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 .
Response to Amendment
The Amendment filed 3/3/2026 has been entered. Claims 1, 3, 6-7, 9, 11, 15, 17 and 19 have been amended. Claims 1-20 are pending in the application.
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 1-20 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.
Independent claim 1 recites in part “visualizing, by a user interface, computations of other permutations of input models to compare to the user-specified prediction model using model comparison metrics.” However, the Applicant’s specification does not reasonably convey possession of the recited limitation for at least two reasons. First, the Applicant’s specification at [0054] describes computing permutations of "model inputs (e.g., independent variables / predictors)," not permutations of "input models." Under their broadest reasonable interpretation consistent with the Applicant’s specification, these terms are not synonymous. "Model inputs" refers to the independent variables or predictors that are fed into a model, as the specification expressly clarifies. "Input models," as recited in the claim, refers under its ordinary meaning to predictive or computational models themselves that are supplied as inputs, a category that the specification does not describe. The Applicant’s specification consistently uses the singular phrase "a user-specified prediction model (e.g., logistic regression)" ([0006], [0020], [0046], [0052], [0054], [0061]) and does not describe any embodiment in which a plurality of distinct "input models" is supplied to or compared by the user interface. Second, the Applicant’s specification does not describe the user interface as "visualizing... computations" of permutations. [0053] describes the UI as visualizing the "signals," allowing the user to move back and forth between modalities. [0054], in contrast, describes the UI as providing "the option to compute other permutations of model inputs." The specification thus discloses two distinct UI functions, visualization of signals ([0053]) and a user-invokable computation option ([0054]), but does not disclose the UI visualizing the computations of permutations themselves. FIG. 4 depicts UI 470 only as a circle connected to behavior prediction engine 460 and provides no further description of the recited visualization. Thus, said claim limitations of claim 1 represents new matter. For the purposes of examination the Examiner will interpret the said limitations as “visualizing, by a user interface, computations of other permutations of model inputs to compare to the user-specified prediction model using model comparison metrics.” as supported by Applicant’s specification [0054].
Independent claims 9 and 17 recites substantially the same new matter as discussed in claim 1 and thus claims 9 and 17 are rejected under 35 U.S.C. 112(a) for the same reasons and are similarly interpreted for the purposes of examination.
Dependent claims 2-8 depend (directly or indirectly) from claim 1; dependent claims 10-16 depend (directly or indirectly) from claim 9; and dependent claims 18-20 depend (directly or indirectly) from claim 17. Each dependent claim incorporates by reference the rejected limitation of its respective parent independent claim and is rejected under 35 U.S.C. 112(a) on the same grounds as its parent. The further limitations recited in the dependent claims do not cure the deficiency of the parent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to software per se.
Claims 17-20 are directed to “A system…comprising: a physiological data collection module…a physiological behavior grouping module…a transfer function learner…a behavior prediction engine…and a user interface…” and the Applicant’s specification does not explicitly preclude these claim elements from being interpreted as pure software elements, the broadest reasonable interpretation of these claims encompass an embodiment that is entirely implemented in a software system. Software per se is not patentable. See MPEP 2106.03. Thus, claims 17-20 are rejected under 35 U.S.C. 101 as it is not directed to patent eligible subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites “A method…comprising”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
A method for learned behavior prediction, comprising: grouping the received physiological data by corresponding, similar behaviors: These limitations recite a mentally performable process of using observation and judgement to group observed physiological data by corresponding, similar behaviors.
predict an individual's future behavior based on an input of physiological data and a user-specified prediction model: These limitations recite a mentally performable process of using judgement to predict an individual’s future behavior based on observation of physiological data and following a user-specified prediction model.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
receiving physiological data from a plurality of different modalities: These additional limitations are recited at a high level of generality and amounts to insignificant extra-solution activity of necessary data gathering and selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g).
learning, by a transfer function learner, a shared latent space, in which embeddings from the different modalities are grouped according to the similar behaviors: These additional elements recite only the idea of learning, by a transfer function learner, a shared latent space, in which embeddings from the different modalities are grouped according to the similar behaviors without details on how this is accomplished. The claim omits any details of what a transfer function learner is, e.g. an algorithm/neural network architecture/machine learning model/etc., and there is no details as to how the transfer function learner learns a shared latent space in which embeddings from the different modalities are grouped according to the similar behaviors, e.g. does the learning obtain the grouping as an initial input and then verifies the grouping through reinforcement learning means, uses the raw physiological data and through unsupervised means form the groupings according to dynamically determined similarities between behaviors. No details or particularities are defined for embeddings from the different modalities. Therefore, these limitations represent no more than mere instructions to implement the abstract idea and is equivalent to adding the words “apply it” to the recited judicial exception. See MPEP 2106.05(f).
utilizing, by a behavior prediction engine, a trained, transfer function learner to: These additional elements are recited at a high level of generality, e.g. a behavior prediction engine and a trained transfer function learner, and merely represent invoking generic computer machinery/algorithm performing in their ordinary capacity to implement the underlying judicial exception. See MPEP 2106.05(f).
visualizing, by a user interface, computations of other permutations of input models (interpreted as model inputs per the 35 U.S.C. 112(a) rejection above) to compare the user-specified prediction model using model comparison metrics: These additional elements are insignificant post-solution output activities and generally links the abstract idea to a technological environment. See MPEP 2106.05(g) and (h).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with the claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of “receiving physiological data from a plurality of different modalities“ and “visualizing, by a user interface, computations of other permutations of input models (interpreted as model inputs per the 35 U.S.C. 112(a) rejection above) to compare the user-specified prediction model using model comparison metrics” which are well-understood, routine, and conventional activity similar to presenting offers and gathering statistics and receiving and transmitting data over a network described in MPEP 2106.05(d)(II). These additional elements also generally links the underlying abstract ideas to a technological environment, include a recitation of the words “apply it” (or an equivalent), and invoke computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
convert the received physiological data into a feature embedding in the shared latent space: These limitations recite a mathematical relationship similar to a conversion between binary coded decimal and pure binary. See MPEP 2106.04(a)(2)(I)(A)(ii).
convert the feature embedding from one modality of the plurality of different modalities to another modality for a given behavior outcome: These limitations recite a mathematical relationship similar to a conversion between binary coded decimal and pure binary. See MPEP 2106.04(a)(2)(I)(A)(ii).
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
in which learning further comprises: training, for each of the plurality of different modalities, an autoencoder network to: These additional elements recite only the idea of learning further comprising training, for each of the plurality of different modalities, an autoencoder network without details on how this is accomplished. The claim omits any details of any particularities of an autoencoder network, e.g. details of the encoder/decoder/functions, and there is no details as to how the autoencoder network is trained for each of the plurality of different modalities, e.g. ensemble training and/or are particular part of the autoencoder network provided particular different modalities/etc. Therefore, these limitations represent no more than mere instructions to implement the abstract idea and is equivalent to adding the words “apply it” to the recited judicial exception. See MPEP 2106.05(f).
using an association and transfer network to: These additional elements are recited at a high level of generality, e.g. an association and transfer network, and merely represent invoking generic computer machinery/algorithm performing in their ordinary capacity to implement the underlying judicial exception. See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with the claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of the words “apply it” (or an equivalent) and invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: a process, as in claim 2.
Step 2A Prong 1: The claim recites, inter alia:
further comprising: computing a transfer loss using explicit cues obtained in a controlled environment to classify whether the received physiological data is obtained from an observed behavior outcome: These limitations recite details of a mathematical calculation of computing a transfer loss.
computing an association loss using implicit cues, including whether a time to group the received physiological data from the plurality of different modalities is synchronous or asynchronous: These limitations recite details of a mathematical calculation of computing an association loss transfer loss.
optimizing the association and transfer network according to a sum of the association loss and the transfer loss: These limitations recite a mathematical relationship organizing and manipulating by optimizing the association and transfer network via mathematically correlating according to a sum of the association loss and the transfer loss similar to organizing information and manipulating information through mathematical correlations described in MPEP 2106.04(a)(2)(I)(A)(iv).
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 4
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
in which utilizing further comprises: converting a physiological data signal received from a corresponding modality to another modality of the plurality of different modalities: These limitations recite a mentally performable process of using judgement with aid of pen and paper to match a physiological data signal observed from a corresponding modality input to another observed modality of the listing of the plurality of different modalities consistent with Applicant Specification [0049].
inferring a predictive contribution of a converted physiological data to the behavior prediction engine: These limitation recite a mentally performable process of using judgement with aid of pen and paper to infer a prediction contribution of an observed converted physiological data that corresponds with the behavior prediction engine consistent with Application Specification [0051].
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
using the transfer function learner: These additional elements are recited at a high level of generality and merely represent invoking generic computer machinery/algorithm performing in their ordinary capacity to implement the underlying judicial exception. See MPEP 2106.05(f).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with the claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking computers or other machinery to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 5
Step 1: a process, as in claim 4.
Step 2A Prong 1: The claim recites the same abstract ideas as claim 4.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
further comprising leveraging an association and transfer network and the shared latent space to generate an embedding for the physiological signal from one of the plurality of different modalities: These additional elements recite only the idea of leveraging an association and transfer network and the shared latent space to generate an embedding for the physiological signal from one of the plurality of different modalities without details on how this is accomplished. The claim omits any details of any particularities of how an association and transfer network and the shared latent space is leveraged to generate an embedding for the physiological signal from one of the plurality of different modalities. There is also no details as to what comprises “an association and transfer network”, e.g. does it comprise neural network or is it a combination of logical functions. Therefore, these limitations represent no more than mere instructions to implement the abstract idea and is equivalent to adding the words “apply it” to the recited judicial exception. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include a recitation of the words “apply it” (or an equivalent). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
exploring relationships between physiological data from the plurality of different modalities: These limitations recite a mentally performable process of using judgement to explore relationships between observed physiological data from the plurality of different modalities.
predicting the individual’s future behavior using an available physiological data and an inferred physiological data: These limitations recite a mentally performable process of using judgement to predict the individual’s future behavior using an observed available physiological data and an observed inferred physiological data.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claim 7
Step 1: a process, as in claim 1.
Step 2A Prong 1: The claim recites, inter alia:
exploring, by a user, inferred signals based on an input to the transfer function learner;: These limitations recite a mentally performable process with aid of pen and paper of using judgement to explore inferred signals based on observed input provided to the transfer function learner and using judgement to visualize with the aid of the paper the inferred signals.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
further comprising: providing a link to scientific research databases…and visualizing, by the user interface, the inferred signals: These additional limitations are recited at a high level of generality and amounts to necessary insignificant extra-solution activity of necessary outputting and selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with the claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of “providing a link to scientific research databases…and visualizing, by the user interface, the inferred signals“ which are well-understood, routine, and conventional activity similar to presenting offers and gathering statistics described in MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8
Step 1: a process, as in claim 7.
Step 2A Prong 1: The claim recites, inter alia:
further comprising enabling the user to select between the plurality of different modalities to identify spatiotemporal relationships between the inferred signals:
These limitations recite certain methods of organizing human activity relating to managing personal behavior including following rules or instructions by enabling the user to filter between the plurality of different modalities to identify spatiotemporal relationships between the inferred signals similar to filtering content. See MPEP 2106.04(a)(2)(II)(C)(i).
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claims 9-16
Step 1: These claims are directed to “A non-transitory computer-readable medium having program code recorded thereon for learned behavior prediction, the program code being executed by a processor and comprising: program code to:”; therefore, these claims are directed to the statutory category of an article of manufacture.
Step 2A Prong 1: claims 9-16 recites substantially the same abstract ideas as in claims 1-8, respectively.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. The only substantive difference between claims 9-16 and claims 1-8 is that claims 9-16 are directed to “A non-transitory computer-readable medium having program code recorded thereon…, the program code being executed by a processor and comprising: program code to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step are substantially the same as that of claims 1-8, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only substantive difference between claims 9-16 and claims 1-8 is that claims 9-16 are directed to “A non-transitory computer-readable medium having program code recorded thereon…, the program code being executed by a processor and comprising: program code to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer-readable medium having program code recorded thereon, the program code being executed by a processor and comprising: program code to, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step are substantially the same as that of claims 1-8, respectively.
Claims 17-20
Step 1: These claims are directed to “A system for learned behavior prediction, the system comprising:”; while the claims are directed to non-statutory subject matter as set for in the 35 U.S.C. 101 rejection above for claims 17-20, a recommended amendment to claim 17 of “A system…the system comprising: a processor configured to implement:” would appear to direct these claims to the statutory category of machines and, per MPEP 2106.03(II), the 2019 PEG analysis proceeds to determine whether such amended claim would qualify as patent eligible.
Step 2A Prong 1: claims 17-20 recite substantially the same abstract ideas as in claims 1-2 and 7-8, respectively.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. The only substantive difference between claims 17-20 and claims 1-2&7-8 is that claims 17-20 are directed to “A system for learned behavior prediction, the system comprising: a physiological data collection module to…; a physiological behavior grouping module to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a system for learned behavior prediction, the system comprising: a physiological data collection module to; a physiological behavior grouping module to, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step are substantially the same as that of claims 1-2&7-8, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only substantive difference between claims 17-20 and claims 1-2&7-8 is that claims 17-20 are directed to “A system for learned behavior prediction, the system comprising: a physiological data collection module to…; a physiological behavior grouping module to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a system for learned behavior prediction, the system comprising: a physiological data collection module to; a physiological behavior grouping module to, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step are substantially the same as that of claims 1-2&7-8, respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-6, 9-10, 12-14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (hereinafter Wu) “Multimodal Generative Models for Scalable Weakly-Supervised Learning” (2018), in view of Khurana et al. (hereinafter Khurana) “A Survey on Neuromarketing Using EEG Signals” (2021), and further in view of Flickinger, US 2021/0059591 A1.
Regarding independent claim 1, Wu teaches a method for learned behavior prediction, comprising (Abstract a method for “weakly-supervised learning” using generative models to predict target variables (labels) from input modalities): receiving data from a plurality of different modalities (Abstract, Section 1 receiving data from a plurality of different modalities describing a “multimodal variational autoencoder (MVAE)” designed to handle “diverse modalities” and learn from multimodal input, Figure 2 demonstrates this using modalities such as images, text, and attributes); grouping the received data by corresponding, similar behaviors (Section 4 grouping received data by corresponding labels (proxies for behaviors) “treating labels as a second modality”. By treating a label (e.g., an attribute) as a modality, groups the input data (e.g., images) according to that label during the learning process); learning, by a transfer function learner, a shared latent space, in which embeddings from the different modalities are grouped according to the similar behaviors (Abstract, Sections 1-2 and 4-5 a “multimodal variational autoencoder (MVAE)” (a transfer function learner) learning (learning by) a “common latent variable, z” (a shared latent space), see Figure 1(a). The model uses inference networks to map diverse modalities into this single, joint distribution. By “treating labels as a second modality”, the MVAE learns a “joint representation” where the embeddings (in which embeddings) are statistically grouped according to those labels (behaviors) and confirms that this joint training shares “statistical strength” between the modalities. (from the different modalities are grouped according to the similar)); utilizing, by a behavior prediction engine, a trained, transfer function learner to predict based on an input of data (Sections 5-5.1 inference network utilizing the trained MVAE to predict a target modality (the label/behavior, x2) based on an input modality (the data, x1) by estimating the conditional likelihood p(x2|x1)) and a user-specified prediction model (Section 5.1 using a specific, secondary model for prediction, disclosing an embodiment where the “internal latent state is used as input to the logistic regression” to predict the target class).
Wu does not expressly teach receiving physiological data; grouping the received physiological data by corresponding, similar behaviors; predict an individual’s future behavior based on an input of physiological data.
However, Khurana teaches receiving physiological data (Abstract, Section I the collection of physiological data from multiple modalities. It states that “various physiological measurement techniques have been proposed… including brain imaging techniques [fMRI, EEG, SST], and various biometric sensors”. It specifically lists “respiratory rate, heart rate, facial expression, skin response and eye tracking” as measures used to determine consumer decisions); grouping the received physiological data by corresponding, similar behaviors (Abstract, Section I, page 735 describes grouping physiological responses based on consumer behaviors, such as “like” vs. “dislike” or “purchase intent”, classifying data based on “cognitive and affective responses” toward products, and how the “behavior” (e.g., a purchase decision or preference) acts as the label modality that groups the corresponding physiological data inputs); predict an individual’s future behavior based on an input of physiological data (Abstract, Section II, page 735, pages 742-743 “4) Comparative Analysis” provide the context of utilizing these techniques for “prediction of consumer behavior” and “purchase intent”, mentions using classifiers (like SVM or Random Forest) to predict “like or dislike” based on brain signals).
Because Wu and Khurana address receiving data from a plurality of different modalities, grouping the received data by similarity, and utilizing the learned data for prediction, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of receiving physiological data; grouping the received physiological data by corresponding, similar behaviors; predict an individual’s future behavior based on an input of physiological data as suggested by Khurana into Wu’s method, with a reasonable expectation of success, such that the method provides the technical architecture for multimodal machine learning and shared latent spaces to address the specific application domain of collecting physiological data to predict human behavior. This modification would have been motivated by the desire to provide advanced machine learning techniques to handle physiological data collection that is a challenge and involves “heterogeneity” across groups (Khurana page 732 last paragraph).
Wu in view of Khurana does not expressly teach utilizing a user-specified model; visualizing, by a user interface, computations of other permutations of input models (interpreted as model inputs per the 35 U.S.C. 112(a) rejection above) to compare to the user-specified prediction model using model comparison metrics.
However, Flickinger teaches utilizing a user-specified model; visualizing, by a user interface, computations of other permutations of model inputs to compare to the user-specified prediction model using model comparison metrics ([0029], [0030], [0036]-[0037], FIG. 5a “the height of the bars represent the intensity of the emotion relative to a baseline” and “confirm or disconfirm feedback, adjust weighting to algorithm and provide other information 270 to tune algorithm”; the user interface displays multiple computations measured against a baseline comparison metric while the user adjusts which algorithm inputs and weightings are supplied, so that computing and visualizing other permutations of the model inputs for comparison against the user-specified prediction model is taught; which reads on the limitation).
Because Wu, Khurana, and Flickinger are analogous art and within the same field of endeavor, specifically machine learning on multimodal signals to characterize and predict the behavior or state of a person, and address the same problem of evaluating and comparing predictive computations produced by such models, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Flickinger’s user-interface visualization of computations measured against a baseline comparison metric, with user-adjustable algorithm inputs and weightings, into the Wu and Khurana’s method, with a reasonable expectation of success, to teach visualizing, by a user interface, computations of other permutations of model inputs to compare to the user-specified prediction model using model comparison metrics. This modification would have been motivated by the desire to allow the user to tune and compare the predictive computations and thereby benefit from being able to proactively prepare, anticipate and channel emotions (Flickinger [0008], [0013], [0031]).
Regarding dependent claim 2, Wu, in view of Khurana and Flickinger, teach the method of claim 1, in which learning further comprises: training, for each of the plurality of different modalities, an autoencoder network to convert the received physiological data into a feature embedding in the shared latent space (see Wu Section 1-2 describes training “inference networks” q(z|x) and “generative models” p(x|z), which form a Variational Autoencoder (VAE) structure (training an autoencoder network), using “an inference network for each combination of modalities” or simplifying to a “product-of-experts” where there is one inference network per modality E1,… ,EN (for each modality), using the inference network to map observations to a “common latent variable, z” (to convert into a feature embedding in the shared latent space) wherein Khurana Abstract and Section I provides the context that the “modalities” being converted are physiological data types (e.g. EEG, eye tracking)); and using an association and transfer network to convert the feature embedding from one modality of the plurality of different modalities to another modality for a given behavior outcome (see Wu Section 2.1, Section 5, Section 6 using the model to “convert between modalities” (using an association and transfer network) by encoding one modality (e.g., an image) into the latent space and using the generative model to decode it into another modality (e.g., text or a transformed image) (to convert the feature embedding from one modality of the plurality of different modalities to another modality for a given behavior outcome)).
Regarding dependent claim 4, Wu, in view of Khurana and Flickinger, teach the method of claim 1, in which utilizing further comprises: converting, using the transfer function learner, a physiological data signal received from a corresponding modality to another modality of the plurality of different modalities (see Wu Abstract, Section 5 explicitly describes using the model to “convert between modalities” and generate missing modalities, Section 7 converts “English (en)” to “Vietnamese (vi)” as two modalities which could be provided by speech as a physiological data signal); and inferring a predictive contribution of a converted physiological data to the behavior prediction engine (see Wu Section 5.1 using the internal latent state (derived from the converted data) as “input to the logistic regression” to predict target classes (behaviors) and evaluates the “predictive task” p(x2|x1) of the neural network using the converted/inferred representations, wherein Khurana Abstract, Section I provides the context that the converted are physiological data types (e.g. EEG, eye tracking)).
Regarding dependent claim 5, Wu, in view of Khurana and Flickinger, teach the method of claim 4, further comprising leveraging an association and transfer network and the shared latent space to generate an embedding for the physiological signal from one of the plurality of different modalities (see Wu Sections 2-2.2, Section 3, Section 7 using the inference network (leveraging an association and transfer network) to generate the latent variable z (and the shared latent space) from a single modality, “given an example with missing data… we can still sample partial data,” effectively generating the embedding from the available modality such as the speech signal for machine translation (to generate an embedding for the physiological signal from one of the plurality of different modalities)).
Regarding dependent claim 6, Wu, in view of Khurana and Flickinger, teach the method of claim 1, further comprising: exploring relationships between physiological data from the plurality of different modalities (see Wu Section 6 describes using the model to “learn image transformations” (relationships like edge detection, colorization) (from the plurality of different modalities) and explore spatiotemporal relationships (in the context of translation) such as gaze direction (exploring relationships between physiological data)); and predicting the individual’s future behavior using an available physiological data and an inferred physiological data (see Wu Abstract, Section 5.1 predicting targets even when data is missing (“weakly supervised learning”) by inferring the missing data or its representation through the shared latent space wherein Khurana Abstract teaches exploring relationships between physiological signals and consumer choices).
Regarding claims 9-10 and 12-14, these are non-transitory computer-readable medium claims that are substantially the same as the method of claims 1-2 and 4-6, respectively. Thus, claims 9-10 and 12-14 are rejected for the same reasons as claims 1-2 and 4-6. In addition, Wu teaches a non-transitory computer-readable medium having program code recorded thereon for learned behavior prediction, the program code being executed by a processor and comprising: program code to (Section 6 mentions using specific software libraries to process data, stating, “we use the Canny detector…from Scikit-Image” and “OpenCV” and page 10 acknowledge utilizing cloud computing resources which are practical applications that exist physically as program code stored on a computer-readable medium and executed by a processor. Thus, suggested by the explicit disclosure of computational algorithms, software libraries, and data processing tasks that require such a hardware/software environment to function).
Regarding claims 17-18, these are system claims that are substantially the same as the method of claims 1-2. Thus, claims 17-18 are rejected for the same reasons as claims 1-2. In addition, Wu teaches a system for learned behavior prediction, the system comprising (Section 1 describes a complete computational framework involving “inference networks,” “generative models,” and a “sub-sampled training paradigm): a physiological data collection module to (Figure 1 and bottom paragraph on page 2 discloses an architecture designed to receive and process”N modalities, x1, …, xN” and describes using specific “inference network[s]” (E1, …, EN) for each modality to map observations to a latent space. These inference networks function as data collection modules that receive input data); a physiological behavior grouping module to (Section 1, Section 4 “treating labels as a second modality”. By modeling labels (e.g., attributes or classes) as a distinct modality within the joint distribution, the system effectively groups the corresponding input data (x1) according to these labels in the shared latent space and notes that this approach allows for the “sharing of statistical strength” across modalities).
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Khurana and Flickinger, as applied in the rejection of claims 2 and 10 above, further in view of Liang et al. (hereinafter Liang) “Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion” (2021), and further in view of Thiam et al. (hereinafter Thiam) “Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective” (2021).
Regarding dependent claim 3, Wu, in view of Khurana and Flickinger, teach the method of claim 2, including the association and transfer network (Wu’s MVAE convert-between-modalities network, as set forth in the rejection of claim 2).
Wu, Khurana, and Flickinger do not expressly teach computing a transfer loss; computing an association loss using implicit cues, including whether a time to group the received physiological data from the plurality of different modalities is synchronous or asynchronous; and optimizing the association and transfer network according to a sum of the association loss and the transfer loss.
However, Liang teaches computing a transfer loss (page 8151 computing “Lp the cross-entropy loss of the downstream prediction task” for the downstream task); computing an association loss (page 8151 computing an “alignment loss” denoted Lm marginal distribution alignment and Le element-level alignment) using implicit cues (page 8152 uses “implicit cues” derived from the model itself because “actual crossmodal correlations … are unknown,” leveraging “information revealed from the crossmodal attention operations”), including whether a time to group the received physiological data from the plurality of different modalities is synchronous or asynchronous (page 8148 Section 1, page 8152 fusing “time-series data” by aligning elements (q and k) based on their temporal attention weights, expressly addressing that “multimodal streams are usually asynchronous due to the variable receiving frequency”); and optimizing the association and transfer network according to a sum of the association loss and the transfer loss (page 8151 optimizing a total objective function L which is a sum of the prediction loss (Lp) and the alignment losses (Lm + Le)).
Because Wu, in view of Khurana and Flickinger, and Liang each address multimodal machine learning, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Liang’s teachings of computing a transfer loss; computing an association loss using implicit cues including whether a time to group the received physiological data is synchronous or asynchronous, and optimizing the association and transfer network according to a sum of the association loss and the transfer loss, into Wu, Khurana, and Flickinger’s method, with a reasonable expectation of success, such that Wu’s association-and-transfer (MVAE convert-between-modalities) network is optimized by Liang’s summed objective. This modification would have been motivated by the desire to provide thorough understanding by fusing time-series data of different modalities for prediction (Liang Abstract).
Wu, Khurana, Flickinger, and Liang do not expressly teach computing a transfer loss using explicit cues obtained in a controlled environment to classify whether the received physiological data is obtained from an observed behavior outcome.
However, Thiam teaches computing a loss to optimize a model (Section 3.1, Eqs. (8) and (9) the task-specific error function is “the mean squared error function” Lf, and “the entire architecture is trained in an end-to-end manner, the entirety of the parameters are optimized by minimizing the following objective function” using explicit cues obtained under a controlled, calibrated experimental protocol (Section 3.4 the BioVid Heat Pain Database, in which “[e]ach sample is labeled with its corresponding level of thermal pain elicitation (T0, T1, T2, T3, T4)” and participants are subjected to “individually calibrated” thermal pain stimulation; the per-sample ground-truth thermal-pain-elicitation labels are explicit cues obtained under that controlled, calibrated protocol) to classify whether received physiological data (Section 3.4 evaluated “uniquely on the physiological signals EMG, ECG, and EDA”) is obtained from an observed behavior outcome (Abstract classifying “observed or experienced pain episodes,” i.e., the elicited level of pain).
Because Wu, in view of Khurana, Flickinger, and Liang, and Thiam each address computing a loss associated with physiological data, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Thiam’s teaching of computing a transfer loss using explicit cues obtained in a controlled environment to classify whether the received physiological data is obtained from an observed behavior outcome, into Wu, Khurana, Flickinger, and Liang’ method, with a reasonable expectation of success, to teach computing a transfer loss using explicit cues obtained in a controlled environment to classify whether the received physiological data is obtained from an observed behavior outcome; computing an association loss using implicit cues, including whether a time to group the received physiological data from the plurality of different modalities is synchronous or asynchronous; and optimizing the association and transfer network according to a sum of the association loss and the transfer loss. This modification would have been motivated by the desire to provide automatic assessment tools where this ability is negatively affected by various psycho-physiological dispositions and distinct physical traits (Thiam page 1).
Regarding dependent claim 11, this is a non-transitory computer-readable medium claim that is substantially the same as the method of claim 3. Thus, claim 11 is rejected for the same reason as claim 3.
Claims 7-8, 15-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Khurana and Flickinger, as applied in the rejection of claims 1, 9, and 17 above, further in view of Menin et al. (hereinafter Menin) “Covid-on-the-Web: Exploring the COVID-19 scientific literature through visualization of linked data from entity and argument mining” (2021).
Regarding dependent claim 7, Wu, in view of Khurana and Flickinger, teach all the elements of claim 1.
Wu, Khurana, and Flickinger do not expressly teach further comprising: providing a link to scientific research databases; exploring, by a user, inferred signals based on an input to the transfer function learner; and visualizing, by the user interface, the inferred signals.
However, Menin teaches providing a link to scientific research databases (page 1312 Table 1 Listing technique lists the items…displays the list of publications, Section 5.5 Figure 11 the user interface (b) each item of the list contains a link to a descriptive web page of the publication, where the user can obtain more information about it “selected document from the PubMed server,” whereupon “the LDViz system launches the ACTA service by redirecting the user to the given URL”); exploring, by a user, inferred signals based on an input to the transfer function learner (Section 5.5, Figure 11 the user “right-click[s] on a document and explore[s] it using the ACTA interface, where we can (c) visualize the argumentative graph and (d) explore where the claims, evidence and PICO elements appear in the document’s abstract”; the argumentative components and PICO elements are the inferred signals, and “[t]he models used in ACTA are trained with SciBert, a language model for scientific text” (an input to a transfer function learner)); and visualizing, by the user interface, the inferred signals (Section 3, Section 5.5, Figure 11 LDViz/MGExplorer visualize the inferred signals through the “Node-link Diagram,” “ClusterVis,” and “IRIS” techniques, and ACTA “visualize[s] the argumentative graph” of the publication).
Because Wu, in view of Khurana and Flickinger, and Menin each address machine-learned representations and the analysis of complex data associated with pre-trained learner models, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Menin’s teachings of providing a link to scientific research databases, exploring inferred signals based on an input to a transfer function learner, and visualizing the inferred signals by a user interface, into Wu, Khurana, and Flickinger’s method, with a reasonable expectation of success, such that the inferred signals from the Wu, Khurana, and Flickinger transfer function learner can be visualized and explored with links to external research databases. This modification would have been motivated by the desire to assist researchers in solving domain-related tasks and to perform exploratory analyses through use-case scenarios (Menin Abstract).
Regarding dependent claim 8, Wu, in view of Khurana, Flickinger, and Menin, teach the method of claim 7, further comprising enabling the user to select between the plurality of different modalities (Section 4.1 and Section 5 the “query management interface” allows users to create and edit their own SPARQL queries, and a public “vitrine” lets the user select “a predefined query to explore the results with MGExplorer,” the user thereby selecting between different network views of the data, e.g., a “co-occurrence network” of named entities versus a “co-authorship network,” which reads on enabling the user to select between the plurality of different modalities of data) to identify spatiotemporal relationships between the inferred signals (Section 2 and Section 5.5 the views “display the temporal distribution of publications” through a time-series chart (the temporal aspect), while the “Node-link Diagram,” “ClusterVis,” and “IRIS” techniques present the structural arrangement and clustering of the items (the structural spatial aspect), so that the user identifies relationships among the inferred signals both spatially and over time and reads on “to identify spatiotemporal relationships between the inferred signals”).
Regarding dependent claims 15-16, these are non-transitory computer-readable medium claims that are substantially the same as the method of claims 7-8, respectively. Thus, claims 15-16 are rejected for the same reasons as claims 7-8.
Regarding claims 19-20, these are system claims that are substantially the same as the method of claims 7-8, respectively. Thus, claims 19-20 are rejected for the same reasons as claims 7-8.
Response to Arguments
Applicant’s claim amendments and Remarks filed 3/3/2026 with respect to the rejections under 35 U.S.C. 101 have been fully considered but are not persuasive. The rejections are maintained as set forth above. Each argument is addressed below per MPEP 707.07(f).
Argument 1 (Step 1 / software per se — claims 17-20): Applicant submits that “[e]ach of the claims fall within at least one statutory category (machine, claims 9-20; and process, claims 1-8), thus passing Step 1.”
This argument is not persuasive as to claims 17-20. Claims 1-8 are processes and claims 9-16 are articles of manufacture, and the Examiner does not contend otherwise. However, claims 17-20 recite “A system … comprising: a physiological data collection module …; a physiological behavior grouping module …; a transfer function learner …; a behavior prediction engine …; and a user interface …” without reciting any hardware, processor, or other structure, and Applicant’s specification does not explicitly preclude these elements from being interpreted as pure software. Under the broadest reasonable interpretation, the recited “modules,” “learner,” “engine,” and “interface” encompass an embodiment implemented entirely in software, which is software per se and is not directed to any of the four statutory categories. See MPEP 2106.03. Applicant’s conclusory assertion that claims 9-20 are a “machine” does not address claims 17-20’s lack of any recited structure. The rejection of claims 17-20 under 35 U.S.C. 101 as non-statutory subject matter is maintained. As noted in the rejection, amending claim 17 to recite, e.g., “a processor configured to implement” the recited modules would direct claims 17-20 to the statutory category of a machine.
Argument 2 (Step 2A Prong 1 — not a mental process): Applicant submits that elements a)-c) “cannot realistically be performed by the human mind and are not fairly considered mental processes because the human mind is not equipped to perform latent state learning for predicting human behavior,” and that performing them mentally would exceed pen-and-paper aids.
This argument is not persuasive because it mischaracterizes which limitations are identified as the abstract idea and which are treated as additional elements. As set forth in the Step 2A Prong 1 analysis, the judicial exception identified in claim 1 is the limitation of “grouping the received physiological data by corresponding, similar behaviors” and “predict[ing] an individual’s future behavior based on an input of physiological data and a user-specified prediction model” grouping observed data by similarity and predicting future behavior from observed data are observations, evaluations, and judgments that can be practically performed in the human mind, and are mental processes (MPEP 2106.04(a)(2)(III)). Applicant’s elements a) (the transfer function learner / shared latent space), b) (the behavior prediction engine), and c) (the visualizing user interface) are precisely the additional elements that the rejection treats at Step 2A Prong 2, they are not the identified abstract idea. The claim recites these elements only at a high level of generality and does not recite any particular algorithm, architecture, or computational technique for “latent state learning”; the claim therefore claims the result of grouping and predicting, not a specific non-mental technique for doing so, and the abstract idea remains within the mental-process grouping under the level-of-generality guidance. Reciting that a generic computer performs the mental steps does not remove the limitations from the mental-process grouping. The Step 2A Prong 1 finding is maintained.
Argument 3 (Step 2A Prong 2 — practical application / meaningful limit / improvement): Applicant submits that elements a)-c), alone and/or in combination, integrate the exception into a practical application of “model comparison to solve the technical problem of finding a preferred model to an inference task,” that the transfer function learner is an “unconventional solution,” and that one of ordinary skill would recognize an “improvement in technology,” relying on the specification at [0054] (UI 470, a library of models, computing other permutations of model inputs, model comparison metrics).
This argument is not persuasive. First, the features Applicant relies upon, “a library of models,” “UI 470,” selecting inferred signals “based on … the linked scientific articles,” and computing “other permutations of model inputs”, are described in the specification at [0054] but are not recited in claim 1 at the level argued. Features disclosed only in the specification and not claimed cannot integrate the exception into a practical application; the claim is evaluated as a whole on what it recites. See MPEP 2145(I). Second, the additional elements that are recited, “a transfer function learner,” “a behavior prediction engine,” and “visualizing, by a user interface, computations of other permutations of [model] inputs … using model comparison metrics”, are recited at a high level of generality with no detail of how they are implemented; they amount to mere instructions to apply the abstract idea on generic computer machinery (MPEP 2106.05(f)), insignificant extra-solution data gathering and post-solution output (MPEP 2106.05(g)), and generally linking the abstract idea to a technological environment (MPEP 2106.05(h)), as set forth in the rejection. Third, “finding a preferred model to an inference task” is itself the abstract idea (a mental evaluation/comparison), not a technical improvement to a computer or other technology; comparing models using comparison metrics is the judicial exception, not a practical application of it. Applying the two-step improvement analysis of Ex Parte Desjardins, while the specification describes a cross-modal/shared-latent-space mechanism, claim 1 as recited does not include the components or steps that would provide any such improvement (the claim omits any details of the transfer function learner, the shared latent space, or how the grouping/conversion is accomplished), so the claim does not reflect the asserted improvement and does not benefit from the MPEP 2106.05(a) consideration. Applicant’s assertion that the transfer function learner is an “unconventional solution” is a Step 2B (significantly more) contention raised at Prong 2 and is conclusory; it is addressed and rejected in the Step 2B analysis of the rejection (the elements are recited so generically that they invoke generic computer machinery in its ordinary capacity). The Step 2A Prong 2 finding is maintained.
Argument 4 (eligibility of claim 1 and dependent claims): Applicant submits that claim 1 is patent eligible under Step 2A Prong Two, that the independent claims and newly submitted claims are similar, and that the dependent claims are eligible at least by virtue of their dependence on an allowable base claim.
This argument is not persuasive for the reasons given for Arguments 2 and 3, claim 1 (and independent claims 9 and 17) remains directed to an abstract idea without significantly more. Dependence on a base claim that is itself rejected under 35 U.S.C. 101 does not confer eligibility on a dependent claim; as set forth in the rejection, each dependent claim was separately analyzed and either recites a further abstract idea (e.g., the mathematical loss computations of claim 3, the mental selection/identification of claim 8) or recites only additional elements at a high level of generality that do not integrate the exception or amount to significantly more. The 35 U.S.C. 101 rejections of claims 1-20 are maintained.
Applicant’s claim amendments and Remarks filed 3/3/2026 with respect to the rejections under 35 U.S.C. 103 have been fully considered but are not persuasive. The rejection is maintained, with Wu retained as the primary reference as in the Office Action dated 12/17/2025, updated for the claims as amended. Flickinger is added as a further reference applied to independent claims 1, 9, and 17 for the amended “visualizing, by a user interface, computations of other permutations of [model inputs] to compare to the user-specified prediction model using model comparison metrics” limitation; Liang and Thiam are applied to claims 3 and 11; and Menin is applied to claims 7-8, 15-16, and 19-20 for the link, explore, and visualize-inferred-signals limitations. To the extent the addition of Flickinger constitutes a new ground, it is necessitated by Applicant’s amendment, which placed the user-interface visualization/model-comparison limitation at issue; the primary reference is unchanged. Applicant’s contention that Wu does not teach a user-interface-based evaluation of the secondary prediction model is addressed: Wu in view of Khurana teaches the user-specified prediction model and the prediction (Wu Section 5.1), and Flickinger expressly teaches the user interface that visualizes computations measured against a baseline comparison metric while the user adjusts the supplied inputs and weightings (Flickinger [0029], [0030], [0036]-[0037], FIG. 5a). Applicant’s contention that Menin does not teach a user-specified prediction model is moot, as Menin is no longer relied upon for that limitation. One cannot show nonobviousness by attacking the references individually where the rejection is based on their combination (MPEP 2145(IV); In re Keller, 642 F.2d 413 (CCPA 1981)); each reference is relied upon only for the limitation it teaches.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Heneghan et al., US 11,191,466 B1 (Dec. 7, 2021) (ABSTRACT — Physiological variables, metrics, biomarkers, and other data points can be used, in connection with a non-invasive wearable device, to screen for, and predict, mental health issues and cognitive states. In addition to metrics such as heart rate, sleep data, activity level, gamification data, and the like, information such as text message and email data, as well as vocal data obtained through a phone and/or a microphone, may be analyzed, provided user authorization. Applying predictive modeling, one or more of the monitored metrics can be correlated with mental states and disorders. Identified patterns can be used to update the predictive models, such as via machine learning-trained models, as well as to update individual event predictions. Information about the mental state predictions, and updates thereto, can be surfaced to the user accordingly).
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 KUANG FU CHEN whose telephone number is (571)272-1393. The examiner can normally be reached M-F 9:00-5:30pm ET.
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/KC CHEN/Primary Patent Examiner, Art Unit 2143
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