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 .
Election/Restrictions
Claims 12-13 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/17/2026.
Claim Objections
Claims 2-7 and 9-11 are objected to because of the following informalities:
Claims 2-7 state “A method according to…”. They should state “The method according to…” to avoid being mistaken as an independent claim. Appropriate correction is required.
Claims 9-11 state “A system according to…”. They should state “The system according to…” to avoid being mistaken as an independent claim. 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.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-3 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.
MPEP 2161.01 I. recites in part:
"... original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV. .."
The following limitations define the invention in functional language, but the specification lacks the algorithm or steps/procedure for performing the functions or are not explained in sufficient detail:
Regarding claim 2, the limitation “A method according to claim 1, wherein the method further comprises using the load estimation as a second input to a second neural network, via the computing arrangement, wherein the second neural network is trained to provide a modelled hormonal response related to the load estimation using the second input, and the modelled hormonal response is used to refine the recovery estimation value” lacks a proper written description.
Regarding claim 3, the limitation “using the modelled recovery estimation to recalculate the refined recovery estimation, via the computing arrangement, to obtain the recovery estimation value to be provided to a user” lacks a proper written description.
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 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 the limitation "the physical exercise". There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 2, the limitation “…the modelled hormonal response is used to refine the recovery estimation value” is indefinite. In particular, the claim does not establish what modification of the recovery estimation value constitutes “refining” the value. The term “refine” suggests that the recovery estimation value is made more accurate, precise, or otherwise improved, but the claim provides no objective criteria for determining when a modified recovery estimation value has been sufficiently refined. Thus, it is unclear whether merely changing, updating, adjusting, or replacing the recovery estimation value based on the modelled hormone response satisfies the limitation. Accordingly, the scope of the limitation cannot be determined with reasonable certainty.
Regarding claim 4, the limitation “…wherein the exposed load is a load related to at least one of: a physical exercise, a mental exercise, a workload, a stress load” is indefinite. Independent claim 1 states, in part, “…provide a load estimation related to the physical exercise using the first input”. This appears to already establish that the exposed load is related to physical exercise, which would rule out the exposed load being related to a mental exercise, workload, or stress load.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
Claim Rejections - 35 USC § 101
Claims 1-11 and 14-16 are rejected under because they are directed to an abstract idea without significantly more. Claim 1 recites (additional limitations crossed out):
A method for providing a recovery estimation value for a user related to an exposure to a load, the method comprising:
providing a first set of questions to the user,
receiving, after the exposure to the load, a first set of respective answers from the user, to the first set of questions,
using the first set of questions and the respective answers as a first input
calculating from the load estimation the recovery estimation value,
The above limitations as drafted, is a process that, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, as well as mental processes. That is, other than reciting the steps as being performed by a “computing arrangement”, and a “first neural network” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, or mental processes. For example, but for the “computing arrangement”, and “first neural network” language, the limitations describe a system providing a first set of questions, receiving a first set of answers to the questions, providing a load estimate based on the questions and answers, and calculating a recovery estimation value based on the load estimation. The limitations describe the management of personal behavior, as well as mental processes. An analog example of the claims would be a physical trainer asking a client about their Rate of Perceived Exertion (RPE) after their workout (e.g., “How do you feel after the workout?”), determining the load of the workout based on the reported RPE (e.g., “this Crossfit workout left him extremely exhausted), determining a recovery time based on the load (e.g., “Don’t work out for at least two days”). If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a “computing arrangement”, and a “first neural network” to perform the claimed steps. These elements recited at a high level of generality (see at least page 21) such that it amounts to no more than mere instructions to apply the exception using generic computing components.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo). Also see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), the Federal Circuit explained, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” 134 F.4th at 1216.
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “computing arrangement”, and “first neural network” elements. This is not sufficient to amount to significantly more than the judicial exception. The claims are therefore still directed to an abstract idea.
Claim 8 recites (additional limitations crossed out):
A system for providing a recovery estimation value for a user, the system comprising:
The above limitations as drafted, is a process that, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, as well as mental processes. That is, other than reciting the steps as being performed by a “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, and a “user device” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, or mental processes. For example, but for the “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, and a “user device” language, the limitations describe providing a first set and second set of questions, collecting a first set and second set of answers to the questions, providing the collected answers to a computer, and receiving a recovery estimation value to provide to a user. The limitations describe the management of personal behavior, as well as mental processes. An analog example of the claims would be a physical trainer asking a client a series of questions, placing their answers in a computer (i.e., mere use of a computer to perform a mental or mathematical process), and receiving a recovery estimation value. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, and a “user device” to perform the claimed steps. The “user device” is recited at a high level of generality (see at least page 21) such that it amounts to no more than mere instructions to apply the exception using generic computing components. In regards to the “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, it is considered to be generic computer function and/or field-of-use/“general link” implementations and do not meaningfully limit the claim (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”). Moreover, the functionality intended to be performed by the “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, appears to be based on very rudimentary constraints (e.g., questions and respective answers, load estimation). Without some prohibition in the claims regarding scalability, computation load, etc., this “computing arrangement comprising a first neural network, a second neural network, and a third neural network” could reasonably be considered merely being applied to an additional abstract idea in the “mental process” category or in the “mathematical concepts” category, but for which is simply automated (i.e., “apply it”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore still directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a “computing arrangement comprising a first neural network, a second neural network, and a third neural network”, and a “user device”” to perform the claimed steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, the claims are not found to be patent eligible.
Claims 2-7 are dependent on claim 1 and include all the limitations of claim 1. Claims 9-11 are dependent on claim 8 and include all the limitations of claim 8. Therefore, they are also found to be directed to an abstract idea. Claim 2 features a “second neural network” and claim 3 features a “third neural network”. However, these neural networks are merely applied to the judicial exception. The remaining dependent claims have not been found to integrate the judicial exception into a practical application or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Association between internal load responses and recover ability in U19 professional soccer players: A machine learning approach”, by Gulielmo Pillitteri1, available April 2023, hereinafter referred to as Pillitteri, in view of “A Data-Driven Fatigue Prediction using Recurrent Neural Networks” by Arsalan Lambay, available June 25, 2021, hereinafter referred to as Lambay2, and Ahmed (US 2022/0293236)
Regarding claim 1, Pillitteri discloses A method for providing a recovery estimation value for a user related to an exposure to a load, the method comprising:
providing a first set of questions to the user, via a computing arrangement;
receiving, after the exposure to the load, a first set of respective answers from the user, to the first set of questions, at the computing arrangement;
using the first set of questions and the respective answers as a first input to
See Page 4 – “Internal load assessment – Internal TL for each participant was calculated according to the session-RPE method by Foster et al. (2001) [68]. In detail, 30 min after each session, the duration (minutes) and each RPE training session were recorded for players using smartphone applications (i.e., through a WhatsApp broadcast list). TL from each session for each player was calculated from the product of the session duration and the player’s RPE (Foster et al., 2001) [68]. The training session duration included warm up, cool down, and rest between tasks.”
While Pillitteri discloses calculating a load estimation based on answered questions (see “Internal load assessment above”), Pillitteri does not explicitly disclose using a first neural network, via the computing arrangement (See Lambay, page 4 – “The representation every fatigue was by RPE scale in our dataset as a vector of the size 1x23, where 23 is the number of features which were include in our prediction. We created a matrix with all of the Fatigue RPE scale and their features that are considered in the prediction and divided all of the tasks into fixed-size windows. Then we reshape all of the matrix's vectors to create a NumPy array. It is now a shape vector (W - l - f), where W is the number of windows, l is the length of A window, and f is the number of features. The dataset was then divided into train and test sets, with the training set accounting for 70% of the dataset and the test set accounting for 30%. As it has the highest significance, we train the LSTM Model to determine the tiredness of the modified by different feature input for each training.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pillitteri to utilize an LSTM Model since it would automate the assessment of the internal load determined in Pillitteri.
Pillitteri does not explicitly disclose calculating from the load estimation the recovery estimation value, using the computing arrangement. (See Ahmed, Para. [0155] – “The recovery score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's heart rate variability (HRV), resting heart rate, sleep quality and recent physiological strain (indicated, in one example, by the intensity score of the user).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pillitteri to further determine a recovery score since Pillitteri speculates that the ability to recover depends mainly on the quantity and quality of the load performed in the previous day (See page 8 of Pillitteri).
Regarding claim 4, Pillitteri discloses A method according to claim 1, wherein the exposed load is a load related to at least one of: a physical exercise, a mental exercise, a workload, a stress load. (See at least page 1 – “The objective of soccer training load (TL) is enhancing players’ performance while minimizing the possible negative effects induced by fatigue”
Regarding claim 14, Pillitteri discloses Use of the method of any one of claim 1 for at least one of:
providing recovery estimation after physical exercise,
providing hormonal value estimations after a real or modelled load
The claim merely invokes intended use and fails to result in a manipulative difference between the claimed invention and the prior art.
Claims 15 and 16 feature limitations similar to those of claim 1 and are therefore rejected using the same rationale.
Claim(s) 2 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Association between internal load responses and recover ability in U19 professional soccer players: A machine learning approach”, by Gulielmo Pillitteri, available April 2023, hereinafter referred to as Pillitteri, in view of “A Data-Driven Fatigue Prediction using Recurrent Neural Networks” by Arsalan Lambay, available June 25, 2021, hereinafter referred to as Lambay, and Ahmed (US 2022/0293236), and in further view of Faghih (US 2022/0142556)
Regarding claim 2, Pillitteri, Lambay, and Ahmed do not explicitly disclose A method according to claim 1, wherein the method further comprises using the load estimation as a second input to a second neural network, via the computing arrangement, wherein the second neural network is trained to provide a modelled hormonal response related to the load estimation using the second input, and the modelled hormonal response is used to refine the recovery estimation value. (In light of the 112 rejection, see Faghih, at least Para. [0009] – “In such aspects, the estimate of the state of the nervous system may be an estimate of a state of cortisol-related energy production.”, and [0074] – “As indicated at 410 and 420 of FIG. 4, the physiological condition data, e.g., skin conductance data or cortisol level data, and the external input(s), respectively, are obtained and are input to the neural network system to perform state estimation, as indicated at 430. The neural network system, based on the state estimation performed, outputs an estimation of the state, as indicated at 440. 410-440 may be performed via processing device 140 of system 100 (FIGS. 1A-2B) or any other suitable processing device(s) in any other suitable system. The external input(s) may include some or all of the additional input data noted above. In particular, the external input may include location data ( e.g., from a GPS system), to enable the neural network system to consider whether the user is in a low noise environment ( e.g., a workplace office) or a high noise environment (e.g., a gym). Biological rhythm data may also be utilized. User labels (e.g., how the user is feeling emotionally, whether the user is tired or energetic, etc.) may additionally or alternatively be utilized as part of the external input.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pillitteri, Lambay, and Ahmed to utilize the teachings of Faghih since it would provide insight regarding the state of a person’s nervous system (See Para. [0005]).
Regarding claim 6, Pillitteri, Lambay, and Ahmed do not explicitly disclose A method according to claim 2, wherein the hormonal response comprises: a cortisol value, a testosterone value and/or a ratio between the testosterone value to the cortisol value. (See Faghih, at least Para. [0009] – “In such aspects, the estimate of the state of the nervous system may be an estimate of a state of cortisol-related energy production.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pillitteri, Lambay, and Ahmed to utilize the teachings of Faghih since it would provide insight regarding the state of a person’s nervous system (See Para. [0005]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Association between internal load responses and recover ability in U19 professional soccer players: A machine learning approach”, by Gulielmo Pillitteri, available April 2023, hereinafter referred to as Pillitteri, in view of “A Data-Driven Fatigue Prediction using Recurrent Neural Networks” by Arsalan Lambay, available June 25, 2021, hereinafter referred to as Lambay, and Ahmed (US 2022/0293236), and in further view of Biadsy (US 2022/0414542)
Regarding claim 5, Pillitteri, Lambay, and Ahmed do not explicitly disclose A method according to claims 1, wherein the training of a neural network comprises generic training epochs and user dependent training epochs. (See Biadsy, Para. [0024] – “In particular, in some implementations, training can occur as follows: In a first phase the base model can be trained on general training data to cover the general distribution for a given general population, task, etc. (e.g., ASR). In a second, subsequent phase, training data for a particular user, task, domain, and/or context (e.g., speaker) can be used to train the submodel. Specifically, in some implementations, only the parameters in the submodel can be learned while all other parameters of the base model are held fixed (so that the base model stays unchanged).” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Pillitteri, Lambay, and Ahmed to train models using general training data and training data for a particular user as taught by Biadsy since it would allow for personalized models (See at least Para. [0032])
Examiner Notes
No prior art is applied to claims 3, 7, and 8-11 at this time. While the receiving of input by a neural network to provide a particular output is a fundamental aspect of machine learning. No prior art could be found at this time disclosing the particular inputs and outputs featured in claim 3. Further, while claim 7’s feature of using a previous output as an input to a neural network describes the well-known feature of recurrent neural networks, similar to claim 3, no prior art could be found regarding the use of the particular input used. Regarding claim 8, while trained neural networks are not novel, no prior art could be found featuring the particular training data used to train the claimed neural networks.
Conclusion
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Available at https://www.sciencedirect.com/science/article/pii/S2405844023026610?via%3Dihub
2 Available at https://ieeexplore.ieee.org/document/9461377?source=IQplus
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