DETAILED ACTION
This action is responsive to the claim amendments and Applicant’s Remarks filed 17 July 2026. The Examiner acknowledges the amendments to claims 1, 3-4, and 6-8. Claims 1-8 are pending.
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 Objections
Claim(s) 7 is/are objected to because of the following informalities:
Claim 7 should read “calculated during [[the]] a training of the regression-based model” [line 8].
Appropriate correction is required.
Claim Interpretation
Examiner Notes: the previously presented claim interpretation(s) under § 112(f) with respect to the “AT/AnT module” of claim 8 [see p. 4-7 of the Non-Final Rejection dated 17 April 2026, not presently reproduced] is/are maintained.
Claim Analysis - 35 USC § 112
Examiner’s Note Regarding Machine Learning: the claimed “regression-based model”, “neural networks-based model”, “MLP machine learning model” of claim(s) 1, the claimed “Long Short-term Memory (LSTM)” of claim(s) 5, the claimed “MLP” of claim(s) 6, the claimed “regression-based model” of claim 7, and the claimed “At/AnT Prediction module” of claim(s) 8 was/were considered under § 112(a), wherein the Examiner notes that the disclosure of machine learning models of the Applicant’s Specification [¶¶00040-00042, 00047-00052] is considered to provide sufficient written description support for the claimed machine learning models as presently claimed for one of ordinary skill in the art to understand that the Applicant possessed the instant invention at the time of filing.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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.
Claim(s) 4 and 7-8 is/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 4 recites the limitation “wherein the stability condition for processing temporal observation that is obtained from at least one sensor” [lines 1-2], which is considered to lack antecedent basis, as while the Examiner notes that claim 1 defines “processing the n-second time windows, wherein the processing includes: - identifying points where heart rate and speed signals satisfy a stability condition” [lines 9-10], there is no previous recitation of a “stability condition for processing temporal observation that is obtained from at least one sensor”, such that it is unclear whether the recited limitation of claim 4 is meant to further limit the stability condition of claim 1 [wherein the heart rate and speed signals of claim 1 are not clearly tied to the recited “temporal observation that is obtained from at least one sensor” of claim 4] or define a new/separate stability condition based on the temporal observation obtained from at least one sensor of claim 4. For examination purposes, the Examiner has interpreted either limitation to be applicable in light of any additional rejections.
Claim 4 recites the limitation “tempsignal = tempsignal + (signaldata ≤ StandardDeviationThreshold)” [line 10], which is considered indefinite, as the Examiner notes that the equation features the variable “tempsignal” on both sides of the equation, such that both instances of “tempsignal” would cancel each other out, resulting in the equation reading “0 = signaldata ≤ StandardDeviationThreshold”; furthermore, while claim 4 prefaces the “tempsignal” equation by defining the data used in the equation as accumulated “stable 30 seconds of temporal data” to build “a new filtered temporal signal that comprises both temporal behavior and outlier-free information”, there is no language in claim 4 to indicate that the “tempsignal” on the left side of the equation of claim 4 as being an incrementally updated “tempsignal”; furthermore, as claim 4 later recites “where tempsignal corresponds to heart rate and speed filtered signals” [line 11], it is unclear whether both instances of “tempsignal” are meant to correspond to the same heart rate and speed filtered signals, or only one or the other of each instance of “tempsignal”. For examination purposes, the Examiner has interpreted the “tempsignal” on the left side of the equation to refer to an incrementally updated “tempsignal” and the “tempsignal” on the right side of the equation to correspond to the instant heart rate and speed filtered signals of the instant accumulated stables 30 seconds of temporal data.
Claim 7 recites the limitation “wherein personal features, extracted from user’s anthropometric characteristics, provide the first intermediate prediction rboutput… rb0 to rb3 and c referring to regression-based model parameters and bias, respectively, calculated during the training” [lines 1-3 and 7-8], which is considered indefinite, as claim 1 previously recites “receiving personal data; feeding a regression-based model with the personal data to generate a first intermediate prediction (rboutput)” [lines 4-6], such that it is unclear whether the rboutput as determined in claim 7 is meant to be a separate instance from the rboutput as generated in claim 1 or whether based on the use of the regression-based model parameters and bias of claim 7, the recited indefinite limitation is meant to further limit the rboutput as generated in claim 1 [such that the recited “personal features” of claim 7 are meant to further limit the received “personal data” of claim 1; and wherein the recited rboutput equation of claim 7 is meant to further limit the regression-based model to generate the first intermediate prediction (rboutput)]. For examination purposes, the Examiner has interpreted the indefinite limitation to further limit the rboutput as generated in claim 1 as noted above.
Claim 8 recites the limitation “wherein the predicting of the aerobic threshold (AT) and the anaerobic threshold (AnT) includes prediction of an aerobic/anaerobic threshold, performed in AT/AnT Prediction module” [lines 2-3], which is considered to lack antecedent basis, as there is no previously defined “AT/AnT Prediction module”. For examination purposes, the Examiner has interpreted the recited limitation to define an AT/AnT Prediction module for performing the further limited method of claim 1.
Claim 8 recites the variables “
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u
t
^
” and “
m
l
p
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” [lines 5, 6], which are each considered to lack antecedent basis, as the Examiner notes that each of “
r
b
o
u
t
p
u
t
^
” and “
m
l
p
o
u
t
p
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” have not been previously defined [the Examiner notes that in the claim as written, the variables are not written in the same format as rboutput or mlpoutput], such that it is unclear whether “
r
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u
t
p
u
t
^
” and “
m
l
p
o
u
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” are meant to refer to the previously defined rboutput or mlpoutput of claim 1 or not. For examination purposes, the Examiner has interpreted either identified interpretation to be applicable in light of any additional rejections.
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 1 recite(s):
A method of predicting aerobic threshold (AT) and anaerobic threshold (AnT) and establishing an optimal aerobic training zone (OATZ), the method comprising:
receiving personal data;
feeding a regression-based model with the personal data to generate a first intermediate prediction (rboutput);
capturing temporal signals (x) including heart rate and speed readings;
splitting the temporal signals into n-second time windows;
processing the n-second time windows, wherein the processing includes:
identifying points where heart rate and speed signals satisfy a stability condition;
extracting features of stable temporal signals (xₜ) through a neural networks-based model;
feeding a Multilayer Perceptron (MLP) machine learning model with the features of stable temporal signals to generate a second intermediate prediction (mlpoutput);
combining the first intermediate prediction generated by the regression-based model and the second intermediate prediction generated by the MLP machine learning model, based on a pre-defined threshold tao to a range of tolerance values for each index:
ATtao(f) = ATMLP(f’) + ATregbas(f’’)/nestimators;
AnTtao(f) = AnTMLP(f’) + AnTregbas(f’’)/nestimators;
where tao is related to post-processed AT and AnT final predictions, MLP refers to MLP- model predictions, regbas refers to regression-based model predictions, nestimators is a number of models employed for AT and AnT predictions, f is a descriptor composed of temporal and anthropometric features, and f’, f’’ are subsets of a whole set of features of f, with anthropometric (f) and temporal features (f") features, respectively; and
defining the OATZ as a range of [AT, AnT] in beats per minute and pace according to output of the MLP machine learning model.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“receiving personal data” – may be performed by merely observing known or previously collected data
“capturing temporal signals (x) including heart rate and speed readings” – may be performed by merely observing known or previously collected data
“splitting the temporal signals into n-second time windows” – may be performed by merely observing at least a limited amount of data and identifying portions of said data based on any predetermined amount of windows
“processing the n-second time windows… identifying points where heart rate and speed signals satisfy a stability condition” – may be performed by merely observing known or previously collected data and drawing mental conclusions therefrom
“extracting features of stable temporal signals (xₜ)” – may be performed by merely observing known or previously collected data and drawing mental conclusions therefrom
“combining the first intermediate prediction generated by the regression-based model and the second intermediate prediction generated by the MLP machine learning model, based on a pre-defined threshold tao to a range of tolerance values for each index”; “ATtao(f) = ATMLP(f’) + ATregbas(f’’)/nestimators”; “AnTtao(f) = AnTMLP(f’) + AnTregbas(f’’)/nestimators”; “where tao is related to post-processed AT and AnT final predictions, MLP refers to MLP- model predictions, regbas refers to regression-based model predictions, nestimators is a number of models employed for AT and AnT predictions, f is a descriptor composed of temporal and anthropometric features, and f’, f’’ are subsets of a whole set of features of f, with anthropometric (f) and temporal features (f") features, respectively” – may be performed for at least a limited amount of data by merely using known or derived mathematical formulas/equations for mere input and output of the at least limited amount of data; and these limitations are considered to refer to mathematical concepts [MPEP § 2106.04(a)(2)]
“defining the OATZ as a range of [AT, AnT] in beats per minute and pace according to output of the MLP machine learning model” – may be performed by merely drawing mental conclusions from known or derived data
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) [i.e., “combining the first intermediate prediction generated by the regression-based model and the second intermediate prediction generated by the MLP machine learning model, based on a pre-defined threshold tao to a range of tolerance values for each index”; “ATtao(f) = ATMLP(f’) + ATregbas(f’’)/nestimators”; “AnTtao(f) = AnTMLP(f’) + AnTregbas(f’’)/nestimators”; “where tao is related to post-processed AT and AnT final predictions, MLP refers to MLP- model predictions, regbas refers to regression-based model predictions, nestimators is a number of models employed for AT and AnT predictions, f is a descriptor composed of temporal and anthropometric features, and f’, f’’ are subsets of a whole set of features of f, with anthropometric (f) and temporal features (f") features, respectively”] to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts [Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)]. The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 1 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity of generic computer function [based on the recitation of the use of machine learning models] – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed extra-solution activity, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 2 and 4-8 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic ‘units’ or ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 3 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer function] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps [acquiring, filtering signals, etc.] as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [generic computer function], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer functions, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim 1 recites the limitations “feeding a regression-based model with the personal data to generate a first intermediate prediction (rboutput)”, “extracting features of stable temporal signals (xₜ) through a neural networks-based model”, and “feeding a Multilayer Perceptron (MLP) machine learning model with the features of stable temporal signals to generate a second intermediate prediction (mlpoutput)”; claim 5 recites “a Long Short-term Memory (LSTM)”; claim 6 recites “the MLP which includes i hidden layers and j neurons that has been trained with individuals who had reference measured AT and AnT”; claim 7 recites “rb0 to rb3 and c referring to regression-based model parameters and bias, respectively calculated during the training”; and claim 8 recites “wherein the predicting of the aerobic threshold (AT) and the anaerobic threshold (AnT) includes prediction of an aerobic/anaerobic threshold, performed in AT/AnT Prediction module”. Such machine learning models for mere input and output is/are considered well-understood, routine, and conventional, as known by at least:
Hu (“Intelligent Sensor Networks”, NPL previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Hu, Page 5)]
Huang (“Kernel Based Algorithms for Mining Huge Data Sets”, NPL previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Huang, Page 1)]
Mitchell (“The Discipline of Machine Learning”, NPL previously presented) [For example, we now have a variety of algorithms for supervised learning of classification and regression functions; that is, for learning some initially unknown function f : X [Calibri font/0xE0] Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)]
Claim 3 recites the limitation “wherein temporal data associated with the temporal signals captured from at least one sensor is registered as heart rate and speed signals”. Such a device is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed device, and recites the device and sensors of the device at a high level of generality [the present invention can obtain a reasonably accurate prediction of AT and AnT using only available data from ordinary smartwatch sensors: profile data, heart rate based on photoplethysmogram sensor speed (PPG), computed using accelerometer sensor or GPS libraries, and step frequency estimated using the accelerometer (Applicant’s Specification ¶00025)]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of biometric sensing. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 recite subject matter regarding “defining the OATZ as a range of [AT, AnT] in beats per minute and pace according to output of the MLP machine learning model”, which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Subject Matter Not Taught By Prior Art
The closest prior art of record is Erdogan et al. (“Non-invasive detection of the anaerobic threshold by a neural network model of the heart rate–work rate relationship”, NPL previously presented) and Jang (US-20170079572-A1, previously presented), as previously presented in the Subject Matter Not Taught By Prior Art section of the Non-Final Rejection dated 17 April 2026 [p. 19-22, not presently reproduced]. Claims 1 and those dependent therefrom are not taught by any prior art reference.
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks p. 6, filed 17 July 2026, with respect to the previously presented drawing objections have been fully considered and are persuasive. The drawing objections for reference character(s) not mentioned in the description have been withdrawn.
Applicant’s arguments, see Applicant’s Remarks p. 6, with respect to the previously presented claim objections have been fully considered and are persuasive. The objections to claims 1, 4, and 6-8 have been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 6-7, with respect to the previously presented § 112(f) claim interpretations have been fully considered but they are not persuasive.
The Applicant asserts that claim 8 does not use words that would raise any § 112(f) presumptions [Applicant refers to language such as “means for”, “step for”] and refers to the Specification [Figs. 1 and 5] as describing wearable devices to show the AT/AnT/OATZ predictions. The Applicant specifically directs attention to Applicant’s Fig. 1 and the Specification as describing a structure corresponding to a smartwatch 108, as well as Fig. 5, which depicts “Gait Enhancing and Motivation System (GEMS)”, such that it would be clear to a person skilled in the art that such a system and/or a wearable device for establishing an OATZ would include a structure. As such, the Applicant submits that the pending claims recite sufficient structure that should not be interpreted under § 112(f). However, the Examiner disagrees with the Applicant’s argument that no equivalent means-plus-function is recited in at least claim 8, as the Examiner notes that “module” is considered to be a generic placeholder [With respect to the first prong of this analysis, a claim element that does not include the term "means" or "step" triggers a rebuttable presumption that 35 U.S.C. 112(f) does not apply. When the claim limitation does not use the term "means," examiners should determine whether the presumption that 35 U.S.C. 112(f) does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term "means"). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998) (MPEP § 2181(I)(A))], which satisfies prong (A) of the § 112(f) analysis [the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function (MPEP § 2181(I))]; the generic placeholder is modified by functional language [“wherein the predicting of the aerobic threshold (AT) and the anaerobic threshold (AnT) includes prediction of an aerobic/anaerobic threshold, performed in AT/AnT prediction module” (prong B)]; and the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function [the Examiner notes that claims 1 and 8 provide no language regarding any particular structure to define the claimed “AT/AnT prediction module” (prong C)]. The Examiner further notes that the features upon which applicant relies (i.e., “a smartwatch”, “Gait Enhancing and Motivation System (GEMS)”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). As such, the § 112(f) interpretation of the “AT/AnT prediction module” is considered to be applicable and the Applicant’s argued “interpreted equivalent structure” of a smartwatch and/or “Gait Enhancing and Motivation System (GEMS)” is not considered to be the broadest reasonable interpretation based on the § 112(f) analysis.
Applicant's arguments, see Applicant’s Remarks p. 7-8, with respect to the previously applied rejections under § 112(b) have been fully considered but they are not entirely persuasive.
The Examiner notes that not all of the previously applied rejections under § 112(b) have been overcome by the Applicant’s remarks and corresponding amendments. See above for maintained rejections.
Regarding the § 112(b) rejection of claim 4 regarding “tempsignal”, the Examiner acknowledges the Applicant’s explanation that the tempsignal equation is an incremental operation to update the current value of tempsignal. However, the Examiner notes that claim 4 as presently recited does not clearly indicate that the tempsignal equation is an incremental operation to update the current value of tempsignal, as “accumulating every stable 30 seconds of temporal data” does not clearly define the tempsignal equation as being performed incrementally and instead merely refers to a plurality of instances of the tempsignal equation being applied for each accumulated stable 30 seconds of temporal data. The Examiner further notes that each instance of “tempsignal” in the equation of claim 4 lacks any subscripts/superscripts to indicate an original or new filtered temporal signal that is being incrementally updated. As such, the corresponding rejection under § 112(b) is maintained.
Applicant's arguments, see Applicant’s Remarks p. 8-9, with respect to the previously applied rejections under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that at Step 2A Prong 1, claim 1 recites that the claimed invention performs the steps of “extracting features of stable temporal signals (xt) through a neural networks-based model” and “feeding a Multilayer Perceptron machine learning model with the features of stable temporal signals to generate a second intermediate prediction (mlpoutput)”, such that the claimed processor is not directed to an abstract idea, and rather the claimed invention uses machine learning to “defin[e] the OATZ as a range” and thus does not recite a mental process. However, while the Examiner acknowledges that the cited limitations of claim 1 are not considered to be directed towards an abstract idea [steps using machine learning] as noted in the previously applied § 101 analysis, the Examiner notes that the limitations directed towards using machine learning techniques are further analyzed at Step 2A Prong 2 and Step 2B, wherein the Examiner notes that as indicated in the Step 2B analysis, the use of machine learning for mere input and output is considered to be well-understood, routine, and conventional, such that the use of machine learning techniques is not considered to integrate the abstract idea(s) identified into a practical application at Step 2A Prong 2 or amount to significantly more at Step 2B.
The Applicant asserts that at Step 2A Prong 2, claim 1 as currently recited is integrated into a practical application that is associated with a wearable device that includes “combining the first intermediate prediction generated by the regression-based model and the second intermediate prediction generated by the MLP machine learning model, based on a pre-defined threshold tao to a range of tolerance values for each index of AT and AnT”. However, the Examiner notes that the features upon which applicant relies (i.e., “a wearable device”) are not recited in the rejected claim(s) [See also Examiner’s response to the § 112(f) claim interpretation arguments]. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). As such, the argued “wearable device” is not presently recited and is not considered to integrate the identified abstract idea(s) into a practical application at Step 2A Prong 2. The Examiner also notes that for the sake of compact prosecution, the Applicant’s Specification refers to such a wearable device/smart watch as being well-understood, routine, and conventional [the present invention can obtain a reasonably accurate prediction of AT and AnT using only available data from ordinary smartwatch sensors: profile data, heart rate based on photoplethysmogram sensor speed (PPG), computed using accelerometer sensor or GPS libraries, and step frequency estimated using the accelerometer (Applicant’s Specification ¶00025)], which was previously indicated in the Step 2B analysis of the language of claim 3 referring to “at least one sensor” [previously recited as “device’s sensors”]. Furthermore, the Examiner notes that the argued limitation of “combining the first intermediate prediction generated by the regression-based model and the second intermediate prediction generated by the MLP machine learning model…” is not considered to integrate the identified abstract idea(s) into a practical application, as the argued limitation is considered to be an abstract idea in itself [see Step 2A Prong 1 analysis].
The Applicant further asserts that that the invention of claim 1 provides, according to output of the MLP machine learning model”, optimal training zones based on fitness indicators AT and AnT predicted from wearable data, thus even assuming under Step 2A Prong 1, the claims pertain to a mental process, claim 1 as a whole integrates the mental process into a practical application. However, the Examiner disagrees with the Applicant’s argument, as the Examiner notes that “defining the OATZ as a range of [AT,AnT] in beats per minute and pace according to output of the MLP machine learning model” was previously identified as being an abstract idea at Step 2A Prong 1, such that the argued limitation is not considered to integrate the mental process into a practical application. Furthermore, the Examiner notes that providing a diagnosis is not considered to amount to particular treatment or prophylaxis [MPEP § 2106.04(d)(2)] and is further not considered to integrate the mental processes as providing the OATZ, as merely defining the OATZ as a range does not apply or use the identified mental process in any meaningful way to integrate the mental process as a practical application.
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 SEVERO ANTONIO P LOPEZ whose telephone number is (571)272-7378. The examiner can normally be reached M-F 9-6 EST.
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, Charles Marmor II can be reached at (571) 272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791
