DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is responsive to the response filed April 14, 2026.
Claims 125-135, 137, 141, 142 and 144 have been amended.
Claims 125-142 and 144 are currently pending and have been fully examined.
Specification
The substitute specification filed April 16, 2026 has not been entered because it does not conform to 37 CFR 1.125(b) and (c) because: the statement as to a lack of new matter under 37 CFR 1.125(b) is missing.
Further, there is no marked up text in the ‘marked up’ copy that is evident beyond a replacement title of invention. Applicant’s response indicates that typographical errors in the specification have been addressed, but they are not readily apparent. Applicant should point to the specific paragraphs in which any changes have been made to allow for efficient review.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 125-142 and 144 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 125 recites the limitation "the database" in line 18. There is insufficient antecedent basis for this limitation in the claim as the initial recitation of “a database” was cancelled in the amendment filed 4/14/26.
Claim 141 recites the limitation "the database" in line 16. There is insufficient antecedent basis for this limitation in the claim as the initial recitation of “a database” was cancelled in the amendment filed 4/14/26.
Claim 144 recites the limitation "the database" in line 16. There is insufficient antecedent basis for this limitation in the claim as the initial recitation of “a database” was cancelled in the amendment filed 4/14/26.
Claims 126-140 and 142 depend on claims 125 and 141 respectively and inherit the same deficiency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 125-142 and 144 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process (claim 141), a machine (claim 125), and an article of manufacture (claim 144) which are recited as methods, systems, and non-transitory computer readable media that perform the steps and/or functions of:
define, a risk element for a risk category wherein the risk category influences risk associated with monitoring of a clinical trial;
calculate, first risk profile data of the risk category based on a risk factor and a weighting assigned to the risk element;
train, a machine learning model with the first risk profile data;
receive, by the machine learning model, second risk profile data based on the risk category and the risk element;
analyze, by the machine learning model, the second risk profile data to identify a pattern based on the first risk profile data;
apply at least one of a normalization, standardization or transformation function to the second risk profile data;
predict, by the machine learning model and based on the pattern, an overall risk score for the clinical trial based on a predefined threshold value of the overall risk score;
recommend, by the machine learning model based on the risk elements, one or more of a type of monitoring, a level of monitoring, and the overall risk score; and
update the database with the second risk profile data;
display on a graphical user interface (GUI) the overall risk score along with a correlation chart between the risk category and the overall risk score;
stratify the type of monitoring into one of a low, medium and high categories using the overall risk score; and
fine tune or more parameters of the machine learning model to improve a prediction of the overall risk score, the one or more parameters comprising at least one of a depth of a neural network, a dimensionality of one or more layers of the neural network, a learning rate, and a momentum; and
wherein the machine learning model comprises a feedback layer that enables the machine learning model to learn continually from the second risk profile data and to improve an output of the system by dynamically improving the prediction of the overall risk score in real time and to enable risk based monitoring (RBM) of the clinical trial.
Step 2A: Prong 1
When taken individually and as a whole, the steps corresponds to concepts identified as abstract ideas by the courts, such as “mathematical concepts”, which are mathematical relationships, mathematical formulas or equations, mathematical calculations, and “certain methods of organizing human activity”, which are interactions between individuals that can include: fundamental economic principles or practices; commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
The claim is directed to a system to perform the process of recommending types or levels of monitoring based on a risk score, which is performed by the system performing the above underlined steps in the claimed invention. The steps of calculating the risk profile data, analyzing the second risk profile data to identify a pattern, applying a function to the risk profile data, predicting a risk score based on a predefined threshold value for the risk scores are all mathematical concepts because they include performing mathematical calculations (e.g., generating a logistic regression model, and predicting an overall risk score), and identifying a mathematical relationships (e.g., determining the relationship between the overall risk score and a threshold value). Applying at least one of a normalization, standardization, or transformation function to data recites mathematical concepts because the specification describes these functions as statistical techniques [paragraphs 271-273]: “[t]his is done by the system by employing modules of statistical techniques to perform operations such as normalization, standardization of predicted values and identification of thresholds to classify a risk score as low, medium, and high.” Similarly, tine tuning model hyperparameters, such as depth of a neural network, dimensionality of layers of the neural network, learning rate and momentum rooted in mathematical concepts, which is reinforced by paragraph 271 of the specification: “[t]he model’s hyper parameters such as depth of the network, dimensions, learning rate and momentum can be fine-tuned to improve the power of predictability of risk by leveraging the optimization techniques including, but not limited to, gradient descent, stochastic gradient descent, and their flavors (speed, memory, noise).”
Defining one or more risk categories, calculating the risk profile data, receiving a second risk profile data, making recommendations based on the risk score, displaying the overall risk score along with a correlation chart, and stratifying the type of monitoring using the overall risk score is certain types of organizing human activity because it is managing personal behavior by providing rules or instructions for a person’s behavior by providing rules or instructions based on the predicted risk score.
Overall, the claim recites certain methods of organizing human activity, wherein the mathematical concepts (applying at least one of a normalization, standardization or transformation function; fine tune one or more parameters… comprising at least one of a depth of a neural network, a dimensionality of one or more layers of the neural network, a learning rate, and a momentum) are part of the analysis performed in order to make the recommendations.
Step 2A: Prong 2
The claims do not include additional elements that are sufficient to be considered a practical application because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), generally linking the application of the abstract idea to a particular field of use or technological environment (2106.05(h)), or mere instructions to apply it with a computer (MPEP 2106.05(f)), as discussed below. The additional elements are bolded above, and include the steps of train, a machine learning model with the first risk profile data; analyze, predict and recommend by the machine learning model, update the database with the second risk profile data; display on a graphical user interface (GUI) the overall risk score along with a correlation chart; wherein the machine learning model comprises a feedback layer that enables the machine learning model to learn continually from the second risk profile data and to improve an output of the system by dynamically improving the prediction of the overall risk score in real time and enable risk based monitoring (RBM) of the clinical trial.
Insignificant Extra-Solution Activity
The step of update the database with the second risk profile data are examples of necessary data outputting. Necessary data outputting is an insignificant extra-solution activity (MPEP 2106.05(g)).
Insignificant extra-solution activities are not sufficient to integrate the abstract idea into a practical application or cause the claim to amount to significantly more than the abstract idea (MPEP 2106.05(g))
Generally Linking Implementation a Particular Technological Environment or Field of Use
The steps of: ‘train, a machine learning model with the first risk profile data’ and ‘the machine learning comprises a feedback layer that enables the machine learning model to learn continually…. and to improve an output of the system by dynamically improving the prediction of the overall risk score in real time to enable risk based monitoring (RBM) of the clinical trial’ are steps that are used to generally link the performance of providing recommendations to the field of patient risk based monitoring.
The steps reciting generically recited components of a computer system, such as wherein the machine learning model is a self-learning model comprising a feedback layer that enables the machine learning model to learn from the second risk profile data, only serve to generally link the implementation of the abstract idea to a technological environment, which would be a system that uses a machine learning model with those features.
Generally linking the application of the abstract idea to a particular field of use or technological environment is not sufficient to integrate the abstract idea into a practical application or cause the claim to amount to significantly more than the abstract idea (MPEP 2106.05(h)).
Mere Instructions to Apply the Abstract Idea Using a Computer
The steps reciting the use of computer components, such as performing the steps “by the machine learning model”, serve as mere instructions to apply the abstract idea using a computer. Mere instructions to apply the abstract idea using a computer are not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.05(f)).
The steps reciting ‘display on a graphical user interface the overall risk score along with a correlation chart’. Paragraph 146 of the specification establishes that a computer may have a display device for displaying information to the user, and paragraph 147 of the specification establishes that a computing system includes a front-end component, and paragraph 167 establishes that a “dashboard” is a type of interface that visualizes KPIs or KRIs for a specific goal or process. Paragraph 192 establishes that “interface” can refer to and/or can include a computer-related entity that can be either hardware, a combination of hardware and software, software, or software in execution. Thus, this step is considered as displaying information on a display device or monitor using said display device or monitor. Gathering and analyzing information using conventional techniques and displaying the result (TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48) and Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result (Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018)) are examples that the courts have indicated as not showing an improvement to technology.
Step 2B
The claims also do not include additional elements that are sufficient to be considered a significantly more than the abstract idea because the additional elements amount to: insignificant extra-solution activity (MPEP 2106.05(g)), mere instructions to apply it with a computer (MPEP 2106.05(f)), generally linking the application of the abstract idea to a particular field of use or technological environment (MPEP 2106.05(h)), or a well-understood, routine, and conventional limitation (MPEP 2106.05(d)), as discussed below.
The steps addressed above in Step 2A: Prong 2, when considered again under Step 2B are not considered to make the claims amount to significantly more than the abstract idea because those steps, when considered additionally with regards to Step 2B, are still considered to be either insignificant extra-solution activity, mere instructions to apply an abstract idea with a computer, or generally linking the application of the abstract idea to a particular field of use or technological environment, which are types of limitations that are not sufficient to make the claims amount to significantly more than the abstract idea (MPEP 2106.05.I.A).
The steps recited as either being part of the abstract idea or insignificant extra-solution activity are all examples of at least one of: storing and retrieving data from a memory (retrieving data to be used from an internal source and storing data at an internal storage location), sending and receiving data over a network (receiving data from an external source and transmitting the data to be stored at an external storage location), electronic recordkeeping, or performing repetitive calculations. All of those functions have been identified as well-understood, routine, and conventional functions of a generic computer that are not significantly more than the abstract idea when claimed broadly or as an extra-solution activity (MPEP 2106.05(d).II).
The recited computer components (e.g., the processor and the non-transitory memory) are all generically recited components (see specification, par. [0160], [0154]). Commercially available components, generic computer components, and specially-programmed computer components performing the functions of a generic computer are not considered to be amount to significantly more than the abstract idea (MPEP 2106.05(b)).
When considered as a whole, the components do not provide anything that is not present when the component parts are considered individually. Using the broadest reasonable interpretation, the system as a whole is a system of general purpose computer components receiving data, analyzing the data, and generating recommendations based on the analysis performed on the data. This is a system of general purpose computer components performing the abstract idea and insignificant extra-solution activities through these generically described devices performing well-understood, routine, and conventional functions of a generic computer (MPEP 2106.05(d).II).
Dependent Claim Analysis
Claims 126-140 are ultimately dependent from Claim(s) 125 and includes all the limitations of Claim(s) 125. Therefore, claim(s) 126-140 recite the same abstract idea of claim 125.
Claims 126-127 all recite additional limitations that serve to select by type or source the data to be manipulated by describing the types of data that are to be used as part of the analysis. Selecting by type or source the data to be manipulated is an insignificant extra-solution activity that is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(g)).
Claim 136 further describes the abstract idea by defining the different types of monitoring that are to be recommended based on the overall risk score. The abstract idea and additional limitations that also recite an abstract idea are not considered to be able to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (MPEP 2106.04.II.A.2).
Claims 137-140 recite additional limitations that amount to mere instructions to apply the abstract idea using a computer by reciting the different embodiments of the machine learning model that could be used to perform the steps of the claimed invention. Each of these types of models are known machine learning model types, and the use of any specific machine learning model does not change any other underlying step in the process. Mere instructions to apply the abstract idea using a computer are not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
Claim 142 is ultimately dependent from Claim(s) 141 and includes all the limitations of Claim(s) 141. Therefore, claim 142 recites the same abstract idea of claim 141.
Claim 142 recites additional limitations that serve to select by type or source the data to be manipulated by describing the types of data that are to be used as part of the analysis. Selecting by type or source the data to be manipulated is an insignificant extra-solution activity that is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(g)).
Response to Arguments
Applicant's arguments filed April 14, 2026 have been fully considered but they are not persuasive.
Step 2A Prong 1 arguments
Applicant argues that amended claim 125 is not directed to an abstract idea or a method of organizing human activity as the claim does not recite human decision workflows, contractual arrangements of business rules and instead recites automated, machine-executed operations that compute risk scores and stratify monitoring types through a machine learning model that operates independently of human judgment. Applicant argues that the claimed feedback-drive improvement of prediction accuracy in real time by finetuning parameters of machine learning model is a function that cannot be practically performed by humans, either mentally or using pencil and paper, particularly given the dynamic nature of the underlying data. Applicant argues that amended claim 125 is not directed to a mathematical concept, such as a mathematical formula, calculation or abstract statistical relationship. Applicant argues that claim 125 does not claim mathematical equations in the abstract, but recites the application of data transformation, pattern identification, and predictive modeling within a machine learning system that operates on structured/unstructured data stored in a database, producing outputs that control computerized monitoring operations.
This argument is not persuasive. Claim 141 (the method version of apparatus claim 125) recites a plurality of steps taken to evaluate the risk of a clinical trial in order to make a recommendation on the monitoring of said clinical trial. The fact that it employs computers, in the form of a machine learning model, to do so, does not change the fact that the claims recite an abstract idea.
The ‘normalization, standardization or transformation’ function that is applied to risk profile data are statistical techniques, as per paragraph 273 of the specification: “[t]he prediction results can be stratified into Low, Medium and High. This is done by the system by employing modules of statistical techniques to perform operations such as normalization, standardization of predicted values and identification of thresholds to classify a risk score as low, medium and high”. Further, paragraph 271 of the specification discloses that “the model’s hyper parameters such as depth of the network, dimensions, learning rate and momentum can be fine-tuned to improve the power of predictability of risk by leveraging the optimization techniques including, but not limited to, gradient descent, stochastic gradient descent, and their flavors (speed, memory, noise)”. Gradient descent and stochastic gradient descent are known mathematical, statistical optimization techniques, as are normalization and standardization. When given their broadest reasonable interpretation in view of the disclosure, normalization and/or standardization of data, as well as fine-tuning hyper parameters including depth of neural network, dimensionality of one or more layers of the neural network, learning rate and momentum are mathematical calculations.
Step 2A Prong 2 and Step 2B arguments
Applicant argues that claim 125 integrates any abstract idea into a practical application. Applicant argues the amended claim further requires that the machine learning model includes a feedback layer that enables continual learning from newly received risk profile data and dynamically improves prediction outputs and monitoring decisions in real time by fine tuning parameters of the machine learning model. Applicant argues that this feedback driven improvement directly affects how the system operates over time, allowing the system to adapt to evolving clinical trial conditions and improve the accuracy of subsequent predictions, and therefore does not merely “use” an abstract idea but applies it in a concrete technological manner to achieve improved system performance in a real-world computing environment.
This argument is not persuasive. Improved prediction of overall risk score is the intended or desired result of the continual learning process of the machine learning model. A fundamental characteristic of machine learning models is that they learn and the resultant output improves, as a result of training with additional data. There is no stated improvement to the functioning or performance of the underlying computer or technology, and there is no improvement to the machine learning model. The fine tuning of machine learning parameters does not address any technical problems presented by the machine learning model, nor does it constitute a technical solution. As noted above, fine tuning the model’s hyper parameters such as depth of network, dimensions, learning rate and momentum are rooted in mathematical concepts, which is reinforced by paragraph 271 of the specification.
Applicant argues that claim 125 does not rely on insignificant extra-solution activity, specifically that the steps of updating the database, applying data transformations, finetuning the model parameters, and dynamically improving predictions are not token post-solution activities. Applicant argues updating the database is not a passive storage or post-solution activity. The database stores transformed data and inference outputs that directly affect subsequent prediction cycles. The stored data is reused as an operational input, such that later inferences are conditioned on prior system states. Removing this step would materially alter system behavior and break the feedback loop, demonstrating that it is integral rather than ancillary.
This argument is not persuasive. After the second risk profile data has “at least one of a normalization, standardization or transformation function” applied to it, a database is updated with the second risk profile data. Using a database to store information or data is not an improvement nor inventive. The database itself is not improved, nor is its structure altered or improved. The only change to the database is the data stored within. Per MPEP 2106.05(a)(I), an example that the courts have indicated may not be sufficient to show an improvement in computer-functionality includes: Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018). Further, per MPEP 2106.05(d)(II), the courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
Applicant argues applying data transformations is not mere data preparation. The transformations enforce structural and semantic constraints that enable the prediction model to operate in real time and to maintain consistency across heterogeneous data inputs. These transformations are prerequisite computational steps without which model inference cannot occur in the claimed manner. As such, they form part of the solution logic, not insignificant pre-solution activity.
This argument is not persuasive. Applicant argues features that are not claimed (enforcing structural and semantic constraints, maintaining consistency across heterogeneous data inputs). As addressed above, normalization and standardization are both mathematical optimization functions. Furthermore, the claim does not specify what a “transformation function” encompasses. Presumably, “transformation functions” are a pre-processing algorithm that pre-process the structured, unstructured and image data and then forward the cleaned data to the subsequent modules for further processing and analysis, where incomplete and missing values can be handled using traditional techniques such as imputation, multivariate regression and k-nearest neighbor, noise reduction by removing erroneous data and outliers from the data by multivariate approaches using different similarity measures such as Mahalanobis and Cook, per paragraphs 221-223 of the specification.
Applicant argues dynamically improving predictions is inherently incompatible with a finding of insignificant extra-solution activity. This step requires modifying model behavior based on feedback derived from prior outputs and stored data. This step also requires finetuning model parameters. The claimed improvement is a technical process that alters how future predictions are generated, not a mere use or presentation of results. Applicant argues the claim recites a closed-loop, feedback-driven architecture in which data persistence, transformation, inference, feedback, and presentation are causally linked. The steps operate in a dependent sequence such that each step enables or constrains the next. Applicant submits that these elements are integral to the operation of the claimed system and shape how the solution is achieved. Therefore, these elements cannot be dismissed as insignificant extra-solution activity.
This argument is not persuasive. Applicant argues features that are not claimed (data persistence, transformation, inference, feedback and presentation being causally linked). Even though the machine learning model learns using the feedback layer, the result is that future predictions or outputs from the machine learning model would be expected to be improved. The claim does not specify how any of the machine learning parameters would be fine tuned, or under what basis. For example, if a certain parameter is fine tuned rather than the others to address an issue with the model. Further, the overall risk score (and type of monitoring) is not adjusted for clinical trials that have already been evaluated using the machine learning model. Hyperparameter tuning is a process used to optimize configuration variables (hyperparameters) for use in training a machine learning model. Hyperparameters are set before training begins and governs the learning process, and are not “based on feedback derived from prior outputs and stored data” (e.g., based on the model input into the machine learning model). The hyperparameters do not have an affect on how the data input into the machine learning model generates the output.
Applicant argues that claim 125 also does not fall within the category of claims that merely instruct an abstract idea to be applied on a generic computer. The claim does not recite generic computer components performing routine functions in an unspecified manner. Instead, it recites specific processing operations, including machine learning-based pattern identification, data normalization or transformation, real-time feedback-driven model improvement (as amended), and GUI generation based on computed correlations. Applicant argues that claim 125 is not directed merely to linking an abstract idea to a particular field of use or technological environment, such as clinical trials. Applicant submits that the claim does not simply state that risk assessment is performed "in the context of' clinical trials. Rather, the claim recites specific technical operations and system behaviors that are tailored to computerized clinical trial monitoring, including real-time data ingestion, adaptive model learning, and automated stratification of monitoring types.
This argument is not persuasive. Looking at method claim 141, the claim is silent as to who or what “applies” at least one of a normalization, standardization or transformation function to the second risk profile data. Applicant does not identify, nor is it readily apparent from the claims themselves, what “specific technical operations and system behaviors” are recited in the claim.
The claim does not preclude a human performing these mathematical functions by pencil or paper, or even by employing a computer to automate it. Further, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 has been held by the courts to not be enough to qualify as "significantly more" when recited in a claim with a judicial exception per MPEP 2106.05(A).
Applicant argues the clinical trial context is not a nominal limitation, but a setting in which the claimed technical solution operates to improve computer functionality. The recited clinical trial context imposes technical constraints on system operation, including real-time ingestion of trial data, adaptive model learning based on evolving trial conditions, and automated stratification of monitoring actions. These requirements dictate how the computer processes data and updates models in a domain-specific manner, rather than merely limiting an abstract idea to a particular environment. The claim therefore recites a technological improvement within a specific computing domain, not a field-of-use restriction applied to an otherwise abstract idea.
This argument is not persuasive. The clinical trial context is indeed the contextual setting in which the machine learning model is applied. However, the underlying computer is not improved as a result of the data pre-processing and processing, updating of the database, or applying the machine learning model to the data to predict an overall risk score for a clinical trial. It is noted that evolving conditions of the clinical trial is not recited in the claims.
Step 2B arguments
A key inventive aspect of claim 125 is the recitation of a machine learning model comprising a feedback layer that enables continual learning from newly received risk profile data and dynamically improves prediction outputs by fine tuning parameters of the machine learning model having at least one of depth of a neural network, dimension, learning rate and momentum and monitoring decisions in real time. This limitation defines a closed-loop learning architecture in which system outputs are not static or predetermined but are adaptively refined as new data becomes available. Such real-time, feedback-driven improvement addresses technical challenges associated with evolving data distributions, data drift, and changing trial conditions, and therefore constitutes a technical improvement to computer-based predictive systems. The claimed feedback layer is not a generic instruction to retrain a model, but a structural and functional feature that governs how the system processes successive data inputs and improves future outputs. This adaptive behavior improves prediction accuracy and system responsiveness without manual intervention, which is not a routine or conventional computer operation. As a result, the claim reflects an inventive concept that improves how computers operate in data-intensive environments.
This argument is not persuasive. It is noted that, after the machine learning model predicts an overall risk score, and the machine learning model parameters are fine tuned, the fine tuning of the model would presumably improve subsequent predictions made by the machine learning model. The fine tuned machine learning model is never recited as being used to evaluate new clinical trials to predict an overall risk score or receiving new data, or to re-evaluate the overall risk score of previously evaluated clinical trials.
The claim also recites automated stratification of monitoring types and generation of a graphical user interface that visually displays correlation charts between risk categories and overall risk scores. These limitations are not merely post-solution reporting steps, but part of an integrated system that transforms raw model outputs into actionable, machine-generated control signals and visualizations used to manage computerized monitoring operations. The GUI presentation reflects a technical transformation of data, not a mere display of information.
This argument is not persuasive. Neither the specification nor the claim defines what a “correlation chart” is or what it encompasses; thus, Applicant is not acting as their own lexicographer. A correlation chart is understood in the art to be a visual indication, such as a chart or graph that visualizes a relationship or correlation between data or variables. Neither the claim nor the specification recites how the correlation chart is formulated or constructed, only that it is displayed on a GUI. There is no indication that GUIs are incapable of displaying correlation charts or that the GUI has been improved in a way such that a correlation chart can now be displayed. Similarly, there is no indication that correlation charts were not previously capable of being displayed on a GUI and therefore improved in manner such that it is capable of being displayed. Per MPEP 2106.05(h), one example of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Gathering and analyzing information using conventional techniques and displaying the result (TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48) and Instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result (Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018)) are examples that the courts have indicated as not showing an improvement to technology.
With respect to data transformation, per MPEP 2106.05(C), "Transformation" of an article means that the "article" has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation. For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER H CHOI whose telephone number is (469)295-9171. The examiner can normally be reached M-Th 9am-7pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Namrata Boveja can be reached at 571-272-8105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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