DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Office Action is in response to the Applicant's amendments and remarks filed12/1/2025. Claims 1, 5-6 were amended. Claims 2-4 were cancelled. Claim 9 is new. Claims 1 and 5-9 are presently pending and presented for examination.
Response to Remarks/Arguments
In regards to rejection under 35 U.S.C. § 101: Applicant’s arguments, filed 12/1/2025, with respect to claims 1 and 5-9 have been fully considered and are not persuasive.
In regards to Applicant’s arguments that “As amended, claim 1 does not recite any step of managing, organizing, or directing human conduct, commerce, or interpersonal interactions. Instead, the claim is directed to a specific computer-implemented apparatus that performs technical data processing using sensor-derived information captured by a wearable device and executed by a processor coupled to a memory. The "behavior data" in the present claim is not behavioral instruction or decision- making guidance for people, but rather digitally measured numerical information-such as step counts, heart rate, and time-stamped activity records-collected automatically by hardware sensors. This data serves as raw machine input that the processor converts into statistically structured feature data (e.g., behavior statistic feature data, behavior transition feature data, and behavior difference amount feature data) through programmatic computation. These operations-aggregating sensor signals, computing transition probabilities, labeling data by time intervals, and generating correlation coefficients-are machine-performed mathematical and statistical transformations applied to digital data, not to human affairs. Accordingly, the claimed subject matter is not directed to organizing or managing human activity… This feature produces a real-world improvement in the functioning of the computer system-not merely an improvement in an abstract model. By reducing redundant input dimensions and compressing the model, the apparatus: " Reduces memory usage associated with the behavior feature datasets, and " Lowers computational load of the processor by minimizing the number of matrix operations required during learning and estimation. These are technical effects at the hardware-execution level, improving the performance of the processor and memory subsystem itself... To the contrary, the specification expressly discloses that the claimed data processing pipeline and model compression scheme represent a non-conventional improvement in how computer systems perform machine learning on sensor-based behavior data. First, the claim recites multiple interdependent data-generation stages-including generation of preprocessed behavior data, behavior statistic feature data, behavior transition feature data, and behavior difference amount feature data-each of which is algorithmically defined and interlinked through labeling and date-based partitioning. This structured, hierarchical processing of heterogeneous time-series sensor data is far beyond routine data collection or generic statistical analysis.… In sum, the record contains no evidence that the recited multi-stage feature generation, correlation-based feature selection, model compression, and computational load reduction steps were well-understood, routine, or conventional in the field. Instead, the claimed invention defines a specific, non-conventional combination of hardware- implemented data processing operations that improve how computers execute machine learning on real-world sensor data”, (see remarks , pg. 9-13).
Examiner respectfully disagrees, the current claims are not statutory because they are directed towards an abstract idea without significantly more. The claims recite method for estimating a time discount rate in an estimation phase, which is a method of managing interactions between people, which falls into the methods of organizing human activity grouping, as two individuals along with a database can calculate the numbers to come up with an estimation by pen and paper, additionally falls under mathematical concepts such a mathematical relationships, mathematical formulas or equations and mathematical calculations as the models can be laid out by pen and paper as a human can perform calculation to present a model to present estimation for time discount rate. The computing elements such as “system, processor, memory, artificial intelligence risk model, fitted model, tree model of claim 1; processor, artificial intelligence risk model, fitted model of claim 8; processor, medium, artificial intelligence risk model, fitted model of claim 15” are recited at a high level of generality and are generically recited computer elements. The generically recited computer elements amount to simply implementing the abstract idea on a computer. The combination of these additional elements are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, elements being analyzed for significantly more are mere generic computer components being implemented to implement the abstract idea on a computer.
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 and 5-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites method for estimating a time discount rate in an estimation phase.
Step 2A – Prong 1
Independent Claims 1 and 5-6 as a whole recite a method of organizing human activity. The limitations from exemplary Claim 1 reciting “time discount rate estimation that estimates a time discount rate in a learning phase, the time discount rate estimation comprising: a ; and a coupled to the and configured to: calculate an error between a value obtained by standardizing values of a plurality of behavior features of a predetermined user and then multiplying each of the standardized values by a model parameter indicating each coefficient serving as a weight and a time discount rate serving as ground-truth data based on an answer by the predetermined user and to perform machine learning on the model parameter so as to reduce the error, wherein the plurality of behavior features of the predetermined user is based on behavior data observed by a worn by the predetermined user, wherein the behavior data includes behavior amount data and behavior date and time data, the behavior amount data numerically indicating a behavior of the predetermined user with time, and the behavior date and time data indicating a type of the behavior of the predetermined user and date and time at which the behavior regarding the type has occurred, wherein the is further configured to: generate preprocessed behavior data by calculating a summary statistic amount for each attribute value and calculating a duration of each behavior based on the behavior amount data and the behavior date and time data generate behavior statistic feature data by calculating an average value for each behavior amount based on the preprocessed behavior data; based on the behavior date and time data, generate preprocessed behavior date and time data including a behavior relationship that indicates a relationship between a current behavior and a next behavior and indicating date and time of start of another type of behavior that occurs from the current behavior to start of the next behavior of a same type of the current behavior; generate behavior transition feature data by calculating a transition probability between the current behavior and the next behavior based on the preprocessed behavior date and time data generate behavior difference amount feature data by generating difference amount data regarding behavior statistics, generating difference amount data regarding behavior transition, and combining the difference amount data regarding behavior statistics and the difference amount data regarding behavior transition for a same user, the difference amount data regarding behavior statistics being generated by performing labeling for dividing the behavior statistic feature data according to a predetermined date based on date and time information in the preprocessed behavior data, dividing the behavior statistic feature data based on the label, and calculating an absolute value of an amount of difference between behavior statistic amounts in divided pieces of the data, the difference amount data regarding behavior transition being generated by performing labeling for dividing the behavior transition feature data according to a predetermined date based on date and time information in the preprocessed behavior date and time data, dividing the behavior transition feature data based on the label, and calculating an absolute value of an amount of difference between behavior statistic amounts in divided pieces of the data calculate a Pearson correlation coefficient between the time discount rate regarding time discount rate data serving as the ground-truth data and the behavior statistic feature data, the behavior transition feature data, and the behavior difference amount feature data of the predetermined user and calculate a test statistic amount for the correlation coefficient; generate various types of behavior feature data by selecting a plurality of behavior features having a correlation with the time discount rate of the ground-truth data and having no similar tendency from among behavior features of the behavior statistic feature data, the behavior transition feature data, and the behavior difference amount feature data and combining the plurality of behavior features for each user, and reduce a number of input dimensions by excluding redundant features, thereby compressing the time discount rate estimation model and lowering computational and load required for data processing performed by the , wherein values of the selected plurality of behavior features are standardized” is a method of managing interactions between people, which falls into the certain methods of organizing human activity grouping, additionally mathematical concepts such a mathematical relationships, mathematical formulas or equations and mathematical calculations as the models can be laid out by pen and paper as a human can perform calculation to present a fitted model. The mere recitation of a generic computer (apparatus, memory, processor, wearable device of claim 1; wearable device, apparatus, memory, processor, machine learned model of claim 5; machine learning, computer, wearable device of claim 6) does not take the claim out of the methods of organizing human activity grouping. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Claims 1 and 5-9 and their underlining limitations, steps, features and terms, are further inspected by the Examiner under the current examining guidelines, and found, both individually and as a whole, not to include additional elements that are sufficient to integrate the abstract idea into a practical application. The limitations are directed to limitations referenced in MPEP 2106.05 that are not enough to integrate the abstract idea into a practical application. Limitations that are not enough include, as a non-limiting or non-exclusive examples, such as: (i) adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions, (ii) insignificant extra solution activity, and/or (iii) generally linking the use of the judicial exception to a particular technological environment or field of use.
This judicial exception is not integrated into a practical application because the claim recites the additional elements of (apparatus, memory, processor, wearable device of claim 1; wearable device, apparatus, memory, processor, machine learned model of claim 5; machine learning, computer, wearable device of claim 6). The apparatus, memory, processor, wearable device of claim 1; wearable device, apparatus, memory, processor, machine learned model of claim 5; machine learning, computer, wearable device of claim 6, are recited at a high level of generality and are generically recited computer elements. The generically recited computer elements amount to simply implementing the abstract idea on a computer. The combination of these additional elements are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are ineligible.
Dependent claims 7-9 are also directed to same grouping of methods of organizing human activity. The additional elements of the apparatus in claim 9; processor in claim 9; machine learned model of claim 7; computer in claim 7-8; medium in claim 8; program in claim 8; model in claim 9, are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM EL-BATHY whose telephone number is (571)272-7545. The examiner can normally be reached Monday - Friday 9am - 7pm.
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/IBRAHIM N EL-BATHY/Primary Examiner, Art Unit 3628
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