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 .
Claim 1 is amended. Claims 1-7 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/24/2026 has been entered.
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 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 are drawn to a method for estimating fitness scores from data associated with a wearable device which is within the four statutory categories (i.e. process).
Claim 1 of recites a method of estimating fitness scores from data associated with a wearable device comprising:
receiving user profile data comprising:
age, gender, weight, height and body mass index;
extracting sensor data, which includes bioelectrical impedance analysis (BIA) data, exercise session data and activity level data;
obtaining a data set of sensor data features and respective performance on tests for health-related physical fitness (HRPF) domains, including: muscular endurance, muscular strength, flexibility, body composition and cardiorespiratory;
normalizing training of the data set by subtracting a mean and dividing by a standard deviation of each feature;
obtaining a prediction model for each HRPF domain by training machine learning algorithms, one for each domain, using the normalized data set; and
obtaining predictions for a new data instance by:
a) normalizing a new data feature vector by subtracting the mean and dividing by the standard deviation of each variable in a training set;
b) applying different regression models with source-dependent feature selection and latent variable projection corresponding to each domain based on sensor data extract for the new data instance and obtaining a prediction for each domain; and
c) normalizing each prediction by a distribution corresponding to age and sex of the new data instance,
wherein the predictions for the new data instance obtained is presented as a multidimensional representation of fitness status associated with the user profile based on source-dependent predictions for each HRPF domain.
The bolded limitations, given the broadest reasonable interpretation, cover a mathematical concept and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations and/or fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of abstract idea are underlined and are deemed “additional elements,” and will be discussed in further detail below.
Dependent Claims 2-7 include other limitations, for example Claim 2 recites wherein the extracting of sensor data further comprises obtaining a most recently valid value within 30 days for VO2max and bioelectrical impedance; temporally aggregating activity level data by performing multiple aggregations across different time scales; and temporally aggregating exercise session data features by performing multiple aggregations across different time scales, Claim 3 recites wherein based on VO2Max data being unavailable, estimating the VO2Max data according to an equation as follows: VO2-max = 79.9 - (0.39 X Age) - (13.7 X Gender[0 = male, 1 = female]) - (0.127 X
Weight[lbs]), Claim 4 recites wherein the sensor data from the wearable device is an input and user performance on HRPF tests are ground truth for the prediction model, Claim 5 recites wherein the obtaining of the prediction model for each HRPF domain by training machine learning algorithms further comprises: source-dependent feature selection, in which bioelectrical impedance analysis (BIA), pedometry, calorie, and exercise session data features are dropped based on their Pearson correlation being lower than a threshold, and temporal features are further subject to selection of an aggregation with largest correlation; source-dependent latent projection, in which profile data, BIA, activity level data and exercise session data forwarded by source-dependent feature selection are subject to their respective Principal Component Analysis (PCA) projection, wherein a smallest subset of vectors representing a given proportion of a variance in the training set is preserved; and linear regressions on features transformed via source- dependent latent projection, where muscular endurance domain employs Poisson regression, and remaining domains employ Lasso regression, Claim 6 recites wherein the normalization of a prediction further comprises: obtaining percentiles of a target variable, according to American College of Sports Medicine (ACSM) , for the age and sex of the predicted respective instance; and for each domain, calculating multiple health-related physical fitness domains' scores of the prediction by subtracting a lowest percentile and dividing by a difference between a highest and lowest percentile, wherein the normalization of f(x) follows:
f(x) = 100 x-PL% ,
PH%-PL%
P where x is a value to be normalized, PL% and PH% are the percentiles of a group-specific distribution, L and H are defined by ACSM guidelines for each domain, Claim 7 requires displaying, on a display of the wearable device, a simultaneous depiction of multiple health-related physical fitness domains' scores, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Furthermore, Claims 1-7 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a display of a wearable device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0028] of the present Specification, see MPEP 2106.05(f).
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs [0028] of the Specification discloses that the additional elements (i.e. smartwatch) comprise a plurality of different types of generic computing systems or off the shelf devices that are configured to perform generic computer functions
Dependent Claims 2-7 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than elements recited at an apply it level (the display of the wearable device feature of Claim 7).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-7 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 03/24/2026 have been fully considered. Examiner notes that the claims were not indicated as allowable, but rather that the claims were free from prior art rejections as the claims remain rejected as being directed towards ineligible subject matter.
REJECTION UNDER 35 U.S.C. § 101
Applicant asserts that claim 1 recites “performs source-dependent projections and presents the same in a multidimensional representation” and that under Step 2A, Prong 1, “the claimed invention enables complete characterization of the user's health status based on all health-related fitness domains by ‘obtaining a prediction model for each HRPF domain by training machine learning algorithms, one for each domain, using the normalized data set (Remarks, page 6).’” Examiner maintains that this is part of the abstract idea. The claimed invention is directed towards creating an overall fitness status of a user (Specification [0001]).
Applicant further asserts that under Step 2A, Prong 2, “claim 1 as currently recited is integrated into a practical application of wearable device (Remarks, page 6).” The display of the wearable device is recited at an apply it level, and is not improved by the claimed invention. It is merely used for its intended purpose which is to collect the user’s fitness data.
Applicant asserts that “at least the following feature of independent claim 1 reflects an improvement and a practical application whereby "the predictions for the new data instance obtained is presented as a multidimensional representation of fitness status associated with the user profile based on source-dependent predictions for each HRPF domain.’” Examiner asserts that this limitation is part of the abstract idea, and therefore, it is not considered when looking whether it integrates the abstract idea into a practical application since its part of the abstract idea itself.
Therefore, the claims remain rejected as being directed to an abstract idea without a practical application or significantly more.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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