DETAILED ACTION
Applicant' s arguments, filed 01/09/2026 have 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.
Applicants have amended their claims, filed 06/30/2023, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-7, 11-21, 23, and 26 are hereby the present claims under consideration.
Examiner’s Note: all references to Applicant’s specification are made using the paragraph numbers assigned in the US publication of the present application, US 2024/0049982 A1.
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 Interpretation
The limitation “exercise workload” is defined by Applicant’s specification in paragraphs 0034-0035, and 0039 as data relating to the user’s exercise such as global positioning system (GPS) data, speed data (e.g. running speed data), step-rate, cadence of the individual, and power data captured from a power meter in a stationary exercise machine such as rowing or stationary bike machines. Such a definition is not consistent with the accepted meaning of the term in the art which is a measure of work done during an exercise session. This work would be measured by, for example, an amount of weight times the sum of total distance moved, or in a bicycle by the speed times a crank torque times duration, or for running by speed times distance times elevation change times duration.
The limitation “exercise workload” will be interpreted in accordance to Applicant’s specification as a measure of speed, distance, step rate, cadence, power meter outputs, and their equivalents.
Claim Objections
Claims 5 and 11 are objected to because of the following informalities:
Claim 5 it appears that “an updated probability density function
PNG
media_image1.png
22
158
media_image1.png
Greyscale
” should include some indication in the formula to distinguish the updated function from the original function.
Claim 11 it appears that “p(VO2max)ng” should likely read “p(VO2max) is”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, 11-21, 23, and 26 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “a single exercise workload” but it is unclear what the requisite degree of consistency in workload measurement over what timeframe is required to satisfy this limitation. It is unclear what degree of variation over what timeframe may be acceptable to consider an exercise workload as a “single” exercise workload. For the purposes of this examination any workload measurement without extensive changes in the selected window may be considered a “single” exercise workload for the duration of the window. This rejection is further applied to the similar limitations of claims 23 and 26.
Claim 1 recites, “a maximum heart-rate of the individual” but it is unclear from where this value is pulled from, measured, or otherwise generated. It is unclear if this limitation is meant to refer to a measured maximum heart rate for the exercise or an absolute maximum heart rate for the user. The recitation of averaging the heart rate appears to suggest that the “maximum heart rate” may be the measured maximum value prior to averaging. For the purposes of this examination, the limitation will be interpreted as any measure of absolute maximum heart rate for an individual which may be measured or estimated. This rejection is further applied to the similar limitations of claims 23 and 26.
Claim 1 recites “averaging the heart-rate and the exercise workload to provide an averaged heart-rate and an averaged exercise workload; normalizing the averaged heart-rate with respect to a maximum heart-rate of the individual to provide multiple data pairs of normalized average heart-rate (HRn) and average exercise workload (w), wherein each data pair comprises corresponding HRn and w values”. It is unclear what time period the method is meant to generate the averages over. In particular, the recitation that the exercise session is performed at a single workload followed by a recitation of averaging the heart-rate and workload seems to suggest that one exercise session provides a single average heart-rate and average workload. However the following limitation then recites the presence of multiple data pairs of normalized average heart rate and average exercise workload values. It is thus unclear what time period(s) of the exercise session are used to create the averages. Additionally, the limitations of calculating an average heart rate and workload for “an exercise session at a single exercise workload” followed by the recitation of multiple data pairs of averages seems to suggest that the obtaining and averaging steps are performed for multiple “exercise sessions” since it would seem that each session provides a single average data pair. It is unclear if multiple exercise session measurements are required and what relationship, if any, must be present between the workloads of the different exercise sessions. The recitation of multiple averaged data pairs further renders the limitation of “an exercise session” indefinite because it is unclear what portion of a full exercise event the “exercise session” is meant to entail. For the purposes of this examination, the limitation will be interpreted as any averages taken over one or more “exercise sessions” having any workload relationships to each other. This rejection is further applied to the similar limitations of claims 23 and 26.
Claims 2-7 and 11-21 are rejected by virtue of their dependency on claim 1.
Claim 2 recites “periodically determining throughout the exercise session, the multiple data pairs of HRn and w” but, as described above, the limitation of averaging the heart rate and exercise workload renders the recitation of multiple averages in a single exercise session indefinite because it is unclear what metric, time period, or other event or value is being used to subdivide the exercise session to form multiple averages. It is further unclear what a single “exercise session” entails For the purposes of this examination, the limitation will be interpreted as any method of generating averages from an exercise session.
Claim 11 recites “wherein p(VO2max)ng a prior probability distribution relates” but it is unclear what “p(VO2max)” entails and how or if it relates to “a probability density function p(VO2max|HRn,w)” of claim 3 and/or “a probabilistic model” of claim 1. For the purposes of this examination, “p(VO2max)” is interpreted as referring to “a probability density function p(VO2max|HRn,w)” of claim 3.
Claim Rejections - 35 USC § 112(a)
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, 11, 14, 23, and 26 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.
Claim 1 recites “applying a probabilistic model comprising a Bayesian probabilistic model that relates HRn to w and maximum oxygen uptake to provide an estimate of a maximum oxygen uptake of the individual (VO2max)”. Applicant’s specification recites a probabilistic model but does not appear to describe the model itself. Rather the specification states that the probabilistic model “determines” a number of probability density functions (PDFs), optionally using Bayes rule, which are used to relate the above parameters (Paragraphs 0041-0067). Applicant’s specification describes a number of PDFs which are recited as being able to determine the VO2max, the probabilistic model itself which is seemingly used to generate these PDFs is not seemingly described. Paragraph 0041 recites that the model may determine the PDFs using Bayes’ Rule but such a recitation merely provides a description of how the “model” determines something and does not describe the model itself. Paragraph 0047 recites that the model is derived from a dataset and may optionally be “based on” multivariate gaussian distribution, but these teachings do not provide any substance to the model itself. The probabilistic model is considered to be described as a “black box” algorithm which is generated from a generic database and which outputs the desired PDFs. It is unclear if the specification is meant to convey that the multiple PDFs together form the probabilistic model or if the probabilistic model is separate from the PDFS. The disclosure is not considered sufficient to encompass the claimed scope of any known and as of yet unknown probabilistic model. Claims 23 and 26 recite similar limitations and are rejected on the same basis as claim 1. This rejection is further applied to the similar limitations of claims 23 and 26.
Claim 14 recites “deriving the Bayesian probabilistic model from a dataset containing …, wherein deriving the Bayesian probabilistic model comprises determining parameters of a probability distribution p(HRn|w, VO2max) from the dataset” but the specification does not seemingly describe what the model itself entails or how it is derived from the dataset. Paragraph 0047 repeats this claim language but is not considered sufficient to support the claimed scope of any and all methods of deriving the Bayesian probabilistic model from the given dataset. As described in the above 112(a) rejection of claim 1, the specification does not seemingly describe the meets and bounds of the model itself or how it is derived from the given data. The specification does not appear to describe the particular process of determining parameters of a probability distribution from the dataset
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 1-7, 11-21, 23, and 26 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. Claims 1-7, 11-21, 23, and 26 are directed to a method of processing exercise signals using a computational algorithm, which is an abstract idea. Claims 1-7, 11-21, 23, and 26 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019) and the 2024 Update on Subject Matter Eligibility (Federal Register, Vol 89, No. 137, page 58128, July 17, 2024).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] obtaining, during an exercise session at a single exercise workload, a heart-rate of an individual and the exercise workload of the individual
[B1] averaging the heart-rate and the exercise workload to provide an averaged heart-rate and an averaged exercise workload
[C1] normalizing the averaged heart-rate with respect to a maximum heart-rate of the individual to provide multiple data pairs of normalized average heart-rate,(HRn) and average exercise workload (w), wherein each data pair comprises corresponding HRn and w values
[D1] applying a model that relates HRn to w and maximum oxygen uptake to provide an estimate of a maximum oxygen uptake of the individual (VO2max)
These elements [A1]-[D1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] a first sensor
[B2] a second sensor
[C2] a probabilistic model comprising a Bayesian probabilistic model
These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Additionally, the element [C2] is nothing more than the computer implementation/automation of an abstract mental process of screening a patient, which is what a physician typically does with a patient in a diagnostic setting. Thus [C2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f)
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “obtaining, during an exercise session at a single exercise workload, a heart-rate of an individual by a first sensor and the exercise workload of the individual by a second sensor” does not qualify as significantly more because this limitation merely describes the nature of the received data and does not incorporate the sensors as part of the claimed invention. In another interpretation, the recitation “obtaining, during an exercise session at a single exercise workload, a heart-rate of an individual by a first sensor and the exercise workload of the individual by a second sensor” is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the sensors are nothing more than conventional heart rate and workload sensors such as PPG sensors of GPS sensors. Such sensors are routine, conventional, and/or well-known as evidenced by Applicant’s lack of a particular description regarding their structure and function in the specification.
Further, the element [C2] does not qualify as significantly more because this limitation is simply an instruction to implement the decision making capabilities and pattern recognition of the human mind onto a generic computer. The computer itself is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-7, and 11-21 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 2: a memory
Claim 18: a bicycle power meter;
Claim 19: a power meter of a stationary exercise machine, wherein the stationary exercise machine is a rowing machine or a stationary bike; and
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 18 and 19 are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
The limitation from claim 2 is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of display) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Also, each of the limitations of claims 18 and 19 do not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the bicycle power meter and power meter of a stationary exercise machine are each well-known, routine, and conventional elements used for nothing more than mere data gathering. These elements are well-known, routine, and conventional as evidenced by Applicant’s specification paragraphs 0035, 0039, and 0049 where these elements are referenced as sensors used and no particular description is provided as to their structure or function indicating they are well-known in the art..
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 23 and 26 recite the same abstract idea as claim 1 and are rejected on the same basis as claim 1. The additional elements not already addressed above are addressed below.
The abridged analysis of claims 23 and 26 is as follows:
Step 1: Claims 23 and 26 are drawn to a machine.
Step 2A – Prong One: Claims 23 and 26 recites an abstract idea. In particular, the same abstract idea as claim 1.
Step 2A – Prong Two: Claims 23 and 26 recite the following additional limitations that are beyond the judicial exception and not already addressed in the above rejection of claim 1.
[A2] a processor
[B2] a memory
[C2] a non-transitory computer-readable medium
[D2] a wearable device
These elements [A2]-[D2] of claims 23 and 26 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[D2] are merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Further, the elements [A2]-[D2] do not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 11-14, 16-21, 23, and 26 rejected under 35 U.S.C. 103 as being unpatentable over Saalasti US Patent Application Publication Number US 2019/0029586 A1 hereinafter Saalasti in view of Liu US Patent Application Publication Number US 2021/0353188 A1 hereinafter Liu further in view of Shepherd “Predicting Maximal Oxygen Consumption (VO2max) levels in Adolescents” published 2012 by Brigham Young University pages 1-60.
Regarding claim 1, Saalasti discloses a method (Abstract) comprising:
obtaining, during an exercise session at a single exercise workload, a heart-rate of an individual by a first sensor and the exercise workload of the individual by a second sensor (Paragraph 0048: Fig. 3: the momentary speed and heart rate; Paragraphs 0087-0089: the heart rate sensor and external work sensor; Paragraphs 0024-0025: an accepted period comprises a period where the heart rate is stabilized relative to the external work output. Each accepted period gives a VO2max estimate from the model; Paragraph 0031: the external work output may be considered as a single average for an entire exercise session or as continuous data);
averaging the exercise workload to provide an averaged exercise workload (Paragraph 0031: the external work output may be an average of the entire session)
normalizing the heart-rate with respect to a maximum heart-rate of the individual to provide multiple data pairs of normalized heart-rate,(HRn) and average exercise workload (w), wherein each data pair comprises corresponding HRn and w values (Paragraphs 0048-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value. Relative heart rate and external work; Paragraph 0025: each period is a data pair of corresponding HR and workload values): and
applying a model that relates HRn to w and maximum oxygen uptake to provide an estimate of a maximum oxygen uptake of the individual (VO2max) (Paragraphs 0049-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value; Paragraph 0025: VO2max is estimated for each period.).
Saalasti fails to further disclose the method including averaging the heart-rate to provide an averaged heart-rate and normalizing the average heart-rate; and the model being a probabilistic model comprising a Bayesian probabilistic model.
Liu teaches a method for dynamically acquiring maximal oxygen consumption (Abstract). Thus, Liu falls within the same field of endeavor as Applicant’s invention.
Liu teaches that maximal oxygen consumption measurements may utilize average heart rates and average workload measurements over a predetermined duration (Paragraph 0043).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize average heart rate values over a predetermined duration as taught by Liu in the method of Saalasti for each of the accepted periods of Saalasti because Liu teaches that an average heart rate over a time period is acceptable to utilize in a VO2max determination and Saalasti uses short periods to identify acceptable data (Saalasti: Paragraph 0014) and thus the average would provide an indicative value of heart rate over the entire period and is further a simple substitution of one known element for another with no surprising technical effect.
Saalasti in view of Liu fails to further teach the method including the model being a probabilistic model comprising a Bayesian probabilistic model.
Shepherd teaches a set of Bayesian hierarchical models to predict VO2max levels (Abstract). Thus, Shepherd falls within the same field of endeavor as Applicant’s invention.
Shepherd teaches that VO2max may be predicted using probabilistic models that include factors of exercise data and heart rate (Chapter 5 Discussions and Conclusions, page 29). The probabilistic models may utilize Bayes’ theorem and use probability distribution, or density, functions (pdfs) on discrete data sets. Additionally, the pdfs may be extended into continuous pdfs (Literature Review, Pages 5-8). The models include Bayesian models (Page 10).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize the Bayesian probabilistic modelling taught by Shepherd in the method of Saalasti in view of Liu because Shepherd teaches that the Bayesian predictive models may use less extreme measurements and may improve patient comfort since they do not have to exercise at maximum intensity (Abstract) and produce accurate VO2max estimations (Results, Pages 25-26).
Examiner’s Note: all dependent claims are rejected with the understanding that Saalasti in view of Liu further in view of Shepherd as presented in the above rejection of claim 1 teaches that modified Saalasti may use the probabilistic model and/or probability density functions taught by Shepherd,
Regarding claim 2, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method further comprising: periodically determining, throughout the exercise session, the multiple data pairs of HRn and w; and storing in a memory the multiple data pairs of HRn, and w (Fig. 3: references e, the various instantaneous VO2max determinations; Paragraph 0043: the computer receives continuous real-time data regarding intensity, or heart rate, and external work output received at the same time and stored in registers).
Regarding claims 3-5, Saalasti in view of Liu further in view of Shepherd teaches the method of claims 1 and 2 respectively. Modified Saalasti fails to further disclose the method further comprising, further comprising determining, using the probabilistic model, a probability density function p(VO2max|HRn, w) to obtain the estimate of VO2max, wherein determining the probability density function comprises applying Bayes rule to the multiple data pairs; determining the probability density function p(VO2max I HRn, w) using Bayes' Rule, wherein the Bayes' Rule is:
PNG
media_image2.png
60
362
media_image2.png
Greyscale
; and storing in the memory an updated probability density function p(VO2max I HRn, w) after determining each of the multiple data pairs, wherein the Bayesian probabilistic model updates the probability density function based on each successive data pair.
Shepherd teaches determining probability distribution, or density, functions which are used to calculate VO2max. The pdfs are determined using Bayes’ theorem. The pdf uses posterior distribution where the prior distribution must be set before performing the analysis according to the previous knowledge (Pages 5-7). A number of generated models were kept, or stored, after testing on data (Tables 4.1 and 4.2; pages 21-22). It is further noted that the operation of the posterior distribution pdf is considered to inherently teach the storing of the updated function after performing the determining on each set of data because the posterior distribution function requires the previous knowledge, or previous datasets, to be accounted for.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the posterior distribution pdfs taught by Shepherd into the method of Saalasti because Shepherd teaches these pdfs of the Bayesian predictive models may use less extreme measurements and may improve patient comfort since they do not have to exercise at maximum intensity (Abstract) and produce accurate VO2max estimations (Results, Pages 25-26).
Regarding claims 6-7, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 5. Modified Saalasti as presented in the above rejection of claim 5 teaches that Saalasti may utilize the probability density functions of Shepherd to carry out its recited VO2max estimations. Modified Saalasti further teaches the method wherein storing the probability density function comprises: discretizing the probability density function p(VO2max|HRn, w) over a set of discrete VO2max values to obtain discretized probability values; and storing, in the memory the discretized probability values; wherein discretizing the probability density function comprises: calculating p(VO2max|HRn, w) for a set of discrete VO2max values using the multiple data pairs of HRn and w, to obtain resulting probability values; and storing in the memory the resulting probability values (Figs. 2 and 3; Paragraphs 0047-0052: the VO2max is calculated using instantaneous, simultaneous, data sets repeatedly through the exercise session and uses previous cycle results stored in the memory, or register, to update the determination; Paragraph 0031: the workload may be a single average for the entire exercise session; Paragraph 0043, the system includes a register for storing data. It would be obvious to one of ordinary skill in the art to store the calculated VO2max values. Thus the limitation of “discretizing” the incorporated pdf of shepherd is considered to be obvious since modified Saalasti acts on instantaneous, discrete values. Shepherd further teaches that the function may be used on discrete data on page 6).
Regarding claim 11, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 3. Modified Saalasti further teaches the method, wherein p(VO2max)ng a prior probability distribution relates one or more of an age of the individual, a gender of the individual, a body-mass index of the individual, or aThe probability density function is determined using Bayes rule as taught by Shepherd in the above rejection of claim 3).
Regarding claim 12, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 2. Modified Saalasti further teaches the method, wherein applying the Bayesian probabilistic model comprises determining a mean of a probability density function p(VO2max I HRn, w) over a range of VO2max values to provide the estimate of VO2max (Paragraphs 0025-0027: the VO2max estimate from the model may be an average or weighted average of the accepted periods of data).
Regarding claim 13, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 2. Modified Saalasti fails to further teach the method wherein applying the Bayesian probabilistic model comprises determining a value of the VO2max that maximizes a probability density function p(VO2max| HRn, w) to provide the estimate of VO2max.
Shepherd teaches the determination of a VO2max value using probability density functions (Introduction; Page 2). Shepherd illustrates the output of the models for a given input data set in fig. 4.2 (Results: Pages 25-26) which illustrate that a given VO2max value will have the highest degree of density. It would be obvious to one of ordinary skill in the art to select the value with the highest density as output by the pdf as the VO2max value for a given discrete input dataset.
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the generated pdf density outputs taught by Shepherd into the method of modified Saalasti and select the VO2max value with the highest density as the VO2max value associated with the given input because Shepherd teaches that the models and pdfs utilized in Shepherd are quite accurate at predicting VO2max values (Results: Page 25) and selecting the value with the greatest density provides a consistent parameter with which to select a value out of the given range of values produced by the pdf.
Regarding claim 14, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method, further comprising deriving the Bayesian probabilistic model from a dataset containing exercise workload data of the individual, heart-rate data of the individual, and VO2max obtained from cardiopulmonary exercise tests (Paragraph 0048: the model function is obtained with the aid of numerous exercise sessions performed by the test persons, their fitness level, or VO2max, was measured using a clinical method from obtained data and previously known information).
Saalasti fails to further disclose the method wherein deriving the Bayesian probabilistic model comprises determining parameters of a probability distribution p(HRn|w, VO2max) from the dataset.
Shepherd teaches the determination of probability distribution parameters from a dataset (Pages 10-12: the determination of the number of bins, their respective probabilities, and Bayes factors to determine how well the model fits).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to implement the determination of model distribution parameters as taught by Shepherd into the method of modified Saalasti because Shepherd teaches that the determination of such parameters is critical to ensure the selected model fits well (Shepherd pages 11-13).
Regarding claim 16, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method including selecting suitable periods of data from the recorded data (Paragraphs 0015 and 0020-0024). Thus modified Saalasti contemplates the identification and selection of suitable data.
An obvious variation of modified Saalasti would be to further identify and discard the data pairs that lead to p(HRn, w |VO2max) = 0, VO2max. Such a modification would be obvious because modified Saalasti teaches the selection of suitable heart rate and exercise data (Paragraphs 0015 and 0020-0024), and one of ordinary skill in the art would further recognize that humans must breath and intake oxygen and therefore any dataset resulting in as estimated of VO2max equaling zero indicates the user is not breathing and thus must be invalid for a living person. Thus it would be obvious to one of ordinary skill in the art to alter the method of modified Saalasti such that the selection of suitable heart rate and exercise data further includes discarding any paired data that results in an estimate of VO2max equaling zero.
Regarding claim 17, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method, whereby obtaining the exercise workload by the second sensor comprises determining a running speed of the individual during the exercise session (Paragraph 0031: the external work may be a person’s running speed).
Regarding claim 18, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method, wherein obtaining the exercise workload by the second sensor comprises measuring, cycling power, wherein the second sensor is a bicycle power meter (Paragraphs 0012, 0032 and 0043: the external work output can be provided directly from a variety of exercise devices such as a bicycle ergometer).
Regarding claim 19, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method, wherein obtaining the exercise workload by the second sensor comprises measuring, power of a stationary exercise machine, wherein the second sensor is a power meter, and wherein the stationary exercise machine is a rowing machine or a stationary bike (Paragraphs 0012, 0032 and 0043: the external work output can be provided directly from a variety of exercise devices such as a bicycle ergometer).
Regarding claim 20, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti further teaches the method, further comprising estimating the
Regarding claim 21, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 20. Modified Saalasti further teaches the method further comprising: determining that a maximum measured heart-rate exceeds the maximum heart-rate that is estimated based on the age; and setting, in response to determining that the maximum measured heart-rate exceeds the maximum heart-rate that is estimated based on the age, the maximum measured heart-rate as the maximum heart-rate (Paragraph 0010: it is known that a device can propose increasing the HRmax if a hear rate value higher than the default value appears during use).
Regarding claim 23, Saalasti teaches a wearable device (Paragraph 0093: the invention may be implemented in a wristop, or worn, device ) comprising:
a first sensor configured to obtain heart-rate measurement data during an exercise session at a single exercise workload (Paragraph 0089: the heartrate sensor; Paragraphs 0024-0025: an accepted period comprises a period where the heart rate is stabilized relative to the external work output. Each accepted period gives a VO2max estimate from the model; Paragraph 0031: the external work output may be considered as a single average for an entire exercise session or as continuous data);
a second sensor configured to obtain exercise workload data during the exercise session (Paragraph 0089: the external work sensor; Paragraph 0043: the external work sensor may be a GPS sensor)
a memory configured to store instructions (Paragraph 0088: the memory); and
a processor coupled to the memory, the first sensor, and the second sensor and configured to execute the instructions to cause the wearable device (Paragraphs 0088-0089: the CPU and bus connecting the components) to:
receive the heart-rate measurement data and the exercise workload data for a user of the wearable device (Paragraph 0048: Fig. 3: the momentary speed and heart rate; Paragraphs 0087-0089: the heart rate sensor and external work sensor);
averaging the exercise workload to provide an averaged exercise workload (Paragraph 0031: the external work output may be an average of the entire session)
normalize the heart-rate measurement data with respect to a maximum heart-rate of the user to provide multiple data pairs of normalized heart-rate (HRn) and average exercise workload (w), wherein each data pair comprises corresponding HRn and w values (Paragraphs 0048-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value. Relative heart rate and external work; Paragraph 0025: each accepted period is a data pair); and
apply a model that relates HRn to w and maximum oxygen uptake to provide an estimate of a maximum oxygen uptake of the user (VO2max) (Paragraphs 0049-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value).
Saalasti fails to further disclose the device including averaging the heart-rate to provide an averaged heart-rate and normalizing the average heart-rate; and the model being a probabilistic model comprising a Bayesian probabilistic model.
Liu teaches that maximal oxygen consumption measurements may utilize average heart rates and average workload measurements over a predetermined duration (Paragraph 0043).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize average heart rate values over a predetermined duration as taught by Liu in the device of Saalasti for each of the accepted periods of Saalasti because Liu teaches that an average heart rate over a time period is acceptable to utilize in a VO2max determination and Saalasti uses short periods to identify acceptable data (Saalasti: Paragraph 0014) and thus the average would provide an indicative value of heart rate over the entire period and is further a simple substitution of one known element for another with no surprising technical effect.
Saalasti in view of Liu fails to further teach the device including the model being a probabilistic model comprising a Bayesian probabilistic model.
Shepherd teaches that VO2max may be predicted using probabilistic models that include factors of exercise data and heart rate (Chapter 5 Discussions and Conclusions, page 29). The probabilistic models may utilize Bayes’ theorem and use probability distribution, or density, functions (pdfs) on discrete data sets. Additionally, the pdfs may be extended into continuous pdfs (Literature Review, Pages 5-8). The models include Bayesian models (Page 10).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize the Bayesian probabilistic modelling taught by Shepherd in the device of Saalasti in view of Liu because Shepherd teaches that the Bayesian predictive models may use less extreme measurements and may improve patient comfort since they do not have to exercise at maximum intensity (Abstract) and produce accurate VO2max estimations (Results, Pages 25-26).
Regarding claim 26, Saalasti teaches a computer program product comprising computer-executable instructions that are stored on a non- transitory computer-readable medium and that, when executed by a processor, cause a wearable device (Paragraphs 0088 and 0093: the wristop device including a CPU, a bus connecting the components, and a memory) to:
receive heart-rate measurement data from a first sensor and a single exercise workload from a second sensor for a user of the wearable device during an exercise session at the exercise workload (Paragraph 0048: Fig. 3: the momentary speed and heart rate; Paragraphs 0087-0089: the heart rate sensor and external work sensor; Paragraphs 0024-0025: an accepted period comprises a period where the heart rate is stabilized relative to the external work output. Each accepted period gives a VO2max estimate from the model; Paragraph 0031: the external work output may be considered as a single average for an entire exercise session or as continuous data);
average the exercise workload to provide an averaged exercise workload (Paragraph 0031: the external work output may be an average of the entire session)
normalize the heart-rate measurement data with respect to a maximum heart-rate of the user to provide multiple data pairs of normalized heart-rate (HRn) and average exercise workload (w) (Paragraphs 0048-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value. Relative heart rate and external work; Paragraph 0025: each accepted period is a data pair); and
apply a model that relates HRn to w and maximum oxygen uptake to provide an estimate of a maximum oxygen uptake of the user (VO2max) (Paragraphs 0049-0052: the equation of paragraph 0051 includes HR/HRmax and a speed value).
Saalasti fails to further disclose the device including averaging the heart-rate to provide an averaged heart-rate and normalizing the average heart-rate; and the model being a probabilistic model comprising a Bayesian probabilistic model.
Liu teaches that maximal oxygen consumption measurements may utilize average heart rates and average workload measurements over a predetermined duration (Paragraph 0043).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize average heart rate values over a predetermined duration as taught by Liu in the device of Saalasti for each of the accepted periods of Saalasti because Liu teaches that an average heart rate over a time period is acceptable to utilize in a VO2max determination and Saalasti uses short periods to identify acceptable data (Saalasti: Paragraph 0014) and thus the average would provide an indicative value of heart rate over the entire period and is further a simple substitution of one known element for another with no surprising technical effect.
Saalasti in view of Liu fails to further teach the device including the model being a probabilistic model comprising a Bayesian probabilistic model.
Shepherd teaches that VO2max may be predicted using probabilistic models that include factors of exercise data and heart rate (Chapter 5 Discussions and Conclusions, page 29). The probabilistic models may utilize Bayes’ theorem and use probability distribution, or density, functions (pdfs) on discrete data sets. Additionally, the pdfs may be extended into continuous pdfs (Literature Review, Pages 5-8).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to utilize the probabilistic modelling taught by Shepherd in the device of Saalasti in view of Liu because Shepherd teaches that the Bayesian predictive models may use less extreme measurements and may improve patient comfort since they do not have to exercise at maximum intensity (Abstract) and produce accurate VO2max estimations (Results, Pages 25-26).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Saalasti US Patent Application Publication Number US 2019/0029586 A1 hereinafter Saalasti in view of Liu US Patent Application Publication Number US 2021/0353188 A1 hereinafter Liu further in view of Shepherd “Predicting Maximal Oxygen Consumption (VO2max) levels in Adolescents” published 2012 by Brigham Young University pages 1-60 as applied to claim 1 above and further in view of Teixeira US Patent Application Publication Number US 2012/0277545 A1 hereinafter Teixeira.
Regarding claim 15, Saalasti in view of Liu further in view of Shepherd teaches the method of claim 1. Modified Saalasti fails to further teach the method wherein the probabilistic model is based on a multivariate Gaussian distribution.
Teixeira teaches a probabilistic digital signal processor for medical function. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and/or a blood pressure (Abstract). Thus Teixeira is reasonably pertinent to the problem at hand.
Teixeira teaches that probability distribution functions may be decomposed into several gaussian distribution of labels and that the sum of the several gaussian distributions with different values and moments usually gives an accurate approximation of the true probability distribution function (Paragraph 0134).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to alter the method of modified Saalasti to be based upon multivariate gaussian distributions as taught by Teixeira because Teixeira teaches that the sum of multiple of multiple gaussian distributions give accurate approximations of the true probability distribution function (Teixeira: Paragraph 0134) and thus may provide more accurate estimates of the predicted parameter.
Response to Arguments
Applicant's arguments filed 01/09/2026 have been fully considered but they are not persuasive.
Regarding Applicant’s arguments directed towards the rejections presented under 35 USC 112(b):
Applicant’s amendments are sufficient to overcome some of the previously presented rejections but further necessitate new grounds of rejection.
Regarding Applicant’s arguments directed towards the rejections presented under 35 USC 112(a):
Applicant’s amendments are sufficient to overcome some of the previously presented rejections.
In particular, the previously presented grounds of rejection for claims 1, 14, 23, and 26 are not overcome by amendment because the specification does not describe the particular structure or training method of the probabilistic model.
Applicant’s arguments are not found to be persuasive because the cited portions of the specification merely describe the model and its training in functional language. The specification details the inputs and the desired outputs of the model but the particular structure of the model and the specific training method utilized to generate the model are not seemingly disclosed. As per 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. The specification does not appear to particularly describe how the Bayesian model which generates the probability distribution functions is constructed and/or trained from a dataset.
Regarding Applicant’s arguments directed towards the rejections presented under 35 USC 101:
Applicant’s arguments have been fully considered and are found to be persuasive in part.
In particular, Applicant’s arguments that the claimed method cannot be practically performed in the human mind because the method requires the structure of the first and second sensors are not found to be persuasive because the sensors have been addressed as mere data gathering outside of the abstract idea and do not qualify as significantly more than the abstract idea. The recited processing steps of averaging the received data, normalizing the data, and determining a VO2max estimate via a “Bayesian probabilistic model” (which is considered analogous to the decision making ability of the human mind) may all be performed in the human mind using nothing more than pen and paper.
Applicant’s arguments directed towards the claims not being directed toward mathematical concepts is found to be persuasive. The claims are no longer considered mathematical concepts under 35 USC 101 and are instead considered directed towards an abstract idea.
Applicant’s argument that the claims do not recite a generic computer because the claimed steps and elements are directed towards a specialized physiological monitoring system are not found to be persuasive because all of the steps of the abstract idea are implemented onto generic computer processing components and do not require specialized computational components as evidenced by Applicant’s lack of a particular description of how the computational elements are not generic computer components. The implementation of the abstract idea onto a generic computer does not qualify as a specifically programmed computer (MPEP 2106.05(b)).
The claimed method and system are not directed towards Patent-eligible subject matter, implemented into a practical application, or amount to significantly more than the abstract idea because the method requires only generic sensors that gather data outside of the abstract idea and the processing of the received data through methods capable of being performed in the human mind. The estimation of VO2max levels is not considered an improvement in the field as there are already a variety of methods which can be used to estimate VO2max using the claimed parameters.
Regarding Applicant’s arguments directed towards the rejections presented under 35 USC 103:
Applicant’s arguments have been fully considered but are not found to be persuasive.
In particular , Applicant argues that Saalasti requires multiple levels of exercise intensity to determine VO2max, and thus does not teach the determination being at a single exercise workload
This argument is not found to be persuasive because Applicant mischaracterizes Saalasti. Saalasti teaches the determination of VO2maz at each accepted period where the hear-rate is stabilized to external work output (Paragraphs 0024-0025). Thus any single intensity is sufficient for the determination of VO2max of Saalasti. Saalasti teaches that it may be desirable to estimate VO2max a certain number of times to ensure reliability (Paragraph 0087). But makes no mention of a requirement of varying workloads. Saalasti explicitly recites that the workload may be considered as a single average over the entire session (Paragraph 0031). Thus Saalasti does not require various levels of workload intensity.
Applicant’s arguments directed towards Saalasti not teaching the averaging of heart rate values are considered moot in light of the new grounds of rejection since Saalasti is not relied upon for this teaching.
Applicant’s arguments directed towards Shepherd not teaching a probabilistic model using only a single workload intensity are not found to be persuasive because Shepherd is only relied upon for the teaching of applying a probabilistic model. One of ordinary skill in the art would be able to adapt the probabilistic model of Shepherd which is directed towards the determination of VO2max to the method of Saalasti which does not requires various levels of workload to make a determination and may use averaged values as taught by Saalasti in view of Liu. Shepherd failing to explicitly disclose the model using averaged values or only a single workload is not found to be persuasive to argue that one of ordinary skill in the art could not readily adapt shepherd to the method of Saalasti in view of Liu.
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 MATTHEW ERIC OGLES whose telephone number is (571)272-7313. The examiner can normally be reached M-F 8:00AM - 5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached on Monday-Friday from 9:00AM – 4:00PM at (571) 272 – 7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW ERIC OGLES/Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791