DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Information Disclosure Statement
The information disclosure statement filed 05 April 2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. In particular, foreign patent document cite no. 2 (JP 6550460 B2) is missing a translation to the English language and/or an explanation of relevance.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 112(a) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Provisional Application No. 63/548,341, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
In particular, the disclosure of the prior-filed application is silent regarding the claimed invention. In particular, the specification of US 63/548,341 is a copy of a journal article on Charisma virtual social training that discusses low immersion virtual reality for social skills training but is silent regarding speech to text analysis, any aspect of generating a social competency score, nor the use of artificial intelligence and training an artificial intelligence model, including all of the claimed “systems”. Similarly, the abstract and the single claim in the Provisional Application only recite “a system and method for virtual technology tools to identify, quantify, and improve social skills.”
Thus, claims 1-20 do not gain benefit of priority to US Application 63/548,341. Therefore, claims 1-20 have an effective filing date of 17 January 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 8-10 and 18-20 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 claims 8 and 18, it is unclear what constitutes “an associated score profile”. The disclosure does not aid understanding as it does not define what this is, nor how it is generated beyond claiming using a plurality of non-descript weight analysis systems. In particular, the specification is silent regarding an “associated score profile”.
Regarding claims 9 and 19, it is unclear what constitutes “an associated score”. The disclosure does not aid understanding as it does not define what this is, nor how it is generated beyond claiming using a plurality of non-descript weight analysis systems. In particular, the specification is silent regarding an “associated score” as claimed. Dependent claims 10 and 20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
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 1-20 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 including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The claims are directed to a method and a product which falls under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A system for generating objective assessment criteria of social skills, comprising:
a speech to text system operating on a processor and configured to receive audio data and to convert the audio data into text data for a first speaker and a second speaker;
a dialog management system operating on the processor and configured to process text data for the first speaker and text data for the second speaker to generate a social competency score for the first speaker based on responsiveness of the text data of the first speaker to text data of the second speaker; and
a response verification system operating on the processor and configured to generate a user control for the social competency score and to receive feedback data from the second speaker to accept or modify the social competency score, wherein the text data of the first speaker, the text data of the second speaker, the social competency score data and the feedback data is use to train an artificial intelligence system to improve recognition of responsiveness of the text data of the first speaker to text data of the second speaker.
Independent claim 11 recites:
A method for generating objective assessment criteria of social skills, comprising:
receiving audio data at a speech to text system operating on a processor;
converting the audio data into text data for a first speaker and a second speaker;
processing text data for the first speaker and text data for the second speaker using a dialog management system operating on the processor to generate a social competency score for the first speaker based on responsiveness of the text data of the first speaker to text data of the second speaker;
generating a user control for the social competency score using a response verification system operating on the processor;
receiving feedback data from the second speaker to accept or modify the social competency score; and
wherein the text data of the first speaker, the text data of the second speaker, the social competency score data and the feedback data is use to train an artificial intelligence system to improve recognition of responsiveness of the text data of the first speaker to text data of the second speaker.
All of the foregoing underlined elements amount to the abstract idea grouping of a certain method of organizing human activity because it is managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) as it is merely following rules or instructions by collecting information, analyzing the information, and outputting the results of the collection and analysis. They all also amount to the abstract idea grouping of mental processes as the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind with the aid of pen and paper but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process. Even if humans would use a physical aid to help them complete the recited steps, the use of such physical aid does not negate the mental nature of these limitations. Lastly, the steps in the independent and dependent claims associated with “generating a social competency score” amount to the abstract idea grouping of mathematical concepts because they recite mathematical calculations as defined in MPEP 2106.05(a)(2)(I) which recites that a “claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the ‘mathematical concepts’ grouping” because a “mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word ‘calculating’ in order to be considered a mathematical calculation. For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation."
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claim recites a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the claim does not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a system (claim 1), a speech to text system (claims 1 and 11), a processor (claims 1 and 11), a dialog management system (claims 1 and 11), a response verification system (claims 1 and 11), an artificial intelligence system (claims 1 and 11), a weight analysis system (claims 2 and 12), a strategic attention weight analysis system (claims 3 and 13), a discourse weight analysis system (claims 4 and 14), a theory of mind weight analysis system (claims 5 and 15), an expressive reasoning weight analysis system (claims 6 and 16), a transform weight analysis system (claims 7 and 17), and a plurality of weight analysis systems (claims 8, 9, 18, and 19). This is evidenced by the manner in which these elements are disclosed. See, for example, at least Fig. 1-3 which illustrate the components as non-descript black boxes in a conventional arrangement while Fig. 4-8 illustrate the claimed invention as purely software, and the specification which identifies that the “systems” are, at best, software modules operating on a processor. The claims do not recite any limitations that improve the functionality of the computer system because the claimed steps are merely performing the steps of processing data but are not tied to improving any functionality of a computer system. In particular, para. 16 of the specification incorrectly asserts that an “essential part of artificial intelligence is training to improve the function of the computer systems”. “Training” in the context of artificial intelligence is for “teaching” the model (otherwise referred to as setting the weights in an automated manner) to optimize performance on a dataset of sample tasks resembling its intended use. Similarly, the “response verification system” and its function is merely an identification that the artificial intelligence model is supervised. Thus, the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. The system is merely recited to be used, not improved. For instance, the speech to text system, as claimed and organized, merely adds insignificant extrasolution activity to the judicial exception (e.g., mere data gathering and processing in conjunction with a law of nature or abstract idea) while the remaining additional elements identified above merely indicate a field of use (i.e., use of a computer to implement the judicial exception). Thus, the components, identified above, are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For instance, para. 16 of the specification recites that the “present disclosure provides systems for assisting humans with artificial intelligence processing of psychological counseling data that allow humans to independently assess the accuracy of outputs and to provide training data to the artificial intelligence systems to improve their ability to process data for the purpose of providing psychological counseling.” Again, this is merely just an identification that the artificial intelligence model is supervised. Accordingly, based on all of the considered factors, these additional elements do not integrate the abstract idea into a practical application. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: YES).
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As addressed in Step 2A, Prong 2, above, the system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. The claims do not involve an improvement in a computer or other technology. Although the claims recite components (identified in Step 2A, Prong 2) for performing at least some of the recited functions, these elements are recited at a high level of generality and are not tied to performing any of the steps of the claimed method. This is evidenced by the lack of significant structure in the figures (i.e., Fig. 1-3 which illustrate the components as non-descript black boxes in a conventional arrangement while Fig. 4-8 illustrate the claimed invention as purely software) and the generic nature in which any structural items are described in the claims and the specification (all of the “systems” are merely recited to be loaded into a working memory of a processor to cause the processor to perform the algorithm indicating they are software modules, at best). IThus, the judicial exception is not implemented with, or used in, a particular machine or manufacture. Furthermore, this also evidences that the components are an attempt to link the abstract idea to a particular technological environment, but does not result in an improvement to the technology or computer functions employed. This also applies specifically to the speech to text system which merely adds insignificant extrasolution data-gathering and processing activity to the judicial exception (e.g., mere data gathering in conjunction with a law of nature or abstract idea) also found to not add significantly more, while the remaining additional elements identified above merely indicate a field of use (i.e., use of a computer to implement the judicial exception). For instance, the mere training of an artificial intelligence system, and use artificial intelligence as a whole, does not improve computer functionality as it merely invokes the use of a computer or other machinery in its ordinary capacity to process information. This is at least evidenced by the manner in which this is disclosed that indicates that Applicant believes the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 USC 112(a). The lack of improvement to the computer or other technology is evidenced by the lack of incorporation of specific rules which enable the automation of a computer-implemented task that previously could only be performed subjectively by humans. None of the hardware offer a meaningful limitation beyond, at best, generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Viewed as a whole, the additional claim elements do not provide a meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea of itself (STEP 2B: NO).
Therefore, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Reece et al. (US 2022/0343911, hereinafter referred to as Reece).
Regarding claims 1 and 11, Reece teaches a system (claim 1) and a method (claim 11) for generating objective assessment criteria of social skills, comprising:
a speech to text system operating on a processor and configured to receive audio data and to convert the audio data into text data for a first speaker and a second speaker (Reece, para. 39, “the conversation analytics system is configured to automatically generate a transcript based on the acoustic/video data.”);
a dialog management system operating on the processor and configured to process text data for the first speaker and text data for the second speaker to generate a social competency score for the first speaker based on responsiveness of the text data of the first speaker to text data of the second speaker (Reece, para. 35, “the communication skills of the mentee can be evaluated by performing computational methods (e.g., neural network based analysis) on the acoustic/video data.” Para. 69, “conversation analysis indicators can be one or more scores for the entire conversation or parts of the conversation, such as an overall effectiveness or quality rating for the conversation or parts of the conversation.”); and
a response verification system operating on the processor and configured to generate a user control for the social competency score and to receive feedback data from the second speaker to accept or modify the social competency score, wherein the text data of the first speaker, the text data of the second speaker, the social competency score data and the feedback data is use to train an artificial intelligence system to improve recognition of responsiveness of the text data of the first speaker to text data of the second speaker (Reece, para. 169, “In another implementation, ML training system 1848 communicates with an annotator for supervised learning, as shown in FIG. 11. For example, ML training system 1848 may transmit utterance features to an annotator, receive synthesized conversation features identified by the annotator, and subsequently train conversation synthesis ML system 1844 to automatically identify those conversation features.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 2-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reece as applied to claims 1 and 11 above, in view of Griffin (US 2023/0178217).
Regarding claims 2 and 12, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker (Reece, para. 71, “generates multiple conversation analysis indicators (e.g., conversation scores, conversation analysis indicators, openness scores, engagement scores, ownership scores, goal scores, interruptions scores, ‘time spent listening’ scores”; para. 100, “a conversation including multiple interactions between two speakers may indicate a conversation quality score”)
Reece does not explicitly teach using a weight analysis system that is configured to apply one or more weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a weight analysis system that is configured to apply one or more weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 3 and 13, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate a strategic attention score (Reece, para. 101, “conversation analysis indicators 612 include engagement scores, enthusiasm scores, ownership score, goal score, interruptions score, and ‘time spent listening’ score, and/or attention scores. For example, engagement scores may be influenced by the number of questions asked and the length of statements made. Enthusiasm scores may be influenced by changes in pitch, changes in voice volume, and excited facial expressions. Attention scores may be defined by gaze detection (e.g., on-screen gaze, eye contact gaze) and body pose detection from the video data.”).
Reece does not explicitly teach using a strategic attention weight analysis system that is configured to apply one or more strategic attention weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a strategic attention weight analysis system that is configured to apply one or more strategic attention weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 4 and 14, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate a discourse score (Reece, para. 101, “conversation analysis indicators 612 include engagement scores, enthusiasm scores, ownership score, goal score, interruptions score, and ‘time spent listening’ score, and/or attention scores. For example, engagement scores may be influenced by the number of questions asked and the length of statements made. Enthusiasm scores may be influenced by changes in pitch, changes in voice volume, and excited facial expressions. Attention scores may be defined by gaze detection (e.g., on-screen gaze, eye contact gaze) and body pose detection from the video data.”).
Reece does not explicitly teach using a discourse weight analysis system that is configured to apply one or more discourse weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a discourse weight analysis system that is configured to apply one or more discourse weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 5 and 15, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate a theory of mind score (Reece, para. 101, “conversation analysis indicators 612 include engagement scores, enthusiasm scores, ownership score, goal score, interruptions score, and ‘time spent listening’ score, and/or attention scores. For example, engagement scores may be influenced by the number of questions asked and the length of statements made. Enthusiasm scores may be influenced by changes in pitch, changes in voice volume, and excited facial expressions. Attention scores may be defined by gaze detection (e.g., on-screen gaze, eye contact gaze) and body pose detection from the video data.”).
Reece does not explicitly teach using a theory of mind weight analysis system that is configured to apply one or more theory of mind weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a theory of mind weight analysis system that is configured to apply one or more theory of mind weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 6 and 16, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate an expressive reasoning score (Reece, para. 140, “conversation labels 1210 include conversation effectiveness ratings. Conversation effectiveness ratings may be a subjective score or rating defining the effectiveness of the conversation towards a particular goal or activity. For example, conversation labels 1210 may include coaching effectiveness scores.”).
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using an expressive reasoning weight analysis system that is configured to apply one or more expressive reasoning weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 7 and 17, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate a transform score (Reece, para. 71, “openness scores,… ownership scores”).
Reece does not explicitly teach using a transform weight analysis system that is configured to apply one or more transform weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a transform weight analysis system that is configured to apply one or more transform weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 8 and 18, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate an associated score profile (Reece, para. 71, “conversation analytics system 400 generates multiple conversation analysis indicators (e.g., conversation scores, conversation analysis indicators, openness scores, engagement scores, ownership scores, goal scores, interruptions scores, ‘time spent listening’ scores, and emotional labels).”).
Reece does not explicitly teach using a plurality of weight analysis systems that are each configured to apply one or more weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a plurality of weight analysis systems that are each configured to apply one or more weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 9 and 19, Reece teaches the system of claim 1 and the method of claim 11 wherein the dialog management system is configured to generate the social competency score using the text data of the first speaker and the text data of the second speaker to generate an associated score (Reece, para. 71, “conversation analytics system 400 generates multiple conversation analysis indicators (e.g., conversation scores, conversation analysis indicators, openness scores, engagement scores, ownership scores, goal scores, interruptions scores, ‘time spent listening’ scores, and emotional labels).”).
Reece does not explicitly teach using a plurality of weight analysis systems that are each configured to apply one or more weights.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Reece to include using a plurality of weight analysis systems that are each configured to apply one or more weights because “’training’ a computer-implemented machine learning model refers to any process by which parameter, hyper parameters, weights, and/or any other value related model accuracy are adjusted to improve the fit of the computer-implemented machine learning model to the training data.” See Griffin at para. 157.
Regarding claims 10 and 20, Reece teaches the system of claim 9 and the method of claim 19 wherein the scores for the plurality of weight analysis systems are used to generate proposed interactions between the first speaker and the second speaker (Reece, para. 77, “the conversation analysis indicators can be mapped to coaching suggestion inferences or actions providing the coach suggestions on topics, coaching materials, engagement techniques, etc., to better engage with the mentee or improve coaching.” Para. 207, “rules are applied to conversation analysis indicators to determine actions (e.g., notifications, triggering new coaching pairing, suggesting training materials, selecting coaching techniques, etc.).”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Beaumont et al. (US 2021/0174933) discloses improving social-emotional skills including the use of machine learning techniques to reference metrics derived from reference subject data.
Cummins et al. (US 2021/0202065) discloses analyzing the interactions between a therapist and patient using artificial intelligence and provide recommendations based on the analysis.
MacKay et al. (US 2022/0245354) discloses detecting a psychological affect in a natural language content with a rule-based engine.
Glick et al. (US 2024/0387020) discloses AI-assisted treatment optimization leveraging treatment fidelity data and providing better clinician feedback and customization of treatment plans.
Long (US 2025/0078675) discloses social interaction training using machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
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/DANIEL LANE/Examiner, Art Unit 3715