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 .
Status of Claims
In the amendment filed 09/06/2011 the following occurred: Claims 1, 6, 10, 16, 21-22, 26, and 29 were amended. Claims 1-30 are presented for examination.
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 1-19, 21, 23, and 26-30 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.
Claims 1-19, 21, 23, and 26-30 are drawn to methods and a system, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites receiving textual input from a user in a counseling session; retrieving context data, wherein the context data are derived from and grounded in Acceptance and Commitment Therapy (ACT) counselling knowledge and ACT session-related content; processing the textual input by applying a mental health condition relationship to the textual input to identify the mental health status of the user; monitoring the textual input for a detected interrupt; and providing an output associated with the mental health status of the user.
The respective dependent claims 2-19, 21, 23, and 26-30, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
Said recited limitations, as drafted, under their broadest reasonable interpretation, cover mental processes, as reflected in the specification, which states that the invention is “for providing mental health advice” (see: specification page 1, line 14; claim 1, preamble). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with a pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. The present claims cover mental processes because they address a problem with “[s]ecuring access to effective, comprehensive, and easily obtainable mental health services” (see: specification, page 2, lines 3-8). The recited limitations address this problem with a “one-stop, effective, highly accessible and low-cost mental health advisory system” in order “to alleviate emotional burdens, enhance parental well-being, and improve the overall quality of life for the parents and their children” thereby “enhancing the overall well-being of these vulnerable families” (see: specification page 38, line 27, through page 39, line 11). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “by a computing device…by a Retrieval-Augmented Generation (RAG) framework…from a knowledge database…” (claim 1), “trained by a deep-learning network…” (claim 2), “the deep-learning network is a deep-neural network arranged to…input being labeled by the deep-neural network…” (claim 3), “providing an artificial intelligence (AI) chatbot interface arranged to facilitate…” (claim 6), “the AI chatbot interface is arranged to…” (claim 11), “the AI chatbot interface is arranged to…” (claim 12), “the AI chatbot interface is further arranged to…” (claim 13), “the AI chatbot interface is further supported by an external Large Language Model (LLM) processing engine arranged to enhance the generation of contextually relevant responses to facilitate interacting with the user via the AI chatbot interface” (claim 15), “training the deep-learning network with at least one of collecting data, pre-processing data, tokenization, model prediction, model evaluation, and optimizing hyperparameters” (claim 21), “the knowledge database…” (claim 23), “an AI chatbot interface…wherein the AI chatbot interface is supported by the deep-learning network; and a cloud service and database with a content management system (CMS)” (claim 27), “via the AI chatbot interface…” (claim 28), “an operator interface supported by the Cloud Service and the database with the CMS, wherein the operator interface is arranged to facilitate…” (claim 29), and “a portal arranged to display” (claim 30), which are additional elements that are recited at a high level of generality (e.g., “computing device” is configured to perform functions though no more than a statement than that said functions are performed “by” said computing device; the “Retrieval-Augmented Generation (RAG) framework” and “knowledge database” are configured to though no more than a statement than that data is retrieved “by” said RAG “from” said knowledge database; the “the deep-learning network is a deep-neural network” is configured though no more than a statement than that it is “arranged to” produce a relationship “by applying” said relationship to input; the “AI chatbot interface” is configured though no more than a statement than that it is “arranged to” perform functions; the “external Large Language Model (LLM) processing engine” is configured though no more than a statement than that it is “arranged to” enhance responses “via” the AI chatbot interface it supports; the “training the deep-learning-network” is configured though no more than a statement than that it performed “with” at least one of collecting data, pre-processing data, tokenization, model prediction, model evaluation, and optimizing hyperparameters; “the knowledge database…” is configured through no more than a statement than that CMS data is “retrieved from” the database, said database is also “updated” with data, and an interface is “supported by” said database; the “cloud service” is configured though no more than a statement than that an interface is “supported by” said cloud service; the “operator interface” is configured though no more than a statement than that it is “arranged to” import and display data; the “portal” is configured though no more than a statement than that it is “arranged to” display data) such that they amount to no more than mere instruction to apply the exception using generic computer elements. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer elements. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the concepts of receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-19, 21, 23, and 26-30, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
As per claims 20, 22, and 24-25, the limitations of said claims, when considered in ordered
combination with the limitations of the claims from which they depend, provide significantly more than
the abstract idea(s). If these limitations, and the limitations of the claims from which they depend, were
properly incorporated into their respective independent claim, said independent claim would be
rendered statutory.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-30 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
As per claim 1, the claim teaches context data that is “grounded in” Acceptance and Commitment Therapy (ACT) counselling knowledge and ACT session-related content, but considering this generally means to be firmly based on a foundation, principle, or knowledge, it is either 1) a term of degree not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention, or is 2) unclear as to what “grounded in” refers to in the claimed context as the originally filed specification, on page 15, lines 7-13, where the only instance of “grounded in” appears, states that “one or more predetermined questions which are sourced from a question bank that is grounded in Acceptance and Commitment Therapy (ACT) principles”. To what degree is the data firmly based on ACT principles and/or how does being grounded in ACT principles differ from any other relation to ACT principles?
Claims 2-30 depend from and incorporate the specifically rejected claims above while failing to remedy the limitations shown as indefinite; therefore, they are rejected here for similar reasons.
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 and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2023/0223133 to Aggarwal in view of U.S. Patent Application Publication 2021/0098110 to Periyasamy in view of U.S. Patent Application Publication 2017/0351830 to Burger further in view of U.S. Patent 12,051,205 to Deutsch.
As per claim 1, Aggarwal teaches a method for providing mental health advice, comprising the step of:
receiving, by a computing device, textual input from a user in a counseling session (see: Aggarwal, paragraph 40, is met by a conversation includes a user input into an AI chatbot that is a digital assistant for mental health, where the user input may be text);
processing the textual input by applying a mental health condition relationship to the textual input to identify the mental health status of the user (see: Aggarwal, paragraph 48-49, 53, 56, 58, and 65-66, is met by the AI model automatically identifies the possibility of the mental health condition of the user);
providing the mental health status of the user (see: Aggarwal, paragraph 65 and 70, is met by the user is triaged, a graphical user interface (GUI) of the output data in accordance with the embodiments).
Aggarwal fails to specifically teach monitoring the textual input for a detected interrupt and an output associated with the user status; however, Periyasamy teaches analyzing user data based on real-time user responses to questions posed by a virtual agent chatbot, where the chatbot may alert the user or a mental health expert of the health condition based at least in part on a determination that the level of severity of the user's current or potential mental health condition poses a high risk, immediately upon the determination (see: Periyasamy, paragraph 8, 18, 27, 45, 55, 66, 68, 70, and 76).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the GUI and triage as taught by Aggarwal to include analyzing user data based on real-time user responses to questions posed by a virtual agent chatbot, where the chatbot may alert the user or a mental health expert of the health condition based at least in part on a determination that the level of severity of the user's current or potential mental health condition poses a high risk, immediately upon the determination as taught by Periyasamy with the motivation of providing timely and comprehensive recommendation for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified (see: Periyasamy, paragraph 55).
Aggarwal and Periyasamy fail to specifically teach retrieving context data from a knowledge database, wherein the context data are derived from and grounded in Acceptance and Commitment Therapy (ACT) counselling knowledge and ACT session-related content; however, Burger teaches terms are categorized into discrete groups based on the relevance of the terms to acceptance and commitment therapy, where the categorized terms are retrieved, and the strength of connection of the terms with acceptance and commitment therapy are stored (see: Burger, paragraph 62, 64, 69, 74, and 80).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the received data as taught by Aggarwal and Periyasamy to include terms are categorized into discrete groups based on the relevance of the terms to acceptance and commitment therapy, where the categorized terms are retrieved, and the strength of connection of the terms with acceptance and commitment therapy are stored as taught by Burger with the motivation of creating a semantic relationship graph of associated terms with determined scores of strength of connection (see: Burger, paragraph 62, 64, 69, 74, and 80).
Aggarwal, Periyasamy, and Burger fail to specifically teach retrieving by a Retrieval-Augmented Generation (RAG) framework; however, Barron teaches Retrieval-augmented generation (see: Deutsch, column 8, lines 24-49).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the chatbot system as taught by Aggarwal, Periyasamy, and Burger to include a Retrieval-augmented generation as taught by Deutsch with the motivation of optimizing machine learning models for particular tasks and outputs (see: Deutsch, column 8, lines 24-49).
As per claim 16, Aggarwal, Periyasamy, Burger, and Deutsch teach the invention as claimed, see discussion of claim 1, and further teach:
pre-processing the textual input before applying the mental health condition relationship to the textual input for identifying the mental health status of the user (see: Aggarwal, paragraph 41, is met by the text input received from the user is preprocessed, and then needs to be vectorized so that the text is converted into a numerical representation which the AI model can work with).
As per claim 17, Aggarwal, Periyasamy, Burger, and Deutsch teach the invention as claimed, see discussion of claim 16, and further teach:
wherein pre-processing of the textual input includes processing the textual input with at least one of removing stopwords, lowercasing, punctuation normalizing, back translation and data augmentation (see: Aggarwal, paragraph 41, is met by the text input received from the user is preprocessed that include steps such as spell correction, expansion of contractions, removal of stop words, stemming and lemmatization, and then needs to be vectorized so that the text is converted into a numerical representation which the AI model can work with).
As per claim 18, Aggarwal, Periyasamy, Burger, and Deutsch teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the mental health status includes the psychological inflexibility or psychological flexibility of the user (see: Aggarwal, paragraph 39, 42-49, and 58, is met by determining a mismatch between a sentiment and a polarity of a life situation by gathering context from “past few days” to look for a semantic match among all the representative statements of various high-gravity life events, and where possibility of the mental health condition may be an indicator of a psychological disorder such as bipolar disorder and/or schizophrenia).
As per claim 19, Aggarwal, Periyasamy, Burger, and Deutsch teach the invention as claimed, see discussion of claim 18, and further teach:
wherein the mental health status is associated with the following processes of psychological inflexibility or psychological flexibility: experiential avoidance or acceptance; cognitive fusion or cognitive defusion; conceptualized past and fear of future or present-moment awareness; attachment to conceptualized self or self-as-context; lack of values clarity or values clarifications; and inaction, impulsivity, avoidance persistence or committed action (see: Aggarwal, paragraph 39, 42-49, and 58, is met by determining a mismatch between a sentiment and a polarity of a life situation by gathering context from “past few days” to look for a semantic match among all the representative statements of various high-gravity life events, and where possibility of the mental health condition may be an indicator of a psychological disorder such as bipolar disorder and/or schizophrenia).
Claim(s) 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2023/0223133 to Aggarwal in view of U.S. Patent Application Publication 2021/0098110 to Periyasamy in view of U.S. Patent Application Publication 2017/0351830 to Burger in view of U.S. Patent 12,051,205 to Deutsch further in view of U.S. Patent Application Publication 2024/0330597 to Temraz.
As per claim 2, Aggarwal, Periyasamy, Burger, and Deutsch teach the invention as claimed, see discussion of claim 1, and further teach:
wherein the mental health condition relationship is trained by a learning network (see: Aggarwal, paragraph 11, 23, and 50, is met by machine learning).
Aggarwal fails to specifically teach the machine learning includes a deep-learning network; however, Temraz teaches a deep neural network (see: Temraz, paragraph 50, 77, 165, and 209).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the machine learning as taught by Aggarwal, Periyasamy, Burger, and Deutsch to include a deep neural network as taught by Temraz with the motivation of categorizing data into shared embedding spaces for multiple languages (see: Temraz, paragraph 165).
As per claim 3, Aggarwal, Periyasamy, Burger, Deutsch, and Temraz teach the invention as claimed, see discussion of claim 2, and further teach:
wherein the network arranged to identify the mental health status based on the received textual input being labeled by the network (see: Aggarwal, paragraph 11, 23, and 50, is met by machine learning classifiers trained on a large dataset of sentences labeled with associated sentiment so that the machine learning classifiers learn to classify the at least one sentiment on new sentences, unseen sentence).
Aggarwal fails to specifically teach the machine learning includes where a deep-learning network is a deep-neural such that functions are performed by a deep-neural network; however, Temraz teaches a deep neural network (see: Temraz, paragraph 50, 77, 165, and 209).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the machine learning as taught by Aggarwal, Periyasamy, Burger, Deutsch, and Temraz to include a deep neural network as taught by Temraz with the motivation of categorizing data into shared embedding spaces for multiple languages (see: Temraz, paragraph 165).
Claim(s) 4-15 and 27-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2023/0223133 to Aggarwal in view of U.S. Patent Application Publication 2021/0098110 to Periyasamy in view of U.S. Patent Application Publication 2017/0351830 to Burger in view of U.S. Patent 12,051,205 to Deutsch in view of U.S. Patent Application Publication 2024/0330597 to Temraz further in view of U.S. Patent Application Publication 2022/0028528 to Paull.
As per claim 4, Aggarwal, Periyasamy, Burger, Deutsch, and Temraz teach the invention as claimed, see discussion of claim 3, and further teach:
wherein the textual input includes dialogues provided by the user in response to one or more questions in a question bank based on knowledge in the counseling session (see: Aggarwal, paragraph 40-41, 50, 57, 59, 61, and 65-66, is met by a conversation includes a user input into an AI chatbot that is a digital assistant for mental health, where the user input may be text a user is allowed to respond using free text to a question from an AI chatbot).
Aggarwal fails to specifically teach the questions are predetermined; however, Periyasamy teaches a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition (see: Periyasamy, paragraph 12, 47, 66, 74, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the GUI and triage as taught by Aggarwal, Periyasamy, Burger, Deutsch, and Temraz to include a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition as taught by Periyasamy with the motivation of providing an accurate, reliable, effective and humanized interactions and identification of a potential mental health condition of the user (see: Periyasamy, paragraph 47).
Aggarwal fails to specifically teach that a question from an AI chatbot for mental health is based on Acceptance and Commitment Therapy (ACT); however, Paull teaches providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires (see: Paull, paragraph 62, 68, 72-74, 83, 136, 187, and 371).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the questions as taught by Aggarwal, Periyasamy, Burger, Deutsch, and Temraz to include providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires as taught by Paull with the motivation of managing or alleviating one or more health-related conditions a patient is suffering from (see: Paull, paragraph 136).
As per claim 5, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 4, and further teach:
wherein the question bank is configured to imitate life-contextual and problem-focused interviews between a counselor and the user at a real-person ACT counseling session for an understanding of the user's mental health condition in terms of status or processes of psychological inflexibility and/or psychological flexibility (see: Aggarwal, Fig. 4; Fig. 5A-5B; and Fig. 7-8, and paragraph 41 and 65-66, is met by a conversation between the AI chatbot and the user where during the conversation is performed the checking of the user's mood and classifying their initial sentiment using sentiment detecting Al model, running internet recognition AI model on the user messages to predict a life situation, and finding the gravity and polarity of the situation).
Aggarwal fails to specifically teach that a question from an AI chatbot for mental health is to imitate ACT counseling; however, Paull teaches providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires (see: Paull, paragraph 62, 68, 72-74, 83, 136, 187, and 371).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the questions as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires as taught by Paull with the motivation of managing or alleviating one or more health-related conditions a patient is suffering from (see: Paull, paragraph 136).
As per claim 6, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 5, and further teach:
providing an artificial intelligence (AI) chatbot interface arranged to facilitate asking the user one or more questions and receiving the textual input from the user in the consultation session (see: Aggarwal, Fig. 4; Fig. 5A-5B; and paragraph 40-41, 50, 57, 59, 61, and 65-66, is met by a conversation includes a user input into an AI chatbot that is a digital assistant for mental health, where the user input may be text a user is allowed to respond using free text to a question from an AI chatbot, where context is gathered by probing the user about why they are feeling the way they are).
Aggarwal fails to specifically teach the questions are predetermined; however, Periyasamy teaches a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition (see: Periyasamy, paragraph 12, 47, 66, 74, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the GUI and triage as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition as taught by Periyasamy with the motivation of providing an accurate, reliable, effective and humanized interactions and identification of a potential mental health condition of the user (see: Periyasamy, paragraph 47).
As per claim 7, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 6, and further teach:
wherein one or more questions are provided to the user in the form of a questionnaire and/or one or more chat dialogues (see: Aggarwal, Fig. 4; Fig. 5A-5B; and paragraph 41, 50, 57, 59, 61, and 65-66, is met by a user is allowed to respond using free text to a question from an AI chatbot, where context is gathered by probing the user about why they are feeling the way they are).
Aggarwal fails to specifically teach the questions are predetermined; however, Periyasamy teaches a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition (see: Periyasamy, paragraph 12, 47, 66, 74, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the GUI and triage as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a mental health questionnaire including a plurality of questions customized to the collected user data and tailored to obtain information related to the mental health condition of the user, the information including a type or severity of the mental health condition as taught by Periyasamy with the motivation of providing an accurate, reliable, effective and humanized interactions and identification of a potential mental health condition of the user (see: Periyasamy, paragraph 47).
As per claim 8, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 4, and while Aggarwal teaches conducting a survey (see: Aggarwal, paragraph 64), Aggarwal fails to specifically teach the following limitations met by Paull as cited:
wherein the question bank is generated upon an initial real-person ACT counseling session between the counselor and the user (see: Paull, paragraph 68, 72-74, 136-138, 187, and 280, is met by a patient may consult with one or more healthcare practitioners regarding symptoms that the patient is experiencing, and the healthcare practitioner may determine that the patient is suffering from one or more health-related conditions manageable or alleviable though acceptance commitment therapy (ACT), upon a determination by a healthcare practitioner that the patient is likely to benefit from administration of a guided behavioral therapy intervention regimen, the doctor may prescribe a digital therapeutics system to the patient, and a therapeutic protocol may include questionnaires).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mental health system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a patient consult where a healthcare practitioner may determine that the patient is suffering from one or more health-related conditions manageable or alleviable though acceptance commitment therapy (ACT) and prescribe a digital therapeutic including to deliver a therapeutic protocol including questionnaires as taught by Paull with the motivation of allowing behavioral therapy to be administered to patients in a convenient and flexible, yet structured fashion (see: Paull, paragraph 61).
As per claim 9, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 4, and while Aggarwal teaches conducting a survey (see: Aggarwal, paragraph 64), Aggarwal fails to specifically teach the following limitations met by Paull as cited:
wherein the question bank is generated based on a self-reported mental health assessment completed by the user, wherein the self-reported mental health assessment is associated with an evaluation of anxiety symptoms, depressive symptoms, stress, psychological inflexibility or psychological flexibility of the user (see: Paull, paragraph 68, 72-74, 114, 118, 187, and 280, is met by pre-assessment and/or onboarding process including a questionnaire, where the pre-assessment data is generated independently of first therapeutic module, where pre-assessment data is utilized to generate a personalized intervention regimen for the patient, where a therapeutic protocol may include questionnaires).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mental health system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include pre-assessment and onboarding process including a questionnaire, where the pre-assessment data is generated independently of first therapeutic module, where pre-assessment data is utilized to generate a personalized intervention regimen for the patient, where a therapeutic protocol may include questionnaires as taught by Paull with the motivation of allowing behavioral therapy to be administered to patients in a convenient and flexible, yet structured fashion (see: Paull, paragraph 61).
As per claim 10, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 6, and further teach:
detecting at least one irrelevant response provided by the user in the consultation session (see: Aggarwal, paragraph 43, 49, 61, and 65, is met by the chatbot may quit identifying the possibility of the mental health condition of the user, and, if the life situation is not predicted with a high confidence score, it is checked whether the user has been probed into enough number of times), and obtaining confirmative responses from the user (see: Aggarwal, Fig. 3, ele. 308, 312; Fig. 4, ele. 408, 412; Fig. 5A, ele. 508, 512, 516; Fig. 5B, ele. 520, 524; and paragraph 57-62, is met by the chatbot gathering context from user responses).
As per claim 11, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 10, and further teach:
wherein the AI chatbot interface is arranged to adjust lines of inquiry upon determination of non-pertinent user responses (see: Aggarwal, Fig. 3, ele. 308, 312; Fig. 4, ele. 408, 412; Fig. 5A, ele. 508, 512, 516; Fig. 5B, ele. 520, 524; and paragraph 57-62, is met by the chatbot gathering context from user responses), if a predetermined number of irrelevant responses is detected (see: Aggarwal, paragraph 43, 49, 61, and 65, is met by, if the life situation is not predicted with a high confidence score, it is checked whether the user has been probed into enough number of times).
As per claim 12, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 11, and further teach:
wherein the AI chatbot interface is arranged to facilitate providing an additional round of questions to the user if non-pertinent user responses are detected, to obtain the confirmative responses from the user for an accurate determination of the mental health status of the user (see: Aggarwal, paragraph 43, 49, 61, and 65, is met by, if the life situation is not predicted with a high confidence score, it is checked whether the user has been probed into enough number of times).
As per claim 13, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 12, and further teach:
wherein the AI chatbot interface is further arranged to autonomously initiate (see: Aggarwal, paragraph 57, 59, and 61, is met by AI chatbot starts the conversation with an initial mood check of the user by asking a question) and sustain engagement with the user and steer conversations to extract information of greater relevance for identifying the mental health status of the user (see: Aggarwal, paragraph 43, 49, 61, and 65, is met by, if the life situation is not predicted with a high confidence score, it is checked whether the user has been probed into enough number of times).
As per claim 14, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 11, and further teach:
wherein the output includes an occurrence of irrelevancy in the consultation session (see: Aggarwal, paragraph 43, 49, 61, and 65, is met by the chatbot may quit identifying the possibility of the mental health condition of the user).
As per claim 15, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 6, and while Aggarwal teaches a chatbot (see: Aggarwal, paragraph 40) and Natural Language Processing (NLP) (see: paragraph 41), Aggarwal fails to specifically teach the following limitations met by Temraz as cited:
wherein the AI chatbot interface is further supported by an external Large Language Model (LLM) processing engine arranged to enhance the generation of contextually relevant responses to facilitate interacting with the user via the AI chatbot interface (see: Temraz, paragraph 64-65, 68, and 73, is met by a chatbot using a generative multimodal large language model (LLM) for interpretation and analysis, and a chatbot to provide generative Q&A capabilities using an LLM and handle responses to questions for which an answer may be found in a connected knowledge database).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the chatbot and machine learning as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a chatbot using a generative multimodal large language model (LLM) for interpretation and analysis for generative Q&A capabilities using an LLM and handle responses to questions for which an answer may be found in a connected knowledge database as taught by Temraz with the motivation of providing suggestions regarding which dialogs may be causing unsuccessful exit points in a graph of conversation paths (see: Temraz, paragraph 165).
As per claim 27, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 6, and further teach:
an AI chatbot interface in accordance with claim 6 provided on a user device, wherein the AI chatbot interface (see: Aggarwal, Fig. 1, ele. 104; and paragraph 38 and 40, is met by the user device such as a mobile phone, a Personal Digital Assistant (PDA), a tablet, a desktop computer, or a laptop, and a conversation includes a user input into an AI chatbot that is a digital assistant for mental health, where the user input may be text) is supported by the learning network (see: Aggarwal, paragraph 11, 23, and 50, is met by machine learning).
Aggarwal teaches machine learning (see: Aggarwal, paragraph 11, 23, and 50) and a networked server (see: Aggarwal, Fig. 1, ele. 108; and paragraph 39), but Aggarwal fails to specifically teach a cloud service and database with a content management system (CMS); however, Periyasamy teaches a cloud network and a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified (see: Periyasamy, Fig. 1, ele. 10A, 110; and paragraph 45, 50, 55-56, 70-71, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mental health system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a cloud network and a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified as taught by Periyasamy with the motivation of enabling the user to enjoy a timely and comprehensive recommendation for self-help targeted to overcome the identified symptoms and potential mental health condition, such that the user may follow the list and self-treat him/herself at the
on-set of the symptoms and prevent further deterioration of his/her mental health (see: Periyasamy, paragraph 55 and 57).
Aggarwal fails to specifically teach the machine learning includes a deep-learning network; however, Temraz teaches a deep neural network (see: Temraz, paragraph 50, 77, 165, and 209).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the machine learning as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a deep neural network as taught by Temraz with the motivation of categorizing data into shared embedding spaces for multiple languages (see: Temraz, paragraph 165).
As per claim 28, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 27, and further teach:
wherein the user device is arranged to collect the textual input through a questionnaire and/or conversations via the AI chatbot interface (see: Aggarwal, Fig. 4; Fig. 5A-5B; and paragraph 41, 50, 57, 59, 61, and 65-66, is met by a user is allowed to respond using free text to a question from an AI chatbot, where context is gathered by probing the user about why they are feeling the way they are)
Aggarwal teaches that the user is triaged and a graphical user interface (GUI) of the output data in accordance with the embodiments (see: Aggarwal, paragraph 65 and 70), but Aggarwal fails to specifically teach and to provide functionality including one or more of the followings: process-matched interventions in accordance with one or more processes of psychological inflexibility or psychological flexibility as identified and diagnosed; tailored stepped-care mental health interventions based on the principles; and booking appointments for meetings with counselors and performing interactive actions for mental health servicing; however, Periyasamy teaches a cloud network and a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the self-help includes supportive therapy session, therapeutic music, journal with a To-Do list, a group chat connection, and a telehealth feature (see: Periyasamy, Fig. 1, ele. 110; and paragraph 50, 55-56, 70-71, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the GUI and triage as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified, where the self-help includes supportive therapy session, therapeutic music, journal with a To-Do list, a group chat connection, and a telehealth feature, as taught by Periyasamy with the motivation of enabling the user to enjoy a timely and comprehensive recommendation for self-help targeted to overcome the identified symptoms and potential mental health condition, such that the user may follow the list and self-treat him/herself at the on-set of the symptoms and prevent further deterioration of his/her mental health (see: Periyasamy, paragraph 55 and 57).
Aggarwal and Periyasamy fail to specifically teach that behavioral therapy self-help is for ACT interventions and based on ACT principles; however, Paull teaches providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires (see: Paull, paragraph 62, 68, 72-74, 83, 136, 187, and 371).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the behavioral therapy self-help as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include providing acceptance commitment therapy (ACT) including a therapeutic protocol including questionnaires as taught by Paull with the motivation of managing or alleviating one or more health-related conditions a patient is suffering from (see: Paull, paragraph 136).
As per claim 29, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 27, and further teach:
Aggarwal teaches machine learning (see: Aggarwal, paragraph 11, 23, and 50) and a networked server (see: Aggarwal, Fig. 1, ele. 108; and paragraph 39), but Aggarwal fails to specifically teach being supported by the Cloud Service and the database with the CMS; however, Periyasamy teaches a cloud network and a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified (see: Periyasamy, Fig. 1, ele. 10A, 110; and paragraph 45, 50, 55-56, 70-71, and 78).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the mental health system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a cloud network and a coaching app where the user may only access some cognitive behavioral therapy files for a variety of self-help designed to deal with the specific symptoms and potential mental health condition identified as taught by Periyasamy with the motivation of enabling the user to enjoy a timely and comprehensive recommendation for self-help targeted to overcome the identified symptoms and potential mental health condition, such that the user may follow the list and self-treat him/herself at the on-set of the symptoms and prevent further deterioration of his/her mental health (see: Periyasamy, paragraph 55 and 57).
Aggarwal teaches that the user is triaged and a graphical user interface (GUI) of the output data in accordance with the embodiments (see: Aggarwal, paragraph 65 and 70), but Aggarwal fails to specifically teach the following limitations met by Temraz as cited:
an operator interface wherein the operator interface is arranged to facilitate importing data and visualizing analysis of the mental health status of the user and chat history (see: Temraz, Fig. 1I; Fig. 12; and paragraph 51, 58-59, and 211-213, is met by a computer system for an automatic analysis and visualization for a chatbot administrator including one or more graphs generated to depict how chatbot users interact with the chatbot, where the chatbot users exit the conversations, and/or conversion rates associated with the chatbot).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify GUI system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a computer system for an automatic analysis and visualization for a chatbot administrator including one or more graphs generated to depict how chatbot users interact with the chatbot, where the chatbot users exit the conversations, and/or conversion rates associated with the chatbot as taught by Temraz with the motivation of enabling a chatbot administrator to fully understand how chatbot users are interacting with the chatbot (see: Temraz, paragraph 59).
As per claim 30, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 27, and while Aggarwal teaches that the user is triaged and a graphical user interface (GUI) of the output data in accordance with the embodiments (see: Aggarwal, paragraph 65 and 70), Aggarwal fails to specifically teach the following limitations met by Temraz as cited:
a portal arranged to display the output associated with the mental health status of the user and/or statistics with respect to detected irrelevant contents and frequency/occurrences of irrelevancy (see: Temraz, Fig. 1I; Fig. 12; and paragraph 51, 58-61, 65, and 211-213, is met by a computer system for an automatic analysis and visualization for a chatbot administrator including one or more graphs generated to depict how chatbot users interact with the chatbot, where the chatbot users exit the conversations, and/or conversion rates associated with the chatbot, where an unsuccessful conversation exit point, and a response including a suggestion regarding which dialogs may be causing unsuccessful exit points, such as above a threshold amount, and providing suggestions regarding which conversation paths are too long, such as above a length threshold, which may lead to unsuccessful exit points due to chatbot users not achieving their objective fast enough).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify GUI system as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include a computer system for an automatic analysis and visualization for a chatbot administrator including one or more graphs generated to depict how chatbot users interact with the chatbot, where the chatbot users exit the conversations, and/or conversion rates associated with the chatbot, where an unsuccessful conversation exit point, and a response including a suggestion regarding which dialogs may be causing unsuccessful exit points, such as above a threshold amount, and providing suggestions regarding which conversation paths are too long, such as above a length threshold, which may lead to unsuccessful exit points due to chatbot users not achieving their objective fast enough, as taught by Temraz with the motivation of enabling a chatbot administrator to fully understand how chatbot users are interacting with the chatbot (see: Temraz, paragraph 59).
Claim(s) 20-21 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2023/0223133 to Aggarwal in view of U.S. Patent Application Publication 2021/0098110 to Periyasamy in view of U.S. Patent Application Publication 2017/0351830 to Burger in view of U.S. Patent 12,051,205 to Deutsch in view of U.S. Patent Application Publication 2024/0330597 to Temraz in view of U.S. Patent Application Publication 2022/0028528 to Paull further in view of WO 2023090548 A1 to Jeong.
As per claim 20, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull teach the invention as claimed, see discussion of claim 4, but fail to specifically teach the following limitations met by Jeong as cited:
wherein the deep-learning network is a pre-trained Natural Language Processing (NLP) model based on Bidirectional Encoder Representations from Transformers (BERT) architecture, including Robustly Optimized BERT Pretraining Approach (RoBERTa) architecture, that has learned ACT counseling logic (see: Jeong, page 7, second paragraph, and page 8, third full paragraph, is met by psychological counseling apparatus using acceptance and commitment therapy (ACT) to treat the user and configured to implement an artificial intelligence model to perform natural language processing by training a learning model, including a deep neural network, where natural language processors include bidirectional encoder representation from transformers (BERT) and its applied models such as RoBERTa).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the machine learning as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, and Paull to include psychological counseling using acceptance and commitment therapy (ACT) to treat the user and configured to implement an artificial intelligence model to perform natural language processing by training a learning model, including a deep neural network, where natural language processors include bidirectional encoder representation from transformers (BERT) and its applied models such as RoBERTaas taught by Jeong with the motivation of providing a combination of effective self-focused attention shifting and balancing self-talk (see: Jeong, page 4, last full paragraph).
As per claim 21, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, Paull, and Jeong teach the invention as claimed, see discussion of claim 20, and further teach:
training the deep-learning network with at least one of collecting data, pre-processing data, tokenization, model prediction, model evaluation, and optimizing hyperparameters (see: Aggarwal, paragraph 50, and 64, is met by machine learning classifiers trained on a large dataset of sentences labeled with associated sentiment so that the machine learning classifiers learn to classify the at least one sentiment on new sentences, unseen sentence, and conducting a survey over a certain population and collecting self-reported data on the occurrence of various life events over a predefined time period).
As per claim 26, Aggarwal, Periyasamy, Burger, Deutsch, Temraz, Paull, and Jeong teach the invention as claimed, see discussion of claim 21, and further teach:
wherein further comprises the step of testing the deep-learning network with at least one of segmenting sentences, pre-processing data, predicting, and post-processing (see: Temraz, paragraph 50, 55, and 82-83, is met by an F1 score for performance of a machine learning model, such as a deep neural network, may be generated based on training phrases).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the machine learning as taught by Aggarwal, Periyasamy, Burger, Deutsch, Temraz, Paull, and Jeong to include generating an F1 score for performance of a deep neural network machine learning model based on training phrases as taught by Temraz with the motivation of estimating how a given model may generalize unseen data (see: Temraz, paragraph 83).
Novelty of Claims
As per claims 22-25, the closest prior art of record - U.S. Patent Application Publication 2023/0223133 to Aggarwal, U.S. Patent Application Publication 2023/0223133 to Periyasamy, and U.S. Patent Application Publication 2024/0330597 to Temraz - neither alone nor in combination teach the invention of these claims as they do not teach, in combination with the other claimed limitations from which they depend, “finetuning the deep-learning network by an Adam optimizer with a cosine annealing scheduler and integrating the Retrieval-Augmented Generation (RAG) framework configured to retrieve data from a knowledge database”; therefore, the closest prior art of record does not anticipate or otherwise render the claimed invention obvious.
Response to Arguments
Applicant’s arguments from the response filed on 05/27/2026 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn in view of the amendments because “Claim 1 has been amended to more clearly be directed to statutory subject matter.”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The amendments concern what data is retrieved and what the input is monitored for, which are abstract elements of the claimed invention. The claim amendments did add substantive additional elements such as “by a computing device…by a Retrieval-Augmented Generation (RAG) framework…from a knowledge database…”, but these were recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer elements. For example, the “computing device” is configured to perform functions though no more than a statement than that said functions are performed “by” said computing device. Similarly, the “Retrieval-Augmented Generation (RAG) framework” and “knowledge database” are configured to though no more than a statement than that data is retrieved “by” said RAG “from” said knowledge database. There are statutory claims within the claims tree; however, these were not incorporated into the independent claim(s).
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 112 rejections should be withdrawn in view of the amendments.
The previous rejections are withdrawn in view of the amendments; however, the amendments necessitated further rejection due to the indefinite phrase of “grounded in” with regard to context data.
In the remarks, Applicant argues in substance that (3) the 35 U.S.C. 103 rejections should be withdrawn in view of the amendments because the amended “features are not disclosed by the cited references. Aggarwal is presented as teaching a method for providing mental health advice. However, Aggarwal modified by Periyasamy fails to disclose or suggest retrieving, by a Retrieval Augmented Generation (RAG) framework, context data from a knowledge database and monitoring the textual input for a detected interrupt. Moreover, Aggarwal modified by Periyasamy fails to disclose or suggest an ACT-specific knowledge base or retrieval of ACT-domain contextual data for providing mental health advice, as claimed. In contrast, in the claimed invention, the method for providing mental health advice comprises the steps of retrieving, by a Retrieval-Augmented Generation (RAG) framework, context data from a knowledge database, wherein the context data are derived from and grounded in Acceptance and Commitment Therapy (ACT) counselling knowledge and ACT session-related content; and monitoring the textual input for a detected interrupt. Aggarwal modified by Periyasamy fails to disclose or suggest the claimed features.”
Applicant’s arguments with respect to the amendments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument – see application of prior art Burger and Deutsch. Otherwise, the Examiner respectfully disagrees. Applicant’s arguments are not persuasive. The amendment of monitoring the textual input for a detected interrupt is broadly to such monitoring without limitation upon how such monitoring is performed or the conditions of the interrupt. Periyasamy teaches analyzing user data based on real-time user responses to questions posed by a virtual agent chatbot, where the chatbot may alert the user or a mental health expert of the health condition based at least in part on a determination that the level of severity of the user's current or potential mental health condition poses a high risk, immediately upon the determination (see: Periyasamy, paragraph 8, 18, 27, 45, 55, 66, 68, 70, and 76). Hence, Periyasamy reasonably teaches such monitoring as broadly claimed.
Conclusion
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 ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682
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