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 .
Claims 1-20 are pending for examination in the instant application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/04/2025 is in
compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure
statement is being considered by the examiner.
Priority
The applicant is claiming foreign priority based on foreign applications KR10-2024-
0019718 filed on 02/08/2024. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claims(s) 1, 2, 4-7, 9-10, 15-16 and 18-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moya et al. (Pub. No.: US 2023/0298568 A1), hereinafter “Moya”
5. As to claim 1, Moya discloses a content curation method implemented on a computer device including at least one processor, the content curation method comprising: creating, by the at least one processor, recommended content for at least one topic using original content produced on at least one platform (Moya, [0002], A chatbot is a software application that executes on the site and that is used to interact with the user, often in lieu of a direct human interaction. [0027], the data model comprises an observation history, together with a set of events that have been determined to represent the conversation up to at least one turn.).;
providing, by the at least one processor, the recommended content to a user using a chatbot for content curation (Moya, [0057], the system receives input data, e.g., from a human designer, and in response configures a directed acyclic graph (DAG) that represents a conversation flow. [0056], the bot is controlled to be more deliberate in how it drives the conversation forward proactively.).;
collecting, by the at least one processor, a user response to the recommended content provided by the user to the chatbot (Moya, [0024], a multi-turn conversation is carried out between an end user 100, and a conversational bot software application 102. [0020], a non-linguistic action taken by an actor, e.g., clicking a button or a link on a Graphical User Interface (GUI). [0022], Utterance: a sequence of words that is grammatically complete; usually one sentence.).;
and reflecting, by the at least one processor, the user response in at least one of a user's personalization recommendation and a report related to the recommended content (Moya, [0031], the data model keeps track of any number of events, all of which can be actively “extended” at any time. [0067], By manually clustering groups of utterances, the provider trains a classifier to identify topics (or, more generally, user intents).).
6. As to claim 2, Moya discloses the content curation method of claim 1, wherein the creating comprises classifying the original content by topic and creating the recommended content for each topic (Moya, [0067], the service provider typically analyzes a customer's historical data (e.g., a set of historical human or bot conversational transcripts over some time period) and uses that data for model training. By manually clustering groups of utterances, the provider trains a classifier to identify topics (or, more generally, user intents). [0003], The method begins by configuring a conversational bot using a machine learning model trained to classify utterances into topics.).
7. As to claim 4, Mayo discloses the content curation method of claim 1, wherein the creating comprises creating the recommended content using the original content, and the original content was produced over a recent period of time on the at least one platform (Moya, [0031], the data model keeps track of any number of events, all of which can be actively “extended” at any time. [0030], the multi-turn conversation, the data model comprises the observation history, namely, a hierarchical set of events that have been determined to represent the conversation up to at least one conversation turn.).
8. As to claim 5, Mayo discloses the content curation method of claim 1, wherein the providing of the recommended content comprises providing reference information indicating the original content, and the original content is a source of the recommended content (Moya, [0027], the data model comprises an observation history, together with a set of events that have been determined to represent the conversation. [0037], candidate interpretation is a pointer identifying specific lines of historical data in the set of inter-related tables that comprise that relational database 402.).
9. As to claim 6, Mayo discloses the content curation method of claim 5, wherein the reference information includes information on at least one of a link and a creator of the original content (Moya, [0037], the candidate interpretation is a pointer identifying specific lines of historical data in the set of inter-related tables. [0030], the data model is persisted (and in the depicted tree grows right-ward), the conversation history between the user and the bot is represented. [0026], organized as clusters of utterances 212.).
10. As to claim 7, Mayo discloses the content curation method of claim 1, wherein the recommended content is randomly selected for the user from among one or more recommended contents classified by topic (Moya, [0048], ActionSelectorContinueLog—this is the simplest action selector. Every time critics approve a candidate interpretation, the system records in the data model which line of which transcript that candidate pointed at. This action selector blindly proposes that the next thing to say is whatever was said next in that particular transcript.).
11. As to claim 9, Mayo discloses the content curation method of claim 1, wherein the reflecting comprises extracting a user's personalization information for content recommendation based on the user response (Moya, [0067], the service provider typically analyzes a customer's historical data (e.g., a set of historical human or bot conversational transcripts over some time period) and uses that data for model training. By manually clustering groups of utterances, the provider trains a classifier to identify topics (or, more generally, user intents). [0080], The approach herein enables unsupervised AI-based self-serve model improvement wherein topics are exposed and added to a base AI model (or some other model) to improve its operation with respect to future human/bot conversations.).
12. As to claim 10, Mayo discloses the content curation method of claim 9, wherein the user's personalization information includes at least one of preference by topic and a subscription status (Moya, [0067], the provider trains a classifier to identify topics (or, more generally, user intents). [0003], The method begins by configuring a conversational bot using a machine learning model trained to classify utterances into topics.).
13. As to claim 15 (Moya: [0093-0095], CRM) is rejected for same rational as applied to claim 1 above.
14. As to claim 16 is rejected for same rationale as applied to claim 1 above.
15. As to claim 18 is rejected for same rationale as applied to claims 5, 6 above.
16. As to claim 19 is rejected for same rational as applied to claim 10 above.
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.
17. Claim(s) 3 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moya in view of Banerjee et al. (Pub. No.: US20180365212 A1), hereinafter “Banerjee”.
18. As to claim 3 Moya discloses the content curation method of claim 1, wherein the creating comprises: classifying the original content by topic (Moya, [0067], the service provider typically analyzes a customer's historical data (e.g., a set of historical human or bot conversational transcripts over some time period) and uses that data for model training. By manually clustering groups of utterances, the provider trains a classifier to identify topics.).
Moya however, is silent to discloses explicitly, creating the recommended content by summarizing the original content classified by topic using generative artificial intelligence (AI).
Banerjee discloses the similar concept in same field of endeavor including,
and creating the recommended content by summarizing the original content classified by topic using generative artificial intelligence (AI) (Banerjee, [0006], a method is disclosed for a novel, computerized framework for automatically generating and/or transforming chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. [0042], However, instead of simply outputting this response, as in conventional systems, the disclosed systems and methods automatically transform the initial chatbot response to produce domain-specific response that mimics a native style unique to the particular domain from which the query was entered.).
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Banerjee” in to those of Moya to automatically generate chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains.
19. As to claim 17 is rejected for same rationale as applied to claim 3 above.
20. Claim(s) 8, 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moya in view of Silverstein et al. (Pub. No.: US 20210334471A1), hereinafter “Silverstein”.
21. As to claim 8, Moya disclose the invention as applied above. Moya however is silent to discloses explicitly, wherein the collecting comprises, when it is determined that the user read the recommended content, collecting the user response to the recommended content through a conversation between the chatbot and the user after a certain period of time elapses from a corresponding point in time.
Silverstein discloses the similar concept in same field of endeavor including, wherein the collecting comprises, when it is determined that the user read the recommended content, collecting the user response to the recommended content through a conversation between the chatbot and the user after a certain period of time elapses from a corresponding point in time (Silverstein, Fig 5A [0081], the communications server 402 determines a frequency of responses (author post frequency) posted by a respondent over time. In implementations, author post frequency comprises an inter-arrival time between message postings of a participant. In embodiments, the communications server 402 takes a time of a post “n” and subtracts the time of the previous n-1 post.).
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Silverstein” in to those of Moya to determine a frequency of the responses of the at least one respondent over time and determine an evasiveness score for each of the responses based on natural language processing of the responses.
22. As to claim 11, Moya and disclose the invention as applied above. Moya however is silent to discloses explicitly, wherein the reflecting comprises providing an effect report by a user to a creator of the original content that is a source of the recommended content.
Silverstein discloses the similar concept in same field of endeavor including, wherein the reflecting comprises providing an effect report by a user to a creator of the original content that is a source of the recommended content (Silverstein, [0088], the communications server 402 optionally scores participants based on aggregate evasiveness of their responses over time. [0069], the ranking module 412 is configured to: utilize the bridged discourse model to rank evasiveness of individual responses, determine a display order of responses based on the ranking and change the display order as needed, insert an indicator of evasiveness in a virtual window of the text-based discourse session, score participants based on aggregate evasiveness, and manage participation based on participant scores. [0097], the communication server 402 retrieves metrics from those participants, such as a base evasiveness score based on semantic rating of the chat, the participants' expertise in the topic of the thread, and the velocity of conversation of the topic. The communication server 402 utilizes the bridged discourse model to rank each of the given responses to the question.).
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Silverstein” in to those of Moya where method includes accessing a real-time text-based discourse session comprised of multiple text-based posts published by participants, the posts including a question from an author and responses from at least one respondent; determining relationships between words in the text-based discourse session utilizing corpus linguistics analysis; determining a frequency of the responses of the at least one respondent over time; determining an evasiveness score for each of the responses based on natural language processing of the responses, wherein each of the evasiveness scores indicate a level of relevance of a response with respect to the question; determining rankings for each of the responses based on the determined relationships of words, the frequency of the responses, and the evasiveness scores; and determining a display order for the responses based on the rankings of the responses.
23. As to claim 12, Moya disclose the invention as applied above. Moya however is silent to discloses explicitly, wherein he reflecting comprises providing a statistical report that compiles user responses to the recommended content by topic to a creator of the original content.
Silverstein discloses the similar concept in same field of endeavor including, wherein he reflecting comprises providing a statistical report that compiles user responses to the recommended content by topic to a creator of the original content (Silverstein, [0098], linguistics analytics of step 501 of FIG. 5A along with the evasiveness score for the topic/question and the frequency of responses to output evasiveness rankings for participants.).
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Silverstein” in to those of Moya where evasiveness scores indicate a level of relevance of a response with respect to the question; determining rankings for each of the responses based on the determined relationships of words, the frequency of the responses, and the evasiveness scores; and to display the responses according to the determined rankings in order to present the most relevant and informative response first. Thereby improving the efficiency with a user review and evaluates the collected responses.
24. As to claim 13, Moya and disclose the invention as applied above. Moya however is silent to discloses explicitly, wherein the statistical report includes a summary of positive content and negative content among conversations exchanged with users about the recommended content.
Silverstein discloses the similar concept in same field of endeavor including, wherein the statistical report includes a summary of positive content and negative content among conversations exchanged with users about the recommended content (Silverstein, [0095], the communications server 402 optionally determines if evasiveness of a response is malicious or non-malicious based on sentiment analysis of the text-based discourse session.).
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Silverstein” in to those of Moya to check if an evasiveness score of one of the responses of the at least one respondent is malicious or non-malicious based on a sentiment analysis of the response and at least one selected from the group consisting of: issuing, by the computing device, guidance to one of the participants based on the determining the response is malicious or non-malicious and filtering, by the computing device, additional responses of the at least one respondent based on the determining the response is malicious or non-malicious.
25. As to claim 14, Moya disclose the invention as applied above. Moya however is silent to discloses explicitly, wherein content curation method of claim 1, further comprising: sharing, by the at least one processor, the user response through an open chat related to the recommended content, wherein the sharing includes, when the recommended content is created based on recent conversations of the open chat, summarizing conversations exchanged with users about the content, and sharing the summarized conversations on the open chat.
Silverstein discloses the similar concept in same field of endeavor including, wherein content curation method of claim 1, further comprising: sharing, by the at least one processor, the user response through an open chat related to the recommended content (Silverstein, [0065], the communications server 402 enables chat sessions to take place via one or more virtual rooms or channels using on-screen text, typed in real-time. [0086], responses to a preceding question are original displayed based on a time the responses were posted to the text-based discourse (e.g., chat) session.) , wherein the sharing includes, when the recommended content is created based on recent conversations of the open chat, summarizing conversations exchanged with users about the content, and sharing the summarized conversations on the open chat (Silverstein, [0080], the communications server 402 analyzes the text-based discourse and determines that the term “system” collocates with the term “and,” and determines that the term “and” collocates with the term “methods”.) [0079], At step 501, the communications server 402 analyzes the text-based discourse accessed at step 500 using a corpus of linguistics analysis (corpus linguistics). [0086], Responses to a preceding question are original displayed based on a time the responses were posted to the text-based discourse (e.g., chat) session.
Therefore, before the effective filing date of the instant application it would have
been obvious to one of the ordinary skilled in the art to incorporate the teachings of
“Silverstein” in to those of Moya to text-based discourse analysis and management and, more particularly, to determining evasiveness of responses in text-based discourse and managing communications of a community based thereon.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Luzhnica et. al. (US 11516158 B1) is one of the pertinent art discloses a system and methods and systems for automated or semi-automated generation of complex messages. Provided systems include neural network(s) that are trained with at least an initial training set including message records having specific characteristics, such as size and form characteristics, and recognize certain user inputted content as “instructional prompts.” The neural network(s) use the instructional prompts, training set, and other prompts to generate a distribution of semantic element options for each semantic element the system determines to include in system drafted message(s). The system selects from among such options to generate a plurality of draft messages which are presented to users for evaluation, editing, or transmission, with the instructional prompts treated as priority content. The systems and methods include mechanisms for reviewing and changing the instructional prompts based on factors that can include the content of the system-generated draft messages before further iterations to enhance the accuracy of future messages.
Wang el, al. (US 20220377028A1) is one of the pertinent art discloses a system and computer program products facilitating a process to identify and respond to a primary electronic message are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a determination component can determine that a primary electronic message has not received a response electronic message. An analysis component can generate a generated electronic message addressing the informational or emotional content of the primary electronic message. In one or more embodiments, an updating component can update the analytical model based on one or more feedbacks to the generated electronic message, where the analytical model can remain active while being updated. The one or more feedbacks can comprise feedback from an entity-in-the-loop monitoring outputs of the analytical model including the generated electronic message.
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/M.W./Examiner, Art Unit 2458
/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458
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