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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “304” [Fig. 3, refers to “Feature Vector”]; “404” [Fig. 4, refers to flow chart step “Temporal data within bounds?”].
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim(s) 1, 4, and 6-8 is/are objected to because of the following informalities:
Claim 1 recites both “heart rate and speed readings” [line 6] and “heart rate and speed signals” [line 9; also recited in claims 3, 4], which appear to be used interchangeably. Each instance identified should be amended to only recite one or the other of “readings” or “signals”.
Claim 1 recites “a MLP machine learning model” [line 12] without previously defining the acronym “MLP”. Claim 1 should be amended to define the acronym “MLP” [which is understood to refer to a Multilayer Perceptron (Applicant’s Specification ¶00023)].
Claim 1 should read “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” [lines 14-16], wherein the Examiner notes that the second amendment is to define “each index” as referenced in the equations of claim 1.
Lines 13, 17, and 18 of claim 1 should each end with a semi-colon.
Claim 1 should read “for each index[[;]]:” [line 16], as the limitation of lines 14-16 appears to refer to the equations that follow the end of the identified limitation.
Claim 4 should read “a temporal signal of the temporal signals (x)” [line 3] to clearly indicate that the recited temporal signal refers to the previously defined temporal signals (x).
Claims 1 and 6 appear to refer to “second intermediate prediction (mlpoutput)” [claim 1, line 13] and “intermediate descriptor mlpoutput” [claim 6, line 1] interchangeably. Each instance identified should be amended to only recite one or the other of “second intermediate prediction (mlpoutput)” or “intermediate descriptor mlpoutput”.
Claims 1 and 7 appear to refer to “first intermediate prediction (rboutput)” [claim 1, line 5] and “rboutput descriptor” [claim 7, line 2] interchangeably. Each instance identified should be amended to only recite one or the other of “first intermediate prediction (rboutput)” or “rboutput descriptor”.
Line 3 of claim 7 should end with a semi-colon.
Line 3 of claim 8 should end with a semi-colon.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) 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;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “AT/AnT Prediction module” in claim(s) 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Examiner’s interpretations of the claim limitation(s) that invoke interpretation under § 112(f) are as follows:
Claim(s) 8 recite(s) the limitation “wherein performing the prediction of aerobic/anaerobic threshold, performed in AT/AnT Prediction module”, which recites a generic placeholder [“module”], modified by functional language [“performing the prediction of aerobic/anaerobic threshold”], wherein the identified generic placeholder is not further modified by sufficient structure, material, or acts for performing the claimed function. Accordingly, the Examiner has interpreted the recited limitation of “AT/AnT Prediction module” as being an application executed on a computing device [The proposed technique has several advantages for embedded applications in wearable devices, such as smartwatches. In this sense, 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); The data encoding finds a generalization of temporal data into a one-dimensional feature vector to feed the final AT/AnT predictor using, for instance, machine learning modules, such as LSTM (Applicant’s Specification ¶00064)], or an equivalent thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 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.
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) 3-4 and 6-8 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 3 recites the limitation “wherein device’s sensors register temporal data as heart rate and speed signals” [lines 1-2], which is considered indefinite, as it is not clear whether this limitation is meant to define a new element(s) of a device that comprises sensors that register temporal data as heart rate and speed signals, or whether the recited limitation is meant to limit the limitation of claim 1 of “capturing temporal signals (x) including heart rate and speed readings” [line 6 of claim 1] by further defining a device comprising sensors for capturing the claimed temporal signals. For examination purposes, the Examiner has interpreted either limitation to be applicable in light of any additional rejections.
Claim 4 recites the limitation “wherein the stability condition for processing temporal observation from device’s sensors” [lines 1-2], which is considered to lack antecedent basis, as there is no previously defined device or sensors of the device, such that there is no previously defined “stability condition for processing temporal observation from device’s sensors”, as the Examiner notes that claim 1 only defines “a stability condition” [line 9 of claim 1]. The recited limitation further renders claim 4 indefinite, as it is not clear whether the stability condition of claim 4 is meant to refer to the same stability condition of claim 1 [line 9] or define a new/separate stability condition based on the newly recited device and sensors of the device. 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 9], which is considered indefinite, as the Examiner notes that the equation features the variable “tempsignal” on both sides of the equation, such both instances of “tempsignal” would cancel each other out, resulting in the equation reading “0 = signaldata ≤ StandardDeviationThreshold”; furthermore, as claim 4 later recites “where tempsignal corresponds to heart rate and speed filtered signals” it is unclear whether both instances of tempsignal are meant to correspond to heart rate and speed filtered signals, or only one or the other. For examination purposes, the Examiner has interpreted the “tempsignal” on the left side of the equation to refer to a “new filtered tempsignal” and the “tempsignal” on the right side of the equation to correspond to heart rate and speed filtered signals.
Claim 6 recites the limitation “an MLP which includes i hidden layers with j neurons that has been trained with individuals who had reference measured AT and AnT” [lines 2-3], which is considered indefinite, as it is not clear whether the recited limitation is meant to further limit the previously defined “a MLP machine learning model” of claim 1 [line 12] or define a new MLP machine learning model. For examination purposes, the Examiner has interpreted the indefinite limitation to further limit the MLP machine learning model of claim 1.
Claim 7 recites the limitation “wherein personal features, extracted from user’s anthropometric characteristics, provide rboutput descriptor” [lines 1-2], which is considered indefinite, as claim 1 previously defined the rboutput as being generated by a regression-based model [claim 1, lines 4-5], such that it is unclear whether the rboutput descriptor as determined in claim 7 is meant to be a separate instance from the rboutput as generated in claim 1 or whether based the use of regression-based model parameters and bias of claim 7 [lines 5-6], the indefinite limitation is meant to further limit the rboutput as generated in claim 1 [the regression-based model parameters and bias of claim 7 would be interpreted to refer to the regression-based model of claim 1]. For examination purposes, the Examiner has interpreted the indefinite limitation to further limit the rboutput as generated in claim 1.
Claim 8 recites the limitation “wherein performing the prediction of aerobic/anaerobic threshold, performed in AT/AnT Prediction module” [lines 1-2], which is considered to lack antecedent basis, as there is no previously defined “AT/AnT Prediction module”. The recited limitation is further considered to render claim 8 indefinite, as it is not clear whether the recited limitation is meant to define performing the prediction of aerobic/anaerobic threshold [which is considered to refer to the “combining intermediate predictions…” limitations and the equations of claim 1 (lines 14-18)] as being performed in an “AT/AnT Prediction module” or refer to an undefined prediction performed on an undefined AT/AnT Prediction module. For examination purposes, the Examiner has interpreted claim 8 to define an AT/AnT Prediction module for performing the method of claim 1.
Claim 8 recites the variables “
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” and “
m
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” [lines 3, 4], which are each considered to lack antecedent basis, as the Examiner notes that each of “
r
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” and “
m
l
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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 “
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” and “
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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 appliable 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 [representing all independent claims] 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 MLP machine learning model with the features of stable temporal signals to generate a second intermediate prediction (mlpoutput)
combining intermediate predictions performed by machine learning models and regression-based estimators, 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 [AT, AnT], in beats per minute and pace.
(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 intermediate predictions performed by machine learning models and regression-based estimators, 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 [AT, AnT], in beats per minute and pace” – 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 intermediate predictions performed by machine learning models and regression-based estimators, 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 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 “an 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 preforming the prediction of 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 attached) [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 attached) [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 attached) [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 device’s sensors register temporal data 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 [AT, AnT], in beats per minute and pace”, 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 following is a statement of reasons for the indication of subject matter not taught by the 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 attached) and Jang (US-20170079572-A1).
Erdogan discloses a method of predicting an anaerobic threshold (AnT), the method comprising: receiving personal data [For a more precise estimation of the HR-OBLA, the age, body mass, height, BMI, and playing position were used as additional inputs of the HR from each stage (Erdogan p. 111)]; capturing temporal signals including heart rate and speed readings [The treadmill test consisted of an incremental step protocol involving 3-min steps during which [La]values and HRs were measured. Each test was per-formed at zero grades and the velocities of the first stage and second stage were 8 km/h and 10 km/h respectively. Then the speed was increased by 1 km/h every 3 min until subjective exhaustion (Erdogan p. 111)]; processing the temporal signals by feeding a MLP machine learning model with the temporal signals to generate a prediction of the anaerobic threshold in beats per minute [A good predictive performance of HR-OBLA (r2 ¼ 0.766; SEE, 4.17 beats/min) was attained with the neural network with HR values obtained at last stages of the treadmill testing (Erdogan p. 113); It is fairly easy to convert a trained MLP predictor to a generalized equation for the prediction of HR-OBLA for easy implementation (Erdogan p. 114)]. However, the Examiner notes that Erdogan fails to explicitly disclose further predicting an aerobic threshold (AT) to establish an optimal aerobic training zone (OATZ), as well as the steps of lines 4-5 and 7-24 of claim 1, particularly wherein the prediction is an intermediate prediction that is combined with an additional prediction to define the OATZ as a range in beats per minute and pace.
Jang discloses a method of predicting an anaerobic threshold, the method comprising: receiving personal data [in operation 510, an apparatus for evaluating exercise capacity receives body information of a user. The body information may be physiological information about the user's body… The body information may include, for example, a gender, an age, a height, a weight, a waist-hip ratio (WHR), and a body mass index (BMI) of the user. The BMI may be obtained by dividing a weight by a square of a height. In this example, a unit of the weight is a kilogram (kg) and a unit of the square of the height is a square meter (m.sup.2) (Jang ¶0077)]; feeding a regression-based model with the personal data to generate a first prediction [The apparatus applies heart rate statistics X1, for example, a heart rate kurtosis, and body information X2, for example, a BMI, to a second estimation regression equation, for example, Y=α1×X1+α2×X2+β, to estimate at least one of the maximum oxygen uptake, the ventilatory threshold, the lactate threshold, and the maximal heart rate (Jang ¶0082)]; capturing temporal signals including heart rate [Referring to FIG. 4, in operation 410, an apparatus for evaluating exercise capacity measures a heart rate of a user while the user is performing an exercise. For example, the apparatus measures a heart rate sensed from the user while the user is performing a graded load exercise on equipment or while the user is performing a daily exercise in which an exercise load increases (Jang ¶0066)]; processing the temporal signals, wherein the processing includes: extracting features of the temporal signals [In operation 420, the apparatus generates a heart rate distribution data based on the heart rate measured in operation 410. In operation 430, the apparatus calculates heart rate statistics based on the heart rate distribution data. As an example, the apparatus calculates heart rate statistics with respect to all heart rate data expressed as the heart rate distribution data. Thus, the apparatus does not need to perform a preprocessing for feature point extraction, and minimizes an influence of noise occurring during an exercise (Jang ¶¶0068-0069)]; and feeding a model with the features of the temporal signals to generate a second prediction [In operation 440, the apparatus estimates at least one of a maximum oxygen uptake, a ventilatory threshold, a lactate threshold, and a maximal heart rate of the user based on the heart rate statistics calculated in operation 430. As an example, the apparatus applies the heart rate statistics to a linear estimation regression equation to estimate a metabolic index, for example, the maximum oxygen uptake, the ventilatory threshold, the lactate threshold, and the maximal heart rate of the user (Jang ¶0070)]. However, the Examiner notes that Jang discloses feeding the regression-based model with the personal data to generate a first prediction and feeding a model with the features of the temporal signals to generate a second prediction in similar, yet separate embodiments of the invention of Jang, and that Jang further fails to explicitly disclose predicting an aerobic threshold (AT) to establish an optimal aerobic training zone (OATZ), as well as the steps of lines 7 and 9-24 of claim 1, particularly wherein the first and second predictions are intermediate predictions that are combined to define the OATZ as a range in beats per minute and pace.
Without the benefit of hindsight, it would not have been obvious to one of ordinary skill in the art to have modified the method of Erdogan or Jang, alone or in combination, to employ the limitations identified as not explicitly disclosed by either Erdogan or Jang. Claim 1 and those dependent therefrom are not taught by any prior art reference.
Conclusion
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791
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