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 .
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 13 recites (additional elements crossed out):
A system to generate a treatment plan for a user, the system comprising:
(a)
(b)
(i) collect a set of features from an application on a communication device of a user;
(ii) process the set of features,
(iii) determine an indication of a sentiment of the user based on the encoded sentiment content; and
(iv) generate a treatment plan to the user based on a profile of the user.
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by “one or more processors”, “a memory”, and a “neural network” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the recited computing language, the limitations describe a system for encoding sentiment content from collected features of a user (ex., determining features that indicate a mood), determining an indication of a sentiment of the user based on the sentiment content (ex., determining the mood based on the features indicating the mood), and generating a treatment plan based on a profile of a user. The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “one or more processors”, “a memory”, and a “neural network” to perform the steps. These additional elements are recited at a high level of generality (see at least Paras. [0210] and [0223]-[0235]) such that it amounts to no more than mere instructions to apply the exception using generic computing components.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo). Also see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), the Federal Circuit explained, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” 134 F.4th at 1216.
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “one or more processors”, “memory”, and “neural network” elements. This is not sufficient to amount to significantly more than the judicial exception. The claims are therefore still directed to an abstract idea.
Claim 7 features limitations similar to those of claim 13, and is therefore also found to be directed to an abstract idea without significantly more.
Claim 19 recites (additional elements crossed out):
A system to generate a treatment plan to a user, the system comprising:
(a)
(b)
(i) collect a set of features from an application on a communication device of a user;
(ii)
(iii)
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by “one or more processors”, and “a memory” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the recited computing language, the limitations describe a system for encoding sentiment content from collected features of a user (ex., determining features that indicate a mood), and generating a treatment plan based on a profile of a user. The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “one or more processors” and a “memory” to perform the steps. These additional elements are recited at a high level of generality (see at least Paras. [0223]-[0235]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. The claim also features the additional limitations of “train a first neural network to encode sentiment content from the set of features to determine a marker that is predictive of the user's response to an intervention”, and “train a second neural network to generate a treatment plan to the user based on a profile of the user”. However, the “training” of the neural networks merely equates to applying machine learning to a new field of use (i.e., “apply it”).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo). Also see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), the Federal Circuit explained, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” 134 F.4th at 1216.
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), particularly as it relates to the recited “computer”, “one or more processors”, and “training a first and second neural network” elements. This is not sufficient to amount to significantly more than the judicial exception. The claims are therefore still directed to an abstract idea.
Claim 1 features limitations similar to those of claim 19, and is therefore also found to be directed to an abstract idea without significantly more.
Claims 2-6 are dependent on claim 1, and include all the limitations of claim 1. Claims 8-12 are dependent on claim 7, and include all the limitations of claim 7. Claims 14-18 are dependent on claim 13, and include all the limitations of claim 13. Claims 20-24 are dependent on claim 19, and include all the limitations of claim 19 Therefore, they are also directed to the same abstract idea. The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4-8, 10-14, 16-20, and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misrilall (US 2022/0093253) in view of Athey (US 2020/0234810).
Regarding claim 1, Misrilall discloses A method of training a model for generating a treatment plan for a user, comprising:
(a) collecting a set of features from an application on a communication device of a user;
(b) training a first neural network to encode sentiment content from the set of features to determine a marker that is predictive of the user's response to an intervention; and
(See at least Abstract – “Embodiments disclosed herein generally relate to a mental health platform for clinicians and patients. A computing system generates a plurality of sets of training data. The plurality of sets of training data include portions of journal and inputs to mental health questionnaires corresponding to a plurality of patients. The computing system generates a prediction model to generate a health score of a patient, the health score indicative of the current mental health of the patient.”, Para. [0041] – “In some embodiments, patient device 102 may facilitate submission of a journal entry by presenting a user interface, via application 110, with an input field that allows a user to enter text. In some embodiments, patient device 102 may facilitate submission of a journal entry by granting application 110 access to a microphone device of patient device 102 so that a user may record the journal entry.”, and Para. [0044] – “In operations, ML module 128 may train neural network 132 to output a health score by a patient using various forms of reinforcement learning. For example, ML module 128 may provide neural network 132 with a plurality of training data sets. In some embodiments, the training data sets may include sentences and/or paragraphs as modified by NLP module 126 (e.g., various tags injected into sentences and/or paragraphs to signal semantic tone or sentiment reflected therein) and answers to questionnaires. Using this training data set, generative neural network 132 may be trained to output the health score reflective of a current state of the patient's mental health. In some embodiments, the plurality of training data sets may further be encoded with annotations from clinicians. In this manner, neural network 132 may be trained to output a health score that is reflective of how a clinician analyzes health records of the patient.”
Misrilall does not explicitly disclose (c) training a second neural network to generate a treatment plan based on a profile of the user. (See Athey, Para. [0136] – “In other embodiments, the drug and dose decision server 102 may generate the dosing algorithm using machine learning techniques. For example, the drug and dose decision server 102 may collect dosing information on patients previously prescribed ketamine as training data. The dosing information may include the dosage each patient was prescribed along with indications of whether the patient's dosage was adjusted during treatment and/or whether the patient experienced adverse events. The drug and dose decision server 102 may then analyze the training data to generate a machine learning model ( e.g., a neural network, a decision tree, a hyperplane, a regression model, etc.) to determine the dosage for a new patient based on the new patient's biological characteristics, demographic characteristics, and clinical characteristics. The patient characteristics utilized in the dosing algorithm may include biological data, such as SNPs that have been reported to stratify response to ketamine in humans. The patient characteristics may also include demographic data for the patient, such as the patient's sex, height and weight, age, and ethnicity. Furthermore, the patient characteristics may include clinical data, such as family history, drug-drug interactions, mental illness history, whether the patient smokes or uses nicotine, and Hamilton Scale for Depression (HAM-D) score.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Misrilall to utilize the teachings of Athey since it may allow for the prescribing of proper dosages related to the determined mental health of the patients of Misrilall (Abstract).
Regarding claim 2, Misrilall discloses The method of claim 1, wherein the set of features comprises at least two of the following:(i) voice data;(ii) textual data, wherein the textual data comprises text and character depicted expression;(iii) location data;(iv) application usage data;(v) biometric data;(vi) sleep data;(vii) activity data; and (viii) self-reported data. (See at least Fig. 4C, Para. [0045] – “In some embodiments, the training data set may further include speech recognition data. In some embodiments, the training data set may include facial recognition data. Such speech recognition and/or facial recognition data may be used in sentiment analysis for early detection of depression and mood disorder symptoms.”, and Para. [0091] – “GUI 450 may include text box 452 and graphical element 454. Via text box 452, a patient may provide input relating to how he or she is currently feeling. For example, via text box 452, a patient can submit a journal entry for processing by organization computing system 104.”
Regarding claim 4, Misrilall discloses The method of claim 1, wherein:
the first neural network generates an encoding that (i) discards semantic content from the set of features, and (ii) represents sentiment content that provides an indication of a sentiment of the user. (See Para. [0044] – “For example, ML module 128 may provide neural network 132 with a plurality of training data sets. In some embodiments, the training data sets may include sentences and/or paragraphs as modified by NLP module 126 (e.g., various tags injected into sentences and/or paragraphs to signal semantic tone or sentiment reflected therein) and answers to questionnaires. Using this training data set, generative neural network 132 may be trained to output the health score reflective of a current state of the patient's mental health.”
Regarding claim 5, Misrilall does not explicitly disclose The method of claim 1, wherein the user's profile comprises at least one of the following:
(i) the user's preferences of the application;
(ii) the user's demographic information; and
(iii) the user's engagement with the application.
(See Athey, Para. [0136] – “The drug and dose decision server 102 may then analyze the training data to generate a machine learning model ( e.g., a neural network, a decision tree, a hyperplane, a regression model, etc.) to determine the dosage for a new patient based on the new patient's biological characteristics, demographic characteristics, and clinical characteristics.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Misrilall to utilize the teachings of Athey since it may allow for the prescribing of proper dosages related to the determined mental health of the patients of Misrilall (Abstract).)
Regarding claim 6, Misrilall does not explicitly disclose The method of claim 1, wherein the model is trained using a machine learning algorithm selected from principal component analysis, uniform manifold approximation and projection, artificial neural network, time series modeling, and any combination thereof. (See Athey, Para. [0064] – “The drug and dose decision server may determine the dosage of the drug to administer to the patient and perform other methods described herein using various machine learning techniques, including, but not limited to regression algorithms (e.g., ordinary least squares regression, linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), instance-based algorithms (e.g., k-nearest neighbors, learning vector quantization, self-organizing map, locally weighted learning, etc.), regularization algorithms (e.g., Ridge regression, least absolute shrinkage and selection operator, elastic net, leastangle regression, etc.), decision tree algorithms (e.g., classification and regression tree, iterative dichotomizer 3, C4.S, CS, chi-squared automatic interaction detection, decision stump, MS, conditional decision trees, etc.), clustering algorithms (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, spectral clustering, mean-shift, density-based pharmacogenomic clustering of applications with noise, ordering points to identify the clustering structure, etc.), association rule learning algorithms (e.g., a priori algorithm, Eclat algorithm, etc.), Bayesian algorithms (e.g., naive Bayes, Gaussian naive Bayes, multinomial naïve Bayes, averaged one-dependence estimators, Bayesian belief network, Bayesian network, etc.), artificial neural networks ( e.g., perceptron, Hopfield network, radial basis function network, etc.), deep learning algorithms (e.g., multilayer perceptron, deep Boltzmann machine, deep belief network, convolutional neural network, stacked autoencoder, generative adversarial network, etc.), dimensionality reduction algorithms (e.g., principal component analysis, principal component regression, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, linear discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, flexible discriminant analysis, factor analysis, independent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embedding, etc.)…” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Misrilall to utilize the teachings of Athey since they are both in the same field of endeavor (i.e., determination of mental state of a patient), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.)
Regarding claim 7, Misrilall discloses A method of using a model to generate a treatment plan for a user, comprising:
(a) collecting a set of features from an application on a communication device of a user;
(b) processing the set of features, using a neural network, to encode sentiment content from the set of features;
(c) determining a marker that is predictive of the user's response to an intervention; and
(See at least Abstract – “Embodiments disclosed herein generally relate to a mental health platform for clinicians and patients. A computing system generates a plurality of sets of training data. The plurality of sets of training data include portions of journal and inputs to mental health questionnaires corresponding to a plurality of patients. The computing system generates a prediction model to generate a health score of a patient, the health score indicative of the current mental health of the patient.”, Para. [0041] – “In some embodiments, patient device 102 may facilitate submission of a journal entry by presenting a user interface, via application 110, with an input field that allows a user to enter text. In some embodiments, patient device 102 may facilitate submission of a journal entry by granting application 110 access to a microphone device of patient device 102 so that a user may record the journal entry.”, and Para. [0044] – “In operations, ML module 128 may train neural network 132 to output a health score by a patient using various forms of reinforcement learning. For example, ML module 128 may provide neural network 132 with a plurality of training data sets. In some embodiments, the training data sets may include sentences and/or paragraphs as modified by NLP module 126 (e.g., various tags injected into sentences and/or paragraphs to signal semantic tone or sentiment reflected therein) and answers to questionnaires. Using this training data set, generative neural network 132 may be trained to output the health score reflective of a current state of the patient's mental health. In some embodiments, the plurality of training data sets may further be encoded with annotations from clinicians. In this manner, neural network 132 may be trained to output a health score that is reflective of how a clinician analyzes health records of the patient.”
Misrilall does not explicitly disclose (d) generating a treatment plan based on a profile of the user. (See Athey, Para. [0063] – “In some embodiments, the drug and dose decision server may determine a dosage of the drug to administer to the patient according to a dosing algorithm. The dosing algorithm may be determined using machine learning techniques such as linear regression and may be based on demographic data for the patient, clinical data for the patient, biological data for the patient, etc.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Misrilall to utilize the teachings of Athey since it may allow for the prescribing of proper dosages related to the determined mental health of the patients of Misrilall (Abstract).
Claims 8, 14, and 20 feature limitations similar to those of claim 2, and are therefore rejected using the same rationale.
Claims 10, 16, and 22 feature limitations similar to those of claim 4, and are therefore rejected using the same rationale.
Claims 11, 17 and 23 feature limitations similar to those of claim 5, and are therefore rejected using the same rationale.
Claims 12, 18, and 24 feature limitations similar to those of claim 6, and are therefore rejected using the same rationale.
Claim 13 features limitations similar to those of claim 7, and is therefore rejected using the same rationale.
Claim 19 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Claim(s) 3, 9, 15, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Misrilall (US 2022/0093253) in view of Athey (US 2020/0234810), and in further view of Ahmad (US 2020/0258629)
Regarding claim 3, Misrilall and Athey do not explicitly disclose The method of claim 1, wherein the first neural network is configured to process missing features in the set of features. (See Ahmad, Para. [0075] – “With respect to imputation for missing features, to account for subjects that lack values for the features used in the model, median imputation may be performed, or the subject could be removed. Alternatively, subjects may be assigned to most common value for the feature. For example, in cases where ApoE status is a feature, subjects lacking an ApoE status may be given the mode status i.e. the most common allele combination rather than performing imputation on the median of the data.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Misrilall and Athey to utilize the teachings of Ahmad since they are all in the same field of endeavor (i.e., application of machine learning in mental health treatment), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claims 9, 15, and 21 feature limitations similar to those of claim 3, and are therefore rejected using the same rationale.
Conclusion
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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