

[0142] As used herein, "Credit Memo" is a statement from Scientist.com to the customer, typically issued when goods are returned or if there is a reduction in the amount due.

[0143] As used herein, "Backoffice" signifies the supplier-facing side of the marketplace website, which combines all individual customer-focused marketplaces.

[0144] As used herein, "Storefront" denotes the customer-centric side of the marketplace website. Each customer has a uniquely configured storefront with the degree of customization varying per customer.

[0145] As used herein, "CSM" refers to the Company's Customer Success Management team, a team dedicated to ensuring optimal customer experience and success with the company's services or products.

[0146] As used herein, "53000 - Market Place COGS" refers to the specific account category associated with the direct costs involved in the production of goods or services sold on the marketplace.

[0147] As used herein, "20000 - Accounts Payable" denotes the specific account where the company's short-term obligations or debts to its suppliers or creditors are recorded and kept track of within a defined period.

[0148] As used herein, "20003 - Accounts Payable: Unbilled Marketplace COGS" signifies the particular account responsible for documenting the costs related to goods or services sold on the marketplace that have been incurred but not yet invoiced.

[0149] As used herein, " 13001 - Prepaid Exp’s & Other CA’s: Prepaid Marketplace COGS" refers to the specific account that tracks the payments made in advance for expenses concerning the cost of goods or services that are planned to be sold on the marketplace in the forthcoming period.

[0150] While preferred embodiments of the present invention have been shown and disclosed herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention disclosed herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

[0151] It should be noted that various illustrative or suggested ranges set forth herein are specific to their example embodiments and are not intended to limit the scope or range of disclosed technologies, but, again, merely provide example ranges for frequency, amplitudes, etc. associated with their respective embodiments or use cases. Where values are described as ranges, it will be understood that such disclosure includes the disclosure of all possible subranges within such ranges, as well as specific numerical values that fall within such ranges irrespective of whether a specific numerical value or specific sub-range is expressly stated.

[0152] It should be understood that, unless a term is expressly defined in this patent, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based at least in part on any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this patent is referred to in this patent in a manner consistent with a single meaning, which is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning.

[0153] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0154] Additionally, certain embodiments are disclosed herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a machine-readable medium) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as disclosed herein.

[0155] In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special -purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general -purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry,or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

[0156] Accordingly, hardware modules may encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations disclosed herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

[0157] Hardware modules may provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information). Elements that are described as being coupled and or connected may refer to two or more elements that may be (e.g., direct physical contact) or may not be (e.g., electrically connected, communicatively coupled, etc.) in direct contact with each other, but yet still cooperate or interact with each other.

[0158] The various operations of example methods disclosed herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.

[0159] Similarly, the methods or routines disclosed herein may be at least partially processor- implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

[0160] The performance of certain operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within an office environment, or a server farm). In other example embodiments, the one or more processors or processor- implemented modules may be distributed across a number of geographic locations.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A computer system for monitoring a biological assay, comprising: one or more processors; and one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to:(A) obtain assay data for said biological assay, wherein said assay data comprises an identifier for at least one party corresponding to said biological assay and service data corresponding to said biological assay;(B) analyze said assay data for said biological assay with a trajectory model, wherein said trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to said at least one party or a service corresponding to said service data; and(C) generate, based at least in part on said analyzing in (B), a trajectory for said biological assay.

2. The computer system of claim 1, wherein said at least one party comprises one or both of a supplier or a customer.

3. The computer system of any one of the preceding claims, wherein said service data comprises data measured by one or more scientific instruments from said biological assay.

4. The computer system of any one of the preceding claims, wherein said service data corresponds to at least one biological subject of said biological assay.

5. The computer system of any one of the preceding claims, wherein said trajectory model comprises a machine learning model.

6. The method of claim 5, wherein said machine learning model was trained via operations comprising:(A) obtaining said historical assay data for said historical biological assay comprising a plurality of actual trajectories for said historical biological assays each corresponding to an actual assay data set;(B) classifying said historical assay data into a plurality of subsets of historical assay data that each correspond to a different actual trajectory of said plurality of trajectories or range of different actual trajectories of said plurality of trajectories; and(C) generating said machine learning model using said plurality of subsets of historical assay data.

7. The computer system of any one of claims 5 or 6, wherein said machine learning model comprises a regression model, a decision tree, a random forest, or a neural network.

8. The computer system of any one of the preceding claims, wherein said trajectory comprises predicted time data.

9. The computer system of claim 8, wherein said predicted time data comprises a predicted date of completion of said biological assay.

10. The computer system of claim 8 or 9, wherein said predicted time data comprises a predicted time until said completion of said biological assay.

11. The computer system of any one of the preceding claims, wherein said trajectory comprises predicted invoice data.

12. The computer system of claim 11, wherein said predicted invoice data comprises a predicted total invoice realization following said completion of said biological assay.

13. The computer system of claim 8 or 9, wherein said predicted invoice data comprises a predicted invoice realization for an increment.

14. The computer system of claim 13, wherein said increment is a day, a week, or a month.

15. The computer system of any one of the preceding claims, wherein said biological assay comprises one or more of: a cell-based assay, a genomics or proteomics assay, an imaging assay, a clinical laboratory assay, a drug discovery assay, a metabolism or pharmacokinetics assay, or an immunology assay.

16. The computer system of any one of the preceding claims, wherein said computerexecutable instructions, when executed, further cause said one or more processors to perform operations (A)-(C) with updated assay data for said biological assay once per interval.

17. The computer system of claim 16, wherein said interval is a day, a week, or a month.

18. The computer system of claims 16 or 17 wherein said computer-executable instructions, when executed, cause said one or more processors to perform said operations (A)-(C) with said updated assay data for said biological assay once per said interval until said completion of said biological assay.

19. A method for monitoring a biological assay, comprising:(A) obtaining assay data for said biological assay, wherein said assay data comprises an identifier for at least one party corresponding to said biological assay and service data corresponding to said biological assay;(B) analyzing said assay data for said biological assay with a trajectory model, wherein said trajectory model was trained using historical assay data for a plurality of historicalbiological assays that each correspond to one or both of a party corresponding to said at least one party or a service corresponding to said service data; and(C) generating, based at least in part on said analyzing in (B), a trajectory for said biological assay.

20. One or more non-transitory computer-readable media comprising computer-executable instructions that, when executed by at least one processor, cause the at least one processor to:(A) obtain assay data for said biological assay, wherein said assay data comprises an identifier for at least one party corresponding to said biological assay and service data corresponding to said biological assay;(B) analyze said assay data for said biological assay with a trajectory model, wherein said trajectory model was trained using historical assay data for a plurality of historical biological assays that each correspond to one or both of a party corresponding to said at least one party or a service corresponding to said service data; and(C) generate, based at least in part on said analyzing in (B), a trajectory for said biological assay.

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