DETAILED ACTION
This communication is in response to the Application filed on May 14, 2024.
Claims 1 - 11 are pending and have been examined.
Claims 1 and 4 are independent.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 20, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered by the examiner.
Drawings
The drawings filed on May 14, 2024 have been accepted and considered by the Examiner.
35 U.S.C. 112(f) Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: “interactive platform”, “detection unit”, and “recommendation unit” in Claim 4, “summary unit” in Claim 5, “retrieving unit” in Claims 6 – 7, “counseling unit” in Claims 8 - 9, “converter unit” in Claims 10 - 11. Note the varied definition of this phrase in the supporting Specification which indicates that the “platform” and “unit” were intended as a generic placeholder. These limitations are generic in the context of the art and don’t refer to any specific structure and only serve as placeholders for the structure that performs the associated function(s) without providing any information about what that structure is. MPEP 2181 I A says:
For a term to be considered a substitute for "means," and lack sufficient structure for performing the function, it must serve as a generic placeholder and thus not limit the scope of the claim to any specific manner or structure for performing the claimed function. It is important to remember that there are no absolutes in the determination of terms used as a substitute for "means" that serve as generic placeholders. The examiner must carefully consider the term in light of the specification and the commonly accepted meaning in the technological art. Every application will turn on its own facts.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1 - 11 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1,
Claim 1 recites the limitation “Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, effective coping strategies, etc.” “Etc.” leads to confusion over the intended scope of a claim. This renders the metes and bounds of the Claim unclear and the Claim indefinite.
Claims 2 - 3 are rejected for being dependent upon an indefinite base claim.
Regarding Claim 4,
Claim 4 recites the limitation “Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, effective coping strategies, etc.” “Etc.” leads to confusion over the intended scope of a claim. This renders the metes and bounds of the Claim unclear and the Claim indefinite.
Claims 5 - 11 are rejected for being dependent upon an indefinite base claim.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1 - 7 and 10 - 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zou (WO2022174161A1) in view of Morse et al., (U.S. Patent Application Publication 2025/0201420), hereinafter referred to as Morse.
Regarding Claim 1, Zou teaches:
1. A method for supporting mental health using generative Artificial Intelligence (genAI) Chatbot, the method comprising:
wherein obtaining at least one message from a user; [Zou, “The AI psychotherapy system obtains a patient message (sometimes called user’s message) (i.e., the claimed “message from a user”) from a patient at a user interface.” Par. 0004]
designing a conversation flow from at least one message by detecting keyword(s) from each user’s message and then defining guiding questions to encourage users to express their emotions, thoughts, and experiences in a systematic and user-friendly manner by leveraging fine-tuned large language generative AI models with prompt engineering techniques; [Zou, “The method as recited in any of clauses (G1)-(G5), wherein delivering psychotherapy comprises using (i) closed-domain rule-based model, (ii) closed-domain retrieval-based model, (iii) closed-domain retrieval-generative-hybrid-based model, (iv) closed-domain generative-based model, (v) open-domain rule-based model, (vi) open-domain retrieval-based model, (vii) open-domain retrieval-generative-hybrid-based model, or (viii) open-domain generative-based model (i.e., the claimed “fine-tuned large language generative AI models”), for conversation (i.e., the claimed “conversation flow”) with the patient.” Par. 0034; “According to some implementations, the AI psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models of the above models, fine-tuning (i.e., the claimed “fine-tuned large language generative AI models”), meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “fine-tuned large language generative AI models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Systems and methods according to the techniques described herein provide conversation therapy, evidence-based psychotherapy and other related treatment and therapy.” Par. 0054; “The AI psychotherapy system obtains a patient message (sometimes called user’s message) (i.e., the claimed “message from a user”) from a patient at a user interface.” Par. 0004; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “In some implementations, the task-oriented dialog system may be bi-level. At the NLP level, NLP and machine learning models 116 are components of an example task- oriented dialog system. The task-oriented dialog system may be used to continue and guide the conversation (i.e., the claimed “guiding questions”), and generate and execute the personalized psychotherapy 330, like asking and generating questions (i.e., the claimed “guiding questions”) and answers to users, using the NLP and machine learning models 116 to process, analyze, and understand from the user’s answer (i.e., the claimed “defining guiding questions to encourage users to express their emotions, thoughts, and experiences”). At the evidence-based psychotherapy level, some implementations interacts with the task-oriented dialog system. The two levels may be parallel, meaning it may work mutually and spontaneously.” Par. 00146; “Thus, systems are provided with improved methods for providing on-demand, targeted, effective, personalized, and customized emotional psychotherapy (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”).” Par. 0038; “The user may choose the most suitable words to describe their current emotions (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”).” Par. 0084; “It can be an indirect emotional expression, or a direct emotional expression (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”), or a longer paragraph (such as a diary), or statements about thoughts (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”) of suicide and death.” Par. 0097; “ask user “recall a past experience (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”)” Par. 00130]
detecting the mental health issues based on the conversation flow by integrating an ill-being detection model and a keyword recognition model to identify conversations pertaining to mental health concerns, [Zou, “detect the specific signal of mental disorders,” Par. 0072; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “ill-being detection model”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”? This input is sent as a patient message 818 to the NLP processing methods 400 and other machine learning and NLP models (i.e., the claimed “ill-being detection model”), according to some implementations as described above in reference to Figures 3 A-3B and 4.” Par. 0086; Referring to the Specification Par. 0084, “ill-being detection model identifies user sentences with negative emotions,”; “The user may choose the most suitable words to describe their current emotions. The selected words will be sent to the sentiment classifier 822 (i.e., the claimed “keyword recognition model”).” Par. 0084; Referring to Specification Par. 0017, “keyword recognition model detects and categorizes positive and negative keywords,”;]
the conversation is considered as relating to mental health matters when a sentence within the conversation is flagged by the ill-being detection model or when there is an accumulation of negative keywords beyond a specified threshold within any given domain; and [Zou, “The language model will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “detect the specific signal of mental disorders,” Par. 0072; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “ill-being detection model”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”? This input is sent as a patient message 818 to the NLP processing methods 400 and other machine learning and NLP models (i.e., the claimed “ill-being detection model”), according to some implementations as described above in reference to Figures 3 A-3B and 4.” Par. 0086; Referring to the Specification Par. 0084, “ill-being detection model identifies user sentences with negative emotions,”; “The user may choose the most suitable words to describe their current emotions. The selected words will be sent to the sentiment classifier 822 (i.e., the claimed “keyword recognition model”).” Par. 0084; Referring to Specification Par. 0017, “keyword recognition model detects and categorizes positive and negative keywords,”;]
giving recommendations on mental health support resources to the user based on the detected mental health issues, [Zou, “to recommend best techniques for personal using depends on personal preference, so the function is actually recommendation.” 00226; “The more the user likes a treatment, the more likely the system recommends subsequent treatments (i.e., the claimed “mental health support resources”).” Par. 0233; “one or more machine learning recommendation engines and (ii) a list of cognitive techniques (i.e., the claimed “mental health support resources”) (e.g., any of the 78 cognitive techniques (i.e., the claimed “mental health support resources”) in a list 862).” Par. 00264; “cognitive behavioral therapy,” Par. 0004; “In some implementations, each of the models 204a-h may be based on AI technologies, deep learning and machine learning algorithms, and statistics methods, and other related technologies and tools, combined with CBT (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”), DBT, IPT and other related treatment and therapy.” Par. 00151]
wherein the ill-being detection model and keyword recognition model are developed by building pipeline and training Artificial Intelligence (AI) models with Bidirectional Encoder Representations from Transformers (BERT) models or pre-trained language models; [Zou, “According to some implementations, the AI (i.e., the claimed “Artificial Intelligence (AI) models”) psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models (i.e., the claimed “pre-trained language models”) of the above models, fine-tuning, meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “pre-trained language models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Transformer-based family models and their modifications, (including but not limited to, transformer, GPT, BERT (i.e., the claimed “Bidirectional Encoder Representations from Transformers (BERT) models”), XLM)” Par. 00131; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “ill-being detection model”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”? This input is sent as a patient message 818 to the NLP processing methods 400 and other machine learning and NLP models (i.e., the claimed “ill-being detection model”), according to some implementations as described above in reference to Figures 3 A-3B and 4.” Par. 0086; Referring to the Specification Par. 0084, “ill-being detection model identifies user sentences with negative emotions,”; “The user may choose the most suitable words to describe their current emotions. The selected words will be sent to the sentiment classifier 822 (i.e., the claimed “keyword recognition model”).” Par. 0084; Referring to Specification Par. 0017, “keyword recognition model detects and categorizes positive and negative keywords,”;]
wherein the mental health support resources include Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, effective coping strategies, etc. [Zou, “to recommend best techniques for personal using depends on personal preference, so the function is actually recommendation.” 00226; “The more the user likes a treatment, the more likely the system recommends subsequent treatments.” Par. 0233; “one or more machine learning recommendation engines and (ii) a list of cognitive techniques (e.g., any of the 78 cognitive techniques in a list 862).” Par. 00264; “cognitive behavioral therapy,” Par. 0004; “In some implementations, each of the models 204a-h may be based on AI technologies, deep learning and machine learning algorithms, and statistics methods, and other related technologies and tools, combined with CBT (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”), DBT, IPT and other related treatment and therapy.” Par. 00151; “Coping notification (send user a coping notification with clam music, funny animal video (i.e., the claimed “inspiring movies”) or positive quotes.) (i.e., the claimed “effective coping strategies”)” Par. 00130]
Zou fails to explicitly teach large language model and articles.
However, Morse teaches:
designing a conversation flow from at least one message by detecting keyword(s) from each user’s message and then defining guiding questions to encourage users to express their emotions, thoughts, and experiences in a systematic and user-friendly manner by leveraging fine-tuned large language generative AI models with prompt engineering techniques; [Morse, “At the core of the platform, Artificial Intelligence (AI) is leveraged to allow patients to verbally describe their conditions in natural language; such conversations (i.e., the claimed “conversation flow”) are converted to insightful notes and summaries that provide clinicians with unprecedented valuable patient insights and risk scores.” Par. 0025; “Aiberry fine-tuned this LLM by using a proprietary set of instruction prompts and paired completions to guide the model to generate these questions (i.e., the claimed “designing a conversation flow”).” Par. 0076; “These LLMs can also be “fine-tuned” (i.e., the claimed “fine-tuned large language generative AI models”),” Par. 0030; “In a preferred embodiment, the fact that the model is trained on proprietary Aiberry data and hosted on Aiberry infrastructure ensures that this generative AI (i.e., the claimed “fine-tuned large language generative AI models”)capability is well-bounded and predictable.” Par. 0032]
detecting the mental health issues based on the conversation flow by integrating an ill-being detection model and a keyword recognition model to identify conversations pertaining to mental health concerns, the conversation is considered as relating to mental health matters when a sentence within the conversation is flagged by the ill-being detection model or when there is an accumulation of negative keywords beyond a specified threshold within any given domain; and [Morse, “Stress and Burnout script in an embodiment, and illustrates how the system (i.e., the claimed “key word recognition model”) utilizes the transcribed response to deliver such a follow-up instruction. When the transcript of question 2012 is sent to the server, a “postAction” is executed, in which the variable “_stress_load_followup” is set to the result of the “isUnderWordCountThreshold” equation. If the number of words in the spoken answer is under the threshold,” Par. 0126]
giving recommendations on mental health support resources to the user based on the detected mental health issues, [Morse, “Beyond assessment (i.e., the claimed “detected mental health issues”), the platform leverages these screening insights (i.e., the claimed “detected mental health issues”) to deliver personalized recommendations for relevant mental health resources and content.” Par. 0025]
wherein the mental health support resources include Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, effective coping strategies, etc. [Morse, “Beyond assessment (i.e., the claimed “detected mental health issues”), the platform leverages these screening insights (i.e., the claimed “detected mental health issues”) to deliver personalized recommendations for relevant mental health resources and content.” Par. 0025;’ “According to one or more embodiments, the AI generated assessment information can now integrate to internal and external libraries of content (e.g., videos (i.e., the claimed “inspiring movies”), articles (i.e., the claimed “informative articles”), exercises (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”)), in such a manner that it “intelligently” matches resources specifically to the patient in an effort to improve impact and engagement.” Par. 0137]
Zou and Morse pertain to artificial intelligence therapy systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the artificial intelligence therapy systems art to modify Zou’s teachings of “guide the conversation (i.e., the claimed “guiding questions”), and generate and execute the personalized psychotherapy 330, like asking and generating questions (i.e., the claimed “guiding questions”) and answers to users, using the NLP and machine learning models 116 to process, analyze, and understand from the user’s answer (i.e., the claimed “defining guiding questions to encourage users to express their emotions, thoughts, and experiences”)” (Zou, Par. 00146) with the teachings of “large language models” and “articles” (Morse, Par. 0032, Par. 0137) taught by Morse in order to “deliver proactive interventions”(Morse, Par. 0003).
Regarding Claim 2, Zou in view of Morse has been discussed above. The combination further teaches:
wherein the conversation flow was generated by leveraging fine-tuned large language generative Artificial Intelligence models with prompt engineering techniques and the predefined questions, assessment questions, user’s answers analysis, and resource recommendation, to gain insight into the user’s status and maintain a smooth conversation. [Zou, “The method as recited in any of clauses (G1)-(G5), wherein delivering psychotherapy comprises using (i) closed-domain rule-based model, (ii) closed-domain retrieval-based model, (iii) closed-domain retrieval-generative-hybrid-based model, (iv) closed-domain generative-based model, (v) open-domain rule-based model, (vi) open-domain retrieval-based model, (vii) open-domain retrieval-generative-hybrid-based model, or (viii) open-domain generative-based model (i.e., the claimed “fine-tuned large language generative AI models”), for conversation (i.e., the claimed “conversation flow”) with the patient.” Par. 0034; “According to some implementations, the AI psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models of the above models, fine-tuning (i.e., the claimed “fine-tuned large language generative AI models”), meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “fine-tuned large language generative AI models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Systems and methods according to the techniques described herein provide conversation therapy, evidence-based psychotherapy and other related treatment and therapy.” Par. 0054; “The AI psychotherapy system obtains a patient message (sometimes called user’s message) (i.e., the claimed “message from a user”) from a patient at a user interface.” Par. 0004; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “In some implementations, the task-oriented dialog system may be bi-level. At the NLP level, NLP and machine learning models 116 are components of an example task- oriented dialog system. The task-oriented dialog system may be used to continue and guide the conversation, and generate and execute the personalized psychotherapy 330, like asking and generating questions (i.e., the claimed “predefined questions, assessment questions”) and answers to users, using the NLP and machine learning models 116 to process, analyze, and understand from the user’s answer (i.e., the claimed “user’s answers analysis”). At the evidence-based psychotherapy level, some implementations interacts with the task-oriented dialog system. The two levels may be parallel, meaning it may work mutually and spontaneously.” Par. 00146; “Thus, systems are provided with improved methods for providing on-demand, targeted, effective, personalized, and customized emotional psychotherapy.” Par. 0038; “The user may choose the most suitable words to describe their current emotions (i.e., the claimed “user’s status”).” Par. 0084; “It can be an indirect emotional expression, or a direct emotional expression, or a longer paragraph (such as a diary), or statements about thoughts of suicide and death.” Par. 0097; “ask user “recall a past experience” Par. 00130]; “AI psychotherapy system 100 may then prompt (i.e., the claimed “prompt engineering”) the patient to submit input, shown as health data collection 808.” Par. 0059; Morse, “Aiberry fine-tuned this LLM by using a proprietary set of instruction prompts (i.e., the claimed “prompt engineering”) and paired completions to guide the model to generate these questions (i.e., the claimed “the predefined questions, assessment questions”).” Par. 0076; Zou, “to recommend best techniques for personal using depends on personal preference, so the function is actually recommendation.” 00226; “The more the user likes a treatment, the more likely the system recommends subsequent treatments (i.e., the claimed “mental health support resources”).” Par. 0233; “one or more machine learning recommendation engines and (ii) a list of cognitive techniques (i.e., the claimed “mental health support resources”) (e.g., any of the 78 cognitive techniques (i.e., the claimed “mental health support resources”) in a list 862).” Par. 00264; Morse, “Beyond assessment (i.e., the claimed “detected mental health issues”), the platform leverages these screening insights (i.e., the claimed “detected mental health issues”) to deliver personalized recommendations for relevant mental health resources and content.” Par. 0025; Zou,“personalized psychotherapy 832) executed as block 834, based on conversation history (i.e., the claimed “smooth conversation”) as discussed above,” Par. 00126]
Regarding Claim 3, Zou in view of Morse has been discussed above. The combination further teaches:
wherein the ill-being detection model identifies user sentences with negative emotions, [Zou, “According to some implementations, the AI (i.e., the claimed “Artificial Intelligence (AI) models”) psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models (i.e., the claimed “pre-trained language models”) of the above models, fine-tuning, meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “pre-trained language models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Transformer-based family models and their modifications, (including but not limited to, transformer, GPT, BERT (i.e., the claimed “Bidirectional Encoder Representations from Transformers (BERT) models”), XLM)” Par. 00131; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “ill-being detection model”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”? This input is sent as a patient message 818 to the NLP processing methods 400 and other machine learning and NLP models (i.e., the claimed “ill-being detection model”), according to some implementations as described above in reference to Figures 3 A-3B and 4.” Par. 0086; Referring to the Specification Par. 0084, “ill-being detection model identifies user sentences with negative emotions,”]
while the keyword recognition model detects and categorizes positive and negative keywords across different mental domains by the BERT model. [Zou, “According to some implementations, the AI (i.e., the claimed “Artificial Intelligence (AI) models”) psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models (i.e., the claimed “pre-trained language models”) of the above models, fine-tuning, meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “pre-trained language models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Transformer-based family models and their modifications, (including but not limited to, transformer, GPT, BERT (i.e., the claimed “Bidirectional Encoder Representations from Transformers (BERT) models”), XLM)” Par. 00131; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”?” Par. 0086; “The user may choose the most suitable words to describe their current emotions. The selected words will be sent to the sentiment classifier 822 (i.e., the claimed “keyword recognition model”).” Par. 0084; Referring to Specification Par. 0017, “keyword recognition model detects and categorizes positive and negative keywords,”; Morse, “Stress and Burnout script in an embodiment, and illustrates how the system (i.e., the claimed “key word recognition model”) utilizes the transcribed response to deliver such a follow-up instruction. When the transcript of question 2012 is sent to the server, a “postAction” is executed, in which the variable “_stress_load_followup” is set to the result of the “isUnderWordCountThreshold” equation. If the number of words in the spoken answer is under the threshold,” Par. 0126]
Regarding Claim 4, Zou in view of Morse has been discussed above. The combination further teaches:
an interactive platform where users can openly and comfortably share their moods; [Zou, “According to some implementations, the AI psychotherapy system 100 may implement a human-machine interactive digital psychotherapist (i.e., the claimed “interactive platform”) (e.g., a robot) which may be equipped with voice interaction and text conversation capabilities (e.g., Chinese and other Chinese dialects).” Par. 0057; “According to some implementations, the output of a psychological -related sentiment classifier 822 (sometimes called psychological -related sentiment classifier 350) may be the input of the sentiment and/or mood tracker and forecaster 824 (sometimes called sentiment and/or mood tracker and forecaster 352).” Par. 0059; The user may choose the most suitable words to describe their current emotions (i.e., the claimed “users can openly and comfortably share their moods”).” Par. 0084]
a detection unit for detecting mental health issues from the user’s conversation; and , [Zou, “detect the specific signal of mental disorders,” Par. 0072; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “detection unit”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142]
a recommendation unit for giving recommendations on mental health support resources to the user, [Zou, “to recommend best techniques for personal using depends on personal preference, so the function is actually recommendation.” 00226; “The more the user likes a treatment, the more likely the system (i.e., the claimed “recommendation unit”) recommends subsequent treatments (i.e., the claimed “mental health support resources”).” Par. 0233; “one or more machine learning recommendation engines and (ii) a list of cognitive techniques (i.e., the claimed “mental health support resources”) (e.g., any of the 78 cognitive techniques (i.e., the claimed “mental health support resources”) in a list 862).” Par. 00264; “cognitive behavioral therapy,” Par. 0004; “In some implementations, each of the models 204a-h may be based on AI technologies, deep learning and machine learning algorithms, and statistics methods, and other related technologies and tools, combined with CBT (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”), DBT, IPT and other related treatment and therapy.” Par. 00151]
wherein the interactive platform is configured to design a conversation flow by detecting keyword(s) from each user’s message and then defining guiding questions to encourage users to express their emotions, thoughts, and experiences in a systematic and user-friendly manner by leveraging fine-tuned large language generative AI models with prompt engineering techniques; [Zou, “According to some implementations, the AI psychotherapy system 100 may implement a human-machine interactive digital psychotherapist (i.e., the claimed “interactive platform”) (e.g., a robot) which may be equipped with voice interaction and text conversation capabilities (e.g., Chinese and other Chinese dialects).” Par. 0057; Zou, “The method as recited in any of clauses (G1)-(G5), wherein delivering psychotherapy comprises using (i) closed-domain rule-based model, (ii) closed-domain retrieval-based model, (iii) closed-domain retrieval-generative-hybrid-based model, (iv) closed-domain generative-based model, (v) open-domain rule-based model, (vi) open-domain retrieval-based model, (vii) open-domain retrieval-generative-hybrid-based model, or (viii) open-domain generative-based model (i.e., the claimed “fine-tuned large language generative AI models”), for conversation (i.e., the claimed “conversation flow”) with the patient.” Par. 0034; “According to some implementations, the AI psychotherapy system 100 train model parameters from scratch on self-collected data and public datasets. To achieve the similar or equivalent purpose of training model parameters, including but not limited to using pre-trained models of the above models, fine-tuning (i.e., the claimed “fine-tuned large language generative AI models”), meta-training, few-shot training, or other specific and unspecified training methods.” Par. 00121; “The language model (i.e., the claimed “fine-tuned large language generative AI models”) will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “Systems and methods according to the techniques described herein provide conversation therapy, evidence-based psychotherapy and other related treatment and therapy.” Par. 0054; “The AI psychotherapy system obtains a patient message (sometimes called user’s message) (i.e., the claimed “message from a user”) from a patient at a user interface.” Par. 0004; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “In some implementations, the task-oriented dialog system may be bi-level. At the NLP level, NLP and machine learning models 116 are components of an example task- oriented dialog system. The task-oriented dialog system may be used to continue and guide the conversation (i.e., the claimed “guiding questions”), and generate and execute the personalized psychotherapy 330, like asking and generating questions (i.e., the claimed “guiding questions”) and answers to users, using the NLP and machine learning models 116 to process, analyze, and understand from the user’s answer (i.e., the claimed “defining guiding questions to encourage users to express their emotions, thoughts, and experiences”). At the evidence-based psychotherapy level, some implementations interacts with the task-oriented dialog system. The two levels may be parallel, meaning it may work mutually and spontaneously.” Par. 00146; “Thus, systems are provided with improved methods for providing on-demand, targeted, effective, personalized, and customized emotional psychotherapy (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”).” Par. 0038; “The user may choose the most suitable words to describe their current emotions (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”).” Par. 0084; “It can be an indirect emotional expression, or a direct emotional expression (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”), or a longer paragraph (such as a diary), or statements about thoughts (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”) of suicide and death.” Par. 0097; “ask user “recall a past experience (i.e., the claimed “encourage users to express their emotions, thoughts, and experiences”)” Par. 00130; Morse, “At the core of the platform, Artificial Intelligence (AI) is leveraged to allow patients to verbally describe their conditions in natural language; such conversations (i.e., the claimed “conversation flow”) are converted to insightful notes and summaries that provide clinicians with unprecedented valuable patient insights and risk scores.” Par. 0025; “These LLMs can also be “fine-tuned” (i.e., the claimed “fine-tuned large language generative AI models”),” Par. 0030; “In a preferred embodiment, the fact that the model is trained on proprietary Aiberry data and hosted on Aiberry infrastructure ensures that this generative AI (i.e., the claimed “fine-tuned large language generative AI models”)capability is well-bounded and predictable.” Par. 0032]
wherein the detection unit is configured to detect mental health issues by integrating an ill-being detection model and a keyword recognition model to identify mental health concerns in the conversation input to the interactive platform, the conversation is considered as relating to mental health matters when a sentence within the conversation is flagged by the ill-being detection model or when there is an accumulation of negative keywords beyond a specified threshold within any given domain; and [Zou, “detect the specific signal of mental disorders,” Par. 0072; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “detection unit”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; Zou, “The language model will analyze the letter and track their emotional change or red-flag signal.” Par. 00139; “detect the specific signal of mental disorders,” Par. 0072; “The conversation is provided below as an example of conversation between the digital psychotherapist (T) and Patient (U) (i.e., the claimed “conversation flow”) T : how you feel today? U: Hmm...last night was horrible that I couldn’t fall asleep and frequently woke up. T: Are you still concerned about tomorrow’s business trip? U: <sign>....yes, that’s bad... probably still can’t fall asleep tonight. T : Let me check your out-of-hospital data. T: I saw you are taking a prescription of Benzodiazepines; it can help your sleeping. Try to take it 30 mins right before your sleep. Lavender oil in the bedroom also can make you feel peaceful.” Par. 0060; “According to some implementations, see Figure 11 A and 1 IB for implementation, the continuum cognitive technique may be selected, particularly when the system (i.e., the claimed “ill-being detection model”) detects the user is anxious or concerned about things ranging from little daily things to life events that may or may not continuously happen.” Par. 00142; “In some implementations, the words or sentences may be an indirect expression of emotions, such as “I have insomnia all night,” or an emotional description, such as “I am very depressed now,” or a sentence about suicide (attempt to) or death, such as “when can I leave this world”? This input is sent as a patient message 818 to the NLP processing methods 400 and other machine learning and NLP models (i.e., the claimed “ill-being detection model”), according to some implementations as described above in reference to Figures 3 A-3B and 4.” Par. 0086; Referring to the Specification Par. 0084, “ill-being detection model identifies user sentences with negative emotions,”; “The user may choose the most suitable words to describe their current emotions. The selected words will be sent to the sentiment classifier 822 (i.e., the claimed “keyword recognition model”).” Par. 0084; Referring to Specification Par. 0017, “keyword recognition model detects and categorizes positive and negative keywords,”; Morse, “Stress and Burnout script in an embodiment, and illustrates how the system (i.e., the claimed “key word recognition model”) utilizes the transcribed response to deliver such a follow-up instruction. When the transcript of question 2012 is sent to the server, a “postAction” is executed, in which the variable “_stress_load_followup” is set to the result of the “isUnderWordCountThreshold” equation. If the number of words in the spoken answer is under the threshold,” Par. 0126]
wherein the recommendation unit comprises a database of mental health support resources including Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, effective coping strategies, etc., and is configured to give recommendations to the user based on the detected mental health issues. [Zou, “to recommend best techniques for personal using depends on personal preference, so the function is actually recommendation.” 00226; “The more the user likes a treatment, the more likely the system (i.e., the claimed “recommendation unit”) recommends subsequent treatments (i.e., the claimed “mental health support resources”).” Par. 0233; “one or more machine learning recommendation engines and (ii) a list of cognitive techniques (i.e., the claimed “mental health support resources”) (e.g., any of the 78 cognitive techniques (i.e., the claimed “mental health support resources”) in a list 862).” Par. 00264; “cognitive behavioral therapy,” Par. 0004; “In some implementations, each of the models 204a-h may be based on AI technologies, deep learning and machine learning algorithms, and statistics methods, and other related technologies and tools, combined with CBT (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”), DBT, IPT and other related treatment and therapy.” Par. 00151; “The data may be stored in a database,” Par. 0070; Morse, “Beyond assessment (i.e., the claimed “detected mental health issues”), the platform leverages these screening insights (i.e., the claimed “detected mental health issues”) to deliver personalized recommendations for relevant mental health resources and content.” Par. 0025;’ “According to one or more embodiments, the AI generated assessment information can now integrate to internal and external libraries of content (e.g., videos (i.e., the claimed “inspiring movies”), articles (i.e., the claimed “informative articles”), exercises (i.e., the claimed “Cognitive Behavioral Therapy (CBT) exercises”)), in such a manner that it “intelligently” matches resources specifically to the patient in an effort to improve impact and engagement.” Par. 0137; “The search query is then used in nearest-neighbor search performed with SQL on the resource library's database table, using cosine similarity to find the closest matching search documents, thereby retrieving the associated resources.” Par. 0146]
Regarding Claim 5, Zou in view of Morse has been discussed above. The combination further teaches:
further comprises a summary unit that is configured to summarize each previous conversation, define prompts on the identity, intent, and behavior of the user in each previous conversation, and [Zou, “generate the summarized classification — the psychological and psychotherapeutic intent 322 — which may be used as an input to the cognitive reconstructing generator 348.” Par. 00110; “At the core of the platform, Artificial Intelligence (AI) is leveraged to allow patients to verbally describe their conditions in natural language; such conversations are converted to insightful notes and summaries that provide clinicians with unprecedented valuable patient insights and risk scores.” Par. 0025; “summary generated from the LLM (i.e., the claimed “summary unit”). This view is designed primarily for clinicians, and therefore, the LLM model used will produce the summaries and insights most relevant for users in that context (practitioners). Additional models can address other users of the system, such as patients, by providing unique summaries trained specifically for them.” Par. 0063; “The output allows practitioners-by interacting with this embodiment-to discern a patient's high-level status by looking at a single, detailed longitudinal text summary (i.e., the claimed “summarize each previous conversation”) of the patient's history. Because the longitudinal summary is to be based on an evolving data stream as new screening information is elicited and stored by the method and system, this patient overview will be dynamic and will evolve as the member's mental health situation evolves, keeping the overview for the practitioner constantly fresh and with up-to-date, current information.” Par. 0082; “LLM to derive structured summaries, insights and contextually sentiment information. Next, an instruction prompt is constructed containing the transcription and the derived summaries and analysis information.” Par. 0121]
store the summarized conversation and the related prompts for use to respond to the new message of the user. [Morse, “Because the longitudinal summary (i.e., the claimed “summarized conversation and the related prompts for use to respond to the new message of the user”) is to be based on an evolving data stream (i.e., the claimed “summarized conversation and the related prompts for use to respond to the new message of the user”)as new screening information is elicited and stored by the method and system,” Par. 0082; Zou, “conversation history” 00129]
Regarding Claim 6, Zou in view of Morse has been discussed above. The combination further teaches:
further comprises a retrieving unit that is configured to retrieve information from trusted internet sources to enrich the chatbot’s responses. [Zou, “Retrieval Augmented Generative searching (i.e., the claimed “retrieving unit” to intelligently recommend targeted mental health resources from internal or external libraries (i.e., the claimed “trusted internet sources”)” Par. 0023; “This capability provides a logically controlled mechanism for integrating a platform (i.e., the claimed “retrieving unit”) with external mental health libraries of resources (i.e., the claimed “trusted internet sources”) and leverages score insights to make data-driven and accurate recommendations as to which resources to use.” Par. 0149]
Regarding Claim 7, Zou in view of Morse has been discussed above. The combination further teaches:
further comprises a retrieving unit that is configured to retrieve information from trusted internet sources to enrich the chatbot’s responses. [Zou, “Retrieval Augmented Generative searching (i.e., the claimed “retrieving unit” to intelligently recommend targeted mental health resources from internal or external libraries (i.e., the claimed “trusted internet sources”)” Par. 0023; “This capability provides a logically controlled mechanism for integrating a platform (i.e., the claimed “retrieving unit”) with external mental health libraries of resources (i.e., the claimed “trusted internet sources”) and leverages score insights to make data-driven and accurate recommendations as to which resources to use.” Par. 0149]
Regarding Claim 10, Zou in view of Morse has been discussed above. The combination further teaches:
further comprises a converter unit that is configured to convert speech-to-text and text-to-speech to enhance chatbot-user interactions through voice communication. [Zou, “In some implementations, the AI psychotherapy system 100 (i.e., the claimed “converter unit”) supports both text-to-speech and speech-to-text voice interaction (i.e., the claimed “voice communication”).” Par. 00187; “A chat hub maintains and manages registered patients and guides the AI psychotherapy system 100 to communicate specific messages to specific patients.” Par. 00199]
Regarding Claim 11, Zou in view of Morse has been discussed above. The combination further teaches:
further comprises a converter unit that is configured to convert speech-to-text and text-to-speech to enhance chatbot-user interactions through voice communication. [Zou, “In some implementations, the AI psychotherapy system 100 (i.e., the claimed “converter unit”) supports both text-to-speech and speech-to-text voice interaction (i.e., the claimed “voice communication”).” Par. 00187; “A chat hub maintains and manages registered patients and guides the AI psychotherapy system 100 to communicate specific messages to specific patients.” Par. 00199]
Claims 8 and 9 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zou in view of Morse as applied in claim 1 above, and in further view of Parker-Bello (U.S. Patent 11,240,367), hereinafter referred to as Parker.
Regarding Claim 8, Zou in view of Morse has been discussed above. The combination fails to teach 24/7 counseling unit that is configured to allow users to connect with experienced psychologists whenever they require immediate assistance.
However, Parker teaches:
further comprises a 24/7 counseling unit that is configured to allow users to connect with experienced psychologists whenever they require immediate assistance. [Parker, Figure 1 “Therapists” (i.e., the claimed “experienced psychologists”); “Live chat capability (i.e., the claimed “24/7 counseling unit”) is provided on a 24/7 basis to connect human trafficking victims with mentoring (i.e., the claimed “counseling”, the claimed “whenever they require immediate assistance”), Col. 8:40-41; “In addition, the use of “one system” (i.e., the claimed “counseling unit”) that each anti trafficking agency assigned can add law enforcement, therapists (i.e., the claimed “experienced psychologists”) and a vetted resource provider to be able to connect with our victim/mentee in one place,” Col. 7:34-37]
Zou, Morse and Parker pertain to chat systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the chat systems art to modify Zou’s teachings of “guide the conversation (i.e., the claimed “guiding questions”), and generate and execute the personalized psychotherapy 330, like asking and generating questions (i.e., the claimed “guiding questions”) and answers to users, using the NLP and machine learning models 116 to process, analyze, and understand from the user’s answer (i.e., the claimed “defining guiding questions to encourage users to express their emotions, thoughts, and experiences”)” (Zou, Par. 00146) with the teachings of “large language models” and “articles” (Morse, Par. 0032, Par. 0137) taught by Morse and the teachings of “live chat capability provided on a 24/7 basis” (Parker, Col. 8:40-41) taught by Parker in order to “deliver proactive interventions”(Morse, Par. 0003) and “connect in one place” (Parker, Col. 7:36-37).
Regarding Claim 9, Zou in view of Morse and Parker has been discussed above. The combination further teaches:
further comprises a 24/7 counseling unit that is configured to allow users to connect with experienced psychologists whenever they require immediate assistance. [Parker, Figure 1 “Therapists” (i.e., the claimed “experienced psychologists”); “Live chat capability (i.e., the claimed “24/7 counseling unit”) is provided on a 24/7 basis to connect human trafficking victims with mentoring (i.e., the claimed “counseling”, the claimed “whenever they require immediate assistance”), Col. 8:40-41; “In addition, the use of “one system” (i.e., the claimed “counseling unit”) that each anti trafficking agency assigned can add law enforcement, therapists (i.e., the claimed “experienced psychologists”) and a vetted resource provider to be able to connect with our victim/mentee in one place,” Col. 7:34-37]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Paz et al., (U.S. Patent Application Publication 2026/0245720) teaches artificial intelligence therapy systems.
Lev (U.S. Patent Application Publication 2025/0317624) teaches artificial intelligence therapy systems.
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/EUNICE LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/ Supervisory Patent Examiner, Art Unit 2656
