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 .
Response to Amendment
Claims 1-2, 6-9, 13-16, & 20 were previously pending in this application. The amendment filed 16 September 2025 has been entered and the following has occurred: Claims 1, 8, & 15 have been amended. No claims have been added or cancelled.
Claims 1-2, 6-9, 13-16, & 20 remain pending in the application.
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 1-2, 6-9, 13-16, & 20 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.
The claims recite subject matter within a statutory category as a process (claims 1-2 & 6-7), machine (claims 8-9 & 13-14), and manufacture (claims 15-16 & 20) which recite steps of:
retrieving, by one or more processing units, a plurality of literatures from one or more databases, wherein each of the plurality of literatures describes a risk prediction model for a same disease, the plurality of literatures from one or more databases being published papers published by authors;
extracting, by the one or more processing units performing a natural language processing technique, study features from each of the plurality of literatures, the study features of at least some of the plurality of literatures including authors' achievement describe in the at least some of the plurality of literatures extracted via keyword searches;
extracting, by the one or more processing units, weights of risk factors in the risk prediction model described by each of the plurality of literatures from the plurality of literatures; and
calculating, by the one or more processing units, adjusted weights of risk factors based on extracted study features and the extracted weights of risk factors, to form an adjusted risk prediction model, wherein calculating the adjusted weights of risk factors comprises:
training, by the one or more processing units, a multi-task model using the extracted study features and the extracted weights of risk factors to obtain a coefficient matrix W that follows a Matrix Variate Normal (MVN) distribution;
decomposing, by the one or more processing units, the coefficient matrix W to obtain a matrix Ω representing a relationship between risk factors according the MVN distribution; and
calculating, by the one or more processing units, the adjusted weights of risk factors using the matrix Ω,
the adjusted risk prediction model forming a synthesized risk prediction model of a plurality of risk prediction models identified in the plurality of literatures for a particular disease; and
using the synthesized risk prediction model to support prevention and prognosis of the particular disease in a clinical setting,
wherein training the multi-task model comprises:
setting, by the one or more processing units, a loss function that considers variance within each of the literatures and variance between literatures, wherein the process of training the multi- task model converges by minimizing the loss function, wherein:
the extracted study features are expressed by a matrix X with n*d elements, n is a number of the plurality of literatures, and d is a number of the extracted study features;
the extracted weights of risk factors are expressed by a matrix Y with n*m elements, n is a number of the plurality of literatures, and m is a number of the extracted weights of risk factors;
the loss function is set as minwl(X,Y;W) + η + ϵ, wherein
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η is a term indicating variance between literatures, ϵ is a term indicating the variance within literatures, x represents elements from the matrix X, yi,j represents elements from the matrix Y, and wk,j represents elements from the coefficient matrix W,
wherein ϵ is determined as an average of variances of weights of risk factors over the literatures,
the loss function minimization with the η term and the ϵ term facilitating generating of the synthesized risk prediction model that accounts for relationships between diverse risk factors, weights and prediction performance of risk prediction models for the same disease described in the plurality of literatures.
These steps of retrieving a plurality of literatures from one or more databases, extracting study features and associated weights of risk factors in a risk prediction model, calculating adjusted weights of risk factors based on the extracted study features, training a generic learning model using the extracted features and weights of risk factors, and performing additional mathematical steps such as decomposing a matrix and calculating adjusted weights of risk factors, and/or generating a synthesized risk prediction model from the adjusted risk prediction model according to the equations and variables, e.g. loss function minimization terms, elected by Applicant, under the broadest reasonable interpretation, covers interactions between people, and therefore falls within the “certain methods of organizing human activity” grouping of abstract ideas. When considered as a whole, the steps describe a person collecting literature such as published medical papers to study, calculating disease risk/risk factors based on the information from the literature, and generating a risk prediction model based on parameterization of said model in view of the literature for prevention and prognosis of the disease and loss minimization, but for recitation of generic computer components. Thus, the steps recited in these claims describe managing personal behavior or interactions between, people, for a person to read literature, formulate a model/rules/instructions for determining disease risk, and calculate a disease risk, and accordingly these claims each recite an abstract idea in the form of certain methods of human activity.
In the alternative, under its broadest reasonable interpretation, the claim limitations cover mathematical relationships, mathematical formulas or equations, mathematical calculations, and therefore falls within the “mathematical concepts” grouping of abstract ideas. For instance, the steps of retrieving a plurality of literatures from one or more databases, extracting study features and associated weights of risk factors in a risk prediction model, calculating adjusted weights of risk factors based on the extracted study features, training a generic learning model using the extracted features and weights of risk factors, and performing additional mathematical steps such as decomposing a matrix and calculating adjusted weights of risk factors to generate a risk model for use, include aspects of extracting (mathematical) weights of risk factors, calculating adjusted (mathematical) weights of risk factors, obtaining a coefficient matrix as an output from a machine learning model, (mathematically) decomposing the coefficient matrix to obtain a matrix Ω, and calculating the adjusted weights of risk factors. Additionally, explicit formulae are recited in independent claims 1, 8, & 15, and therefore the independent claims are overwhelmingly directed towards mathematical relationships and/or mathematical calculations for obtaining weights of risk factors within the corpus of an associated medical literature with the various equations and variables, such as loss minimization terms, elected by Applicant. Accordingly, the claims recite an abstract idea, as drafted, under the broadest reasonable interpretation, includes mathematical concepts.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2, 6-7, 9, 13-14, 16, & 20, reciting particular aspects of how predicting a risk/factors of a disease and selecting particular study features may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of one or more processing units/processors, a risk-prediction model, a natural language processing technique, a multi-task model, a memory, a set of computer program instructions, one or more databases, and/or a computer program product amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0094] for processing units/devices/processors, [0088] for a “generalized” risk prediction model, [0054]-[0055] for a natural language processing model stating “other NLP algorithms or NER algorithms could be used”, [0066] for a multi-task model described or well-known in prior art, [0090] for a memory, [0094]-[0095] for computer readable instructions, [0053] for one or more databases, [0090] for computer readable storage medium which describe the generic nature of the additional elements, and the usage of a generalized risk prediction model to support prevention and prognosis of a “particular” disease in a clinical setting, such that these elements amount to no more than mere instructions to “apply it”, see MPEP 2106.05(f) for generically applied additional elements; see MPEP 2106.04(d)(2)(a) for generality of the treatment or prophylaxis in view of the generally applied/synthesized risk prediction model, which states “a claim that recites the same abstract idea and ‘administering a suitable medication to a patient’… is not particular, and is instead merely instructions to “apply” the exception in a generic way”);
add insignificant extra-solution activity to the abstract idea (such as recitation of retrieving a plurality of literatures from one or more databases, describing risk prediction model for a disease and being published papers, extracting study features and/or author achievement, extracting weights of risk factors, obtaining a coefficient matrix as an output from a multi-task model, and/or receiving parameters/formulae for obtaining study features/weight of risk factors amounts to mere data gathering; recitation of calculating adjusted risk factors based on extracted study features and weights of risk factors and using a matrix Ω, decomposing the obtained coefficient matrix, forming a synthesized risk prediction model from the adjusted risk prediction model, and applying various functions or variables for extracting study features/weights of risk factors, the η term and the ϵ term facilitating generating of the synthesized risk prediction model that accounts for relationships between diverse risk factors, weights and prediction performance of risk prediction models for the same disease described in the plurality of literatures and loss function minimization efforts amounts to selecting a particular data source or type of data to be manipulated; recitation of training multi-task model using extracted study features and risk factors amounts to insignificant application that is known to represent simple parameterization of models, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of the literature relating to a disease, the extracted information from the literature relating to said disease, and using the synthesized risk prediction model to support prevention and prognosis of the particular disease in a clinical setting, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 6-7, 9, 13-14, 16, & 20, which recite similar or the same generic computer components as the independent claims additional limitations which amount to invoking computers as a tool to perform the abstract idea; claims 6-7, 13-14, & 20, which specify the gathered/extracted parameters and/or functions to be applied to the parameters, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 2, 9, & 16, which recite limitations relating to predicting a risk of the disease using a prediction model and/or setting a loss function that considers variance within literatures and variance between literatures and/or applying the DerSimonian and Laird method in order to minimize a loss function, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; and claim 2, 6-7, 9, 13-14, 16, & 20 which generally relate the limitations/steps/parameters to calculating disease risk, additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (such as retrieving a plurality of literatures from one or more databases, describing risk prediction model for a disease and being published papers, extracting study features and/or author achievement, extracting weights of risk factors, and obtaining a coefficient matrix as an output from a multi-task model, e.g., receiving or transmitting data, especially over a network, Symantec, MPEP 2106.05(d)(II)(i); calculating adjusted risk factors based on extracted study features and weights of risk factors and using a matrix Ω and/or decomposing the obtained coefficient matrix, forming a synthesized risk prediction model from an adjusted risk prediction model and using said synthesized risk prediction model to support prevention and prognosis of the particular disease in a clinical setting, applying one or more varying functions and/or parameters, such as setting a loss function that considers variance within literatures and variance between literatures and/or the η term and the ϵ term facilitating generating of the synthesized risk prediction model that accounts for relationships between diverse risk factors, weights and prediction performance of risk prediction models for the same disease described in the plurality of literatures and loss function minimization efforts, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), see Wikipedia et al. (“Loss Function” – 15 August 2024 – NPL) which discloses the use of a quadratic loss function and minimization thereof being WURC, especially when using variance analysis, and Hu et al. (“LINKAGE: An Approach for Comprehensive Risk Prediction for Care Management” – 17 June 20244 – NPL) which describes the use of varying matrices being WURC such as via the use of a coefficient matrix and/or decomposed coefficient matrix; maintaining one or more databases of literature that describe risks associated with varying diseases, upkeeping/maintaining records relating to associated risk factors, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing computerized instructions for performance of the steps recited, storing literature in a database, storing extracted risk factors and/or study features, storing one or more architectures for learning models, storing one or more training sets for training the learning models, and storing one or more outputs generated by the models, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); extracting study features and/or risk factors from the plurality of literatures such as by a natural language processing technique, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 6-7, 9, 13-14, 16, & 20 which recite additional limitations that amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields: claims 6-7, 13-14, & 20 which specify the gathered/extracted parameters and/or functions to be applied to the parameters, e.g., receiving or transmitting data, especially over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 2, 9, & 16 which recite limitations relating to predicting a risk of the disease using a prediction model and/or applying the DerSimonian and Laird method such as to minimize a loss function, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), see Haidich et al. for DerSimonian and Laird being WURC and Hu et al. for the a multi-task predictive model and a generalized loss function being WURC; claims 6-7, 13-14, & 20 which recite limitations relating to maintaining or upkeeping one or more parameters/functions, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2, 6-7, 9, 13-14, 16, & 20 which recite limitations relating to storing extracted parameters and particular forms of those parameters, storing computerized instructions for performance of the steps recited, , storing one or more models/functions, and storing one or more outputs generated, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 6-7, 13-14, & 20 which specify the gathered/extracted parameters and/or functions to be applied to the parameters that are extracted from literature via natural language processing techniques, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Response to Arguments
Applicant's arguments filed 16 September 2025 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 101 rejections of claims 1-2, 6-9, 13-16, & 20, Applicant argues on p. 8-10 of Arguments/Remarks that the amendments to independent claims 1, 8, & 15 recite features that integrate any recited judicial exception into a practical application. More specifically, Applicant argues that the “synthesized risk prediction model” that is formed according to the embodiments recited in the independent claims integrate any recited judicial exception into a practical application, because the particularity of the model provides for a particular machine that is significantly more than any recited abstract idea. Examiner respectfully disagrees with Applicant’s arguments. Further specifying the particular formulae and/or parameterization of the formulae seems to amount to mere efforts of data gathering, applying mathematical equations/formulae, and/or mere manipulation of data, and does not seem to represent a technological improvement. Additionally, an improvement to modeling or parameterization of a model seems to amount to improving an abstraction rather than improving the technology being used to implement the abstraction. That is, improving ways of gathering data from literature and how to manipulate said data or perform repetitive calculations still constitutes an abstraction instead of an improvement to the technology implementing said data gathering and/or modeling efforts. Furthermore, these aspects do not seem to amount to significantly more than the recited abstract idea in the form of a particular machine, because the particularities of the formulae and/or parameters elected by Applicant do not add particularities to the machinery or technological components. Rather, they merely further limit the abstraction or abstract ideas at-hand. Therefore, claims 1-2, 6-9, 13-16, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1-2, 6-9, 13-16, & 20, Applicant argues on p. 10-11 of Arguments/Remarks that the amendments to independent claims 1, 8, & 15 recite features that integrate any recited judicial exception into a practical application. More specifically, Applicant argues that the claims as a whole include an improvement to a computer or a technological field, such as difficulties in synthesizing risk prediction models from different papers into a generalized risk prediction model that quantitatively takes into account various aspects of different research papers. Examiner respectfully disagrees with Applicant’s Arguments. While Examiner notes that the limitations may amount to an improved effort of synthesizing one or more risk prediction models, this reads as an improvement to an abstraction, e.g. the mental process and/or associated mathematical concepts of generating a risk prediction model. That is, improving efforts of parameterizing a risk prediction model seems to amount to efforts of improving data gathering and/or data manipulation efforts, which amounts to improvements of abstract concepts. MPEP 2106.05(a) describes that the judicial exception alone cannot provide the improvement, and therefore, improvements alone to these abstract concepts amounts to improvements to the judicial exception alone, and therefore does not represent an improvement to a computer or a technological field, as defined by the Alice/Mayo framework. Therefore, claims 1-2, 6-9, 13-16, & 20 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1-2, 4-9, 11-16, & 18-20, Applicant argues on p. 11-12 of Arguments/Remarks that independent claims 1, 8, & 15 do not recite well-understood, routine, or conventional activity (WURC) in the prior art regarding training or creating of a prediction model, a technique in machine learning technology, and therefore the claims recite patent-eligible subject matter. Examiner respectfully disagrees with Applicant’s Arguments. While Applicant argues in view of the lack of prior art rejections supporting the notion that the elements of the claim when considered as a whole do not constitute WURC, Examiner asserts that while the prior art may not disclose the specifics of the model being built, such as particular parameters, equation terms, etc., the general notion of creating a risk model, minimizing a loss/cost function when creating said model, and applying said models to determine potential risks for a particular disease/trials associated therewith are substantially described in the prior art. That is, as presented above in the “Claim Rejections – 35 U.S.C. 101” section of this Office Action, various references are cited that describe the generally well-known, conventional nature of the steps recited. Additionally, further specifying the abstract portions of the claimed invention, such as particular parameters, equation terms, etc. does not necessarily amount to an inventive concept or significantly more than the recited abstract idea itself. Therefore, claims 1-2, 6-9, 13-16, & 20 remain rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cohen et al. (U.S. Patent Publication No. 2018/0068083) generally discloses a system for predicting risk and/or likelihood for a patient developing or having developed an associated disease based on various information, such as based on journal articles, scientific studies, etc., and parameters associated therewith;
Colley et al. (U.S. Patent Publication No. 2021/0090694) discloses a risk model being generated based on various research or treatment planning perspectives collected from literature and clinical research, including internally weighted c5riteria composed of knowledge of known evolutionary models, functional data, clinical data, hotspot regions within genes, internal and external somatic databases, primary literature, and other features of somatic drivers;
Edelson et al. (U.S. Patent Publication NO. 2015/0332012) discloses a system for aggregated weighted risk scores that utilize parameters from investigators’ analyses, prior literature, and trial and error efforts in order to further personalize said risk scores/analysis.
Applicant's amendment necessitated the new ground 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.
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/H.R./Examiner, Art Unit 3684/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684