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 .
Acknowledgement
Acknowledgement is made of applicant’s amendment made on 04/28/2026. Applicant’s submission filed has been entered and made of record.
Status of the Claims
Claims 1-20 are pending. Claims 8, 10-18 and 20 were withdrawn from consideration.
Response to Applicant’s Arguments
In response to “Therefore, Chow does not disclose or suggest two separate processes for generating a system response. In particular, Chow does not disclose or suggest generating a system response using a first process or a second process, wherein the first process uses at least one trained language model to generate a dynamically determined system response and wherein the second process retrieves a pre-determined system response”.
In view of amendment to claims 1, 9, and 19, rejection under Chow has been withdrawn. Upon further search and consideration, please see details of a new combination of references set forth below.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-7, 9, and 19 are rejected under 35 USC 103(a) as being unpatentable over Darcy et al. (US 2022/0180067 A1) in view of Maitra et al. (US 11854540 B2).
Regarding Claims 1, 9, and 19, according to MPEP 2181I, examiners will apply 35 USC 112(f) to a claim limitation if it meets the following 3-prong analysis:
(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-ATA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f or pre-AJA 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.
In particular, claims 1, 9, and 19 recited “subject safety module configured to…” and “matching module performs…”.
Here, “module” is a nonce word substitute for “means”. Further, “module” is modified by functional terms like “performs” in “matching module performs” and configured to in “subject safety module configured to”. Finally, the claimed functions of the respective “matching module” and “subject safety module” contained no structure, material, or acts for performing the respective function.
Therefore, interpretation under 35 USC 112(f) in view of the specification at Fig. 13(a) is applicable to claims 1, 9, and 19:
Regarding claims 1, 9, and 19, Darcy discloses a dialogue system (Fig. 1A), comprising:
an input configured to receive input data relating to speech or text provided by a user (¶21, provide input phrase by text entry in a chat box or by speaking words aloud for a speech to text processor; e.g., ¶42, user device with a microphone);
an output configured to provide output data relating to speech or text to a user (¶42, speaker in user device 102; ¶69, display data on a display device or via audio signals generated by a speaker); and
one or more processors (Fig. 5, processors 502), configured to:
receive, by way of the input, input data relating to speech or text provided by a user (¶21, provide input phrase by text entry in a chat box or by speaking words aloud for a speech to text processor);
provide the input data to a subject safety module (¶¶38-39, NLP system can process the input phrase by applying a pattern matching classifier and a trained machine learning classifier), the subject safety module configured to receive the input data and evaluate the input data before a system response is output, evaluating the input data (¶22, process the input phrase by a pattern matching classifier and a trained machine learning classifier to determine if at least one classifier returns a trigger signal) comprising performing a first determination on the input data using a matching module (¶26, a pattern matching classifier) and a second determination on the input data using a trained model (¶27, trained machine learning classifier being RNN-LSTM or a pre-trained transformer based model such as GPT-2; ¶56, apply trained machine learning classifier to the input phrase to determine whether or not the input phrase is indicative of a crisis situation), wherein the matching module performs the first determination to determine whether the input data matches one or more items from a pre-determined set of one or more items (¶55, apply pattern matching classifier to input phrase includes searching the input phrases using a set of matching criteria (¶39, a set of regular expressions configured to trigger whenever certain trigger words or patterns of words are present) accessed from local memory as being indicative of a crisis situation; ¶63, identify “I want to kill myself” as matching the matching criteria with a trigger phrase “want to kill”);
parse an output of the evaluation to determine whether to trigger a first process or a second process (¶81, determine a crisis score from the input phrase from the pattern matching classifier and the trained machine learning classifier associated with whether or not the input phrase is likely to indicate a crisis situation; ¶82, determine if the crisis score is outside of a certain threshold; Fig. 4, step 406, perform crisis workflow if crisis score is outside of threshold);
responsive to determining that the second process is to be triggered, generate the system response using the second process, wherein the second process retrieves a pre-determined system response (¶72, engaging crisis management comprising a workflow of prompts and comments used to engage the individual to mitigate the crisis situation; e.g., crisis management tool being a list of actions or tasks to be accomplished, which can be selected to help mitigate the crisis situation);
output, by way of the output, the system response (¶68, accessing preset data (e.g., text strings / sympathetic phrases, images, computer links, and other contents) and present the preset data on a display).
Darcy does not disclose responsive to determining that the first process is to be triggered, generate a system response using the first process, wherein the first process uses at least one trained language model to generate a dynamically determined system response.
Maitra discloses a wellness system utilizing a generative pretrained transformer language model (Col 4, Rows 1-5) to process text, audio, and video to trigger a first process (Col 8, Rows 50-54) by generating a dynamically determined system response (Col 4, Rows 1-13, using a generative pretrained transformer language model to process text, audio, and video to determine a context for response to the user based on the response, the stress level, the overall depression level, the continuous affect prediction, and the emotion; Col 4, Rows 14-18, provide automated empathetic conversations in a proactive, personalized, contextual, and guided manner; Col 4, Rows 25-30, provide personalized conversations, trustworthy interactions, mood-aware interactions, context-aware conversations).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to parse an output of the evaluation corresponding to mental wellness help (Maitra, Col 11, Rows 52-55) and trigger a first process using at least one trained language model to generate a dynamically determined system response in order to provide automated empathetic conversations in a proactive, personalized, contextual, and guided manner (Maitra, Col 4, Rows 14-18).
Further regarding claim 19, Darcy discloses a non-transitory computer readable storage medium comprising computer readable code configured to cause a computer to perform the method of claim 9 and functions of claim 1 (¶95, processors for execution of program instructions stored in memory).
Regarding claim 2, Darcy discloses wherein responsive to the system response being generated by the second process, the one or more processors are further configured to provide a function to the user to contact a third party (¶¶68-69, accessing preset data and presenting preset data to mitigate crisis situation comprising presenting emergency information such as a preset list of emergency contact phone numbers).
Regarding claim 4, Darcy discloses wherein the pre-determined system response is retrieved based on a rule based dialogue flow (Fig. 4, step 406 shows a rule to trigger crisis workflow if crisis score is outside of a threshold; ¶72, engage a crisis management tool comprising a workflow of prompts and comments / list of actions or tasks to be accomplished / series of questions).
Regarding claim 5, Darcy as modified by Maitra discloses wherein the dynamically determined system response is generated by generating a system prompt comprising the input data and providing the system prompt to the at least one trained language model (Maitra, Col 4, Rows 1-13, using a generative pretrained transformer language model to process text, audio, and video to determine a context for response to the user based on the response).
Regarding claim 6, Darcy as modified by Maitra discloses wherein the output of the evaluation is a first output and wherein the subject safety module is configured to generate the first output based on the evaluation of the input data (Darcy, ¶81, determine a crisis score from the input phrase; ¶82, perform a crisis workflow if the crisis score is outside of a certain threshold), wherein the one or more processors are configured to select the second process if the first output includes an indication that the user is in crisis (Darcy, Fig. 4, step 406), and wherein the one or more processors are configured to select the first process if the first output does not include an indication that the user is in crisis (Darcy, ¶77, receiving a crisis confirmation response from the individual being a denial of the crisis situation, perform a modified abatement protocol asking questions to help calm or ground an individual without specifically noting that a crisis situation is occurring; this can be accomplished by guiding conversations to soothe the users per Maitra, Col 4, Rows 25-26; see further Maitra, Col 4, Rows 53-64 and Col 5, Rows 11-13, based on voice tonality, facial expression, body gesture of the individual not happy with a product and wanting his/her money back, generate automated empathetic conversation response for the individual that is fully unscripted).
Regarding claim 7, Darcy discloses wherein the trained model comprises a language model (¶27, machine learning classifier may be a pretrained transformer based GPT-2 model; per Maitra, Col 8, Rows 50-52, GPT-2 is a generative pretrained transformer language model) and the second determination comprises generating a system prompt including instructions to evaluate the input data and the input data, and providing the system prompt to the language model (¶56, apply the trained machine learning classifier to the received input phrase to determine whether or not the input phrase is indicative of a crisis situation; i.e., instructing or prompting the GPT-2 model to determine whether the input phrase indicates crisis).
Claim 3 is rejected under 35 USC 103(a) as being unpatentable over Darcy et al. (US 2022/0180067 A1) in view of Maitra et al. (US 11854540 B2) as applied to claim 1, in further view of Kidd et al. (US 2017/0228520 A1).
Regarding claim 3, Darcy discloses wherein responsive to the system response being generated by the second process, the one or more processors are further configured to communicate with a second user (¶70, opening a telephone app on a smartphone and automatically dialing emergency contact phone number; per ¶71, a family member or caregiver previously selected by the individual).
Darcy does not disclose the one or more processors are further configured to transmit information comprising the input data to the second user.
Kidd discloses transmitting user conversational input in response to detecting emergency situation trigger conditions (¶34).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to configure the processors to transmit information comprising the input data to the second user in order to transmit user conversational inputs and associated data in response to detecting emergency situation trigger condition (Kidd, ¶34).
Conclusion
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 07/08/2026
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