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 .
DETAILED ACTION
Status of the Application
Claims 1, 3-11, and 13-20 are currently pending in this case and have been examined and addressed below. This communication is a Final Rejection in response to the Amendment to the Claims and Remarks filed on 09/18/2025.
Claims 1 and 11 are currently amended.
Claims 2 and 12 remain canceled and are not considered at this time.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claims 1 and 11 recite “using the integrative machine learning model to the integrative program” which appears to be missing some words. Appropriate correction is required.
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 1, 3-11, and 13-20 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 3-10 fall within the statutory category of an apparatus or system. Claims 11 and 13-20 fall within the statutory category of a process.
Step 2A, Prong One
As per Claims 1 and 11, the limitations of produce a viral epidemiological profile related to the user by generating viral epidemiological profile training data by categorizing the viral biomarkers and external data comprising a genomic analysis to training data sets based on user cohorts; identify an integrative signature as a function of the viral epidemiological profile, which includes identifying the integrative signature as a function of the behavioral indicator and the viral epidemiological profile, iteratively updating the integrative training set as a function of an iteratively determined integrative signature, and generate an integrative program as a function of the integrative signature, cover performance of the limitation in the mind but for the recitation of generic computer components. The steps of producing a viral epidemiological profile, identifying an integrative signature, iteratively updating the training set, and generating an integrative program are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The steps of training a viral epidemiological profile machine-learning model with viral epidemiological training data and outputting the viral epidemiological profile from the model; training the integrative machine-learning model as a function of the integrative training set wherein training the model comprises: applying the training data to input nodes of the intervention machine learning model, creating connections between a plurality of nodes of the integrative machine-learning model, and adjusting the connections and weights between the plurality of nodes of the integrative machine-learning model to produce desired values at output nodes of the integrative machine-learning model; identifying the integrative signature as a function of the integrative machine-learning model, and retraining the integrative machine-learning model as a function of the updated integrative training set, under its broadest reasonable interpretation, encompass mathematical concepts such as training and executing a machine learning model, training a model using creating connections between nodes, adjusting the connections and weights between the plurality of nodes to produce desired values, identifying the integrative signature as a function of the machine-learning model, and retraining the integrative machine-learning model as a function of the updated training set. The specification describes a machine-learning process as using a body of training data to generate an algorithm performed by a machine-learning module to produce outputs, paragraph 14, which describes the training and use of a machine-learning model as a mathematical concept. The specification, paragraphs 75 and 77, describes an integrative machine-learning model as any of supervised, unsupervised, or reinforcement machine-learning processes which includes machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG) which are specific mathematical concepts and utilizing a neural net machine-learning process which is a mathematical concept. Additionally, paragraph 77 of the specification describes the updated machine-learning model as incorporating polynomial regression machine-learning process, which is also a mathematical concept.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a computing device for executing the elements of the abstract idea, receiving a user input comprising viral biomarkers and external data comprising a genomic analysis of a user, receiving a behavioral indicator, using an integrative machine-learning model, receiving an integrative training set, outputting the integrative signature from the integrative machine-learning model, and using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program. The computing device in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims recite using an integrative machine learning model to identify the integrative signature and using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program, which amounts to mere instructions to apply an exception as per MPEP 2106.05(f)(2). The integrative and programming machine learning models are recited at a high-level of generality such that it is a commonplace mathematical algorithm (Specification [0075], [0082]) which is applied on a general purpose computer and therefore, amounts to mere instructions to apply the exception. The claims also recite the additional element of receiving a behavioral indicator, receiving an integrative training set, and outputting the integrative signature which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because it is mere data gathering and data outputting in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The system including the "computing device” are recited at a high level of generality and are recited as generic computer components by reciting any computing device including a microcontroller, microprocessor, etc. (Specification, [0009]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also recite the use of an integrative machine learning model and a programming machine-learning model to execute the abstract idea, where the integrative machine learning model is disclosed to be a commonplace mathematical algorithm including any of supervised, unsupervised, or reinforcement machine- learning processes which includes machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG) (Specification [0075], [0082]). Any of the described algorithms are mathematical algorithms which are applied to the abstract idea, which applies the exception, as per MPEP 2106.05(f)(2). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of receiving a user input comprising viral biomarkers and external data comprising a genomic analysis of a user, receiving a behavioral indicator, receiving an integrative training set, and outputting the integrative signature from the integrative machine-learning model which are elements that are well-understood, routine and conventional computer functions in the field of data management because it is claimed at a high level of generality and include receiving or transmitting data and presenting data, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added) and (iii) Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93). There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims 3-10 and 13-20 add further limitations which are also directed to an abstract idea. Claims 3 and 13 include identifying a contagion element which is a mental process for similar reasons to the independent claims. Claims 4 and 14 include determining a travel routine which is a mental process for similar reasons to the independent claims. Claims 5 and 15 include identifying a geographical prevalence which is a mental process for similar reasons to the independent claims. Claims 6-8 and 16-18 include determining a susceptibility element and specifying the susceptibility which is a mental process for similar reasons to the independent claims. Claims 9-10 and 19-20 include generating a viral alleviation program and viral prevention metric which is a mental process for similar reasons to the independent claims. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Response to Arguments
Applicant’s arguments, see Pages 7-14, “Rejection of Claims under 35 U.S.C. §101”, filed 09/18/2025 with respect to claims 1, 3-11, and 13-20 have been fully considered but they are not persuasive.
Applicant argues that the steps of the independent claims are not directed to a mental process because the steps cannot be performed in the human mind or with mental processing using the aid of pen and paper. Specifically, Applicant argues that the newly amended claim limitation including “generate an integrative program as a function of the integrative signature, using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program” does not recite a mental process. Examiner notes that the newly amended claim limitation of using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program to apply the steps of abstract idea, here to generate an integrative program as a function of the integrative signature, is an additional element which amounts to mere instructions to apply the exception. The programming machine-learning model is a mathematical algorithm, as described in the specification ([0082], [0084]) as any of simple linear regression, etc. The use of a mathematical algorithm to apply the exception is found to be mere instructions to apply the exception and does not integrate the abstract idea into a practical application or provide significantly more.
Applicant argues that the steps of the claims are not directed to a mathematical concept because the newly amended claim limitation including “generate an integrative program as a function of the integrative signature, using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program” does not recite a mathematical concept. As described above, the use of a programming machine-learning model to execute the step of the abstract idea including generating an integrative program as a function of the integrative signature is an additional element which amounts to mere instructions to apply the exception and is not directed to the abstract idea itself.
Applicant additionally argues that this claim element is not directed to an abstract idea because it does not recite an abstract idea similar to Example 39. Examiner respectfully disagrees. The presently amended claims include the step of “generate an integrative program as a function of the integrative signature, using a programming machine-learning model wherein the programming machine-learning model is trained using a program training set configured to correlate an integrative signature generated using the integrative machine-learning model to the integrative program” which does not include positively reciting the training of the model similar to Example 39. Instead, the present claim recites the use of a machine learning model to execute steps of the abstract idea, which is mere instructions to apply the exception, as per MPEP 2106.05(f)(2). The use of a machine learning model to apply the exception, as in the present claims, is an additional element and not part of the abstract idea, and thus any arguments with regard to whether it falls into the abstract category of a mathematical process are moot.
Applicant argues that the present claims integrate the abstract idea into a practical application because the claims present a technical improvement over traditional systems by improving program generation by enabling the automatic synthesis of integrative programs from learned correlations, replacing manual, static, or rule-based systems. Applicant further argues that direct application of machine learning to solve a technical problem aligns with the principles of Example 47. Examiner respectfully disagrees. The automatic synthesis of integrative programs from learned correlations which replaces a manual, static, rule-based system is an example of mere automation of manual processes, which as per MPEP 2106.05(a) has been found by the courts to not be sufficient to show an improvement in computer functionality. As per Example 47, Claim 3, the claim is eligible because of a recited improvement in the technical field of network security which is specifically described in the background. In the present application, generating an integrative program is not a technical problem but is applying the additional elements of the machine learning model applied to a general purpose computer to replace a manual, static, or rule-based system. This amounts to mere instructions to apply the exception and does not integrate the abstract idea into a practical application.
Applicant argues that the claims integrate the abstract idea into a practical application similar to Example 48, Claim 2 because the claims improve upon traditional aptitude measurement and program-generation systems by integrating a trained machine-learning model into a technical process. Examiner respectfully disagrees. The present claims apply the machine-learning models to the abstract idea of generating an integrative program. This amounts to mere instructions to apply the exception. The ability for the machine learning model to be refined based on new data is merely applying the updated data to the machine learning model which is a mathematical algorithm. This still falls into mere instructions to apply the exception. The present claims do not provide an improvement to machine-learning as a technology, but rather use known, established machine-learning technology to apply to the abstract idea in order to determine the results of the epidemiological profile and the integrative signature. Applicant additionally argues that by iteratively refining the model using updated training data which optimizes the models performance, this results in a practical application. Examiner respectfully disagrees. Improving the performance of the model through continued training and updating is not an improvement or advance in machine-learning and thus does not provide a technical improvement. The improved result of applying the machine-learning model to the data is a result of the abstract idea itself and not to an improvement to the technology of machine-learning. Therefore, the claims do not integrate the abstract idea into a practical application.
Applicant argues that the claims contain limitations such as non-conventional and specific arrangement of steps which provides a technical improvement. Applicant further argues that the claimed invention improves computer functionality by enabling automated, data-driven generation of integrative programs. Examiner respectfully disagrees. As discussed above, the use of known computer components and mathematical algorithms to apply the abstract idea does not provide an improvement in computer functionality but rather applies the abstract idea with the use of the known computer components and mathematical algorithms. As discussed above, the automation of a manual process does not provide an improvement in computer functionality as per MPEP 2106.05(a). Therefore, the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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 Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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/EVANGELINE BARR/Primary Examiner, Art Unit 3682