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 .
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 the abstract idea of dialog/communication analysis without significantly more. The claims 1, 10 and 19 recite steps of cause a domain sentiment AI model to detect a domain-specific negative sentiment, which is associated with a designated domain, in a statement of a first user, which is included in a domain-specific dialog that represents a domain-specific conversation between the first user and a second user, in real-time during the domain-specific conversation by providing a sentiment AI prompt together with at least a portion of the domain-specific dialog as inputs to the domain sentiment AI model, the sentiment AI prompt requesting that the domain sentiment AI model determine whether the domain- specific dialog includes the domain-specific negative sentiment, wherein the portion of the domain-specific dialog comprises context regarding the sentiment AI prompt (i.e., a data evaluation step), receive a response to the sentiment AI prompt from the domain sentiment AI model, the response to the sentiment AI prompt comprising an indication of the domain-specific negative sentiment (i.e., a data evaluation step), cause the indication of the domain-specific negative sentiment to be converted into a query that describes a domain-specific issue (i.e., a data evaluation step), cause a passage ranking Al model to rank passages, which are included in domain-specific documents, to provide relevancy ranks, which represent relevancies of the passages with regard to mitigation of the domain-specific issue, by providing a ranking AI prompt together with the passages and the query as inputs to the passage ranking Al model, the ranking Al prompt requesting that the passage ranking AI model rank the passages based on relevancy to the mitigation of the domain-specific issue, wherein the passages and the query comprise context regarding the ranking AI prompt (i.e., a data evaluation step), receive a response to the ranking AI prompt from the passage ranking AI model, the response to the ranking AI prompt comprising an indication of the relevancy ranks of the passages (i.e., a data evaluation/judgement step), identify a subset of the passages such that the relevancy rank of each passage in the subset satisfies a relevancy criterion (i.e., a data evaluation step), cause a response to the query to be generated using information in the subset of the passages, the response to the query specifying a mitigating factor that mitigates the domain-specific issue (i.e., a data evaluation step), and present the response to the query to the second user via a user interface (i.e., a judgement or post solutional activity), corresponding to steps achievable manually or mentally by a human in detecting issues provided in a communication by analyzing the communication, and identifying ways to resolve the issues, and providing response/resolutions to the issues, corresponding to the mental processes as well as the “certain methods of organizing human activity” categories of abstract idea. This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with additional generic computer elements, where the generically recited computer elements (system, memory, processor system, computing system, computer program product, AI model) do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because steps “identify a subset of the passages such that the relevancy rank of each passage in the subset satisfies a relevancy criterion, cause a response to the query to be generated using information in the subset of the passages, the response to the query specifying a mitigating factor that mitigates the domain-specific issue and present the response to the query to the second user via a user interface” correspond to well-understood, routine, conventional computer functions of “collecting information, analyzing it, and displaying certain results of the collection and analysis” and “gathering and analyzing information using conventional techniques and displaying the result” as recognized by the court decisions listed in MPEP § 2106.05 and as provided by cited references Can (IDS), Bajaj and Austraat (PTO 892 form).
The dependent claims also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
Claim Rejections - 35 USC § 102
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 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.
1. Claims 1, 7-10 and 16-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Can US 11,563,852 B1 (“Can”)
Per claim 1, Can discloses a system comprising:
a processor system (col. 22, ln 10-12); and
a memory that stores computer-executable instructions that are executable by the processor system (col. 22, ln 26-30) to at least:
cause a domain sentiment AI model to detect a domain-specific negative sentiment, which is associated with a designated domain, in a statement of a first user, which is included in a domain-specific dialog that represents a domain-specific conversation between the first user and a second user, in real-time during the domain-specific conversation by providing a sentiment AI prompt together with at least a portion of the domain-specific dialog as inputs to the domain sentiment AI model, the sentiment AI prompt requesting that the domain sentiment AI model determine whether the domain- specific dialog includes the domain-specific negative sentiment, wherein the portion of the domain-specific dialog comprises context regarding the sentiment AI prompt (col. 1, ln 65-67; the technology described herein includes a plurality of machine learning models related to customer experience combined in an infrastructure to support call center agents in real-time while interacting with customers … For example, an incoming call is routed to a call agent based on an inferred topic (call routing machine learning model). This call is recorded, punctuated and classified based on one or more inferred sentiments (real-time customer dissatisfaction machine learning model) of a current caller's speech. …the machine learning system classifies the sentiment of customer speech and, if a negative sentiment is detected, identifies a negative emotion (anger, confusion, discontent, or dissatisfaction) present…., col. 2, ln 3-31);
receive a response to the sentiment AI prompt from the domain sentiment AI model, the response to the sentiment AI prompt comprising an indication of the domain-specific negative sentiment (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated …, col. 2, ln 31-34);
cause the indication of the domain-specific negative sentiment to be converted into a query that describes a domain-specific issue (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching …, col. 2, ln 31-36);
cause a passage ranking Al model to rank passages, which are included in domain-specific documents, to provide relevancy ranks, which represent relevancies of the passages with regard to mitigation of the domain-specific issue, by providing a ranking AI prompt together with the passages and the query as inputs to the passage ranking Al model, the ranking Al prompt requesting that the passage ranking AI model rank the passages based on relevancy to the mitigation of the domain-specific issue, wherein the passages and the query comprise context regarding the ranking AI prompt (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching for one or more similar callers to the current caller. Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments …, col. 2, ln 37-41; Automated system assistance module 114 subsequently analyzes these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success …, col. 4, ln 9-14);
receive a response to the ranking AI prompt from the passage ranking AI model, the response to the ranking AI prompt comprising an indication of the relevancy ranks of the passages (col. 4, ln 14-15);
identify a subset of the passages such that the relevancy rank of each passage in the subset satisfies a relevancy criterion (col. 4, ln 14-15);
cause a response to the query to be generated using information in the subset of the passages, the response to the query specifying a mitigating factor that mitigates the domain-specific issue (For detected complaints, similar customer module 112 will search for similar customers with similar issues and successful resolutions of previous complaints. Automated system assistance module 114 will subsequently analyze these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success…., col. 4, ln 9-16); and
present the response to the query to the second user via a user interface (For example, call agent 104 may receive a list of actions displayed on their computer screen …, col. 4, ln 16-17).
Per claim 7, Can discloses the system of claim 1, wherein the computer-executable instructions are executable by the processor system further to at least: select the passages from a plurality of passages based at least on the passages being created on or after a specified date (Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments…., col. 2, ln 37-41; col. 12, ln 1-8, selecting from history/previously successful outcomes/resolutions as implying a creation date of successful outcomes/resolutions).
Per claim 8, Can discloses the system of claim 1, wherein the computer-executable instructions are executable by the processor system further to at least: cause a citation that cites the information in the subset of the passages, which is used to generate the response to the query, to be generated (Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments…., col. 2, ln 37-41); and
present the citation to the second user via the user interface (For example, call agent will receive phrases displayed on their computer screen. Phrases may include, or be combined with, negative sentiment/emotion diffusing phrases or additional contextual information, such as product descriptions, product suggestions, customer options or steps that may provide technical assistance, col. 4, ln 16-21).
Per claim 9, Can discloses the system of claim 1, wherein the computer-executable instructions are executable by the processor system to at least: cause the domain sentiment AI model to detect the domain-specific negative sentiment in the statement of the first user based at least on a tone of the first user that is indicated by the statement (col. 2, ln 2-31).
Per claim 10, Can discloses a method implemented by a computing system, the method comprising:
detecting a domain-specific negative sentiment, which is associated with a designated domain, in a statement of a first user, which is included in a domain- specific dialog that represents a domain-specific conversation between the first user and a second user, in real-time during the domain-specific conversation by providing a sentiment AI prompt together with at least a portion of the domain-specific dialog as inputs to a domain sentiment AI model, which causes the domain sentiment AI model to generate a response to the sentiment AI prompt, wherein the sentiment AI prompt inquires whether the domain-specific dialog includes the domain-specific negative sentiment, wherein the portion of the domain-specific dialog comprises context regarding the sentiment AI prompt, and wherein the response to the sentiment Al prompt comprises an indication of the domain-specific negative sentiment (col. 1, ln 65-67; the technology described herein includes a plurality of machine learning models related to customer experience combined in an infrastructure to support call center agents in real-time while interacting with customers … For example, an incoming call is routed to a call agent based on an inferred topic (call routing machine learning model). This call is recorded, punctuated and classified based on one or more inferred sentiments (real-time customer dissatisfaction machine learning model) of a current caller's speech. …the machine learning system classifies the sentiment of customer speech and, if a negative sentiment is detected, identifies a negative emotion (anger, confusion, discontent, or dissatisfaction) present…., col. 2, ln 3-31);
converting the indication of the domain-specific negative sentiment into a query that describes a domain-specific issue (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching …, col. 2, ln 31-36);
ranking passages, which are included in domain-specific documents, to provide relevancy ranks, which represent relevancies of the passages with regard to mitigation of the domain-specific issue, by providing a ranking AI prompt together with the passages and the query as inputs to a passage ranking AI model, which causes the passage ranking Al model to generate a response to the ranking Al prompt, wherein the ranking AI prompt requests that the passage ranking AI model rank the passages with regard to relevancy to the mitigation of the domain-specific issue, wherein the passages and the query comprise context regarding the ranking AI prompt (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching for one or more similar callers to the current caller. Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments …, col. 2, ln 37-41; Automated system assistance module 114 subsequently analyzes these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success …, col. 4, ln 9-14), and
wherein the response to the ranking AI prompt comprises an indication of the relevancy ranks of the passages (col. 4, ln 14-15);
identifying a subset of the passages such that the relevancy rank of each passage in the subset satisfies a relevancy criterion (col. 4, ln 14-15);
generating a response to the query using information in the subset of the passages, the response to the query specifying a mitigating factor that mitigates the domain-specific issue (For detected complaints, similar customer module 112 will search for similar customers with similar issues and successful resolutions of previous complaints. Automated system assistance module 114 will subsequently analyze these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success…., col. 4, ln 9-16); and
presenting the response to the query to the second user via a user interface (For example, call agent 104 may receive a list of actions displayed on their computer screen …, col. 4, ln 16-17).
Per claim 16, Can discloses the method of claim 10, further comprising: selecting the passages from a plurality of passages based at least on the passages being created on or after a specified date (Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments…., col. 2, ln 37-41; col. 12, ln 1-8, selecting from history/previously successful outcomes/resolutions as implying a creation date of successful outcomes/resolutions)
Per claim 17, Can discloses the method of claim 10, further comprising: causing a citation that cites the information in the subset of the passages, which is used to generate the response to the query, to be generated (Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments…., col. 2, ln 37-41);
wherein presenting the response to the query to the second user comprises: presenting the citation and the response to the query to the second user via the user interface (For example, call agent will receive phrases displayed on their computer screen. Phrases may include, or be combined with, negative sentiment/emotion diffusing phrases or additional contextual information, such as product descriptions, product suggestions, customer options or steps that may provide technical assistance, col. 4, ln 16-21).
Per claim 18, Can discloses the method of claim 10, wherein the domain-specific negative sentiment is detected in the statement of the first user based at least on a tone of the first user that is indicated by the statement (col. 2, ln 2-31).
Per claim 19, Can discloses a computer program product comprising a computer-readable storage medium having instructions recorded thereon for enabling a processor-based system to perform operations, the operations comprising:
causing a domain sentiment AI model to detect a domain-specific negative sentiment, which is associated with a designated domain, in a statement of a first user, which is included in a domain-specific dialog that represents a domain-specific conversation between the first user and a second user, in real-time during the domain- specific conversation by providing a sentiment AI prompt together with at least a portion of the domain-specific dialog as inputs to the domain sentiment AI model, the sentiment AI prompt requesting that the domain sentiment AI model determine whether the domain-specific dialog includes the domain-specific negative sentiment, wherein the portion of the domain-specific dialog comprises context regarding the sentiment AI prompt (col. 1, ln 65-67; the technology described herein includes a plurality of machine learning models related to customer experience combined in an infrastructure to support call center agents in real-time while interacting with customers … For example, an incoming call is routed to a call agent based on an inferred topic (call routing machine learning model). This call is recorded, punctuated and classified based on one or more inferred sentiments (real-time customer dissatisfaction machine learning model) of a current caller's speech. …the machine learning system classifies the sentiment of customer speech and, if a negative sentiment is detected, identifies a negative emotion (anger, confusion, discontent, or dissatisfaction) present…., col. 2, ln 3-31);
receiving a response to the sentiment AI prompt from the domain sentiment AI model, the response to the sentiment AI prompt comprising an indication of the domain-specific negative sentiment (col. 2, ln 31-36);
causing the indication of the domain-specific negative sentiment to be converted into a query that describes a domain-specific issue (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching …, col. 2, ln 31-36);
causing a passage ranking AI model to rank passages, which are included in domain-specific documents, to provide relevancy ranks, which represent relevancies of the passages with regard to mitigation of the domain-specific issue, by providing a ranking Al prompt together with the passages and the query as inputs to the passage ranking AI model, the ranking AI prompt requesting that the passage ranking AI model rank the passages based on relevancy to the mitigation of the domain-specific issue, wherein the passages and the query comprise context regarding the ranking AI prompt (The real-time customer dissatisfaction machine learning model determines, based on the call classification, that a complaint has been articulated and initiates an automated assistance (automated assistance machine learning model) by searching for one or more similar callers to the current caller. Successful call outcomes associated with one or more similar callers may be used to suggest one or more previously successful phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments …, col. 2, ln 37-41; Automated system assistance module 114 subsequently analyzes these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success …, col. 4, ln 9-14);
receiving a response to the ranking AI prompt from the passage ranking AI model, the response to the ranking AI prompt comprising an indication of the relevancy ranks of the passages (col. 4, ln 14-15);
identifying a subset of the passages such that the relevancy rank of each passage in the subset satisfies a relevancy criterion (col. 4, ln 14-15);
causing a response to the query to be generated using information in the subset of the passages, the response to the query specifying a mitigating factor that mitigates the domain-specific issue (For detected complaints, similar customer module 112 will search for similar customers with similar issues and successful resolutions of previous complaints. Automated system assistance module 114 will subsequently analyze these successful resolutions to determine which actions may have contextually contributed (e.g., based on relevance) to the success…., col. 4, ln 9-16); and
presenting the response to the query to the second user via a user interface (For example, call agent 104 may receive a list of actions displayed on their computer screen …, col. 4, ln 16-17).
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.
2. Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Can in view of Bajaj et al US 2025/0286950 A1 (“Bajaj”)
Per claim 5, Can discloses the system of claim 1,
Can does not explicitly disclose wherein the computer-executable instructions are executable by the processor system to at least: cause a response generation AI model to generate the response to the query by providing a generation AI prompt together with the subset of the passages and the query as inputs to the response generation AI model, the generation AI prompt requesting that the response generation AI model generate the response to the query, wherein the subset of the passages and the query comprise context regarding the generation AI prompt or receive a response to the generation AI prompt from the response generation AI model, the response to the generation AI prompt comprising the response to the query
However, these features are taught by Bajaj:
wherein the computer-executable instructions are executable by the processor system to at least: cause a response generation AI model to generate the response to the query by providing a generation AI prompt together with the subset of the passages and the query as inputs to the response generation AI model, the generation AI prompt requesting that the response generation AI model generate the response to the query, wherein the subset of the passages and the query comprise context regarding the generation AI prompt (para. [0025]; para. [0078]; para. [0081]); and
receive a response to the generation AI prompt from the response generation AI model, the response to the generation AI prompt comprising the response to the query (para. [0084])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Bajaj with the system of Can in arriving at the missing features of Can, because such combination would have resulted in enhancing call center features (Baja, Abstract).
Per claim 14, Can discloses the method of claim 10,
Can does not explicitly disclose wherein causing the response to the query to be generated comprises: causing the response to the query to be generated by providing a generation AI prompt together with the subset of the passages and the query as inputs to a response generation AI model, which causes the response generation AI model to generate a response to the generation AI prompt, wherein the generation AI prompt requests generation of the response to the query, wherein the subset of the passages and the query comprise context regarding the generation AI prompt or wherein the response to the generation AI prompt comprises the response to the query
However, these features are taught by Bajaj:
wherein causing the response to the query to be generated comprises: causing the response to the query to be generated by providing a generation AI prompt together with the subset of the passages and the query as inputs to a response generation AI model, which causes the response generation AI model to generate a response to the generation AI prompt (para. [0025]; para. [0078]; para. [0081])
wherein the generation AI prompt requests generation of the response to the query (para. [0025]; para. [0078]; para. [0081]);
wherein the subset of the passages and the query comprise context regarding the generation AI prompt (para. [0025]; para. [0078]; para. [0081]); and
wherein the response to the generation AI prompt comprises the response to the query (para. [0084])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Bajaj with the method of Can in arriving at the missing features of Can, because such combination would have resulted in enhancing call center features (Baja, Abstract).
3. Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Can in view of Austraat US 2024/0194178 A1 (Austraat”)
Per claim 6, Can discloses the system of claim 1,
Can does not explicitly disclose wherein the response to the query has at least one of the following: a tone that corresponds to a reference tone of the second user, a style that corresponds to a reference style of the second user, or a personality that corresponds to a reference personality of the second user
However, this feature is taught by Austraat (para. [0179); para. [0183])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Austraat with the system of Can in arriving at the missing features of Can, because e such combination would have resulted in facilitating an optimal outcome for each user (Austraat, para. [0183])
Per claim 15, Can discloses the method of claim 10,
Can does not explicitly disclose wherein the response to the query has at least one of the following: a tone that corresponds to a reference tone of the second user, a style that corresponds to a reference style of the second user, or a personality that corresponds to a reference personality of the second user
However, this feature is taught by Austraat (para. [0179); para. [0183])
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the teachings of Austraat with the method of Can in arriving at the missing features of Can, because e such combination would have resulted in facilitating an optimal outcome for each user (Austraat, para. [0183]).
Allowable Subject Matter
Claims 2-4, 11-13 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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