DETAILED ACTION
Applicant’s response, filed 13 April 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Claim Status
Claims 1-17 are pending and examined herein.
Claims 1-17 are rejected.
Claim Objections
The objection of claim 2 in Office action mailed 12 January 2026 is withdrawn in view of the amendment “a likelihood of inpatient hospital visits…” received 13 April 2026.
Claim Rejections - 35 USC § 112
The rejection on the ground of 112/b of claims 2, 8, 9, and 16 in Office action mailed 12 January 2026 is withdrawn in view of the amendment of “for each organ-system-specific health condition of the plurality of organ-system-specific health conditions for the respective patient” received 13 April 2026.
112/d
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 17 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 17 does not further limit claim 15 because claim 15 already requires a first (and second) set of models applied to a first (and second) group of feature sets to predict the first (and second) score of the first (and second) condition of the patient. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Response to Arguments
Applicant's arguments filed 13 April 2026 have been fully considered but they are not persuasive.
Applicant argues that claim 17 properly limits claim 15 because it provides temporal context to the operations recited in claim 15 (Reply p. 10). Applicant argues that claim 17 requires that “the first set of one or more machine learning models is applied to the first group of one or more feature sets…” while “the second set of one or more machine learning models is applied to the second group of one or more feature sets…” whereas claim 15 does not require the two operations to take place simultaneously (Reply p. 10).
This argument has been fully considered but found to be not persuasive. The BRI of the claim in light of the instant disclosure encompasses the interpretation that the first set of one or more machine learning models is applied to the first group of one or more feature sets while (which is interpreted to show contrast between the first set of one or more machine learning models and the second set of one or more machine learning models) the second set of one or more machine learning models is applied to the second group of one or more feature sets. This interpretation of claim 17 does not properly limit claim 15. Further, the BRI of the claim in light of the instant disclosure does not encompass the interpretation that these operations take place simultaneously because there is no disclosure of these machine learning model operations being performed simultaneously. Thus, under the BRI of the claim in light of the instant disclosure claim 17 does not properly limit claim 15.
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 1-17 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)
Claims 1-11 and 13-17 fall under the statutory category of a process and claim 12 falls under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1 and 12 recite mental processes of “extracting, from a database, data items…”, “aggregating one or more of the data items…”, and “generating a report that indicates a health care plan for the respective patient based on the total health score in relation to a particular age group…”.
Independent claims 1 and 12 recite mathematical concepts of “applying one or more machine learning models to the one or more feature sets to predict a respective risk score for the respective health condition for a respective patient…” and “computing a total health score based on the predicted respective risk score for each health condition for the respective patient”.
Dependent claim 3 recites a mathematical concept of “applying the respective machine learning model for the respective health condition to the one or more feature sets to predict the respective risk score for the respective health condition for a respective patient”. Dependent claim 6 recites a mental process of “aggregating the one or more of the data items into one or more feature sets further based on selecting…”. Dependent claim 8 recites mathematical concepts of “performing steps of inversion, scaling to 0-100, and normalization by age, on the respective score, for generating the report”. Dependent claim 10 recites a mathematical concept of “calculating correlation between the respective score for each health condition and the total health score, while generating the report”. Dependent claim 13 recites a mathematical concept of “training the one or more machine learning models by performing risk classification analysis on the data items…”. Dependent claim 14 recites mathematical concepts “applying a first set of one or more machine learning models to the one or more feature sets to predict a first risk score…”, “applying a second set of one or more machine learning models, distinct from the first set of one or more machine learning models, to the one or more feature sets to predict a second risk score…”, and “computing the total health score based at least on the first risk score and the second risk score…”. Dependent claim 15 recites mathematical concepts of “applying a first set of one or more machine learning models to a first group of one or more features…”, “applying a second set of one or more machine learning models, distinct from the first set of one or more machine learning models, to a second group of one or more feature sets…”, and “computing the total health score based at least on the first risk score and the second risk score…”.
The claims recite steps of making observations and organizing data as “extracting, from a database, data items…”, “aggregating one or more of the data items…”, and the claims recite a step of analyzing/evaluating data and making judgments as “generating a report that indicates a health care plan for the respective patient based on the total health score in relation to a particular age group…”. The human mind is capable of organizing data, analyzing/evaluating data and making judgments. The claims recite mathematical concepts of mathematical calculations of applying machine learning models to features to predict a respective risk score (such as a gradient boosted classifier as shown in [0014], a gradient boosted tree model that outputs calibrated likelihoods as shown in [0012], a gradient-boosted tree classifier as shown in [0014]), or logistic regression model (which encompasses a logistic regression equation) in [0072] which intake numerical values for features and output numerical values representing risk scores, computing a total health score based on respective risk scores which encompasses intaking respective risk score numerical values and outputting a total health score, performing mathematical operations of inversion, scaling, and normalization on numerical data, calculating correlations, and training machine learning models by performing risk classification analysis which is a series of mathematical calculations to tune the parameters of the model using training data. Dependent claims 2, 4, 5, 7, 9, 11, 16, and 17 further limit the mental process/mathematical concept recited in the independent claim but do not change their nature as a mental process/mathematical concept. Thus, claims 1-17 recite abstract ideas.
(Step 2A prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claims 1 and 12 of using a generic computer to perform judicial exceptions does not integrate the judicial exceptions into a practical application because this is simply applying the judicial exception to a generic computer without an improvement to computer technology. This additional element only interacts with the judicial exceptions by utilizing the computer as a tool to perform the judicial exceptions.
The additional element in claims 1 and 12 of outputting data (displaying data) does not integrate the judicial exceptions into a practical application because this is a step of insignificant extra solution activity of outputting data. This additional element only interacts with the judicial exceptions by outputting the solution of the judicial exceptions. It is noted that the content of the data displayed falls under the abstract idea itself.
Thus, the additional elements do not integrate the judicial exceptions into a practical application.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claims 1 and 12 of using a generic computer to perform judicial exceptions is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
The additional element in claims 1 and 12 of outputting data (displaying data) is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II). It is noted that the content of the data displayed falls under the abstract idea itself.
The combination of additional elements in claims 1 and 12 of using a generic computer and outputting data is conventional see MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exceptions because they are conventional alone and in combination.
Response to Arguments
Applicant's arguments filed 13 April 2026 have been fully considered but they are not persuasive.
Argument 1:
Applicant argues that the claimed subject matter does not recite any mental processes, because a human cannot practically perform the steps recited in the claims. Applicant provides that claim 1 requires that “respective risk scores” be calculated “for each organ-system-specific health condition of a plurality of organ-system-specific health conditions for a respective patient” which may increase the volume of data by at least an order of magnitude (Reply p. 11). Applicant argues that a human cannot practically perform operations recited in claim 1 (Reply p. 11). Applicant provides that claim 1 is amended to clarify that the data is digital data retrieved from a database, this clarifying that the data in the claims are not abstract and bringing the claims more aligned to those in example 39 (Reply p. 13).
This argument has been fully considered but found to be not persuasive. It is noted that the calculation of respective risk scores for each organ-system-specific health condition of a plurality of organ-system-specific health conditions for a respective patient was not identified as being mental processes (these limitations were identified as mathematical concepts). The limitations identified as reciting mental processes are “extracting, from a database, data items…” (which encompasses making observations to extract data items from a database), “aggregating one or more of the data items…” (which encompasses organizing the extracted data items), and “generating a report that indicates a health care plan for the respective patient based on the total health score in relation to a particular age group…” (which encompasses performing an analysis based on the total health score in relation to a particular age group to make judgments on a health care plan for a respective patient). Further, “extracting, from a database, data items…” recites a mental process because the human mind is capable of making observations of data in a database and extracting the data items.
Argument 2:
Applicant argues that the model recited in the claims is not a generic numerical processor, but rather a specific model operating on age-agnostic patient data to generate a clinical risk assessment (Reply p. 12). Applicant argues that the one or more machine learning models in the claims are not a mathematical concept in the abstract, but rather, specific computational architecture implemented in a specific way on a computer to perform a specific function. Characterizing the machine learning models as "math" would render virtually every artificial intelligence software invention patent-eligible, which is precisely the result the Supreme Court in Alice and the Federal Circuit in Enfish cautioned against (Reply p. 14).
This argument has been fully considered but found to be not persuasive. It is noted that the machine learning model is characterized as a mathematical concept because it encompasses a model which itself is a series of mathematical calculations. As in the case of a gradient boosted tree classifier (which is encompassed by the claims) the data is accessed against several inequalities to generate an output of several weak learnings (decision trees) and then aggregating (e.g., averaging) the numerical output of the plurality weak learners. Thus, these machine learning models are interpreted as being a mathematical concept because the models themselves are a series of mathematical calculations. Further, the instant disclosure provides that the machine learning model may be a logistic regression model (instant disclosure [0072]) and encompasses a logistic regression equation and using a logistic regression model to generate scores is a mathematical calculation of calculating a numerical value using this logistic regression equation.
Argument 3:
Applicant argues that "generating a report that indicates a health care plan for the respective patient based on the total health score in relation to a particular age group, wherein generating the report includes concurrently displaying the total health score and a breakdown of the total health score in terms of the respective score for each organ-system-specific health condition, a comparison of the total health score of the respective patient to other patients in same age group as the respective patient, vitals, and/or data used to compute the total health score, in addition to a health care plan for alleviating at least some of the organ-system-specific health conditions," integrates any alleged judicial exception into a practical application because the generated report is not merely informational output, but rather a direct connection between the computational output to specific real-world clinical action (Reply p. 15). As described in claim 1, the report comprises not just the generated score and analytics of the score, but also "a health care plan for alleviating at least some of the organ-system-specific health conditions," which is a connection between the multimorbidity score and specific, actionable clinical measure. Rather than the report being used as a method of displaying information, the report is used to tie the score to a practical clinical action such as treatment planning for a respective patient. Thus, the additional elements of the claim integrates any alleged judicial exception into a practical application, and the claims are patent-eligible at Step 2A, Prong 2 of the patent-eligibility analysis (Reply p. 15).
This argument has been fully considered but found to be not persuasive. It is noted that the step of generating a report falls under the judicial exception itself (which encompasses performing an analysis based on the total health score in relation to a particular age group to make judgments on a health care plan for a respective patient) and the content of the data contained in the report further falls under the abstract idea while the step of displaying this report is the additional element of outputting data (displaying data). This additional element constitutes as insignificant extra solution activity of outputting data because this additional element only interacts with the judicial exception in a manner of outputting/displaying the output of the judicial exception.
Argument 4:
Applicant argues that when the claim is considered as a whole, the claims present a technical improvement over existing technologies because the claimed method in claim 1 reflects a specific technical improvement over conventional age-stratified scoring approach for multimorbidity score calculations (Reply p. 16). Applicant further argues the claims provide a technical solution to the problem identified in paragraph [0002] by using age-agnostic data to generate a multimorbidity score that does not result in incomplete, patchwork representations of patient health, and generates an age-specific report that then tailors the age-agnostic multimorbidity score for a respective patient's demographic (Reply p. 17). Applicant argues that this approach presents a technical improvement over existing methods, because the report generated from the score provides a single, unified, and actionable clinical assessment specific to the patient but without integration of multiple scores and with a higher number of input data from age-agnostic data, compared to a more limited input dataset from age-specific data (Reply p. 17).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. The scoring approach for multimorbidity score calculations is interpreted as falling under the judicial exception. Thus, the argued improvement of an improvement in the scoring approach constitutes as an improvement provided by the judicial exception of the scoring approach itself. Further, the improvement is not provided by the additional elements (the generic computer and outputting data) or the combination of the judicial exceptions of this scoring approach and these additional elements. The MPEP states “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)” (see MPEP 2106.05(a)(I)). The combination of the judicial exceptions of the steps of performing this scoring approach and the additional element of the generic computer does not provide an improvement in computer technology because the computer is invoked as a tool to perform the improved abstract idea (i.e., the computer itself is not functioning in a different/improved manner). Thus, the argued improvement is provided by the judicial exception alone which does not constitute as an improvement to technology.
Argument 5:
Applicant argues that claim 1 recites elements that improve conventional computer systems and claims and 12 recite specific combination of claim features in a particular order (Reply p. 18).
This argument has been fully considered but found to be not persuasive. The MPEP states “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)” (see MPEP 2106.05(a)(I)). In the instant claims the improvement is provided by the judicial exception alone which invokes the generic computer as a tool to perform the abstract data analysis. The computer itself does not function in a different/improved manner rather the instant claims provide an improved abstract idea of generating a risk score which is implemented on a general-purpose computer.
Argument 6:
Applicant argues that claims 11, 15, and 17 are patent eligible under at least Step 2A, Prong Two, because the multiple-model structure wherein each model is specific to an organ-system-specific health condition; trained for each score, each with a distinct label set and input feature set; and subsequently calibrated using isotonic regression with 3-fold cross validation over the training set; reflects specific technical improvements in accuracy to the clinical assessment process. Moreover, the improvements to technology are driven by the model architecture (system design) itself, not solely from an abstract idea. The Applicant respectfully submits that these dependent claims are thus patent-eligible under at least Step 2A, Prong Two (Reply p. 20).
This argument has been fully considered but found to be not persuasive. The MPEP states at 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements… In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception”. It is noted that these machine learning models are interpreted as falling under the judicial exception itself and thus cannot provide the improvement. Further, the argued improvement in accuracy to clinical assessment process (through generating risk scores utilizing organ-system-specific health conditions) is an improvement in the abstract data analysis itself. The MPEP states “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)” (see MPEP 2106.05(a)(I)). Although the abstract data analysis is implemented on a computer, the computer itself does not function in a different/improved manner, rather the abstract idea of generating risk scores which improve the accuracy of an assessment is improved.
Conclusion
No claims are allowed.
Claims 1-17 are free of the prior art for the reasons discussed in the Office action mailed 25 June 2025.
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.
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/J.E.H./Examiner, Art Unit 1685
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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