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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/17/2025 has been entered.
Response to Arguments
Applicant's arguments filed 11/17/2025 have been fully considered but they are not persuasive. After further consideration, Examiner notes that Sivaraj and Mcmahan teach the inventions in claims 1, 3, 11-12, 20.
Applicant’s specification defines maximum or near maximum exertion HRR as measuring an individual’s heart rate immediately after a high-exertion activity, then measuring the decrease of the HR after a time interval [0004]. Therefore, maximum or near-maximum exertion HRR is defined as the HRR after a high-exertion activity.
Mcmahan details the definition/calculation of HRR as a difference between HR during exercise and HR at 1, 2, 3, minutes after exercise [0095].
Mcmahan additionally teaches the calculation of HRR after exertion in addition to determination of the exertion level [0155].
Mcmahan discloses in Table 8 that HRR measurements can be associated with periods of higher exertion. Therefore, Mcmahan, in combination with Sivaraj, teaches the invention in claims 1, 3, 11-12, 20 as claimed by applicant, specifically the estimation of maximum or near maximum exertion HRR.
Mcmahan additionally teaches the estimation of input features used to determine the maximum or near maximum HRR estimation. See human performance tracking module 850, Fig. 10, [0095] that details the analysis of biometric data, including HR, HRR, training/activity load, and other various metrics.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, 20, contain the limitation “estimating…, during the observation window, the maximum or near maximum exertion HRR of the user based on a machine learning model and the input features.” Applicant is claiming the intended result of estimating maximum/near maximum exertion HRR with a machine learning model without setting forth any particular steps regarding how the model actually arrives at the claimed result.
Applicant's arguments filed 07/07/2025 cites paragraph [0028] of the specification as providing a written description of the machine learning model, which states “the machine learning model is a linear regression framework with the input features being the contributing features, or a parameterized physiological model where the modifying features are functions of the input features.” This does not set forth any particular way in which the model(s) is/are particularly trained to provide specific outputs for a specific set of features. As such, the machine learning algorithm amounts to a “black box” where the specific design of the model is unclear.
Additionally, Applicant argues “one with ordinary skill in the art would...know how to use a linear regression framework or physiological model because it is conventional or well known to one of ordinary skill in the art.” The Examiner is not arguing that linear regression functions are not conventional or well-known. The issue is the adaptation of a generic model or framework to the specific instance of estimating maximum or near maximum exertion HRR as claimed. Applicant has not set forth any specific steps indicating how the model is trained, adapted or parameterized and simply repeats the intended function of estimating maximum or near maximum exertion HRR (see Abstract; par. [0005, 0015, 0035, 0039]). Lastly, as noted in MPEP §2161.01, “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas under mental processes groupings, without significantly more.
The framework for establishing a prima facie case of lack of subject matter eligibility requires that the Examiner determine: (1) Does the claim fall within the four categories of patent eligible subject matter; (2a) Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon and (2a) Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application; and (2b) Does the claim recite additional elements that amount of significantly more than the judicial exception.
Step (1)
The claimed invention in claims 1-20 are directed to a system or a method, and thus, the claims all fall under one of the four patent eligible categories.
Step (2a) Prong 1 (Judicial Exception)
Regarding claims 1-20, the recited steps are directed to mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (I)).
Independent claims 1, 11, 20 recite the limitations of: determining an energy expenditure, determining if the energy expenditure is within an eligible range, determining time demarcations for an observation window, identifying an observation window, estimating input features, and estimating the maximum or near maximum exertion HRR. Under the broadest reasonable interpretation, these are limitations that, as drafted, cover that which can be wholly performed in a person’s mind via a series of mental observations and judgements. In particular, a person can determine an observation window or a particular subset of HR data to be examined, then estimate values that correspond to HRR, namely HR immediately after a high exertion activity and HR after a selected time period, further finding the difference between these values to determine the maximum or near maximum exertion HRR. These are data gathering and processing steps (determining, identifying, estimating) that reflect mental processes.
Accordingly, independent claims 1, 11, 20 are directed to a judicial exception including one or more abstract ideas, specifically mental processes.
The dependent claims recite additional limitations for estimating maximum or near maximum exertion HRR, including identifying relevant time periods and analyzing HR sample characteristics. These limitations also fall within the judicial exceptions of one or more mental processes.
Step (2a) Prong 2 (Integration into a Practical Application)
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. MPEP 2106.04(d).
For claims 1-20, the judicial exception is not integrated into a practical application.
Regarding claims 1, 11, 20, the additional limitations of obtaining sensor data and obtaining a heart rate of the user are nothing more than the pre-solution activity of mere data gathering.
Regarding claims 1, 11, 20, the additional element of “at least one processor” amounts to recitation of a generic processor. As in Alice Corp v. CLS Bank, 573 U.S. 208, 223 (2014), limiting an abstract idea to a field of use or adding generic hardware does not integrate the exception into a practical application.
Regarding claims 11, 20, the additional element of a “memory” amounts to recitation of a generic memory. As in Alice Corp v. CLS Bank, 573 U.S. 208, 223 (2014), limiting an abstract idea to a field of use or adding generic hardware does not integrate the exception into a practical application.
Claims 3-10, 12-19 further limit the abstract ideas of claims 1, 11, respectively, without introducing any additional elements.
Furthermore, when the claims, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it is still in the mental processes grouping unless the claim limitation cannot practically be performed in the mind. Likewise, performance of a claim limitation using generic computer components does not preclude the claim limitation from being in the mental processes grouping.
Step (2b) (Inventive Concept)
The claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements of a processor and memory in the field of HR monitoring are well-understood, routine and conventional activities previously known in the industry as indicated in the following references:
Sivaraj (US Patent No. 9,737,761) teaches a processor (Fig. 2, processor 205) and memory (Fig. 2, memory 230).
Mcmahan et al. (US Pre-Grant Publication 2021/0321883) teaches a processor [0213] and memory (Fig. 1B, memory 155).
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations in claims 1, 11, 20 of obtaining sensor data and obtaining a heart rate of the user are directed to nothing more than the pre-solution activity of mere data gathering, which does not amount to an inventive concept.
Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-20 are thus rejected under 35 USC 101 for reciting patent-ineligible subject matter- abstract ideas.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 11-12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj (US Patent No. 9,737,761), hereinafter ‘Sivaraj’, in view of Mcmahan et al. (US Pre-Grant Publication 2021/0321883), hereinafter ‘Mcmahan’.
Regarding claims 1, 11, 20, Sivaraj teaches a method, system, and apparatus for estimating fitness parameters (col. 5, ll. 11-15), further comprising:
obtaining, with at least one processor (Fig. 2, processor 205), sensor data from a wearable device worn on a wrist of a user (col. 7, ll. 1-5: wrist worn heart monitor) (heart rate monitor 165, Fig. 1);
obtaining, with the at least one processor, a heart rate (HR) of the user;
determining, with the at least one processor, an energy expenditure of the user during an activity based on at least one of the sensor data or HR (col. 7, ll. 9-17: collects information on the user’s energy expenditure);
determining, with the at least one processor, if the energy expenditure feature is within an eligible range of exertion (col. 16, ll. 65-68: comparing HR to energy expenditure to estimate anaerobic threshold); and
in accordance with the energy expenditure feature being within the eligible range of exertion,
determining, with the at least one processor, time demarcations for an observation window (adaptive training window 1500, Fig. 15A) (col. 12, ll. 42-49, graph of HR and power over a time period);
identifying, with the at least one processor, the observation window of the sensor data and HR based on the time demarcations (col. 12, ll. 42-49, window 1500 indicates when HR is anaerobic level and provides information relating to recovery).
Sivaraj does not teach the estimation of input features to determine an estimation of HRR near maximum or near maximum exertion, for use in a machine learning model.
Mcmahan teaches a system and method for a method of sensing biometrics of a subject [0007], further comprising:
estimating, with the at least one processor [0213] during the observation window, input features for estimating maximum or near maximum exertion HR recovery (HRR) of the user based on the sensor data and HR (human performance tracking module 850, Fig. 10) ([0095], training and activity load 1002, HRR metrics 1008, HR metrics 1026); and
estimating, with the at least one processor during the observation window, the maximum or near maximum exertion HRR of the user based on a machine learning model and the input features (Fig. 10, machine learning 1032).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj to incorporate the teachings of Mcmahan to include the estimation of HRR during/after high periods of exertion. Doing so would allow for the identification of signs of nonfunctional overreaching and overtraining syndrome, as recognized by Mcmahan [0161].
Regarding claims 3, 12, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively. Sivaraj teaches the method/system further comprising:
determining, with the at least one processor, a period of steady state recovery based at least in part on the energy expenditure of the user (col. 15, ll. 36-40, heart rate response can be period of time representing the corresponding period of energy expenditure); and
determining, with the at least one processor, the time demarcations for the observation window based at least in part on the period of steady state recovery (col. 15, ll. 36-40, marker of time).
Claim(s) 4, 8-9, 13, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj (US Patent No. 9,737,761) in view of Mcmahan et al. (US Pre-Grant Publication 2021/0321883), further in view of Cox et al. (WO 2023/208665), hereinafter ‘Cox’.
Regarding claims 4, 13, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively, but do not teach the limitations of claims 4, 13. Cox teaches a system/method of patient recovery monitoring (Fig. 1), further comprising:
determining, with the at least one processor, a minimum number of HR samples within the observation window (Fig. 3, S305, [0044], obtaining data from recovering patient) ([0049], processes performed periodically until appropriate set of data is obtained);
determining, with the at least one processor, a goodness of fit of the HR samples to a prototypical shape expectation ([0061], deviation of patient data from reference curve); and
determining, with the at least one processor, a consistency of HR pre and post end of activity ([0075], combination of vital sign/activity parameters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj and Mcmahan to incorporate the teachings of Cox to include determine the minimum number of HR samples to be collected, a goodness of fit of the samples, and a consistency of the samples. Doing so would allow for a training model to apply a data set for an objective determination of patient recovery, as recognized by Cox [0046].
Regarding claims 8, 17, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively, but do not teach the limitations of claims 8, 17. Cox teaches a system/method of patient recovery monitoring (Fig. 1), further comprising:
the machine learning model ([0084], Fig. 4, S455, machine learning algorithms) is a linear regression framework ([0084], linear regression) with the input features being the contributing features ([0085], training prediction model, Fig. 4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj and Mcmahan to incorporate the teachings of Cox to include a linear regression machine learning model. Doing so would allow for a training model to apply a data set for an objective determination of patient recovery, as recognized by Cox [0046].
Regarding claims 9, 18, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively, but do not teach the limitations of claims 9, 18. Cox teaches a system/method of patient recovery monitoring (Fig. 1), further comprising:
wherein the machine learning model is a parameterized physiological model with modifying parameters that are functions of the input features ([0076], health status parameters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj and Mcmahan to incorporate the teachings of Cox to include modifying parameters in the machine learning model. Doing so would allow for a training model to apply a data set for an objective determination of patient recovery, as recognized by Cox [0046].
Claim(s) 5, 10, 14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj (US Patent No. 9,737,761) in view of Mcmahan et al. (US Pre-Grant Publication 2021/0321883), further in view of Snell (US Patent No. 6,904,313), hereinafter ‘Snell’.
Regarding claims 5, 14, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively, but do not teach the limitations of claims 5, 14. Snell teaches a system/method for monitoring heart rate recovery (Fig. 3), further comprising:
wherein the input features include at least one of an estimate of decrease of HR during a recovery period, an estimate of recovery rate scaling factors, an estimate of pre-recovery and during recovery load or an estimate of steady state HR (Fig. 6, col. 10, ll. 67 – col.11, ll. 24, derive a model by monitoring patient’s HR).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj and Mcmahan to incorporate the teachings of Snell to include input features that include HR during recovery and activity load. Doing so would allow for model to be built associated with a particular patient, as recognized by Snell (col. 11, ll. 5-7).
Regarding claims 10, 19, Sivaraj and Mcmahan teach the method of claim 1 and the system of claim 11, respectively, but do not teach the limitations of claims 10, 19. Snell teaches a system/method for monitoring heart rate recovery (Fig. 3), further comprising:
determining a quality of HR samples based on at least one of a minimum number of HR samples within the observation window of a specified quality or a goodness of fit of the HR samples to a prototypical shape expectation (Fig. 5, col. 9, ll. 49-66, determining if a heart rate recovery value is “normal”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj and Mcmahan to incorporate the teachings of Snell to include an analysis of the HR sample quality. Doing so would allow for the determination of if the patient’s HRR is trending in a particular direction, as recognized by Snell (col. 11, ll. 29-30).
Claim(s) 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj (US Patent No. 9,737,761) in view of Mcmahan et al. (US Pre-Grant Publication 2021/0321883), further in view of Snell (US Patent No. 6,904,313), further in view of Steinarsson et al. (WO 2021/166000), hereinafter ‘Steinarsson’.
Regarding claims 6, 15, Sivaraj, Mcmahan, and Snell teach the method of claim 5 and the system of claim 14, respectively, but do not teach the limitations of claims 6, 15. Steinarsson teaches a method of analyzing heart rate data (pg. 2, ll. 27-30), further comprising:
wherein the input features include an estimate of decrease of HR, and the estimate of decrease of HR is smoothed to remove noise (Fig. 36, pg. 40, ll. 25-27, smoothing heart rate data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj, Mcmahan, and Snell to incorporate the teachings of Steinarsson to include data smoothing. Doing so would allow for the reduction of noise in the data for enhanced sensitivity of analysis, as recognized by Steinarsson (pg. 41, ll. 14).
Claim(s) 7, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraj (US Patent No. 9,737,761) in view of Mcmahan et al. (US Pre-Grant Publication 2021/0321883), further in view of Snell (US Patent No. 6,904,313), further in view of Hadley (US Pre-Grant Publication 2007/0249949), hereinafter ‘Hadley’.
Regarding claims 7, 16, Sivaraj, Mcmahan, and Snell teach the method of claim 5 and the system of claim 14, respectively, but do not teach the limitations of claims 7, 16. Hadley teaches a system for analyzing exercise-induced heart rate recovery metrics [0017], further comprising:
determining at least one HRR scaling factor and correcting the estimate of decrease in HR based on the at least one HRR scaling factor (Figs. 7A, 7B, [0046], scaling factor for normalization).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Sivaraj, Mcmahan, and Snell to incorporate the teachings of Hadley to include an HRR scaling factor. Doing so would allow for the normalization of the data, as recognized by Hadley [0046].
Conclusion
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/E.L.O./Examiner, Art Unit 3792
/ALLEN PORTER/Primary Examiner, Art Unit 3796