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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/06/2026 has been entered.
Status of Claims
This action is a Non-Final Action in response to the communications filed on 03/06/2026.
Claims 1, 10 – 11, and 18 – 20, have been amended.
Claims 1 – 3, 6 – 13, and 15 – 20, are currently pending in this application.
Response to Remarks
Examiner’s Response to Remarks:
Response to Rejections under 35 U.S.C. § 101.
Examiner’s Response to Rejections under 35 U.S.C. § 101.
Applicant argues the claims show an improvement in computer functionality that integrates any abstract idea into a practical application.
Examiner respectfully disagrees. Applicant’s claim 1 recites an abstract idea of mathematical concepts without significantly more. Claim 1 merely performs calculations to compute scores, discretize data based on associations and correlations, provides a recommendation, and tunes the model to continue predicting a score. For example, independent claim 1 receives sensor data, identifies a category for the sensor data, determines a measure value, transforming a value to another value, determining a predicted utility score, generating a parameter used for training a model to make a recommendation, detecting new activity data, generating a recommended activity, modifying the parameter, and tuning the model. However, these are merely the abstract ideas of mathematical calculations and mathematical relationships. Claims 11 and 19 substantially recite the same subject matter as claim 1 and include the same abstract ideas as claim 1.
The claims do not integrate the judicial exception into a practical application. Claim 1 recites the additional elements of one or more processors and one or more biometric sensors, monitored entity, training by the one or more processors, tuning by the one or more processors, and predictive data analysis system. However, with these additional elements and the additional elements of a system and one or more non-transitory computer readable media storing processor executable instructions, one or more non-transitory computer-readable storage media and executed by one or more processors, the additional elements recited are not significantly more than the judicial exception, as these are all generic computer components performing generic computer functions. The training a parameter, tuning a parameter, as well as the predictive data analysis system are recited at a high level of generality without any detailed description of these additional elements. Claim 1 as a whole does not amount to significantly more than the judicial exception. In addition, everything here is executed by a processor; as there is no improvement to a computer nor is there an improvement to a technological field. Applicant is merely executing mathematical concepts with a processor. Even with the steps of training a parameter and tuning a parameter, the claim is nothing more than mathematical concepts; and here the claimed invention is merely predicting a score and providing a recommended activity, and thus resolving a business problem of recommending an activity.
The dependent claims encompass the same abstract ideas as the independent claims and add nothing that is significantly more than the judicial exception. Accordingly, claims 1 – 3, 6 – 13, and 15 – 20 remain rejected under 35 U.S.C. 101.
Applicant’s claims are not similar to the claims discussed in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) Memorandum December 5, 2025, hereinafter “Desjardins Memorandum”. Desjardins Memorandum describes the claimed invention where there is a method of training a machine learning model on a series of tasks. However Applicant’s instant claims merely train a parameter for predicting a recommended activity and tunes the parameter based on the score; and here the training parameter is the same as tuning the parameter. The instant claimed invention is not similar to Desjardins.
Claim Rejections: 35 U.S.C. § 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, 6 – 13, and 15 – 20, are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 11, and 19 recite:
A computer-implemented method comprising: receiving, biometric data for a monitored entity;
identifying, a plurality of utility discretization categories that is associated with a plurality of sub-ranges of a total range for a utility measure, wherein (i) the utility measure is associated with one biometric utility sub-category in a set of biometric utility sub-categories of a biometrics utility category and (ii) the set of biometric utility sub- categories is associated with the biometric data;
determining, a risk distribution measure value based at least in part on a statistical distribution of a plurality of risk measures that is associated with a utility discretization category, of the plurality of utility discretization categories, for the monitored entity;
transforming, the risk distribution measure value into a utility distribution measure value based at least in part on a mapping of the risk distribution measure value from a risk domain into a utility measure value of a utility domain based at least in part on the utility discretization category of the plurality of utility discretization categories;
determining, a predicted utility score for the monitored entity based at least in part on the utility distribution measure value, wherein the predicted utility score is associated with the utility discretization category;
determining, an activity score of a candidate activity of a plurality of candidate activities based at least in part on the predicted utility score;
training, a parameter of a predictive data analysis system to provide the candidate activity as a recommended activity
detecting, activity data that corresponds to the recommended activity;
determining, an updated predicted utility score by modifying the predicted utility score based at least in part on the activity data;
determining, an updated activity score by modifying the activity score of the candidate activity based at least in part on the updated predicted utility score.
The limitations of claim 1, under its broadest reasonable interpretation, recites the abstract idea of mathematical concepts. Claim 1 recites observing, biometric data for a monitored entity; observing, a plurality of utility discretization categories that is associated with a plurality of sub-ranges of a total range for a utility measure, wherein (i) the utility measure is associated with one biometric utility sub-category in a set of biometric utility sub-categories of a biometrics utility category and (ii) the set of biometric utility sub- categories is associated with the biometric data; evaluating, a risk distribution measure value based at least in part on a statistical distribution of a plurality of risk measures that is associated with a utility discretization category, of the plurality of utility discretization categories, for the monitored entity; evaluating, the risk distribution measure value into a utility distribution measure value based at least in part on a mapping of the risk distribution measure value from a risk domain into a utility measure value of a utility domain based at least in part on the utility discretization category of the plurality of utility discretization categories; evaluating, a predicted utility score for the monitored entity based at least in part on the utility distribution measure value, wherein the predicted utility score is associated with the utility discretization category; evaluating, an activity score of a candidate activity of a plurality of candidate activities based at least in part on the predicted utility score; detecting, activity data that corresponds to the recommended activity; evaluating, an updated predicted utility score by modifying the predicted utility score based at least in part on the activity data; and evaluating, an updated activity score by modifying the activity score of the candidate activity based at least in part on the updated predicted utility score. However, the claim limitations merely collect data, manipulate data, and evaluate correlations to determine mathematical relationships between data and determine a score with mathematical calculations. Claims 11 and 19 are substantially similar and recite the same subject matter as claim 1. Accordingly, claims 1, 11, and 19 recite mathematical concepts.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2 and 12 are directed towards observing a risk distribution measure value describing a centroid statistical distribution measure for a risk measure value for the monitored entity, wherein the monitored entity is associated with a demographic profile and a particular utility discretization category; claims 3 and 13 are directed towards observing the risk measure values is associated with a risk measure that comprises an episode risk group (ERG) measure; claim 6 is directed towards observing the utility measure is associated with one exercise utility sub-category in a set of exercise utility sub-categories of an exercise activity utility category and (ii) the set of exercise utility sub- categories is associated with a recorded count of performance of a particular exercise activity; claims 7 and 15 are directed towards evaluating user interface data that describes a relationship between the predicted utility score and a distribution of predicted utility scores for the plurality of utility discretization categories; claims 8 and 16 are directed towards observing the utility distribution measure value is determined based at least in part on an inverse of a select risk distribution measure value of a plurality of risk distribution measure values that is associated with an observed utility discretization category; claims 9 and 17 are directed towards observing the predicted utility score is determined based at least in part on: the utility distribution measure value, a minimal predicted utility score for the plurality of utility discretization categories, and a maximal predicted utility score for the plurality of utility discretization categories; and claims 10, 18, and 20 are directed towards evaluating combining one or more predicted utility scores for one or more utility measure values of a plurality of utility measure values of the monitored entity to generate a categorical utility measure value for a particular utility category of a plurality of utility categories; determining a plurality of categorical utility weights for the plurality of utility categories; determining an overall predicted utility score based at least in part on one or more predicted utility values and the plurality of categorical utility weights; and performing one or more prediction-based actions based at least in part on the overall predicted utility score; however all are merely observation and evaluation of data. Accordingly, the dependent claims encompass the same abstract ideas as well.
These judicial exceptions are not integrated into a practical application. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., a number of processors). Claim 1 recites the additional elements of one or more processors and from one or more biometric sensors, monitored entity, training, by the one or more processors, a parameter of a predictive data analysis system to provide the candidate activity as a recommended activity based at least in part on the activity score of the candidate activity, wherein (i) the activity score (a) satisfies an activity score threshold and (b) represents a recommendation value that is associated with the monitored entity performing the recommended activity in accordance with the predicted utility score and (ii) the recommended activity comprises a health action. In addition to reciting the additional elements of claim 1, claim 11 recites a system and one or more non-transitory computer readable media storing processor executable instructions. In addition to reciting the additional elements of claim 1, claim 19 recites the additional elements of one or more non-transitory computer-readable storage media and executed by one or more processors. However, these additional elements are merely generic computer components performing generic computer functions as per Applicant’s Specifications shown below:
“[0037] FIG. 2 provides a schematic of a predictive data analysis computing entity 106 according to one embodiment of the present invention. In general, the terms computing entity, computer, entity, device, system, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. Such functions, operations, and/or processes may include, for example, transmitting, receiving, operating on, processing, displaying, storing, determining, creating/generating, monitoring, evaluating, comparing, and/or similar terms used herein interchangeably. In one embodiment, these functions, operations, and/or processes can be performed on data, content, information, and/or similar terms used herein interchangeably. [0038] As indicated, in one embodiment, the predictive data analysis computing entity 106 may also include one or more communications interfaces 220 for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein interchangeably that can be transmitted, received, operated on, processed, displayed, stored, and/or the like.”
and thus are not practically integrated nor significantly more.
The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., one or more processors). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Therefore, the claims are directed to an abstract idea. The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 3, 6 – 10, 12 – 13, 15 – 18, and 20, when analyzed both individually and in combination are also held to be ineligible for the same reasons above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually, add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 3, 6 – 13, and 15 – 20, are not patent eligible under 35 U.S.C § 101.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Reier, Mike (U.S. Patent No. 9,727,885) discloses a method including the operations of receiving responses for a plurality of assessments for an individual, the responses including responses for assessments of at least one physical and at least one psychological component of the individual's wellness; calculating numerical indicators for a plurality of wellness components for the individual based on the plurality of responses.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Frank Alston whose telephone number is 703-756-4510. The Examiner can normally be reached 9:00 AM – 5:00 PM Monday - Friday.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
3/27/2026
/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625