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 .
Formal Matters
Applicant's response, filed 05/14/2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-5, 7-16, and 18-22 are currently pending and have been examined.
Claims 1, 12, 16, and 19 have been amended.
Claims 1-5, 7-16, and 18-22 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. EP22305114.5, filed on 08/01/2024d.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 15 Nov. 2016 claiming benefit to EP22305114.5.
Objections
Examiner acknowledges that appropriate correction to the previous objections have been made and withdraws the objections accordingly.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
signal processing module (claim 1);
hierarchical scoring module (claims 1 and 15);
forecasting module (claim 1-4 and 8);
processing module (claims 1 and 16);
fusion module (claims 4 and 8)
The specification provides that the modules are software components performed on a computer with corresponding hardware and algorithms (see the instant specification on p 9 lines 7-10 and in the Drawings in fig. 1). Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-16, and 18-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-5, 7-16, and 18-22 are drawn to a system or method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for forecasting the evolution of a mental state of a living subject likely to suffer from a mental disorder in part performing the steps of generating, from the time-dependent signals, at least two feature sets comprising time-series of a plurality of extracted features, each feature set relating to a symptom of the mental disorder; processing the at least two feature sets and generating for each feature set, an evaluation of a respective temporal sub-score, and processing the evaluated temporal sub-scores through at least one … algorithm and generating a prediction of the evolution of at least one indicator related to the mental state of the subject and/or of the mental state of the subject.
Independent claim 19 recites a method for forecasting the evolution of a mental state of a living subject likely to suffer from a mental disorder in part performing the steps of b) generating at least two feature sets comprising time-series of a plurality of features extracted from these signals, each feature set relating to a symptom of the mental disorder; c) using at least one … algorithm for generating, for each feature set, an evaluation of a respective temporal sub-score between said first and second date; d) using at least one .. algorithm to process the evaluated temporal sub-scores and generate a prediction of the evolution of a least one indicator related to the mental state of the subject and/or of the mental state of the subject.
These steps of collecting sensor data and processing said data to determine a mental state of a person amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)).
Dependent claim 3 recites, in part, generate a forecast for each temporal sub-score.
Dependent claim 4 recites, in part, aggregating together the forecasts of the sub-scores and computing the forecast of a global score representative of the mental state of the subject.
Dependent claim 5 recites, in part, informing a user of the at least one indicator related to the mental state of the subject and/or of the mental state of the subject.
Dependent claim 7 recites, in part, wherein the mental disorder comprises major depression disorder (MDD) or post-traumatic stress disorder (PTSD).
Dependent claim 8 recites, in part, aggregating together at least two evaluated temporal sub-scores and computing a global temporal score representative of the mental state of the subject.
Dependent claim 9 recites, in part, wherein the time-dependent signals comprise time series of physiological measurements.
Dependent claim 10 recites, in part, the physiological measurements comprising one or more among electro-cardiogram (ECG), body temperature, photoplethysmogram (PPG), activity records (ACC), electrodermal activity (EDA).
Dependent claim 11 recites, in part, the plurality of extracted features comprising data related to actimetry, sleep and wake periods, sleep phases and/or step counting, cardio-respiratory autonomous nervous system and/or heart rate variability (HRV).
Dependent claim 12 recites, in part, the feature sets comprising one or more sleep quality, anxiety, psychomotor retardation, and diurnal activity.
Dependent claim 13 recites, in part, the feature sets comprising: an anxiety feature set comprising features relating to the EDA activity over the subject's sleeping period, and/or activity and heart rate of the subject during a daily period after cessation of social activities, a psychomotor retardation feature set comprising features relating to the activity and/or the heart rate of the subject during the first moments after wake-up, a diurnal activity feature set comprising features relating to the daily number of steps of the subject and/or the daily activity of the subject, and a sleep quality feature set comprising features relating to the activity and/or the sleep phases and/or the sleep and wake periods and/or the heart rate and/or breathing rate of the subject during the night.
Dependent claim 16 recites, in part, an encoder for building a point or a distribution of probability in a latent space using the temporal sub-scores, and neural ordinary differential equations (ODEs) predicting the evolution of said point or said distribution of probability and/or the evolution of the temporal sub-scores using an ordinary differential equation.
Dependent claim 20 recites, in part, computing at step c), with the at least one … algorithm, a global temporal score from the temporal sub-scores between said first and second date, the global score being representative of the mental state of the subject between said first and second date.
Dependent claim 21 recites, in part, generating with the at least one … algorithm at step d) a forecast for each temporal sub-score
Dependent claim 22 recites, in part, comprising aggregating together the forecasts of the sub-scores and computing the forecast of a global score as an indicator of the mental state of the subject.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 19 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites a computer with a signal processing module, hierarchical scoring module comprising at least one predictor, and a forecasting module comprising a processing module. Claims 4 and 8 recite a fusion module. Claim 5 recites a user interface. The specification provides that the modules are software components performed on a computer with corresponding hardware and algorithms (see the instant specification on p 9 lines 7-10 and in the Drawings in fig. 1). The specification lists generic hardware for the computer device and corresponding user interface (see the instant specification on p. 11 line 10 – p. 12 line 5). The use of the computer, user interface, and software modules is recited as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 2, and 3 recite using the machine learning algorithm of the forecasting module comprising a neural network. Claim 14 recites wherein the at least one ML algorithm of the predictor comprises an interpretable machine learning algorithm comprising one or more among logistic regression, shallow multi-layer perceptron or Neural 2-Choquet integrals. Claim 15 recites wherein the ML algorithm of the hierarchical scoring module is a Neural 2-Choquet integrals network. Claims 19 and 20 recite a machine learning algorithm for generating, for each feature set, an evaluation. Claims 19 and 21 recite a machine learning algorithm for processing the evaluated temporal sub-scores and generate a prediction. The instant specification does not provide specific details on the structure of the machine learning models – that is, the specification does not provide sufficient detail on the model arrangements outside merely reciting the model type (a neural network, integral network, logistic regression… etc.) known in the art to perform the intended tasks and the inputs and outputs of said model (see the instant specification in at least p. 16 line 29 – p. 18 line 14). The use of a first machine learning algorithm, in this case to generate, for each feature set, an evaluation, only recites the machine learning algorithm as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). The use of a second machine learning algorithm, in this case to process the evaluated temporal sub-scores and generate a prediction, only recites the machine learning algorithm as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 18 recites an acquisition device comprising at least one of an accelerometer, an optical sensor, a heart rate detector and an electrodermal activity sensor. Claim 19 recites acquiring during a monitoring period between a first and a second date time-dependent signals from at least one acquisition device sensing the subject. The specification provides generic embodiments of different sensors for acquiring data from a user (see the instant specification on p. 4 lines 6-25). The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites a computer with a signal processing module, hierarchical scoring module comprising at least one predictor, and a forecasting module comprising a processing module. Claims 4 and 8 recite a fusion module. Claim 5 recites a user interface. Claims 1, 2, and 3 recite using the machine learning algorithm of the forecasting module comprising a neural network. Claim 14 recites wherein the at least one ML algorithm of the predictor comprises an interpretable machine learning algorithm comprising one or more among logistic regression, shallow multi-layer perceptron or Neural 2-Choquet integrals. Claim 15 recites wherein the ML algorithm of the hierarchical scoring module is a Neural 2-Choquet integrals network. Claims 19 and 20 recite a machine learning algorithm for generating, for each feature set, an evaluation. Claims 19 and 21 recite a machine learning algorithm for processing the evaluated temporal sub-scores and generate a prediction.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 18 recites an acquisition device comprising at least one of an accelerometer, an optical sensor, a heart rate detector and an electrodermal activity sensor. Claim 19 recites acquiring during a monitoring period between a first and a second date time-dependent signals from at least one acquisition device sensing the subject. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Furthermore, the use of a wearable device to collect patient health data is well-understood, routine, and conventional activity. This position is supported by (1) Kakkar et al. (US Patent App. No. 2016/0089089) teaching on the system receiving biomarker data for the user from a wearable user device in the Detailed Description in ¶ 0062 and ¶ 0073; (2) An et al. (US Patent App. No. 2013/0116578) teaching on a patient wearable device for collecting biomarker data in the Background in ¶ 0004-5; and (3) Kaleal (US Patent App. No. 2016/0086500) teaching on a biomarker collection device worn by the user in the Detailed Description in ¶ 0049 and ¶ 0052. Therefore, the acquisition and transfer of patient sensor data from a wearable device to a computer is not sufficient to amount to significantly more than the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-5, 7-16, and 18-22 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
Examiner acknowledges that appropriate corrections to the claim for the previous 35 U.S.C. 112(b) rejection has been made and withdraws the rejection accordingly.
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.
Claims 1-5, 7-12, and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Spathis et al, Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data, KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2886 - 2894 (July 25, 2019)[hereinafter Spathis] as further evidenced by Taylor et al., Personalized Multitask Learning for Predicting Tomorrow’s Mood, Stress, and Health, 11(2) IEEE Transactions on Affective Computing 200-213 (June 2020)[hereinafter Taylor]. Although the invention is not identically disclosed or described as set forth in 35 U.S.C. 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 designer having ordinary skill in the art to which the claimed invention pertains, the invention is not patentable.
As per claim 1, Spathis teaches on the following limitations of the claim:
a computer-based forecasting system for generating a prediction of the evolution of a mental state of a living subject likely to suffer from a mental disorder, the prediction being generated from time-dependent signals acquired beforehand on the subject, the system comprising is taught in the § Abstract on p. 2886, § 3 METHOD on p. 2889 col 1, and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) for evaluating a historical emotional state and predicting future emotional state values)
a. a signal processing module for generating, from the time-dependent signals, at least two feature sets comprising time-series of a plurality of extracted features, each feature set relating to a symptom of the mental disorder is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting time-dependent mental health data from patient self-report surveys and passive measures from wearable devices for training data (treated as synonymous to feature sets) in a multi-task learning model)
b. a hierarchical scoring module comprising at least one predictor comprising at least one machine learning (ML) algorithm for processing the at least two feature sets and generating for each feature set, an evaluation of a respective temporal sub-score, and is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores via a regression analysis by the LSTM Encoder model)
c. a forecasting module comprising a processing module for processing the evaluated temporal sub-scores through at least one machine learning (ML) algorithm and generating a prediction of the evolution of at least one indicator related to the mental state of the subject and/or of the mental state of the subject is taught in the § 3 METHOD on p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the second task is associated with evaluating the historical mood scores by the LSTM Decoder model to predict a future state/s of the patient)
One of ordinary skill in the art would combine the multi-task analysis of feature data of Spathis to include the physiological sensor data as feature data of the alternate embodiment as further evidenced by Taylor as the alternate embodiment contains the same structural similarity (see MPEP § 2144) and the motivation to “build upon this piece of literature [Taylor] of employing deep learning on mood prediction” (Spathis in the § 7 Related Work on p. 2894 col 1).
As per claim 2, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, wherein the at least one machine learning algorithm of the forecasting module comprises a neural network (NN) is taught in the § 3 METHOD on p. 2889 col 1 and Figure 3 on p. 2889 (teaching on the LSTM Decoder as a neural network model to predict a future state/s of the patient)
As per claim 3, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, wherein the at least one ML algorithm of the forecasting module is configured to generate a forecast for each temporal sub-score is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores via a regression analysis by the LSTM Encoder model)
As per claim 4, Spathis further evidenced by Taylor discloses all of the limitations of claim 3. Spathis also discloses the following:
the system of claim 3, wherein the forecasting module comprises a fusion module for aggregating together the forecasts of the sub-scores and computing the forecast of a global score representative of the mental state of the subject is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1, equation (1) on p. 2888, and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores that are combined to create a single output (treated as synonymous to a global score) for the decoder)
As per claim 5, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, comprising a user-interface for informing a user of the at least one indicator related to the mental state of the subject and/or of the mental state of the subject is taught in the Figure 3 and Figure 4 on p. 2889 (teaching on outputting the predicted mood (treated as synonymous to the mental state of the subject) for the next 2 days)
As per claim 7, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, wherein the mental disorder comprises major depression disorder (MDD) or post-traumatic stress disorder (PTSD) is taught in the § 7 Related Work on p. 2894 col 2 (teaching on the mental state for which the symptoms are being evaluated relating to an extreme depression disorder)
As per claim 8, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, wherein the hierarchical module comprises a fusion module for aggregating together at least two evaluated temporal sub-scores and computing a global temporal score representative of the mental state of the subject is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1, equation (1) on p. 2888, and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores that are combined to create a single output (treated as synonymous to a global score) for the decoder)
As per claim 9, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, wherein the time-dependent signals comprise time series of physiological measurements is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models as evidence by Taylor incorporated by reference in the teachings of Spathis)
As per claim 10, Spathis further evidenced by Taylor discloses all of the limitations of claim 9. Spathis also discloses the following:
the system of claim 9, the physiological measurements comprising one or more among electro-cardiogram (ECG), body temperature, photoplethysmogram (PPG), activity records (ACC), electrodermal activity (EDA) is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models as evidence by Taylor on p. 206 wherein Taylor teaches on the physiological sensor data including accelerometer data (treated as synonymous to activity data), skin conductance data (treated as synonymous to electrodermal activity),and skin temperature data)
As per claim 11, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, the plurality of extracted features comprising data related to actimetry, sleep and wake periods, sleep phases and/or step counting, cardio-respiratory autonomous nervous system and/or heart rate variability (HRV) is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models including activity monitoring data (treated as synonymous to data related to actimetry) as evidence by Taylor incorporated by reference in the teachings of Spathis)
As per claim 12, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, the feature sets comprising one or more sleep quality, anxiety, psychomotor retardation, and diurnal activity is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models including activity monitoring data (treated as synonymous to diurnal activity data) as evidence by Taylor incorporated by reference in the teachings of Spathis)
As per claim 18, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis also discloses the following:
the system of claim 1, comprising an acquisition device sensing the subject for generating said time-dependent signals, the acquisition device comprising at least one of an accelerometer, an optical sensor, a heart rate detector and an electrodermal activity sensor is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models as evidence by Taylor on p. 206 wherein Taylor teaches on the physiological sensor data including accelerometer data (treated as synonymous to activity data), skin conductance data (treated as synonymous to electrodermal activity),and skin temperature data)
As per claim 19, Spathis teaches on the following limitations of the claim:
a computer-implemented method for forecasting the evolution of a mental state of a living subject likely to suffer from a mental disorder, the method comprising is taught in the § Abstract on p. 2886, § 3 METHOD on p. 2889 col 1, and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) for evaluating a historical emotional state and predicting future emotional state values)
a) acquiring during a monitoring period between a first and a second date time-dependent signals from at least one acquisition device sensing the subject is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting physiological sensor data for use in the multi-task predictive models as evidence by Sara A. Taylor et al. 2017. Personalized Multitask Learning for Predicting Tomorrow’s Mood, Stress, and Health. IEEE Transactions on Affective Computing (2017) incorporated by reference in the teachings of Spathis)
b) generating at least two feature sets comprising time-series of a plurality of features extracted from these signals, each feature set relating to a symptom of the mental disorder is taught in the § 2 The Problem and The Data on p. 2887 and § 7 Related Work on p. 2894 (teaching on collecting time-dependent mental health data from patient self-report surveys and passive measures from wearable devices for training data (treated as synonymous to feature sets) in a multi-task learning model)
c) using at least one machine learning (ML) algorithm for generating, for each feature set, an evaluation of a respective temporal sub-score between said first and second date is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores via a regression analysis by the LSTM Encoder model)
d) using at least one ML algorithm to process the evaluated temporal sub-scores and generate a prediction of the evolution of a least one indicator related to the mental state of the subject and/or of the mental state of the subject is taught in the § 3 METHOD on p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the second task is associated with evaluating the historical mood scores by the LSTM Decoder model to predict a future state/s of the patient)
One of ordinary skill in the art would combine the multi-task analysis of feature data of Spathis to include the physiological sensor data as feature data of the alternate embodiment as further evidenced by Taylor as the alternate embodiment contains the same structural similarity (see MPEP § 2144) and the motivation to “build upon this piece of literature [Taylor] of employing deep learning on mood prediction” (Spathis in the § 7 Related Work on p. 2894 col 1).
As per claim 20, Spathis further evidenced by Taylor discloses all of the limitations of claim 19. Spathis also discloses the following:
the method of claim 19, comprising computing at step c), with the at least one ML algorithm, a global temporal score from the temporal sub-scores between said first and second date, the global score being representative of the mental state of the subject between said first and second date is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores via a regression analysis by the LSTM Encoder model)
As per claim 21, Spathis further evidenced by Taylor discloses all of the limitations of claim 19. Spathis also discloses the following:
the method of claim 19, comprising generating with the at least one ML algorithm at step d) a forecast for each temporal sub-score is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1 and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores via a regression analysis by the LSTM Encoder model)
As per claim 22, Spathis further evidenced by Taylor discloses all of the limitations of claim 21. Spathis also discloses the following:
the method of claim 21, comprising aggregating together the forecasts of the sub-scores and computing the forecast of a global score as an indicator of the mental state of the subject is taught in the § 3 METHOD on p. 2888 col 2 - p. 2889 col 1, equation (1) on p. 2888, and Figure 3 on p. 2889 (teaching on a multi-task (treated as synonymous to a first and second machine learning algorithm) wherein the first task is associated with evaluating a historical emotional state for the patient to create a state/time memory unit to predict mood scores that are combined to create a single output (treated as synonymous to a global score) for the decoder)
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Spathis et al, Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data, KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2886 - 2894 (July 25, 2019)[hereinafter Spathis] as further evidenced by Taylor et al., Personalized Multitask Learning for Predicting Tomorrow’s Mood, Stress, and Health, 11(2) IEEE Transactions on Affective Computing 200-213 (June 2020)[hereinafter Taylor] in further view of Shieh et al., Applying a complexity-based Choquet integral to evaluate students’ performance, 36 Expert Systems with Applications 5100-5106 (2009)[hereinafter Shieh].
As per claim 14, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis fails to disclose the following; Shieh however does disclose:
the system of claim 1, wherein the at least one ML algorithm of the predictor comprises an interpretable machine learning algorithm comprising one or more among logistic regression, shallow multi-layer perceptron or Neural 2-Choquet integrals is taught in the § 1. Introduction on p. 5100 (teaching on replacing a linear regression analysis with a Choquet integral for analyzing human behavior features for a neural network encoder model)
One of ordinary skill in the art before the effective filing date would replace the linear regression analysis of Spathis with the Choquet integral of Shieh with the motivation of “When interactions among criteria exist, the discrete Choquet integral is proved to be an adequate aggregation operator by further taking into accounts the interactions” (Shieh in the § Abstract on p. 5100).
As per claim 15, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis fails to disclose the following; Shieh however does disclose:
the system of claim 1, wherein the ML algorithm of the hierarchical scoring module is a Neural 2-Choquet integrals network is taught in the § 1. Introduction on p. 5100 (teaching on replacing a linear regression analysis with a Choquet integral for analyzing human behavior features for a neural network encoder model)
One of ordinary skill in the art before the effective filing date would replace the linear regression analysis of Spathis with the Choquet integral of Shieh with the motivation of “When interactions among criteria exist, the discrete Choquet integral is proved to be an adequate aggregation operator by further taking into accounts the interactions” (Shieh in the § Abstract on p. 5100).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Spathis et al, Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data, KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2886 - 2894 (July 25, 2019)[hereinafter Spathis] as further evidenced by Taylor et al., Personalized Multitask Learning for Predicting Tomorrow’s Mood, Stress, and Health, 11(2) IEEE Transactions on Affective Computing 200-213 (June 2020)[hereinafter Taylor] in further view of Adeline Fermanian et al., Framing RNN as a kernel method: Aneural ODE approach, 35th Conference on Neural Information Processing Systems (NeurIPS 2021) (2021)[hereinafter Fermanian].
As per claim 16, Spathis further evidenced by Taylor discloses all of the limitations of claim 1. Spathis fails to disclose the following; Fermanian however does disclose:
the system of claim 1, wherein the processing module comprises: an encoder for building a point or a distribution of probability in a latent space using the temporal sub-scores, and neural ordinary differential equations (ODEs) predicting the evolution of said point or said distribution of probability and/or the evolution of the temporal sub-scores using an ordinary differential equation is taught in the § 2.1 From discrete to continuous time on p. 3-4 and § 5 Discussion and conclusion on p. 10 (teaching on replacing a RNN with a neural ODE when modeling sequential, time series data).
It would have been obvious to one of ordinary skill in the art at the time of the invention to replace the recurrent neural network of Spathis with the improvement discussed in the neural ODE of Fermanian with the predictable results of the neural ODE being “better at learning long-term dependencies” (Fermanian in the § Introduction on p. 1) as needed in Spathis.
Response to Arguments
Examiner acknowledges that appropriate correction to the previous objections have been made and withdraws the objections accordingly.
Examiner acknowledges that appropriate corrections to the claim for the previous 35 U.S.C. 112(b) rejection has been made and withdraws the rejection accordingly.
Applicant's arguments filed 05/14/2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. First, Applicant asserts that Examiner has oversimplified the claims by looking at the generally. Examiner notes that the explanatory restatement of the abstract idea, after presenting, in Applicant’s exact language, the limitations identified as abstract, serves to explain why Examiner has identified the limitations as abstract as required under MPEP § 2106.07(a) Formulating a Rejection For Lack of Subject Matter Eligibility. Applicant’s assertion that the combination of “modules” and receiving data from a sensor as evidence that the claim does not recite an abstract idea is not persuasive. The modules are computer programs performing the logical steps of the abstract idea. While the modules are treated as additional elements, the steps performed by said modules are abstract in nature most similar to example iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982).
Next, Applicant asserts that the claims amount to an improvement to technology under Step 2A Prong 2, and specifically the use of a machine learning model configuration to solve the problem with the prior art. Examiner disagrees. The sections outlined by Applicant demonstrate a problem with the abstract idea – that is, the challenging nature of digesting large historical and current medical data to make a mental health diagnosis for a patient, and applying said data to a known machine learning environment similar to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025) stating “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved” (id. p. 15). An improvement to the abstract ideas of modeling and diagnosing a patient mental health state does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Applicant asserts that acquisition step and device do not amount to pre-solution activity or mere data gathering as the sensors gather “specific types of physiological time-series data that are then processed through the claimed hierarchical pipeline”. Examiner notes that the relevancy of the use of the data is not the test under Step 2A Prong 2 analysis – the necessity of the data gathering is not a consideration under MPEP § 2106.05(g). Examiner points Applicant to the (3) consideration stating, “below are examples of activities that the courts have found to be insignificant extra-solution activity: • Mere Data Gathering: i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)”.
Applicant asserts that the combination of additional elements, specifically the hierarchical architecture of the diagnostic model, is not well understood, routine, or conventional. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea, here the hierarchical architecture of the diagnostic model identified as abstract under Step 2A Prong 1, is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant's arguments filed 05/14/2026 with respect to 35 USC § 103 have been fully considered but they are not persuasive. Applicant asserts that Spathis fails to teach on the fundamental model architecture of the instant claims primarily the two feature sets structure. Applicant further relies on this distinguishment by attacking the individual embodiment for the machine learning algorithm stating “Spathis’s encoder does not process multiple symptom-specific feature sets”. Examiner is not persuaded. As clearly stated in the rejection and the basis for the single reference 35 USC 103 rejection instead of a 35 USC 102 rejection, one of ordinary skill in the art would combine the multi-task analysis of feature data of Spathis to include the physiological sensor data as feature data of the alternate embodiment as further evidenced by Taylor as the alternate embodiment contains the same structural similarity (see MPEP § 2144) and the motivation to “build upon this piece of literature [Taylor] of employing deep learning on mood prediction” (Spathis in the § 7 Related Work on p. 2894 col 1).The test for obviousness is not whether the features of a secondary reference (or here, the secondary embodiment including additional feature data) may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Next, Applicant asserts that Fermanian fails to teach on the same architecture of the independent claim, and that the “theoretical context” of discussing the neural ODEs is not in the same “specific architecture recited in claim 16, arguing that “Fermanian does not address the problem of forecasting mental states from symptom-specific temporal sub-scores”. Examiner is not persuaded. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/JORDAN L JACKSON/
Primary Examiner, Art Unit 2857
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