Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
The office action sent in response to Applicant’s communication received on 11/19/2024 for the application number 18951699. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure was submitted on 3/5/2025 before the mailing data of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note:
Although a double patenting rejection is not made since the claims of current application and the copending application 18/827737 is obvious variant of each other at this time. However, through amendments, if the claims become obvious variant of each other, an appropriate double patenting rejection could be made at later stage of prosecution.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 101.
Claim 1 includes
A computer implemented method for providing a digital agent that interacts with a user, the digital agent providing audio and visual outputs and accepting audio inputs, the method comprising: (a) accepting audio input from a user and processing the audio input substantially in real-time to generate a transcription of the audio input; (b) determining from a specified category, a context of the audio input; performing with the context a contextual trade-off analysis of the transcription to generate an inferred intent of the audio input; (c) generating and providing to the user one or more outputs in the form of questions as a function of the inferred intent and (d) receiving further inputs from the user to determine one or more modified contexts and one or more inferred intents; and (e) providing outputs to the user in the form of visual representations of at least a portion of a human being speaking audio output, the audio output synchronized with the visual representations of the human being, the outputs providing a substantially live interaction with the user.
Steps (a)-(d) can be performed mentally since a human can accept the audio and transcribe it. Further humans can do a tradeoff the requirement received from the user and ask additional information to infer intent
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least a method hence a process. Thus, the claim is reciting a statutory category of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of steps (a)-( e) those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. As discussed, a person can accept the audio and transcribe it. Then a person can do a tradeoff the requirement received from the user and ask additional information to infer intent Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making doing analysis on the given data which are mental tasks humans routinely do,’” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as an abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Claim requires providing outputs to the user in the form of visual representations of at least a portion of a human being speaking audio output, the audio output synchronized with the visual representations of the human being, the outputs providing a substantially live interaction with the user. This limitation is merely an insignificant extra solutional activity of generating an output using avatar. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. At Step 2A, Prong Two, the additional element of usage of avatar or audiovisual output was found to be an extra solutional activity. The analysis under Step 2A, Prong Two is carried through to Step 2B. These additional elements should be re-evaluated in Step 2B, in which the extra-solution activity consideration takes into account whether or not an extra-solution activity is well-known. Using an avatar to output the response is well known in the art. Therefore, the additional elements individually or in combination with the judicial exception do not provide an inventive concept; so, the claim as a whole does not amount to significantly more than a generic instruction to “apply” the judicial exception. (Step 2B: NO). The claim is not eligible.
Regarding claims 2-7, based on given criteria human can respond and hence an abstract idea.
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.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claim(s) -- are rejected under 35 U.S.C. 103 as being unpatentable over Shiloni ( US 20240212826) and further in view of Micaelian ( US 6714929 )
Regarding claim 1, Shiloni teaches a computer implemented method for providing a digital agent that interacts with a user, the digital agent providing audio and visual outputs and accepting audio inputs, the method (agent, Fig 3 and 4) comprising:
accepting audio input from a user and processing the audio input substantially in real-time to generate a transcription of the audio input( If the input is in the form of speech, this is generally converted to text, which is then input into further subsystems as described above, which are adapted to generate appropriate responses, Para 0036) ;
determining from a specified category, a context of the audio input ( determine the current content – weather, brand etc., Para 0031, 0036, 0092) ; performing analysis of the transcription to generate an inferred intent of the audio input ( infer users goal/objective, Para 0036, 0040) ;
generating and providing to the user one or more outputs in the form of questions as a function of the inferred intent and receiving further inputs from the user to determine one or more modified contexts and one or more inferred intents ( If such user data is not available, then it is within provision of the invention that they be determined, for instance by a process of questioning or probing of the user by the agent. A number of options may thereby be determined, and presented to the user for instance by means of a list, points on a map, a set of images, or the like. The user may then choose one of these options, which is then booked, called, or otherwise contacted by the agent if appropriate, and the user agent may further offer help getting a delivery, or getting to the pharmacy, for example offering to call a taxi, Uber, or other transport, providing walking or public transport directions, or the like., Para 0036) ; and
providing outputs to the user in the form of visual representations of at least a portion of a human being speaking audio output, the audio output synchronized with the visual representations of the human being, the outputs providing a substantially live interaction with the user ( When a response has been generated by the contextual center (for instance in the form of a piece of speech to be generated, an image or set of images, map location, etc.), it is output to one or more of the appropriate media (e.g. speaker, screen, or any other human-machine interface output means) by way of a media interpreter. Thus for instance if the text to be output is ‘How about some fried chicken for dinner?’, this is sent to a text-to-speech media interpreter. This module incorporates information from the avatar to generate the appropriate audio waveform and avatar animation. The output will generally also take into account variables such as the user's state of mind (thus speech to an annoyed user may be short and to-the-point while speech to a relaxed user may be slower, in general matching the user's speed of speech). Once speech and animation is output, the high-level representation of the output is stored in the short term memory, and further input can then take this into account, Para 0096; wherein avatar is a visual representation of the agent, for instance an animated face or full 3D model of a human being or other figure, Para 0064)
Shiloni does not explicitly teach performing with the context a contextual trade-off analysis of the transcription to generate an inferred intent of the input
However, Micaelian performing with the context a contextual trade-off analysis of the transcription to generate an inferred intent of the input ( infer intent- for example based on trade-off of different weights, give the search results, Claim 1, Fig 10-13)
It would have been obvious having the teachings of Shiloni to further incorporate the teachings of Micaelian for POSITA before effective date to determine the importance of search criteria ( Abstract, Micaelian)
Regarding claim 2, Shiloni as above in claim 1, teach modifying the visual representations to adapt tone, personality and attitude of the visual representations in accordance with the context ( the appearance and behavior of the avatar of the invention are carefully tailored in order to elicit a bond (possible including affection, empathy, friendship, desire for acceptance and/or approval, and the like) between the avatar and the user. Once the user is invested in the companionship of the avatar, various therapies can be performed more effectively. The appearance and behavior mentioned include a host of physical cues including eye contact, gestures and other body language, tone of voice, vocabulary chosen, cadence, facial expressions, and so on as may be gleaned from research in the psychology of human interaction, as well as testing, possibly using the system itself. It is further within provision of the invention to read the tone, vocabulary, speech, cadence, body language, physical gestures, facial expressions, tics and other bodily expressions of emotion of the user, in an attempt to deduce the mindset, emotional state, and other aspects of the user., Para 0117)
Regarding claim 3, Shiloni as above in claim 1, wherein determining from a specified category, a context of the audio input comprises: retrieving the category ( for e.g. pharmacy, Para 0036) ; and generating a plurality of parameters that correspond to the category and that provide the context ( Then NLP of any variety known in the art reaches the conclusion that the user is looking for a pharmacy in his local environment, and user data may then be consulted to determine the user's preferences (e.g. brands of pharmacies, favorite locations and opening hours, and the like, Para 0036)
Regarding claim 6, Shiloni modified by Micaelian as above in claim 1, teach wherein generating and providing to the user one or more outputs in the form of questions as a function of the inferred intent comprises: retrieving the questions from a questions library and supplementing the questions with additional text retrieved from a fillers library (additional questions, Para 0036; wherein Once the user is invested in the companionship of the avatar, various therapies can be performed more effectively. The appearance and behavior mentioned include a host of physical cues including eye contact, gestures and other body language, tone of voice, vocabulary chosen, cadence, facial expressions, and so on as may be gleaned from research in the psychology of human interaction, as well as testing, possibly using the system itself. It is further within provision of the invention to read the tone, vocabulary, speech, cadence, body language, physical gestures, facial expressions, tics and other bodily expressions of emotion of the user, in an attempt to deduce the mindset, emotional state, and other aspects of the user., Para 0117, Shiloni; wherein the question library ( criteria database) is explained in Micaelian ) ; and modifying the questions retrieved from the questions library and the additional text retrieved from the fillers library in accordance with predetermined emotion, attitude, language and locale ( Once the user is invested in the companionship of the avatar, various therapies can be performed more effectively. The appearance and behavior mentioned include a host of physical cues including eye contact, gestures and other body language, tone of voice, vocabulary chosen, cadence, facial expressions, and so on as may be gleaned from research in the psychology of human interaction, as well as testing, possibly using the system itself. It is further within provision of the invention to read the tone, vocabulary, speech, cadence, body language, physical gestures, facial expressions, tics and other bodily expressions of emotion of the user, in an attempt to deduce the mindset, emotional state, and other aspects of the user., Para 0117))
Regarding claim 7, Shiloni modified by Micaelian as above in claim 1, teach wherein the digital agent provides audio and visual outputs and accepts audio inputs in connection with an application that permits the user to select a choice among a plurality of choices ( user can choose a pharmacy and brand, Para 0036, Shiloni; user choices, Col 2, line 53-60, Fig 7-9, Micaelian) , the method further comprising: (i) receiving further audio input from the user indicative of criteria for selection of a choice among a plurality of choices (user can keep interacting with the system, Para 0036-0037, Shiloni; user input for criteria selection, Fig 3, Micaelian ) ; (ii) determining from the choice a modified context ( avatar keeps modifying based on the goals and interruption etc., Para 0040, Shiloni; user selected criteria, Fig 3, Micaelian) ; (iii) generating a question to the user to cause the user to provide an answer to the question ( probing the user, Para 0036-0040; give user criteria , Fig 8-13, Micaelian) ; (iv) generating, from the question, a translated question in accordance with predetermined emotion, attitude, language and locale ( responding or asking to the user in a particular style, Fig 1-2, Para 0117) ; (v) generating audio-visual output to provide the translated question to the user ( avatar, Para 0040, Para 0117) ; (vi) receiving a response from the user to the translated question ( responses, Para 0046-0047, ) ; (vii) determining if the response is within the modified context ( system is able to detect detours along the route, Para 0040) ; (viii) if the response is within the modified context ( detect detours, Para 0040) , repeating operations (i) through (vii) a predetermined number of times ( whether the answer is correct or not, Para 0111) ; (ix) generate from a final response of the user a result corresponding to the user's selection of a choice among a plurality of choices ( based on user response for e.g. in pharmacy example get the brand, location hours etc., Para 0040, Shiloni; or final result based on user criteria, Fig 3, 10-13, Micaelian) ; and (x) present to the user in audiovisual output the result ( avatar can respond via different ways, Para 0077, 0117)
Regarding claim 8, Micaelian as above in claim 7, does not explicitly teach wherein the operation to generate from a final response of the user a result corresponding to the user’s selection of a choice among a plurality of choices comprises: performing tradeoff scoring with a weighted preference generator and a weighted preference data search engine ( trade off analysis, Col 2, line 15-20, claim 1) ; wherein the weighted preference generator accepts inputs from user that comprise, selection of search criteria, adjustment of weights with respect to the search criteria, and an indication of subjective ordering of at least one of the search criteria (adjustments of weights, Claim 1) ; wherein the weighted preference data searching includes determining weighted preference information including a plurality of the search criteria ( plural search criteria, Claim 1) and a corresponding plurality of the weights signifying the relative importance of the search criteria ( relative importance, Claim 2) , and querying an information source and ranking results of the querying based upon the weighted preference information ( querying a data source and ranking the results based upon said weighted preference information, Claim 2)
Regarding claim 9, Micaelian as above in claim 8, wherein determining weighted preference data comprises: determining whether or not there should be further input from the user; without further user input, providing at least one of default and automatically heuristically determined weights to the weighted preference data search engine; if further user input is taken, determining whether the user should be allowed to select criteria; if the user is not allowed to select criteria, providing at least one of default and automatically heuristically determined criteria selections for the user; if the user is allowed to select criteria, accepting criteria from the user; determining whether the user should be able to adjust weights and if not then providing at least one of default and automatically heuristically determined weights; and if the user is allowed to select weights, accepting weights from the user ( In operation 60, it is determined whether the weights are to be adjusted by the user. If not, default weights are assigned in an operation 64 (using the same definition as above), and operational control is turned over to operation 66 to determine if the user should be allowed subjective ordering. If operation 60 determines that the user is allowed to address the weight, an operation 68 inputs the adjusted weights on desired criteria from the user. Operational control is then turned over to operation 66., Col 6, line 24-33)
Regarding claim 10, Micaelian as above in claim 9, teach requesting subjective ordering by the user and if the user does not provide subjective ordering, then generating an ordering ( Fig 3)
Regarding claim 11, Shiloni teaches a server computer system that provides a virtual agent which mimics human-to-human interactions with a user (agent, Fig 3 and 4) , the server computer system comprising:
a plurality of questions to be provided to the user ( probing user or series of question for interrogating the user, 0036, 0105) ; a fillers library comprising additional conversation to be provided to the user( short term local context, Para 0036-0037) ; and a category that defines a category for interaction by the virtual agent ( determine the category, weather etc., Para 0036; The short and long term memories may be represented in terms of sets of processed data, which for instance have been categorized, classified, or otherwise reduced to representative form, Para 0098) ; one or more processors that execute instructions that cause the one or more processors to implement the virtual agent, the instructions comprising code that when executed by the one or more processors implements: a listening and transcription module that generates transcribed audio input spoken by a user into text input ( If the input is in the form of speech, this is generally converted to text, which is then input into further subsystems as described above, which are adapted to generate appropriate responses, Para 0036) ; an intent extraction module that receives the transcribed audio input and generates an inferred intent as a function of a context component generated by the listening and transcription module (Then NLP of any variety known in the art reaches the conclusion that the user is looking for a pharmacy in his local environment, and user data may then be consulted to determine the user's preferences (e.g. brands of pharmacies, favorite locations and opening hours, and the like, Para 0036, 0089) ;
a context generation module that retrieves the category ( for e.g. Pharmacy, Para 0036 or goal for e.g. car maintenance, Para 0105) and generates a plurality of parameters that correspond to the category ( for e.g. brand, location, hour etc., Para 0036, for steps to accomplish, Para 0105) and that provide a context to user input ( context – looking for pharmacy or car maintenance etc., Para 0105) ; a question generation module that generates, from the context, questions for the user from the questions library and that generates additional conversation with the user from the fillers library (interrogating the user based on the goal, Para 0105-0106) ;
a translation module that identifies a language, locale( given language, Para 0078) , emotional tone ( tonal analysis, Fig 2) and attitude for interaction with the user ( personality, Fig 1- 2) and that modifies questions and additional conversation generated by the question generation module in accordance with the language, locale, emotional tone and attitude (This system incorporates verbal, nonverbal, and environmental inputs and acts upon them using a set of expressive engines to (for example) output audio, animate a video character, send data over the internet, and in general operate any actuator that is connected to the system, Para 0083-0087) ; a speaking module that generates audiovisual output to the user from text generated by the question generation module and modified by the translation module, the audiovisual output including a live visual representation of a person or avatar speaking the text generated by the question generation module and modified by the translation module ( This system incorporates verbal, nonverbal, and environmental inputs and acts upon them using a set of expressive engines to (for example) output audio, animate a video character, send data over the internet, and in general operate any actuator that is connected to the system, Para 0068, 0083-0087) ; and a flow control module that coordinates operation of the listening and transcription module, the intent extraction module, the context generation module, the question generation module, the translation module, and the speaking module ( Fig 2-4)
Shiloni does not teach data storage having stored therein, a questions library comprising a plurality of questions to be provided to the user
However, Micaelian teach data storage having stored therein, a questions library comprising a plurality of questions to be provided to the user ( criteria is presented to the user to be selected, Fig 8-10; complex database queries can be made that have a degree of "fuzziness" which are based upon user or other client input as to the importance or "weight" of particular search criteria. By providing this functionality, the search engine can provide results that are ranked by factoring a number of weighted search criteria to obtain results that best match the client's specifications, Col 4, line 1-10)
It would have been obvious for POSITA having the teachings of Shiloni to further incorporate the concept from Micaelian before effective filing date to make the results more relevant ( Col 2, line 30-38, Micaelian )
Regarding claim 13, Shiloni modified by Micaelian as above in claim 1, teach wherein the context generation module generates the plurality of parameters by retrieving the plurality of parameters from a set of stored parameters ( for e.g. brand, location, hour etc., Para 0036, for steps to accomplish, Para 0105, Shiloni; fig 8-13, Micaelian)
Regarding claim 14, Micaelian as above in claim 11, teach wherein the intent extraction module generates the inferred intent by performing with the context component a contextual trade-off analysis of the transcribed audio ( plurality of weights signifying the relative importance of said search criteria and allowing tradeoffs expressed as a plurality of normalized fixed sum weights wherein determining weighted preference information; wherein the information is based on user input, Fig 3, Claim 1)
Regarding claim 16, Micaelian as above in claim 14, teach wherein the contextual trade-off analysis comprises: receiving one or more preferences provided by the user; assigning a weight to each preference provided by the user in accordance with inputs received from the user; and providing each preference and each associated weight to a recommendation engine ( fig 3)
Regarding claim 17, rejections analogous to claim 7, are applicable.
Regarding claim 18, rejection analysis to claim 11, are applicable.
Regarding claim 19, rejection analysis to claim 12, are applicable.
Regarding claim 20, rejection analysis to claim 13, are applicable.
Claims 4-5, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Shiloni ( US 20240212826) and further in view of Micaelian ( US 6714929 ) and further in view of Spiegel (US 20240249318)
Regarding claim 12, Shiloni modified by Micaelian as above in claim 1, does not teach wherein the intent extraction module generates the inferred intent by generating prompts, in accordance with the context component, to one or more large language models
However, Spiegel teaches wherein the intent extraction module generates the inferred intent by generating prompts, in accordance with the context component, to one or more large language models (prompt LLM and infer intent, Para 0089, Fig 3A)
It would have been obvious for POSITA having the teachings of Shiloni and Micaelian to further include the concept of Spiegel before effective filing date since LLM became well known in the art for their high adaptability and just better for intent recognition.
Regarding claim 15, Micaelian as above in claim 14, teach wherein the contextual trade-off analysis comprises: generating, from the context component, with a rule based expert system (tradeoff of weighted preferences, Fig 3, Claim 1), and receiving responses and identifying the inferred intent from the responses (based on user preference and weights output the results, Fig 8-13)
Shiloni modified by Micaelian does not teach generating, from the context component, one or more prompts and providing the prompts to a large language model; and receiving responses from the large language model and identifying the inferred intent from the responses
However, Spiegel teaches generating, from the context component, with a rule based expert system, one or more prompts and providing the prompts to a large language model (prompt LLM, Para 0089); and receiving responses (receive responses, Para 0089) from the large language model and identifying the inferred intent from the responses (infer intent, Fig 3A)
Shiloni modified by Micaelian has a concept of tradeoff analysis using a model, they differed by the claimed invention based on the concept that they don’t use LLM to generate the output, Spiegel teaches this concept and it would have been obvious for POSITA before effective filing date since LLM became well known in the art for their high adaptability and just better for intent recognition.
Regarding claim 4, rejection analysis to claim 15, are applicable. In addition, Micaelian teaches trade off of the analysis of the transcription to generate an inferred intent of the audio input (trade off based on users input and generate intent based on weights, Fig 3; intent here is what user wants for e.g. search for cars and color model etc.)
Regarding claim 5, Micaelian as above in claim 4, teach wherein performing with the context a contextual trade-off analysis of the transcription to generate an inferred intent of the audio input comprises: receiving one or more preferences provided by the user ( user preference, fig 3-8-13) ; assigning a weight to each preference provided by the user in accordance with inputs received from the user; and providing each preference and each associated weight to a recommendation engine ( provide search based on weights and preference, Fig 8-13, Claim 1)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jootoo ( US 20260003874) discloses he LLM service (122) employs techniques such as beam search, top-k sampling, or nucleus sampling to generate the response. These techniques help balance the trade-off between the quality and diversity of the generated output. Beam search maintains multiple candidate responses and selects the most likely one based on a scoring function. Top-k sampling restricts the sampling space to the top k most likely next words, while nucleus sampling sets a probability threshold and samples from the smallest set of words whose cumulative probability exceeds that threshold ( Para 0194)
Xia (US 20260197358 ) teach The mechanisms collect real-time online communication data of a participant computing device and executes first artificial intelligence (AI) computer model(s) on the collected real-time online communication data to extract key features indicative of an emotional state of a participant associated with the participant computing device. The mechanisms execute second AI computer model(s) to classify the extracted key features into an emotional feedback classification. The mechanisms map the emotional feedback classification to an emotional feedback element, in a library of emotional feedback elements, corresponding to the emotional feedback classification. The mechanisms modify a data stream, of real-time online communication, associated with the participant computing device to include the emotional feedback element.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Phan Hai can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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