DETAILED ACTION
This office action is in response to Applicant’s submission filed on 1/9/2025. Claims 1-20 are pending in the application of which Claims 1, 11, and 19 are independent and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s)(IDS) submitted on 6/23/2026 has been considered by the examiner.
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 non-statutory subject matter without significantly more. The claims as whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
The independent claims 1, 11 and 19 disclose the same inventive concept with slightly different claim language. In this analysis, claim 1 is analyzed as a representative of the other two independent claims. The independent claim 1 recites: “ … a query response communicated by a conversational agent, via a conversational agent device, to a device associated with a user in response to a query received from the device, determining, by a system comprising at least one processor, a conditional aspect based sentiment score associated with a statement, received from the device in response to the query response, based on a result of evaluating statement data representative of the statement, wherein the statement is based on user input associated with the user that is obtained in response to the query response; and determining, by the system, whether the statement is classified as positive feedback associated with the user with respect to the query response based on the conditional aspect based sentiment score and a defined threshold conditional aspect based sentiment score that is a positive feedback indicator.
This application describes a way for a chatbot or other conversational agent to infer whether a user liked its answer without asking the user for explicit feedback. After the agent answers a user’s first question, it watches what the user says next. The next message may be a follow-up question, a new question, or a short statement. The system compares that message with both the original question and the answer that was given. It also analyzes the sentiment of the user’s message, including the aspect-based sentiment. Those scores are combined into a conditional aspect-based sentiment score (CASS). If the score passes one of the defined thresholds, the system treats the message as positive feedback or, in some cases, negative feedback. The approach is intended to reduce the need for surveys, thumbs-up/down prompts, or other explicit feedback requests.
The aforementioned limitations, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “conversational agent”, and “processor”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “conversational agent”, and “processor” language, “a “query response” communicated by a conversational agent, via a conversational agent device, to a device associated with a user in response to a “query received” from the device, “determining”, by a system comprising at least one processor, a “conditional aspect based sentiment score” associated with a statement, “received” from the device in response to the query response, based on a result of “evaluating statement data” representative of the statement, wherein the statement is based on user input associated with the user that is obtained in response to the query response; and “determining”, by the system, whether the statement is classified as positive feedback associated with the user with respect to the query response based on the conditional aspect based sentiment score and a defined threshold conditional aspect based sentiment score that is a positive feedback indicator. In the context of this claim encompasses the human obtain a response to his query given to a chatbot, and based on the response being correct or not, the follow-up query to the chatbot can be used by the bot (implicit) to decides if the response to the original query was on the point, or wrong. The following steps which deal with generating a response is a matter of design by the user/developer. The conversational AI is doing what the AI model is programmed to do which is to output a response. Therefore, the AI is nothing but generic tool being used to apply the abstract idea.
All of these steps can be performed in the mind and/or using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements - using a “conversational AI”, and “processor” to perform all of the above-mentioned steps. The use of a “conversational AI”, and “processor” is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The only element mentioned is the usage of a “conversational AI”, which due to lack of specificity can be considered as a generic processor. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processor is merely for the purpose of data gathering and/or insignificant extra-solution activity that amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The dependent Claims do not add limitations that could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim:
Similarly, dependent claims 2- 10, 12-18, and 20 are also not patent eligible as they include additional steps that are directed towards evaluating, determining follow-up response, determining sentiment score, determining implicit feedback, determining relationship between queries, determining sentiment of the query and follow-up query, analyzing a similarity between queries, performing analysis, determining overall aspect based sentiment score, determining average sentiment score, etc. and classifying follow-up query as positive feedback, and thus are also directed towards an abstract idea as they can be practically performed in the mind without being integrated into a practical application, or including any additional elements sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-3, 7, 9, 11 - 12, 15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Davis et al. (US20210049476A1)(herein "Davis"), and further view of Narayanan et al. (Sentiment Analysis of Conditional Sentences)(herein “Narayanan”).
Regarding claims 1, 11 and 19 Davis teaches [A method comprising – claim 1], [A system, comprising: at least one memory that stores computer executable components; and at least one processor that executes computer executable components stored in the at least one memory, wherein the computer executable components comprise: - claim 11], and [A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations, comprising: - claim 19] (Davis, Par. 0007:” … computer usable program product. The computer usable program product includes one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices.”, and Par. 0008:” … The computer system includes one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories.”, and Par. 0088:” … A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media … “).
[in connection with a query response communicated by a conversational agent, via a conversational agent device, to a device associated with a user in response to a query received from the device, - claim 1], [a score determinator that, with regard to a query response transmitted by a conversational agent, via a first device, to a second device associated with a user in response to a query received from the second device, - claim 11], [with regard to a query response communicated by an interactive agent, via a first device, to a second device, associated with a user identity that identifies a user, in response to a first query received from the second device, - claim 19] (Davis, Par. 0072:” Chat 400 is a transcript of a portion of an interaction [query/response] between a user and a chatbot [ conversational agent] including application 300. Chat 400 includes welcome 402, a welcome message from the chatbot, and query 404, a natural language query about vacations in Italy in the summer.”, and Par. 0073:” Application 300 applies query 404 to compendium 410, which includes responses 412, 414, 416, and 418. …”).
[determining, by a system comprising at least one processor, a [[conditional aspect based]] sentiment score associated with a statement, received from the device in response to the query response, based on a result of evaluating statement data representative of the statement, wherein the statement is based on user input associated with the user that is obtained in response to the query response; and – claim 1], [determines a [[conditional aspect based]] sentiment score associated with a message received, via the second device based on input from the user, in response to the query response, based on a result of an analysis of message information representative of the message; and – claim 11], [determining a [[conditional aspect based]] sentiment value associated with a message received from the second device in response to the query response based on a result of analyzing message information representative of the message associated with the user identity, wherein the message is a second query or a user response to the query response; and – claim 19] (Davis, Par. 0008:” … devices for execution by at least one of the one or more processors via at least one of the one or more memories.”, and Par. 0031:” … Another embodiment collects implicit feedback, by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative. Another embodiment collects implicit feedback by analyzing an amount of time the querier spends interacting with the response or the information included in the response.”)
[determining, by the system, whether the statement is classified as positive feedback associated with the user with respect to the query response based on the [[conditional aspect based]] sentiment score and a defined threshold conditional aspect based sentiment score that is a positive feedback indicator. – claim 1], [a feedback evaluator that determines whether the message is representative of positive feedback associated with the user with respect to the query response based on the [[conditional aspect based]] sentiment score and a defined threshold conditional aspect based sentiment score that indicates whether feedback is positive. – claim 11], [determining whether the message is indicative of positive feedback or negative feedback associated with the user identity with respect to the query response based on the [[conditional aspect based]] sentiment value and a defined threshold [[conditional aspect based]] sentiment criterion that is indicative of feedback types comprising the positive feedback and the negative feedback. – claim 19] (Par. 0031:” … conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”, and Par. 0080:” … Chats 720 and 730 depict solicitation of implicit feedback. In chat 720, response 722 presents the information in text 614. The querier responds with query 724, which application 300 analyzes to determine that the querier is satisfied with the response. In chat 730, response 722 also presents the information in text 614. This time the querier responds with query 734, which application 300 analyzes to determine that the querier is not satisfied with the response.”)
[claim 19 only] user identity (Davis, Par. 0086:” In some other cases, the SaaS implementation of the application may permit a possible exception of limited user-specific application configuration settings [user identity]”.
Davis, does not teach conditional aspect based sentiment, however, Narayanan teaches Abstract:” … sentiment analysis of conditional sentences. The aim is to determine whether opinions expressed on different topics in a conditional sentence are positive, negative or neutral.”)
Narayanan is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis further in view of Narayanan to provide sentiment analysis of conditional sentences. Motivation to do so would allow to eliminate false generalization.
Regarding claim 2, Davis, as modified above, teaches the method claim of 1.
Davis, as modified above, further teaches wherein the query is a first query received by the conversational agent, via the conversational agent device, from the device, (Davis, Par. 0028:” … For example, if an incoming [first] query asks about vacations in Italy, and an embodiment provided a response about vacations in France, the querier [user] might respond with an additional [second] query such as, “I asked about Italy, not France!” By analyzing this additional query, an embodiment concludes that the previously-provided response was incorrect, and that a new, correct, response is needed.”)
wherein the statement is a second query or a user response received by the conversational agent, via the conversational agent device, from the device, and wherein the second query or the user response is received in response to the query response. (Davis, Par. 0028:” … For example, if an incoming [first] query asks about vacations in Italy, and an embodiment provided a response about vacations in France, the querier [user] might respond with an additional [second] query such as, “I asked about Italy, not France!” By analyzing this additional query, an embodiment concludes that the previously-provided response was incorrect, and that a new, correct, response is needed.”)
Regarding claims 3, and 12, Davis, as modified above, teaches the method, and the system claims of 1, and 11, respectively.
Davis, as modified above, further teaches [wherein the statement comprises implicit feedback associated with the user that is not explicit feedback requested by the conversational agent. – claim 3], [wherein the input is second input, wherein the query is a first query received by the conversational agent, via the first device, based on first input from the user, via the second device, wherein the message is a second query or a user response received by the conversational agent, via the first device, based on the second input from the user, via the second device, wherein the second query or the user response is received in response to the query response, and wherein the message is representative of implicit feedback associated with the user that is not explicit feedback requested or solicited by the conversational agent from the user. – claim 12] (Davis, Par. 0031:” … Another embodiment collects implicit feedback, by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement/second input/message]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative. Another embodiment collects implicit feedback by analyzing an amount of time the querier spends interacting with the response or the information included in the response.”)
Regarding claims 7, and 15, Davis, as modified above, teaches the method, and the system claims of 1, and 11, respectively.
Davis, as modified above, further teaches [determining, by the system, that the conditional aspect based sentiment score associated with the statement satisfies the defined threshold conditional aspect based sentiment score; and – claim 7], [wherein the feedback evaluator determines that the conditional aspect based sentiment score associated with the message satisfies the defined threshold conditional aspect based sentiment score, and, - claim 15] (Davis, Par. 0031:” … For example, a sentiment of a follow-up query [statement] such as “great, thanks” could be analyzed as “positive”, or an emotion of the follow-up query could be analyzed as high in joy or low in anger.”) Note: sentiment being positive or high in joy, reads on statement satisfying the threshold.
[classifying, by the system, that the statement represents the positive feedback based on determining that the conditional aspect based sentiment score associated with the statement satisfies the defined threshold conditional aspect based sentiment score. – claim 7], [based on the conditional aspect based sentiment score being determined to satisfy the defined threshold conditional aspect based sentiment score, determines that the message is representative of the positive feedback. – claim 15] (Davis, Par. 0032:” If user feedback is above a threshold level of satisfaction with a presented result, one embodiment uses information in the presented result to form a response and add the response to the compendium for use in answer future queries. “, and Par. 0073:” … In particular, application 300 uses a natural language classifier to classify query 404 as matching one or more stored potential responses in the compendium. The results are shown in classifier output 420, in which each of responses 412, 414, 416, and 418 is scored at 0.4 on a 0-1 scale. Because each response has a score in between a higher threshold (here, 0.5) and a lower threshold (here, 0.25), application 300 concludes that query 404 is sufficiently relevant to the subject matter of compendium 410 that a new response to query 404 should be added to compendium 410.”) Note once classifying query as matching, reads on satisfying threshold.
Regarding claims 9, and 17, Davis, as modified above, teaches the method, and the system claims of 1, and 11, respectively.
Davis, as modified above, further teaches [wherein the conversational agent comprises or is associated with a trained artificial intelligence-based model, and wherein the method further comprises: determining, using the trained artificial intelligence-based model of the system, first query response data relating to the query response based on an artificial intelligence-based analysis of query data relating to the query and document data of a group of electronic documents, wherein the group of electronic documents is determined to be relevant to the query and is retrieved, utilizing an electronic document retrieval process, in connection with the query; and – claim 9], [ wherein the conversational agent comprises or is associated with a trained artificial intelligence-based model, wherein the trained artificial intelligence- based model determines first query response information relating to the query response based on an artificial intelligence-based analysis of query information relating to the query and document information of a group of electronic documents determined to be associated with the query, - claim 17] (Davis, Par. 0029:” … a natural language analysis [trained AI] of the query to construct a search for information that could constitute a new response to be added to the compendium. One embodiment can use, as a data source for the search, a corpus of documents or other narrative text that has been analyzed and indexed by a natural language analysis tool. Another embodiment can utilize any search engine [trained artificial intelligence-based model] tool that has an application program interface (API) capable of being used by a software application to find and retrieve the information. The information is typically in the form of narrative text.”, and Par. 0030:”An embodiment scores and ranks the search results according to each result's relevance to the natural language query. One embodiment ranks the search results according to a score returned by the search engine corresponding to the degree to which the result corresponds to the search. Another embodiment ranks the search results by analyzing both the query and each result to count keywords, concepts, entities, or a combination in common between the query and each result. Keywords, concepts, and entities can be identified by using any available natural language analysis technique. Another embodiment uses a statistical model [trained artificial intelligence-based model] to combine the search engine score and the common keywords, concepts, entities, or a combination into one combined numerical score, then uses the combined numerical store to rank the results. Other techniques of computing a relevance between a result and a query are also possible and contemplated within the scope of the illustrative embodiments. In addition, an embodiment need not rank all the results, but instead stop scoring and ranking once one, or a particular number of, results above a threshold relevance score have been obtained.”)
[communicating, by the conversational agent of the system, via the conversational agent device, second query response data relating to the query response to the device for rendering to the user, wherein the second query response data is the first query response data or is based on the first query response data. – claim 9], [wherein the conversational agent transmits, via the first device, second query response information relating to the query response to the second device for presentation to the user, and wherein the second query response information is the first query response information or is based on the first query response information. – claim 17] (Davis, Par. 0004:” A chatbot or conversational interface is software that conducts a natural language conversation with a human user. Typically, the natural language conversation is conducted in text form. However, input to the chatbot can also be converted from another modality, such as speech, into text for processing, then output from the chatbot converted back into speech a human can hear.”, and Par. 0031:” … For example, if the response, to conserve screen space in a chat displayed on a mobile device with a small screen, included only a one sentence response and a uniform resource locator (URL) to consult for further detail, and the querier selected the URL and spent more than a threshold amount of time at the site denoted by the URL, this could indicate the querier's satisfaction with the response.”, and Par. 0040:” … Network 102 is the medium used to provide communications links between various devices and computers connected together within data processing environment 100. …”, and Par. 0068:” User feedback module 340 presents one or more of the ranked results to the querier, and attempts to collect feedback on the presented results. Module 340 can solicit explicit feedback, asking the querier to select a specific feedback item, answer a question regarding the quality of the response, or otherwise explicitly provide a feedback response.”)
Claims 4, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Davis, and Narayanan, and further view of Zhu et al. (US 9117006 B2)(herein "Zhu").
Regarding claims 4, and 13, Davis, as modified above, teaches the method, and the system claims of 1, and 11, respectively.
Davis, as modified above, does not teach, however, Zhu teaches [determining, by the system, a first similarity score that indicates a first relationship between the query and the statement based on a first analysis of respective keywords of query data representative of the query and the statement data representative of the statement, wherein the first similarity score indicates a first level of similarity, or a level of logical continuity, between the query and the statement; - claim 4], [wherein the analysis is a third analysis, wherein the result is a third result, wherein the score determinator determines a first similarity score that is representative of a first relationship between the query and the message based on a first result of a first analysis of respective keywords of query information representative of the query and the message information representative of the message, wherein the first similarity score is representative of a first level of similarity or a level of logical continuity between the query and the message, - claim 13] (Zhu, Claim 1: “ … determine a first similarity value between one or more industries associated with the first candidate keyword and one or more industries associated with sets of product information that are relevant to the first candidate keyword; …”).
[determining, by the system, a second similarity score that indicates a second relationship between the query response and the statement based on a second analysis of query response data representative of the query response and the statement data, wherein the second similarity score indicates a second level of similarity between the query response and the statement, and – claim 4], [wherein the score determinator determines a second similarity score that is representative of a second relationship between the query response and the message based on a second result of a second analysis of query response information representative of the query response and the message information, wherein the second similarity score is representative of a second level of similarity between the query response and the message, wherein the second level of similarity between the query response and the message is derived based on the second result of the second analysis, and – claim 13] (Zhu, Claim 1: “ … determine a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and …”).
[claim 4 only] wherein the determining of the second level of similarity between the query response and the statement comprises deducing the second level of similarity based on the second analysis; and (Zhu, Claim 1: “ … determine a second similarity value between the one or more industries associated with the first candidate keyword and one or more industries of one or more seller users associated with the sets of product information that are relevant to the first candidate keyword; and
[determining, by the system, a query continuity score based on the first similarity score and the second similarity score. – claim 4], [wherein the score determinator determines a query continuity score as a function of the first similarity score and the second similarity score. – claim 13] (Zhu, Claim 1: “ … determine the industry index value [continuity score] associated with the first candidate keyword based at least in part on a combination of the first similarity value and the second similarity value; and …”).
Zhu is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Zhu to determine, by the system, a first similarity score that indicates a first relationship between the query and the statement based on a first analysis of respective keywords of query data representative of the query and the statement data representative of the statement, wherein the first similarity score indicates a first level of similarity, or a level of logical continuity, between the query and the statement; determining, by the system, a second similarity score that indicates a second relationship between the query response and the statement based on a second analysis of query response data representative of the query response and the statement data, wherein the second similarity score indicates a second level of similarity between the query response and the statement, and wherein the determining of the second level of similarity between the query response and the statement comprises deducing the second level of similarity based on the second analysis; and determining, by the system, a query continuity score based on the first similarity score and the second similarity score. Motivation to do so would measure the current query's distance from the previous query, preventing the system from drifting into irrelevant topics during multi-step conversations.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Davis, Narayanan, and Zhu, and further view of Shahzad Khan (US 20130311485 A1)(herein " Khan ").
Regarding claim 5, Davis, as modified above teaches the method claim of 4.
Davis, as modified above, further teaches wherein the respective keywords are respective first keywords, and wherein the method further comprises: determining, by the system, respective aspect based sentiment scores indicative of a sentiment of the statement with respect to the query response based on [[a third analysis of respective second keywords of the statement data representative of the statement, wherein the respective second keywords comprise at least some of the respective first keywords]]; and (Davis teaches determining, by the system, respective aspect based sentiment scores indicative of a sentiment of the statement with respect to the query response based on [[a third analysis of respective second keywords of the statement data representative of the statement, wherein the respective second keywords comprise at least some of the respective first keywords]]; (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”)
determining, by the system, an overall aspect based sentiment score indicative of the sentiment of the statement with respect to the query response based on the respective aspect based sentiment scores, (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”) Note: identification of a sentiment of text in general, reads on an overall sentiment.
wherein the determining of the conditional aspect based sentiment score associated with the statement comprises determining the conditional aspect based sentiment score associated with the statement based on the query continuity score and the overall aspect based sentiment score. (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”, and Par. 0064:” … query is sufficiently relevant to the subject matter …) Note: when query is sufficiently relevant to the subject matter, reads on based on the query continuity score.
Davis, as modified above, does not teach, however, Khan teaches a third analysis of respective second keywords of the statement data representative of the statement, wherein the respective second keywords comprise at least some of the respective first keywords; (Khan, Par. 0095:”… item of content without any prior consideration or analysis [third analysis] and hence may be an item of content … thereby allowing an organization the ability to monitor sentiments in essentially real-time. … It would be further evident that input term 410 may be initially filtered for an occurrence of a particular keyword [second keyword], subset of a set of keywords [comprise at least some of the respective first keywords], or all keywords in a set of keywords. Optionally the content may also be processed such that locations of the negative and positive sentiment seed terms relative to one or more keywords are determined and only those meeting a predetermined threshold condition are counted into the respective negative and positive sentiment seed co-occurrence counts.”)
Khan is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Khan to conduct a third analysis of respective second keywords of the statement data representative of the statement, wherein the respective second keywords comprise at least some of the respective first keywords. Motivation to do so would indicate the user is comparing options or drilling down toward a transaction rather than just casually browsing.
Claims 6, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Davis, Narayanan, Zhu and Khan, and further view of Phani Sai Ganesh (US 20260154506 A1)(herein "Ganesh").
Regarding claim 6, Davis, as modified above, teaches the method claim of 5.
Davis, as modified above, does not teach, however, Ganesh teaches wherein the determining of the overall aspect based sentiment score indicative of the sentiment of the statement with respect to the query response based on the respective aspect based sentiment scores comprises determining the overall aspect based sentiment score based on an average value or a median value of the respective aspect based sentiment scores. (Ganesh, Par. 0041]:”For aspect weights, embodiments determine the overall sentiment of the text, by using a weighted average of the aspect sentiments. The LLM is prompted to assign weights to various aspects, considering the job context to ensure an accurate weighting. This context-aware approach helps in accurately capturing the overall sentiment, providing a comprehensive view of the performance review.”)
Ganesh is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Ganesh to determine the overall aspect based sentiment score based on an average value or a median value of the respective aspect based sentiment scores. Motivation to do so would make it easy to track sentiment trends over time and benchmark against competitors.
Regarding claim 14, Davis, as modified above, teaches the method claim of 5.
Davis, as modified above, further teaches wherein the respective keywords are respective first keywords, wherein the score determinator determines respective aspect based sentiment scores representative of a sentiment of the message with respect to the query response based on [[a fourth result of a fourth analysis of respective second keywords of the message information representative of the message, wherein the respective second keywords comprise at least some of the respective first keywords,]] (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”)
wherein the score determinator determines an overall aspect based sentiment score representative of the sentiment of the message with respect to the query response as a function of the respective aspect based sentiment scores, (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”) Note: identification of a sentiment of text in general, reads on an overall sentiment.
wherein the score determinator determines the conditional aspect based sentiment score associated with the message as a function of the query continuity score and the overall aspect based sentiment score. (Davis, Par. 0031:” … by analyzing one or more queries received from the querier reacting to the presented result. One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query [statement]. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, either in general or towards a particular entity in the text, on a numerical scale from positive through neutral to negative.”, and Par. 0064:” … query is sufficiently relevant to the subject matter …) Note: when query is sufficiently relevant to the subject matter, reads on based on the query continuity score.
Davis, as modified above, does not teach, however, Khan teaches a fourth result of a fourth analysis of respective second keywords of the message information representative of the message, wherein the respective second keywords comprise at least some of the respective first keywords, (Khan, Par. 0095:”… item of content without any prior consideration or analysis [fourth analysis] and hence may be an item of content … thereby allowing an organization the ability to monitor sentiments in essentially real-time. … It would be further evident that input term 410 may be initially filtered for an occurrence of a particular keyword [second keyword], subset of a set of keywords [comprise at least some of the respective first keywords], or all keywords in a set of keywords. Optionally the content may also be processed such that locations of the negative and positive sentiment seed terms relative to one or more keywords are determined and only those meeting a predetermined threshold condition are counted into the respective negative and positive sentiment seed co-occurrence counts.”)
Khan is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Khan to conduct a fourth result of a fourth analysis of respective second keywords of the message information representative of the message, wherein the respective second keywords comprise at least some of the respective first keywords. Motivation to do so would indicate the user is comparing options or drilling down toward a transaction rather than just casually browsing.
Davis, as modified above, does not teach, however, Ganesh teaches wherein the score determinator determines the overall aspect based sentiment score as a function of an average value or a median value of the respective aspect based sentiment scores, and (Ganesh, Par. 0041]:”For aspect weights, embodiments determine the overall sentiment of the text, by using a weighted average of the aspect sentiments. The LLM is prompted to assign weights to various aspects, considering the job context to ensure an accurate weighting. This context-aware approach helps in accurately capturing the overall sentiment, providing a comprehensive view of the performance review.”)
Ganesh is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Ganesh to wherein the score determinator determines the overall aspect based sentiment score as a function of an average value or a median value of the respective aspect based sentiment scores. Motivation to do so would make it easy to track sentiment trends over time and benchmark against competitors.
Claims 8, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Davis, Narayanan, and further view of Vivek Kumar (US 20230368262 A1)(herein "Kumar").
Regarding claims 8, and 16, Davis, as modified above, teaches the method, and the system claims of 1, and 11, respectively.
Davis, as modified above, further teaches [classifying, by the system,that the statement represents the positive feedback with respect to the query response based on determining that the conditional aspect based sentiment score associated with the statement satisfies the second defined threshold conditional aspect based sentiment score; or determining, by the system, that the conditional aspect based sentiment score associated with the statement is lower than the first second defined threshold conditional aspect based sentiment score and higher than the second defined threshold conditional aspect based sentiment score, and resultingly does not satisfy the first second defined threshold conditional aspect based sentiment score and the second defined threshold conditional aspect based sentiment score, and – claim 8], [based on the conditional aspect based sentiment score being determined to satisfy the second defined threshold conditional aspect based sentiment score, determines that the message is representative of the positive feedback with respect to the query response; or the feedback evaluator determines that the conditional aspect based sentiment score associated with the message is lower than the first second defined threshold conditional aspect based sentiment score and higher than the second defined threshold conditional aspect based sentiment score, and resultingly does not satisfy the first second defined threshold conditional aspect based sentiment score and the second defined threshold conditional aspect based sentiment score, and, - claim 16] (Davis, Par. 0031:” … One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, … For example, a sentiment of a follow-up query such as “great, thanks” could be analyzed as “positive”, …”, and Par. 0070:” If user feedback is above a threshold level of satisfaction with a presented result, compendium revision module 340 uses information in the presented result to form a response and add the response to the compendium for use in answer future queries.”)
[classifying, by the system, that the statement represents negative feedback associated with the user, and does not represent the positive feedback, with respect to the query response based on determining that the conditional aspect based sentiment score associated with the statement does not satisfy the first second defined threshold conditional aspect based sentiment score and the second defined threshold conditional aspect based sentiment score. – claim 8], [based on the conditional aspect based sentiment score being determined to not satisfy the first second defined threshold conditional aspect based sentiment score and the second defined threshold conditional aspect based sentiment score, determines that the message is representative of negative feedback associated with the user, and is not representative of the positive feedback, with respect to the query response. – claim 16] (Davis, Par. 0031:” … One embodiment conducts a natural language sentiment or emotion analysis, using a commercially available sentiment analysis technique, to determine a sentiment of a follow-up query. A natural language sentiment analysis attempts to identify a sentiment in a portion of narrative text, ..., a sentiment of a follow-up query such as “no, I meant . . . ” could be analyzed as “negative”, or an emotion of the follow-up query could be analyzed as high in anger. … On the other hand, if the querier did not select the URL and ceased interacting with the chatbot, this could indicate the querier's lack of satisfaction with the response.”, and Par. 0075:” … Because this score is above the high threshold score (0.5), this score indicates that response 418 is a relevant response to query 404. …”) Note: Since above threshold score is considered positive and relevant, conversely must hold true as well, where lower than threshold is considered negative and irrelevant.
Davis, as modified above, does not teach, however, Kumar teaches [determining, by the system, that the conditional aspect based sentiment score associated with the statement is at or lower than, and resultingly satisfies, a second defined threshold conditional aspect based sentiment score that is lower than the first defined threshold conditional aspect based sentiment score, and – claim 8], [the feedback evaluator determines that the conditional aspect based sentiment score associated with the message is at or lower than, and resultingly satisfies, a second defined threshold conditional aspect based sentiment score that is lower than the first defined threshold conditional aspect based sentiment score, and, - claim 16] (Kumar, Claim 15:” … based on determining the first effectiveness score (a) equals or exceeds the first [second] threshold value, and (b) is lower than the second [first] threshold value “).
Kumar is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Kumar to determine, by the system, that the conditional aspect based sentiment score associated with the statement is at or lower than, and resultingly satisfies, a second defined threshold conditional aspect based sentiment score that is lower than the first defined threshold conditional aspect based sentiment score. Motivation to do so would trigger a more precise early-warning system for detecting critical issues, customer dissatisfaction, or market volatility.
Claims 10, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Davis, Narayanan, and further view of Lee et al. (US 20250363378 A1)(herein "Lee").
Regarding claims 10, and 18, Davis, as modified above, teaches the method, the system of claims of 9, and 17, respectively.
Davis, as modified above, does not teach, however Lee teaches [wherein the result is a first result, and wherein the method further comprises: determining, by the system, a modification to the trained artificial intelligence-based model, the electronic document retrieval process, or the conversational agent based on a second result of determining whether the statement is classified as the positive feedback associated with the user with respect to the query response, wherein the second result indicates whether the statement is classified as the positive feedback or negative feedback associated with the user with respect to the query response; and – claim 10], [wherein the result is a first result, wherein the computer executable components comprise: an updater that determines an update to the trained artificial intelligence-based model, the conversational agent, or a retrieval procedure for retrieval of electronic documents in response to queries based on a second result of the determination of whether the message is representative of the positive feedback associated with the user with respect to the query response, wherein the second result indicates whether the message is representative of the positive feedback or negative feedback associated with the user with respect to the query response, and – claim 18] (Lee, Par. 0058:” In one embodiment, in response to determining that the feedback message M1 is the positive feedback message, the processor 104 may update the machine learning model to enforce this behaviour.”)
[modifying, by the system, the trained artificial intelligence-based model, the electronic document retrieval process, or the conversational agent based on modification data of the modification to facilitate refining the trained artificial intelligence-based model, the electronic document retrieval process, or the conversational agent. – claim 10], [wherein the updater updates the trained artificial intelligence-based model, the conversational agent, or the retrieval procedure based on update information of the update to facilitate enhancing the trained artificial intelligence-based model, the conversational agent, or the retrieval procedure. – claim 18] (Lee, Par. 0004:” … updating, by the server, the machine learning model according to the feedback message.”)
Lee is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Lee to determine, by the system, a modification to the trained artificial intelligence-based model, whether the statement is classified as the positive feedback associated with the user with respect to the query response, wherein the second result indicates whether the statement is classified as the positive feedback or negative feedback associated with the user with respect to the query response; and modifying, by the system, the trained artificial intelligence-based model, to facilitate refining the trained artificial intelligence-based model. Motivation to do so would improve the performance and efficiency of the whole system (Lee, Par. 0064).
Regarding claim 20, Davis, as modified above, teaches the medium of claim 20.
Davis, as modified above, further teaches wherein the interactive agent comprises or is associated with a trained artificial intelligence-based model, wherein the result is a first result, and wherein the operations further comprise: determining, using the trained artificial intelligence-based model, first query response information relating to the query response based on an artificial intelligence-based analysis of query information relating to the query and document information of a group of electronic documents retrieved to facilitate the artificial intelligence-based analysis in connection with the query; (Davis, Par. 0029:” … a natural language analysis [trained AI] of the query to construct a search for information that could constitute a new response to be added to the compendium. One embodiment can use, as a data source for the search, a corpus of documents or other narrative text that has been analyzed and indexed by a natural language analysis tool. Another embodiment can utilize any search engine [trained artificial intelligence-based model] tool that has an application program interface (API) capable of being used by a software application to find and retrieve the information. The information is typically in the form of narrative text.”, and Par. 0030:”An embodiment scores and ranks the search results according to each result's relevance to the natural language query. One embodiment ranks the search results according to a score returned by the search engine corresponding to the degree to which the result corresponds to the search. Another embodiment ranks the search results by analyzing both the query and each result to count keywords, concepts, entities, or a combination in common between the query and each result. Keywords, concepts, and entities can be identified by using any available natural language analysis technique. Another embodiment uses a statistical model [trained artificial intelligence-based model] to combine the search engine score and the common keywords, concepts, entities, or a combination into one combined numerical score, then uses the combined numerical store to rank the results. Other techniques of computing a relevance between a result and a query are also possible and contemplated within the scope of the illustrative embodiments. In addition, an embodiment need not rank all the results, but instead stop scoring and ranking once one, or a particular number of, results above a threshold relevance score have been obtained.”)
communicating, by the interactive agent, via the first device, second query response information relating to the query response to the second device associated with the user identity, wherein the second query response information is the first query response information or is based on the first query response information; (Davis, Par. 0004:” A chatbot or conversational interface is software that conducts a natural language conversation with a human user. Typically, the natural language conversation is conducted in text form. However, input to the chatbot can also be converted from another modality, such as speech, into text for processing, then output from the chatbot converted back into speech a human can hear.”, and Par. 0031:” … For example, if the response, to conserve screen space in a chat displayed on a mobile device with a small screen, included only a one sentence response and a uniform resource locator (URL) to consult for further detail, and the querier selected the URL and spent more than a threshold amount of time at the site denoted by the URL, this could indicate the querier's satisfaction with the response.”, and Par. 0040:” … Network 102 is the medium used to provide communications links between various devices and computers connected together within data processing environment 100. …”, and Par. 0068:” User feedback module 340 presents one or more of the ranked results to the querier, and attempts to collect feedback on the presented results. Module 340 can solicit explicit feedback, asking the querier to select a specific feedback item, answer a question regarding the quality of the response, or otherwise explicitly provide a feedback response.”)
Davis, as modified above, does not teach, however, Lee teaches determining an update to the trained artificial intelligence-based model, the interactive agent, or a retrieval process for retrieving electronic documents in response to queries based on a second result of determining whether the message is classified as the positive feedback or the negative feedback associated with the user identity with respect to the query response, wherein the second result indicates whether the message is classified as the positive feedback or the negative feedback; and (Lee, Par. 0058:” In one embodiment, in response to determining that the feedback message M1 is the positive feedback message, the processor 104 may update the machine learning model to enforce this behaviour.”)
modifying the trained artificial intelligence-based model, the interactive agent, or the retrieval process based on update information of the update to facilitate refining the trained artificial intelligence-based model, the interactive agent, or the retrieval process. (Lee, Par. 0004:” … updating, by the server, the machine learning model according to the feedback message.”)
Lee is considered to be analogous to the claimed invention because it is in the same field of endeavor. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Davis, as modified above, further in view of Lee to determine, an update to the trained artificial intelligence-based model, based on a second result of determining whether the message is classified as the positive feedback or the negative feedback associated with the user identity with respect to the query response, wherein the second result indicates whether the message is classified as the positive feedback or the negative feedback; and modifying the trained artificial intelligence-based model, to facilitate refining the trained artificial intelligence-based model. Motivation to do so would improve the performance and efficiency of the whole system (Lee, Par. 0064).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Linfeng Song (US 20230118506 A1) teaches in Par. 0004:” … extract internal knowledge from dialogues, which can be used for understanding fine-grained sentiment information and aid in dialogue understanding. The present disclosure adapts aspect based sentiment analysis to conversational scenario sentiment analysis. As an example, according to embodiments of the present disclosure, conversational aspect sentiment analysis may extract user opinions, polarity, and the corresponding mentions from dialogues. Based on the understanding that humans often express their emotions in relation to the entities they are talking about, extracting sentiment, polarity, and mentions may provide helpful features and general domain understanding. More specifically, accurately extracting people's emotions and corresponding entities from their dialogues may help chatbots plan subsequent topics and make the chatbots more active in multi-turn conversations.”
Examiner's Note: Examiner has cited particular columns and line numbers and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARIOUSH AGAHI whose telephone number is (408)918-7689. The examiner can normally be reached Monday - Thursday and alternate Fridays, 7:30-4:30 PT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
DARIOUSH AGAHI, P.E.
Primary Examiner
/DARIOUSH AGAHI/Primary Examiner, Art Unit 2656
Prosecution Insights