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 .
DETAILED ACTION
In the response filed on 06 April 2026, the following has occurred: claim 1 has been amended.
Now claims 1-10 are pending.
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 1-10 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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a system for cognitive behavioral therapy (CBT) training. The limitations of:
a plurality of digital patients that each have: a character trait database having a plurality of human character traits; a message database having a plurality of patient memories; […] access to the character trait database and the message database, […] having a communication [… with …] a digital therapist; software […] for [… obtaining …] a patient memory and a set of character traits for a test patient and formulating an inner voice of the test patient based on the patient memory and the character traits; software […] for combining a new message with the inner voice to compose a reply of the test patient; software […] to update the patient memory based on the new message and the reply; and [… providing …] the reply to the digital therapist using the communication […]; a plurality of digital therapists that each have: the message database […]; software […] with access to the message database as well as environmental factors for generating an assessment; a database of curated messages processed to generate a numerical representation of each curated message for storage together with the curated message; software […] for numerically representing the assessment and/or the reply and for searching the curated message database for matches; software […] for generating a message using at least one of the reply, the summary, the assessment, and any matching curated content; software […] for skeptically analyzing the message and either returning it to the message generating software for revision or forwarding it […]; and a training subsystem comprising: […] in communication with the plurality of digital patients and the plurality of digital therapists, […] having access to the message database; software executing […] for analyzing a message history for a particular digital patient to determine improvement or regression over time and/or over volume of communications through the digital patient and the digital therapist; and software executing […] for ranking digital therapists by diagnosis and providing samples of higher ranked replies, wherein the system generates multiple potentially useful digital patients and digital therapists and [… builds …] each digital therapist against more than one of the digital patients.
, as drafted, is a system which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with a computer and a mobile computing device, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, via human interaction with a computer and a mobile computing device, the claim encompasses organization of messages between a patient and a therapist for determination of improvement or regression of a patient and ranking of therapists. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer and a mobile computing device, which implements the abstract idea. The computer and a mobile computing device are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 10, paragraph [0090]-[0094]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “a communication link… retrieving… transmitting… a communication channel for transmission…” and “trains…”. The “a communication link… retrieving… transmitting… a communication channel for transmission…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “trains…” is recited at a high-level of generality (i.e., training and using a generic off-the-shelf computer-vision based machine learning model in a generic way) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer and a mobile computing device to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a communication link… retrieving… transmitting… a communication channel for transmission…” and “trains…” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The “a communication link… retrieving… transmitting… a communication channel for transmission…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “trains…” have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Day (20240050003): paragraph [0011]; Duan (20190163692): paragraph [0055]; Dutta (20220200934): paragraph [0027]; training a machine learning model using artificial intelligence is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-10 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 2 further describes the training, however training was already considered above and is incorporated herein.
Claim 3 recites tracking of data, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 4 recites updating of data, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 5 and 8 recites semantic analysis (i.e., organization of data), however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 6-7 and 9 further describes the ranking, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
claim 10 recites generation of data (i.e., organization of data), however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 and 3-9 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20240050003 (hereafter “Day”), in view of U.S. Patent Pub. No. 20190163692 (hereafter “Duan”), in further view of U.S. Patent Pub. No. 20190163692 (hereafter “Dutta”).
Regarding (Currently Amended) claim 1, Day teaches a cognitive behavioral therapy training system (Day: paragraph [0011], “systems and methods for creating software and/or hardware to assist therapists in their work with patients and to help users during self-therapy, by identifying unconsciously-held agendas that impact health and well-being, using machine learning to validate the line of questioning by the therapist, chatbot (etc.), of the patient”, paragraphs [0013]-[0016], “Cognitive-behavioral therapy (CBT) being the most successful can be improved with this invention by assisting a therapist in the process”) comprising:
a plurality of digital patients that each have (Day: paragraph [0021], “a population of users”):
a character trait database having a plurality of human character traits (Day: paragraph [0020], “a digitally recorded user profile of the user, the digitally recorded user profile comprising personal information of the user, personal information comprising aspirations, personality traits and preferences, a belief system of the user, a record of discovered unconscious agendas, conditions, observation by other humans expressed in reports, a list of diagnosed disease conditions, personal life stories, or defined traumas throughout life”, paragraph [0053], “The database may include a structured collection of records or data organized according to a database model… The database may include a database management system”, paragraph [0118], “characteristics may include demographic data (e.g., age, gender, location, time period of lifetime, etc.), behavioral data (e.g., access emotions, unconscious agendas, events, traumas as well as questioning techniques, etc.), stylistic content of data (e.g., style, diction, tone, voice, intent, sentence/dialogue length and complexity, etc.), psychographic data (e.g., opinions, values, attitudes, tempered responses, etc.), and the like”, paragraph [0159], “human characteristics data associated with the user 1220 and may store the data and/or provide access to data sources include data for the one or more characteristics in server datastores 1202A-F”);
a message database having a plurality of patient memories (Day: paragraph [0126], “one or more conversational data sources… conversational data may be collected from the Metaverse, and stored in, for example, a Metaverse chat index. The Metaverse chat index may include Metaverse users' perceptions, opinions and knowledge, their intention, emotions, thoughts, feelings, etc. regarding the actions, communications and/or events relating to one or more specific avatars/agents, a period of time, or one or more events”. The Examiner notes a conversation history reads on memories under the broadest reasonable interpretation);
a computer with access to the character trait database and the message database, said computer having a communication link between said computer and a digital therapist (Day: Figures 1, 9-10, 19, paragraph [0017]-[0020], “CBT is conducted by a therapist helping a patient to identify negative thinking that produces negative emotions and undesired behavior and/or a disease condition… The system includes one or more computer processors configured to provide a digital framework including a pattern recognition module configured to measure and validate the response of the user. The digital framework includes a digitally recorded library… the digital agent configured to interact with the user in real time, to ask questions, to engage the user, and to direct the user to perform a task, wherein the digital agent comprises at least one of a chatbot and an avatar”. The Examiner notes the computer provides a framework (i.e., communication link) for the various libraries (i.e., databases) and the digital agents (i.e., digital therapist));
software executing on the computer for retrieving a patient memory and a set of character traits for a test patient and formulating an inner voice of the test patient based on the patient memory and the character traits (Day: paragraph [0003], “identifying unconsciously-held agendas that impact health and well-being, using machine learning”, paragraph [0011], “provide access to unconscious agendas in the unconscious that determine what emotions are experienced consciously”, paragraph [0021], “recording and reporting at least one of: a physiological state or an emotional state of the user while responding to a plurality of questions posed by the at least one of the chatbot and the avatar during the question-answer session; integrating a first input from the digitally recorded library of human emotions and unconscious agendas, a second input from the digitally recorded set of rules, a third input from the digitally recorded user profile of the user, and a fourth input from the plurality of sensors; generating a baseline measure for the user based on the response of the user and an user reaction to a set of baseline questions posed by at least one of the chatbot or the avatar based on the first input, second input, third input and the fourth input”, paragraphs [0082]-[0084], “access to their inner self, and benefit from it as proven… a therapist and his/her users can augment a therapy session with an AI Therapy Assistant that identifies the exact feeling being felt by the user and the unconscious agenda that causes it”, paragraphs [0137]-[0141], “an exemplary input processing unit 700 for digitizing emotions and unconscious agendas for correlating to user-data 730… an intuition or inner voice”);
software executing on the computer for combining a new message with the inner voice to compose a reply of the test patient (Day: paragraphs [0020]-[0022], “determine and differentiate among a valid answer, an invalid answer and inconclusive answer… recording and reporting at least one of: a physiological state or an emotional state of the user while responding to a plurality of questions posed by the at least one of the chatbot and the avatar during the question-answer session… generating a baseline measure for the user based on the response of the user and an user reaction to a set of baseline questions posed by at least one of the chatbot or the avatar based on the first input, second input, third input and the fourth input… recording and reporting a response of a user”);
software executing on the computer to update the patient memory based on the new message and the reply (Day: paragraphs [0020]-[0022], “recording and reporting a response of a user”, paragraph [0180], “communication models for understanding, responses of the control group of users, (iii) a human-tempered response framework, (iv) interpretation of questions posed to the control group of users, and (v) interpretation of answers of the control group of users, and (3) digitally recording a user profile of the user”); and
transmitting the reply to the digital therapist using the communication link (Day: Fig. 14, paragraphs [0011]-[0012], “a question-answer session with a therapist, chatbot, avatar, coach, friend as a means of engaging the question engine, each of the plurality of sensors configured to communicate, record and report at least one of: a physiological state or an emotional state of the user while responding to a plurality of questions posed by the therapist, chatbot, avatar, coach, friend as a means of engaging the question engine during the question-answer session… evaluating the user's responses and reactions to the questions posed by the therapist, chatbot, avatar”, paragraph [0028], “data is gathered and responded to in the Metaverse between the avatar contemplated in this disclosure and another person or group”, paragraph [0042], “sending back responses to the clients”);
a plurality of digital therapists that each have (Day: paragraph [0011], “assist therapists in their work with patients and to help users during self-therapy, by identifying unconsciously-held agendas that impact health and well-being, using machine learning to validate the line of questioning by the therapist, chatbot (etc.), of the patient”, paragraphs [0017]-[0020], “CBT is conducted by a therapist helping a patient to identify negative thinking that produces negative emotions and undesired behavior and/or a disease condition… A CBT therapist would ask you… implementing chatbots to interact with their customers… digital agent configured to interact with the user in real time, to ask questions, to engage the user, and to direct the user to perform a task, wherein the digital agent comprises at least one of a chatbot and an avatar”, paragraph [0122], “one or more avatars or agents… a data store accessible to interface 202, such as data stores 204. Data store(s) 204 may be configured to store and/or organize data according to various criteria. For instance, data store(s) 204 may store photos and videos, human characteristic data, colors, colors matched to words, meanings of words, and or emotions or intent”):
the message database accessible by the computer; software executing in the computer with access to the message database as well as environmental factors for generating an assessment (Day: paragraph [0014], “There are environmental factors”, paragraph [0017]-[0020], “The system includes one or more computer processors configured to provide a digital framework including a pattern recognition module configured to measure and validate the response of the user. The digital framework includes a digitally recorded library… the digital agent configured to interact with the user in real time, to ask questions, to engage the user, and to direct the user to perform a task, wherein the digital agent comprises at least one of a chatbot and an avatar”, paragraph [0136], “determine the responses in any environment”,);
a database of curated messages processed to generate a numerical representation of each curated message for storage together with the curated message (Day: paragraph [0073], “include the use of latent semantic indexing, latent Dirichlet processing, word and/or sentence embedding models, collaborative filtering techniques, entity graphs, Jaccard similarity, cosine similarity and/or translation models”, paragraphs [0117]-[0118], “index engine 210 may access the knowledge base and the personal information of the user collected by interface 202 and/or stored by data store(s) 204. Index engine 210 may search for and collect questions, and/or answers identified in the request… scoring or comparison algorithm/model may generate and/or assign scores or labels to the evaluated characteristics”, paragraph [0123]-[0126], “Index engine 206 may be further configured to access one or more conversational data sources and/or APIs. In aspects, index engine 206 may have access to one or more data sources”. The Examiner notes indexing and use of scoring of messages reads on what is required of processing of the database under the broadest reasonable interpretation);
software executing in the computer for numerically representing the assessment and/or the reply and for searching the curated message database for matches (Day: paragraphs [0117]-[0118], “index engine 210 may access the knowledge base and the personal information of the user collected by interface 202 and/or stored by data store(s) 204. Index engine 210 may search for and collect questions, and/or answers identified in the request… The scoring or comparison algorithm/model may generate and/or assign scores or labels to the evaluated characteristics. The scoring or comparison algorithm/model may use the generated scores/labels to determine a similarity score or metric questions, answers, responses”, paragraph [0123]-[0126], “Index engine 206 may be further configured to access one or more conversational data sources and/or APIs. In aspects, index engine 206 may have access to one or more data sources”, paragraph [0141], “determine a similarity score or metric for an AI therapy assistant. The similarity score/metric may represent the estimated similarity between multiple therapist questions 902 multiple user answers 904, and/or multiple AI responses 930 940”, paragraph [0163], “matching results data”);
software executing in the computer for generating a message using at least one of the reply, the summary, the assessment, and any matching curated content (Day: paragraph [0020], “a question engine configured to interrogate the user using a second digitally recorded library of predetermined questions”, paragraph [0032], “directing the line of questioning”, paragraph [0095], “ask questions based on a list of emotions and unconscious agendas”, paragraph [0107]-[0110], “Index engine 210 may search for and collect questions… determine a hierarchal data traversal process for collecting and analyzing therapist question, user reply data”);
software executing in the computer for skeptically analyzing the [… response …] and […] forwarding it to a communication channel for transmission to a mobile computing device (Day: paragraph [0011], “using machine learning to validate the line of questioning by the therapist, chatbot (etc.), of the patient”, paragraphs [0020]-[0021], “a validation engine to validate an accuracy of the response of the user to a plurality of validation questions posed by at least one of the chatbot and the avatar”, paragraph [0173], “analysis of whether the answer is valid or invalid and recording it so”, paragraphs [0191]-[0192], “validate by a validation engine during the question-answer session”. The Examiner notes validation reads on skeptically analyzing under the broadest reasonable interpretation); and
a training subsystem comprising: a computer in communication with the plurality of digital patients and the plurality of digital therapists, said computer having access to the message database (Day: paragraph [0017]-[0020], “The system includes one or more computer processors configured to provide a digital framework including a pattern recognition module configured to measure and validate the response of the user. The digital framework includes a digitally recorded library… usage of chatbots continues to increase as they become smarter through the implementation of AI/ML/NLP… the digital agent configured to interact with the user in real time, to ask questions, to engage the user, and to direct the user to perform a task, wherein the digital agent comprises at least one of a chatbot and an avatar”, paragraph [0043], “a model may be a rule-based model, a machine-learning regressor, a machine learning classifier, a neural network, or any combination thereof”, paragraph [0088], “The rules portion of this invention trains the AI/ML how to ask questions, what list of emotions and unconscious agendas are and how to translate them, and biometric sensor results interpretation training”, paragraph [0105], “train the digital framework to ask questions based on a list of emotions and unconscious agendas… This correlation can occur as a result of AI/ML learning”, paragraph [0159], “data associated with the user 1220 and may store the data and/or provide access to data sources include data for the one or more characteristics in server datastores 1202A-F”);
software executing on the computer for analyzing a message history for a particular digital patient to determine improvement or regression over time and/or over volume of communications through the digital patient and the digital therapist (Day: paragraph [0140], “machine learning engine 908 of the validity of user answers 904 with the repository of previous questions, answers, and AI responses 930, matched, digitized emotions, unconscious agendas and disease conditions 990”, paragraph [0163], “The resulting data creates the rules, analysis, and decision engine 1306 that is then used in other implementations of one or more aspects provided in this application. It's the volume of data of avatars, agent users 1302 with matching results data to improve all aspects of this disclosure”. One of ordinary skill in the art would find it prima facie obvious that Volume of data communicated is taught under the broadest reasonable interpretation, which teaches what is required of the claim under the broadest reasonable interpretation); and
[…], wherein the system generates multiple potentially useful digital patients and digital therapists and trains each digital therapist against more than one of the digital patients (Day: paragraph [0011], “assist therapists in their work with patients and to help users during self-therapy, by identifying unconsciously-held agendas that impact health and well-being, using machine learning to validate the line of questioning by the therapist, chatbot (etc.), of the patient”, paragraph [0017]-[0021], “usage of chatbots continues to increase as they become smarter through the implementation of AI/ML/NLP”, paragraph [0043], “a model may be a rule-based model, a machine-learning regressor, a machine learning classifier, a neural network, or any combination thereof… a population of users”, paragraph [0088], “The rules portion of this invention trains the AI/ML how to ask questions, what list of emotions and unconscious agendas are and how to translate them, and biometric sensor results interpretation training”, paragraph [0105], “train the digital framework to ask questions based on a list of emotions and unconscious agendas… This correlation can occur as a result of AI/ML learning”, paragraph [0122], “one or more avatars or agents… a data store accessible to interface 202, such as data stores 204. Data store(s) 204 may be configured to store and/or organize data according to various criteria. For instance, data store(s) 204 may store photos and videos, human characteristic data, colors, colors matched to words, meanings of words, and or emotions or intent”).
Day may not explicitly teach (underlined below for clarity):
software executing in the computer for skeptically analyzing the message and either returning it to the message generating software for revision or forwarding it to a communication channel for transmission to a mobile computing device; and
software executing on the computer for ranking […] and providing samples of higher ranked replies, wherein the system generates multiple potentially useful digital patients and digital therapists and trains each digital therapist against more than one of the digital patients.
Duan teaches software executing in the computer for skeptically analyzing the message and either returning it to the message generating software for revision or forwarding it to a communication channel for transmission to a mobile computing device (Duan: Figures 2-4, paragraphs [0056]-[0060], “At 310, it is determined whether the selected sentence is suitable for the conversation, which is corresponding to action 208 in the method 200… f the selected sentence is determined at 310 to be suitable for the conversation, at 312, the selected sentence is presented as a response to the message in the conversation… On the other hand, if the selected sentence is determined at 310 to be unsuitable for the conversation, the selected sentence will not be presented as the response to the message. In this case, at 314, a chitchat response may be determined”); and
software executing on the computer for ranking […] and providing samples of higher ranked replies, wherein the system generates multiple potentially useful digital patients and digital therapists and trains each digital therapist against more than one of the digital patients (Duan: paragraph [0055], “a machine learning ranking model may be trained to rank a plurality of sentences… By applying the trained machine learning ranking model to the ranking model 144, the sentences can be automatically ranked in the chatbot scenario”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using ranking and message analysis as taught by Duan within the authentication of Q/A between a user and therapist for CBT as taught by Day with the motivation of “the adaptability of the chatbot system on different chatting topics is significantly improved” (Duan: paragraph [0087]).
Day and Duan may not explicitly teach (underlined below for clarity):
software executing on the computer for ranking digital therapists by diagnosis and providing samples of higher ranked replies, wherein the system generates multiple potentially useful digital patients and digital therapists and trains each digital therapist against more than one of the digital patients.
Dutta teaches software executing on the computer for ranking digital therapists by diagnosis and providing samples of higher ranked replies, wherein the system generates multiple potentially useful digital patients and digital therapists and trains each digital therapist against more than one of the digital patients (Dutta: Figures 1-4, paragraphs [0003]-[0004], “determining, by the computing system, a ranking of the chatbot profiles based on the scores for the chatbot profiles; and selecting, by the computing system, a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles”, paragraph [0039], “Profile design system 114 may train conversation model 218 to generate responses based on the transcribed speech”. Also see, paragraph [0097]).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using ranking as taught by Dutta within the various therapists for real-time CBT as taught by Day and Duan with the motivation of “increased interaction between the user and the chatbot profile” (Dutta: paragraph [0021]).
Regarding (Previously Presented) claim 3, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the system tracks efficacy of various interventions by a digital therapist against various iterations of a digital patient (Day: paragraph [0043], “data that is collected, stored and used”, paragraph [0068], “efficacy rate”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 4, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the system updates one or more digital therapists' professional knowledge based on higher rated therapist responses for a given digital patient (Day: paragraph [0109], “create a learned data model based on historical variables, and the training thereof, and modify and update the data model based on newly obtained single or multiple observations”, paragraph [0138]-[0140], “the processed customized data may be used to create, organize, populate or update rules training for an AI Therapy Assistant… the scoring data may be used to create, organize, populate or update an AI therapy assistant that determines true, false or don't know answers 940”. The Examiner notes higher scored data is used to update, which teaches what is required under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 5, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the software for ranking therapists implements a semantic analysis technique on a plurality of sequences of messages and replies, each of the sequences being associated with a patient and a therapist (Day: paragraphs [0017]-[0020], “CBT is conducted by a therapist helping a patient to identify negative thinking that produces negative emotions and undesired behavior and/or a disease condition… A CBT therapist would ask you… implementing chatbots to interact with their customers… digital agent configured to interact with the user in real time, to ask questions, to engage the user, and to direct the user to perform a task, wherein the digital agent comprises at least one of a chatbot and an avatar”, paragraph [0073], “using machine learned techniques and/or natural language processing techniques and may also include the use of latent semantic indexing, latent Dirichlet processing, word and/or sentence embedding models, collaborative filtering techniques, entity graphs, Jaccard similarity, cosine similarity and/or translation models”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 6, Day, Duan and Dutta teach the limitations of claim 5, and further teach wherein the software ranks a plurality of therapists based on respective assessments of state, development, and/or progress in therapy for one or more patients associated with each of the plurality of therapists (Dutta: paragraph [0026], “ranks a plurality of chatbot profiles based at least in part on… positive emotional responses”. The Examiner notes an emotional response is a state under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 7, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the software for ranking therapists analyzes the message history using a semantic technique to assess each reply against key attributes of therapeutic praxis, said attributes including at least two of: engaging, helpful, language, empathic, actionable, relevant, accurate, appropriate, accepting, clear, empowering (Day: paragraph [0020], “validate an accuracy… includes a statistical level of confidence score”, paragraphs [0071]-[0073], “training of empathy … using machine learned techniques and/or natural language processing techniques and may also include the use of latent semantic indexing, latent Dirichlet processing, word and/or sentence embedding models, collaborative filtering techniques, entity graphs, Jaccard similarity, cosine similarity and/or translation models”., paragraph [0081], “language understanding and speech synthesis, semantics”, paragraph [0096], “engagement between the therapist and the patient”. The Examiner notes one of ordinary skill in the art before the effective filing date would understand a confidence is equivalent to at least one of relevant or appropriate, under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 8, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the software for ranking therapists analyzes the message history using a semantic technique to assess at least one of the patient's emotional spectrum, development, and activation (Day: paragraph [0020], “record and report at least one of: a physiological state or an emotional state of the user”, paragraph [0030], “digitize emotions and unconscious agendas”, paragraph [0073], “using machine learned techniques and/or natural language processing techniques and may also include the use of latent semantic indexing, latent Dirichlet processing, word and/or sentence embedding models, collaborative filtering techniques, entity graphs, Jaccard similarity, cosine similarity and/or translation models”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 9, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the software for ranking therapists provides higher ranked or higher rated therapist responses as feedback to the therapist reply system (Day: paragraph [0020], “the digital agent configured to interact with the user in real time”, paragraph [0141], “identify and recommend reprogramming”, paragraph [0245], “provide consumer feedback”; Dutta paragraph [0030], “Scoring system 116 may determine a ranking of chatbot profiles 124 based on the scores for chatbot profiles 124. Scoring system 116 may select a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with user 110 based on the ranking of chatbot profiles 124”).
The motivation to combine is the same as in claim 1, incorporated herein.
Claim(s) 2 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20240050003 (hereafter “Day”), U.S. Patent Pub. No. 20190163692 (hereafter “Duan”) and U.S. Patent Pub. No. 20190163692 (hereafter “Dutta”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20250103902 (hereafter “Herzog”).
Regarding (Previously Presented) claim 2, Day, Duan and Dutta teach the limitations of claim 1, and further teach wherein the system repeatedly resets digital patients to their starting conditions so that the digital therapists may be trained in multiple iterations.
Herzog teaches wherein the system repeatedly resets digital patients to their starting conditions so that the digital therapists may be trained in multiple iterations (Herzog: paragraph [0117], “randomly initialising the parameters of the models and resetting the parameters after every training iteration, which may also be considered equivalent to “pruning at initialisation””).
One of ordinary skill in the art before the effective filing date would have found it obvious to include iteration and initialization of parameters for iterative training as taught by Herzog with the training of various digital therapists as taught by Day, Duan and Dutta with the motivation of “provide improvements to federated learning by compressing a size of each update sent between nodes within a distributed system” (Herzog: paragraph [0013]).
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20240050003 (hereafter “Day”), U.S. Patent Pub. No. 20190163692 (hereafter “Duan”) and U.S. Patent Pub. No. 20190163692 (hereafter “Dutta”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20230215531 (hereafter “Chevalier”).
Regarding (Previously Presented) claim 10, Day, Duan and Dutta teach the limitations of claim 1, but may not explicitly teach wherein the software executing on the computer is adapted to generate synthetic training data for the patient messaging system and/or the therapist reply system.
Chevalier teaches (Chevalier: paragraph [0058], “training the artificial intelligence models is performed using one or more datasets based on synthetic data, where the synthetic data is related to one or more synthetic models. The training can include generating patient-specific synthetic geometrics based on features extracted from the medical imaging data, then generating one or more synthetic models based on the synthetic geometries and the indices”, paragraph [0095], “or generating synthetic geometries in step 502, data could be in the form of altering existing models. Alternatively, data could be created without any extraction from medical images”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include generation and use of synthetic data as taught by Chevalier with the digital therapists trained as taught by Day, Duan and Dutta with the motivation of “improving on their accuracy” (Chevalier: paragraph [0004]).
Response to Arguments
Applicant's arguments filed on 18 February 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 18 February 2026.
Rejections under 35 U.S.C. § 112
Regarding the rejection of claim 1, in view of the amendments the antecedent basis rejection has been removed.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-10, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that this characterization does not accurately describe the claimed invention. Claim 1 does not recite a method of organizing human activity… These operations are specific to the claimed computer architecture and cannot be performed through pen-and-paper or human interaction… The specific multi-agent architecture, with its dedicated supervisory quality-control mechanism, its diagnosis-based ranking system, and its iterative training methodology, imposes meaningful limits on any abstract concept of therapy management. The claims recite a particular technological implementation, not a result: the claimed system achieves its training function through a specific pipeline of interacting agents (patient simulators, therapist agents, and a training subsystem) with defined data flows and feedback loops. Others remain free to build CBT training systems using different architectures.
The Examiner respectfully disagrees.
It is respectfully submitted, that the claims under the broadest reasonable interpretation collect and organize data, to provide to a human user an output of the organized data via human interaction with generic computer components, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claims are directed toward an abstract idea.
Only the additional elements can provide a practical application and/or significantly more, none of the drafted additional elements recite a technical solution to a technical problem recited in Applicant’s specification. Applicant argues that the claims recite “a particular technological implementation”, but does not argue any portions of their specification for recitations of a technical problem, let alone even arguing a technical problem rooted in computer hardware technology, the Examiner notes implementing an abstract idea using generic computer components is at best, using generic computer components to “apply it”, and is not a technical solution to a technical problem recited in Applicant’ s specification. Therefore, unlike the argued McRO the claimed additional elements, do not recite a technical solution to a technical problem recited in Applicant’ specification, and the argument is not persuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-10, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
The prior art references cited by the Examiner each solve different problems in different ways… Assembling these disparate teachings into the specific multi-agent architecture claimed by Applicant, complete with digital patient simulators, digital therapist agents, a supervisory quality-control mechanism with iterative revision, and a diagnosis-based ranking and training subsystem, requires precisely the kind of hindsight reasoning that Graham prohibits… The Examiner's Proposed Combination Fails to Teach "Skeptically Analyzing" the Message… The Examiner maps this limitation to Day's "validation engine"… Day's validation engine validates the accuracy of user responses to questions posed by the chatbot… Day's validation engine never evaluates the system's own outputs; it evaluates only the user's inputs. These are fundamentally different operations… Duan's system selects pre-existing sentences from a knowledge base and checks suitability; it does not compose original messages through a generative process and then subject those compositions to critical review… The Examiner's Proposed Combination Fails to Teach Ranking Digital Therapists "By Diagnosis"… The Examiner relies on Duan for sentence ranking (Duan, paragraph [0055]) and Dutta for chatbot profile ranking… Neither reference teaches ranking therapists by diagnosis… so that a therapist agent can be assessed for its performance with depression patients separately from its performance with anxiety patients, for example… It requires the system to maintain diagnostic categories and to evaluate therapist performance within those categories, enabling the training subsystem to identify which therapists perform well for which conditions
The Examiner respectfully disagrees.
It is respectfully submitted, that firstly both argued limitations are currently drafted as intended use of implementing software, as drafted the claim only requires execution of software with an intended use of “for skeptically analyzing” and “for ranking” that is not required to occur. These feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the software execution, nevertheless, arguendo, the argued “for skeptically analyzing” is taught by the combination of Duan within Day, in particular Day explicitly teaches validation of responses (see above but at least paragraph [0011]), and although may not explicitly teach validation of messages, Duan explicitly teaches the validation and routing of messages (see above but at least paragraphs [0056]-[0060]), and would be prima facie obvious to combine with the motivation of “the adaptability of the chatbot system on different chatting topics is significantly improved” (Duan: paragraph [0087]). With respect to “for ranking”, Applicant argues multiple diagnosis, but the claim does not require this, a single ranking for any diagnosis is all that is required and the ranking of Dutta explicitly teaches a ranking for a scored emotional response (i.e., specific diagnosis), which teaches what is required of the claim under the broadest reasonable interpretation of the claim with the motivation of “increased interaction between the user and the chatbot profile” (Dutta: paragraph [0021]).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Applicant further argues:
Claim 2: Herzog Does Not Teach Resetting Digital Patients to Starting Conditions… Applicant respectfully submits that resetting model parameters is not the same as resetting digital patients to their starting conditions… The Examiner's motivation to combine, drawn from federated learning improvements (Herzog, paragraph [0013]), does not explain why one of ordinary skill in the art working on a CBT training system would look to federated learning parameter compression to address the need for iterative patient scenario training… Claim 10: Chevalier Is lnapposite Prior Art from a Different Technical Field… Applicant respectfully submits that Chevalier is inapposite prior art. The claimed invention generates synthetic conversational training data for dialog agent systems, namely synthetic message exchanges for training patient messaging systems and therapist reply systems. Chevalier generates synthetic geometric models derived from medical imaging data for training computer vision models
The Examiner respectfully disagrees.
It is respectfully submitted, that the broadest reasonable interpretation of “resets digital patients” is taught by Herzog (see above but at least paragraph [0117]), Applicant appears to argued not claimed language, as none of the language argued is explicitly claimed nor explicitly defined in Applicant’s specification, and therefore the broadest reasonable interpretation is taught, and would be prima facie obvious with the motivation of to improve the machine learning models (Herzog: paragraph [0013]). Finally, with respect to claim 10, both references are directed to healthcare machine learning models, and are analogous prior art that one of ordinary skill in the art prima facie obvious to incorporate to improve machine learning use in healthcare (Chevalier: paragraph [0004]).
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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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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