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
This action is in response to Application as filed on September 9, 2024. Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 11, 2025 is in compliance with the provisions of 37 CFR § 1.97. Accordingly, the IDS has been considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,109,454. Although the claims at issue are not identical, they are not patentably distinct from each other because the inventions are directed to substantially similar subject matter. The subject matter claimed in the instant application is anticipated by the referenced US Patent, as follows: the difference between independent claim 1 of instant application, 18/448075. and Claim 1 of US Patent No. 12,109,454 are highlighted in the following table.
Claim 1 of 18/828,426
Claim 1 of 12,109,454
A method for determining a current capability of a user, the method comprising:
A method for determining a current capability of a user, the method comprising:
accessing an exercise history for a user, the exercise history comprising an exercise performed by the user and a capability of the user each time the user performed the exercise,
accessing an exercise history for a user, the exercise history-comprising an exercise performed by the user and performance statistics of the user for the exercise, the performance statistics further comprising a capability of the user each time the user performed the exercise;
training, by a processor, a machine-learned model using the accessed exercise history for the user, the machine-learned model configured to produce a target weight to recommend to the user for the exercise based on performance statistics of the user;
wherein the exercise history is organized into time periods;
partitioning, by the processor, the exercise history into a plurality of time periods;
computing, by the processor, an aggregate capability of the user for the exercise during each time period based on the capability of the user each time they performed the exercise during the time period;
for each time period, computing, by the processor, an aggregate capability of the user for the exercise based on the capabilities of the user in performing the exercise during the time period;
determining, by the processor, a moving average of the user’s capability for the exercise, wherein determining the moving average of the user’s capability comprises:
determining, by the processor, a moving average of the user's capability for the exercise based on the aggregate capabilities, wherein determining the moving average of the user's capability comprises:
determining the moving average of the user’s capability based on by weighting the aggregate capability of each time period based on the recency of the time period; and
assigning each aggregate capability a weight based on a recency of the time period corresponding to the aggregate capability; determining the moving average of the user's capability based on each weighted aggregate capability; and
discounting the moving average of the user’s capability where a threshold amount of time has passed since the user last performed the exercise;
discounting the moving average of the user's capability where a threshold amount of time has passes since the user last performed the exercise;
determining a current capability of the user for the exercise based on the moving average of the user’s capability;
determining a current capability of the user for the exercise based on the moving average of the user's capability;
applying, by the processor, the machine-learning model to the current capability of the user for the exercise to determine a current target weight to recommend to the user for the exercise; and
applying, by the processor the machine-learning model to performance statistics of the user to determine a current target weight to recommend to the user for the exercise, wherein the performance statistics comprise at least the current capability of the user for the exercise
modifying, by the processor in real-time, a graphical user interface displayed by a client device of the user to display the current target weight in response to an input from the user requesting the target weight.
modifying, by the processor in real-time, a graphical user interface displayed by a client device of the user to display the current target weight in response to an input from the user requesting the target weight.
Furthermore, independent claims 9 and 17 of the instant application, and claims 9 and 17 of the referenced US Patent include substantially the same limitations as those shown for the method in the table above except that they are directed to the statutory classes of a non-transitory computer-readable storage medium and a system that implement the above method. Dependent claims 2-8, 10-16, and 18-20 of the instant application are identical to the claimed subject matter of claims 2-8, 10-16, and 18-20 of the referenced US Patent.
Claim Objections
Claims 1, 9, and 17 are objected to because of the following informalities:
Claim 1 recites “determining the moving average of the user’s capability based on by weighting the aggregate capability of each time period based on the recency of the time period.” The same language “based on by” also appears in claims 9 and 17. It is believed this should read --determining the moving average of the user’s capability by weighting the aggregate capability of each time period based on the recency of the time period--. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
In re claims 1, 9, and 17, the claims recite the limitations “the machine-learning model” at line 20. There is insufficient antecedent basis for these limitations in the claim. In addition, claims 1, 9, and 17 recite “the target weight.” This term lacks clear antecedent basis. It is unclear if this term refers to the current target weight or a different target weight.
In re claims 4 and 12, the claims recite “a threshold amount of time.” This term is unclear. It is unclear whether “a threshold amount of time” recited in claims 4 and 12, is the same as or different than the threshold amount of time recited in claims 1 and 9.
Claims 2-8, 10-16, and 18-20 depend from a rejected base claim, and therefore are rejected for at least the reasons provided for the base claim.
Prior Art
The Examiner notes that after a thorough search on the claims, the claims currently overcome the prior art. The closest prior art found to date are the following:
Jang (US 2019/0046107) teaches an exercise feedback system determines muscle stress measurements using physiological data generated by a sensor-equipped athletic garment. A muscle stress measurement represents an accumulated normalized signal from one or more of the sensors corresponding to a given muscle or set of muscles over a period of time. By normalizing the physiological data, the exercise feedback system may determine muscle stress measurements, biofeedback, or other metrics for comparison between different muscle groups of an athlete or different athletes.
Lyke (US 2021/0093919) teaches a workout and readiness/recovery history for the population of users is analyzed to create one or more expected profiles. Each expected profile is associated with the heuristics and/or performance metrics for a subset of users having similar physiology and psychology. In one exemplary embodiment, the expected profiles are generated based on artificial intelligence (AI) and/or machine learning to identify similar users and their corresponding heuristics, rules, and/or patterns. The initial expected profiles may be corrected and improved gradually over time as more and better crowd-sourced workout history is collected.
Toivonen (US 10,918,908) teaches determining a weekly training load (WTL) that describes an amount of training the user has performed during the past week including today. A training plan is divided into cycles of blocks of training intensities, called meso-cycles (of blocks). A meso-cycle may comprise one/more four-day micro-cycle(s) of training days comprising medium and high intensity training days having a low intensity training day after each medium and high intensity training day. A present phase of the micro- and/or meso-cycle may be detected. Detecting the present phase of meso-cycle enables determination of the current following next workout recommendation (NWR) being at a block of low, moderate/medium or high intensity training. By determining two previous performed phases of the micro-cycle, the present phase of the micro-cycle, relating to the training to be recommended, may be determined based on predetermined successive four-day micro-cycles.
However, the prior art does not teach or suggest determining a moving average of the user’s capability for the exercise, wherein determining the moving average of the user’s capability comprises: determining the moving average of the user’s capability based on by weighting the aggregate capability of each time period based on the recency of the time period; and discounting the moving average of the user’s capability where a threshold amount of time has passed since the user last performed the exercise; determining a current capability of the user for the exercise based on the moving average of the user’s capability; applying a machine-learning model to the current capability of the user for the exercise to determine a current target weight to recommend to the user for the exercise in conjunction with the other limitations in the claim. Therefore, no rejection over the prior art is made at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed on the attached Notice of References Cited.
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/ANDREW BODENDORF/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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