DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Response to Election / Restriction received 10/27/2025 electing claims 1-24 without traverse.
Claims 25-34 have been withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 10/27/2025.
Claims 1-24 have been examined and are pending.
Information Disclosure Statement (IDS)
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by applicant on 10/08/2024.
(AIA ) Examiner Note
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 is rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Dependent claim 16 recites, “…the set of boundary instructions…”. However, this limitation lacks proper antecedent basis. The term “the” is a definite article. As such, this term must refer to a definite previous recitation of the noun which it modifies. However, no previous recitation of “set of boundary instructions” has been provided. It is wholly unclear to what set of boundary instructions the applicant is attempting to reference as no such set has previously been mentioned. Therefore, this limitation lacks an antecedent basis for the claimed step. Because this limitation lacks an antecedent basis, the claim limitation is indefinite.
For the purpose of compact prosecution, the feature in question is interpreted as follows: “…[a] set of boundary instructions…” Nonetheless, clarification or correction is required.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claims 1, 15, 20:
[claim 1] a generative artificial intelligence (GAI) engine trained on answering customer queries; wherein the one or more service provider servers and the GAI engine participate in the generation of a knowledgebase including information used to answer the customer queries.
[claim 15] a generative artificial intelligence (GAI) engine trained on answering customer queries; wherein the one or more service provider servers and the GAI engine participate in the generation of an answer to a customer query using a knowledgebase of information relating to the merchant
[claim 20]
…generate a prompt for analysis by a generative artificial intelligence (GAI) engine trained on answering customer queries;
wherein the prompt comprises: the customer query; a knowledgebase of information regarding a merchant to which the customer query is directed; and a set of boundary instructions instructing the GAI engine to limit its answer to the customer query to the information within the knowledgebase.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the steps of each claim, as drafted, are merely business decisions in an answering service business. The claimed steps are not technical in nature. Applicant has not invented a GAI nor any particular new AI model by which to generate answers to queries nor by which to generate a knowledgebase of information from which to select/generate answers to queries. Instead, applicant’s entire disclosure is devoid of technical improvements to GAI or AI models and appears to focus on using off-the-shelf pre-existing models to support various business scenarios regarding a question/answer service (a type of business) – these are merely business decisions regarding how to use existing tools (e.g. existing generically recited GAI model(s)). For example, the business decision to include certain information in a “prompt” to a generic GAI model is wholly a business decision and one which is only recited at the highest of levels of generality. Thus, the claims fall into Certain Methods of Organizing Human Activity. Furthermore, the mere nominal recitation of a generic computer components (e.g. generic servers which are configured to receive a customer question/query, hosts the question/answer models, i.e. the GAI models, and respond with answers) does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts or, link them to a field of use (i.e. in this case using generic generative AI models to support a question/answer service – a type of business) or, serve as insignificant extra-solution activity (e.g. data-gathering and data transmittal). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are as exemplified per limitations of claim 20: “A system for automated answering of customer queries, comprising: one or more service provider servers configured to receive a customer query of the customer queries…”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a particular server or server technology nor a method or technique or protocol for receiving data such as a customer query. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts within a computing environment or, link them to a field of use or, are insignificant extra-solution activity (i.e. gathering customer query data upon which the abstract idea operates when generating an answer to the query) and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. by one or more servers) and “link” them to a field of use (i.e. use of GAI models to provide answers to customer queries), or as insignificant extra-solution activity (e.g. receiving customer queries and/or retrieving information upon which a GAI may be trained). For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 3 recites the following: “wherein the GAI engine automatically generates the knowledgebase without need of manual curation.” However, automating an otherwise manual task without any particular means by which automation is effected is nothing more than an abstraction when recited at this very high-level of generality. There is no technical solution to the automation and no technical problem being solved.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 102 (AIA )
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10, 12, 15-18, 20-24 are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by Khosla et al. (U.S. US 2025/0005052 A1; hereinafter, "Khosla").
Claim 1:
Pertaining to claim 1, Khosla as shown teaches the following:
A system for automated answering of customer queries, comprising:
one or more service provider servers configured to receive the customer queries (Khosla, see at least Figs. 1-3 and at least [0010]-[0019], teaching, e.g.: “…The environment 100 can include a network 116, the network connecting a number of customer computing devices 122 to one or more network-based services, illustratively, a natural language question answer service 102 [service provider server]. Furthermore, the natural language question answering service 102 can receive natural language questions [configured to receive a customer query] (e.g., about a certain network-based service) from the customer computing devices 122…”)
and a generative artificial intelligence (GAI) engine trained on answering customer queries (Khosla, see citations noted supra including at least [0013] in view of [0019]; e.g.: “…The natural language question answering service 102 may utilize machine-learned algorithms, such as generative AI model algorithms [a generative artificial intelligence (GAI) engine], to provide answers (e.g., information regarding a specific network-based service, passages associated with a network-based service, links to webpages, application programming interface (API) calls, etc.) to natural language questions catered to network-based services … The natural language question answering service 102 may comprise a trained LLM that is trained at least on the QA pairs from the search systems 124 in order to provide answers to questions and prompts…”);
wherein the one or more service provider servers and the GAI engine participate in the generation of a knowledgebase including information used to answer the customer queries (Khosla, see citations noted supra, including again Fig. also at least [0011]-[0012] in view of [0022]-[0024], teaching Khosla’s “natural language question answer service 102” [service provider server] which contains a LLM including a generative AI (GAI) generate an answer to a customer query, called a question answer (QA) pair. This QA pair may be stored in a network-based storage service and customer knowledge graph(s) thereby forming a portion of a knowledgebase which may later be retrieved and used by the GAI to answer a new or different customer query, e.g. Khosla herein teaches: “…The LLM component 106 may receive the prompt from the aggregator component 104, the user context (optionally) from user context component 105, …The LLM component 106 may take the prompt and the user context received from the aggregator component 104 and utilize a generative AI model (e.g., Retrieval Augmented Generation (RAG) utilizing natural language processing (NLP) architecture) to determine an answer to the natural language question… the LLM component 106 may utilize a trained generative AI model (e.g., trained on QA pairs from a network-based storage service and customer knowledge graphs) to determine an answer…”).
Claim 2:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1 wherein the one or more service provider servers and the GAI engine further participate in the generation of an answer to a customer query using the knowledgebase (Khosla, see citations noted supra, including again Fig. also at least [0022]-[0024], teaching, e.g.: “…The LLM component 106 may receive the prompt from the aggregator component 104, the user context (optionally) from user context component 105, and generate one or more answers based on the prompt and the user context. The LLM component 106 may be trained on at least QA pairs generated from the search systems 124…”)..
Claim 3:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1 wherein the GAI engine automatically generates the knowledgebase without need of manual curation (Khosla, see citations noted supra in view of at least [0078], teaching: “…All of the processes described herein may be fully automated via software code modules…”).
Claim 4:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1 wherein the GAI engine automatically generates the knowledgebase upon receiving a text file including information regarding a merchant’s business to which the customer query is directed (Khosla, see citations noted supra in view of at least [0021], e.g.: “…The search systems 124 may be a plurality of search systems which can provide, but are not limited to, passages, documents, and QA pairs to the natural language question answering service 102. The natural language question answering service 102 may take
that information and formulate an answer to a natural language question related to a network-based service and/or computer domain. For example, a search system 124 may be a data store which contains frequently asked questions (FAQ) (and associated answers) concerning a specific network-based service (e.g., network-based storage or database)…”).
Claim 5:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 4, wherein the information regarding the merchant’s business is scraped from the merchant’s website (Khosla, see citations noted supra in view of at least [0037], e.g.: “…The aggregator component 104 may utilize Python functions (e.g., BeautifulSoup) to scrape the webpages to extract QA pairs ( e.g., using the title of each page as a potential answer to a question in a QA pair)…”).
Claim 6:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 4, wherein the text file is generated by the one or more service provider servers (Khosla, see citations noted supra, again e.g. [0021], teaching documents [text files] received by the system are provided by search systems where “the search systems 124 may also be hosted outside the network 116 (e.g. a third-party service) [service provider servers]…”).
Claim 7:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1, wherein the GAI engine is further trained to identify facts from a received data file containing text from a website of a merchant to which the customer query is directed (Khosla, see citations noted supra in view of at least [0036], teaching, e.g.: “…For example, but not limited to, a search system may be network-based system that contains documents describing how to use a network-based service (e.g., manuals regarding how to use a network-based artificial intelligence and machine learning service via a graphical user interface (GUI)). The aggregator component 104 may identify QA pairs associated with this type of question by tagging the titles of each document and associating each title with question where the title is an answer to a question…”).
Claim 8:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1, wherein upon receipt of information relating to a merchant business to which the customer query is directed, the GAI engine uses the information to generate labels containing facts about the merchant business, the labels used to form the knowledgebase (Khosla, again see at least [0036], teaching, e.g.: “…For example, but not limited to, a search system may be network-based system that contains documents describing how to use a network-based service (e.g., manuals regarding how to use a network-based artificial intelligence and machine learning service via a graphical user interface (GUI)). The aggregator component 104 may identify QA pairs associated with this type of question by tagging [generate a label] the titles of each document and associating each title with question where the title is an answer [a fact] to a question…”)..
Claim 9:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 1, wherein the one or more service provider servers scrape a website of a merchant to which the customer query is directed to generate a textual representation of the website, and transmits the textual representation of the website to the GAI engine for analysis (Khosla, see citations noted supra, e.g. [0021] in view of at least [0037], e.g.: “…The aggregator component 104 may utilize Python functions (e.g., BeautifulSoup) to scrape the webpages to extract QA pairs ( e.g., using the title of each page as a potential answer to a question in a QA pair)…” and per [0021]: “…“the search systems 124 may also be hosted outside the network 116 (e.g. a third-party service) [service provider servers]…”; therefore, the Examiner understands that Khosla contemplates the search service which provides the webpage scraping service may be a third-party service [service provider server]).
Claim 10:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 9, wherein upon receipt of the textual representation of the website, the GAI engine uses the textual representation of the website to generate labels containing facts about the merchant business, the corpus of labels forming the knowledgebase (Khosla, see citations noted supra, e.g. per at least [0036]-[0037] teaching: the information received, such as scarping from webpages and document text, is used by the GAI to create tags [labels], where a tag may be a title and the title is associated with a question and the answer [fact about the business] to a question is the title [label] which forms a component of the knowledgebase. Note per [0054] the system may create “knowledge graphs” based on such information. See also at least [0028], [0044], [0065]).
Claim 12:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 10, wherein two or more labels determined by the GAI engine to be related to each other are hyperlinked to each other (Khosla, see citations noted supra, e.g. [0026] in view of at least [0067]: “...For example, the attribution component 109 may provide reference links and titles to the retrieved passages used by the LLM component 106 (e.g., retrieved passages used as context to generate the answer), which may allow the submitter of the question to get more details on the referenced passages…”).
Claim 15:
Pertaining to claim 15, Khosla as shown teaches the following:
A system for automated answering of customer queries for a merchant, comprising:
one or more service provider servers configured to receive the customer queries (Khosla, see at least Figs. 1-3 and at least [0010]-[0019], teaching, e.g.: “…The environment 100 can include a network 116, the network connecting a number of customer computing devices 122 to one or more network-based services, illustratively, a natural language question answer service 102 [service provider server]. Furthermore, the natural language question answering service 102 can receive natural language questions [configured to receive a customer query] (e.g., about a certain network-based service) from the customer computing devices 122…”);
and a generative artificial intelligence (GAI) engine trained on answering customer queries (Khosla, see citations noted supra including at least [0013] in view of [0019]; e.g.: “…The natural language question answering service 102 may utilize machine-learned algorithms, such as generative AI model algorithms [GAI engine], to provide answers (e.g., information regarding a specific network-based service, passages associated with a network-based service, links to webpages, application programming interface (API) calls, etc.) to natural language questions catered to network-based services … The natural language question answering service 102 may
comprise a trained LLM that is trained at least on the QA pairs from the search systems 124 in order to provide answers to questions and prompts…”);
wherein the one or more service provider servers and the GAI engine participate in the generation of an answer to a customer query using a knowledge base of information relating to the merchant (Khosla, see citations noted supra, including again Fig. also at least [0022]-[0024], teaching, e.g.: “…The aggregator component 104 can retrieve passages from the search systems 124 based on the natural language question and create a prompt for the LLM component 106. For example, the aggregator component 104 may analyze the natural language question by using string matching techniques (e.g., partial string matching, dense passage retrieval, etc.) to determine the meaning of the natural language question.…The LLM component 106 may receive the prompt from the aggregator component 104, the user context (optionally) from user context component 105, and generate one or more answers [generation of an answer to a customer query] based on the prompt and the user context [using a knowledgebase of information]. The LLM component 106 may be trained on at least QA pairs generated from the search systems 124 [e.g. related to a merchant]…”).
Claim 16:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 15, wherein the GAI engine is constrained in its answer by the set of boundary instructions to prevent hallucination by the GAI engine (Khosla, see at least [0066]: “…At (7), the LLM component 106 sends the generated answer and retrieved passages to the verifier component 108 [instructions to constrain the answer]. At (8), the verifier component 108 determines if the answer was generated in error (e.g., hallucinated). As stated above, the verifier component 108 may look for textual overlap between an answer and retrieved passages, determine whether there is a contradiction between the answers and the retrieved passages, use head/tail/relational triples to confirm faithfulness, use membership inference attacks techniques to confirm whether a question (e.g., or similar) is in a dataset, and/or a score of any of the four combined. At (9), if the answer was not hallucinated, the verifier component 108 sends the answer and retrieved passages to the attribution component 109…”).
Claim 17:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 15, wherein the GAI engine returns a uniform resource locator (URL) in response to the query, (Khosla, see at least [0019], e.g.: “…The natural language question answering service 102 may utilize machine-learned algorithms, such as generative AI model algorithms, to provide answers (e.g., information regarding a specific network-based service, passages associated with a network-based service, links to webpages [a uniform resource locator (URL) to a webpage],…”) the one or more service provider servers forwarding a link to the URL to the customer (Khosla, see at least [0026]: “…For example, the attribution component 109 may provide links (e.g., uniform resource identifiers (URI)) or titles as references (e.g., links to webpages, links to online documents, links to images, audio, video, etc.) to documents whose passages were retrieved by the aggregator component 104…”).
Claim 18:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 15, wherein the GAI engine assists in generating the knowledgebase prior to receipt of the query (Khosla, see citations noted supra, including [0036]-[0044], e.g.: “…As another example, a search system may be a network-based system (or associated with a network-based system) that contains knowledge graphs of customers for a
network-based service (e.g., what kind of services they have, their usage activity, questions the customers have previously asked, types of questions customers have asked and their occurrence, their preferences regarding answers, etc.). The aggregator component 104 may utilize the knowledge graphs to create QA pairs where information about a customer may be an answer in a QA pair (e.g., the customer has 25 buckets in a network-based storage service) and a question from the customer may be a question in the QA pair (e.g., "how many buckets do I have in this network-based storage service?")…”; QA pairs are created before customer makes query.).
Claim 20:
As shown below, Khosla teaches the following:
A system for automated answering of customer queries, comprising:
one or more service provider servers configured to receive a customer query of the customer queries (Khosla, see at least Figs. 1-3 and at least [0010]-[0017], teaching, e.g.: “…The environment 100 can include a network 116, the network connecting a number of customer computing devices 122 to one or more network-based services, illustratively, a natural language question answer service 102 [service provider server]. Furthermore, the natural language question answering service 102 can receive natural language questions [configured to receive a customer query] (e.g., about a certain network-based service) from the customer computing devices 122…”), and configured to generate a prompt for analysis by a generative artificial intelligence (GAI) engine trained on answering customer queries (Khosla, see citations noted supra, including at least [0012], teaching, e.g.: “…Illustratively, the natural language question answer service can utilize an aggregator to retrieve passages (and corresponding question answer pairs (QA pairs) for those passages) based on the natural language question, to modify, update, or supplement the natural language question, or the like, and produce a prompt. The aggregator may analyze the natural language question and determine what search systems to retrieve passages and QA pairs from, to answer that question. The aggregator may retrieve those passages (and related QA pairs) and use them, along with the question, to formulate a prompt. For purposes of the present application, prompt can correspond to a few selected passages (e.g., from all the passages retrieved) and the QA pairs of the selected passages, along with the natural language question (e.g., or a form of the question where question may be a prompt or a command)…”; see also at least );
wherein the prompt comprises:
the customer query (Khosla, see citations noted supra, including again at least [0012]-[0013], teaching: “…For purposes of the present application, the prompt can correspond to a few selected passages (e.g., from all the passages retrieved) and the QA pairs of the selected passages, along with the natural language question [i.e. the customer query]…”);
a knowledgebase of information regarding a merchant to which the customer query is directed (Khosla, see citations noted supra, including at least [0012]-[0013], teaching: “…For purposes of the present application, the prompt can correspond to a few selected passages (e.g., from all the passages retrieved) and the QA pairs [knowledgebase of information] of the selected passages [regarding a merchant], along with the natural language question [i.e. the customer query]…”; see also at least [0022]); and
a set of boundary instructions instructing the GAI engine to limit its answer to the customer query to the information within the knowledgebase (Khosla, see citations noted supra, including at least Fig. 4 and [0074], teaching the GAI answer is constrained by “verifier component” to ensure the GAI has not “hallucinated”, etc… and also note [0012]-[0013] in view of at least [0022], teaching: “…For example, the aggregator component 104 may analyze the natural language question …to determine the meaning of the natural language question. After determining the meaning of the natural language question, the aggregator component 104 may then determine which [a boundary instruction] of the search systems 124 the aggregator component 104 may retrieve passages from (e.g., retrieve from a network-based storage service QA pair system but not a network-based AI service QA pair system) based on the natural language question…”; i.e. the instruction is that the GAI is limited to retrieving answers from a particular network-based storage service QA pair system [i.e. a particular knowledgebase] ).
Claim 21:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 20, further comprising a GAI engine configured to form an answer to the customer query as an automated customer service representative (Khosla, see citations as noted supra, e.g. at least Fig. 4 and [0076]-[0078] the answer which is provided to the customer in response to the customer’s natural language question is fully automated; e.g. “All of the processes described herein may be fully automated via software code modules…”; note applicant does not stipulate any particular scope for the “customer service rep” and therefore reads on Khosla’s service which provides an answer, to a customer, on behalf of merchant.).
Claim 22:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 20, further comprising a GAI engine configured to form an answer to the customer query, wherein the GAI engine is constrained in its answer by the set of boundary instructions to prevent hallucination by the GAI engine (Khosla, see citations noted supra, including again Fig. 4 and [0074], teaching the GAI answer is constrained by “verifier component” to ensure the GAI has not “hallucinated”, etc…).
Claim 23:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 20, further comprising a GAI engine configured to form an answer to the customer query, wherein the GAI engine returns a uniform resource locator (URL) in response to the query (Khosla, see at least [0019], e.g.: “…The natural language question answering service 102 may utilize machine-learned algorithms, such as generative AI model algorithms, to provide answers (e.g., information regarding a specific network-based service, passages associated with a network-based service, links to webpages [a uniform resource locator (URL) to a webpage],…”) the one or more service provider servers forwarding a link to the URL to the customer (Khosla, see at least [0026]: “…For example, the attribution component 109 may provide links (e.g., uniform resource identifiers (URI)) or titles as references (e.g., links to webpages, links to online documents, links to images, audio, video, etc.) to documents whose passages were retrieved by the aggregator component 104…”).
Claim 24:
Khosla teaches the limitations upon which this claim depends. Furthermore, Khosla as shown teaches the following:
The system of claim 20, wherein the GAI engine assists in generating the knowledgebase prior to receipt of the query (Khosla, see citations noted supra, including [0036]-[0044], e.g.: “…As another example, a search system may be a network-based system (or associated with a network-based system) that contains knowledge graphs of customers for a
network-based service (e.g., what kind of services they have, their usage activity, questions the customers have previously asked, types of questions customers have asked and their occurrence, their preferences regarding answers, etc.). The aggregator component 104 may utilize the knowledge graphs to create QA pairs where information about a customer may be an answer in a QA pair (e.g., the customer has 25 buckets in a network-based storage service) and a question from the customer may be a question in the QA pair (e.g., "how many buckets do I have in this network-based storage service?")…”; QA pairs are created before customer makes query.).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claim 19 is rejected under 35 U.S.C. 103 as obvious over Khosla et al. (U.S. US 2025/0005052 A1; hereinafter, "Khosla") in view of Official Notice.
Claim 19:
Although Khosla teaches the limitations upon which this claim depends, Khosla may not explicitly teach all of the below recited nuances. However, Khosla in view of Official Notice teaches the following:
The system of claim 15, further comprising a speech-to-text (STT) engine on the one or more service provider servers, the STT engine converting the audio of the received query to text for inclusion in the prompt (Khosla, see at least [0059], e.g.: “…The question (or prompt) may also be input via different methods (e.g., textual input of the question or prompt, audio input of the question or prompt, inputs generated by other generated models, etc.). Moreover, the input may be received via a graphical user interface, via APis, or the like…”; Examiner notes that because the GAI must take input in computer readable form, the audio prompt must inherently be converted to some computer readable form. Furthermore, Examiner takes Official Notice of the following fact: speech to text engines were well-known before the effective filing date of the claimed invention and therefore implementation of such was within the level of skill of a person of ordinary skill in the art before the effective filing date of the claimed invention. Therefore, in view of these findings, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use such well-known speech to text technology to convert the audio, which Khosla teaches his system receives, into text such that it is in some computer readable form necessary to enable his disclosure of using such audio as a natural language query operated on by his system and GAI and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.)
Claims 11, 13, 14 are rejected under 35 U.S.C. 103 as obvious over Khosla et al. (U.S. US 2025/0005052 A1; hereinafter, "Khosla") in view of Cole et al. (U.S. 2022/0210268 A1; hereinafter, "Cole").
Claim 11:
Although Khosla teaches the limitations upon which this claim depends, Khosla may not explicitly teach the nuance as recited below. However, regarding this feature, Khosla in view of Cole teaches the following:
The system of claim 10, wherein upon receipt of the textual representation of the website, the GAI engine suggests labels related to the business of the merchant that are not found in the textual representation of the website (Cole, see at least Figs. 4-5 and [0055]-[0060], e.g.: “…The user interface provides an option to annotate 328 (e.g., add labels) the conversation, such as to edit the suggested labels generated by the AI models, edit the transcript suggested by the NLP, tag the states, and validate values of identified parameters….”; note the label(s) may be created for a summary of a conversation. In some example embodiments, the summary is a textual abstract of the content of the conversation. In some example embodiments, the summary is generated by an ML model. As noted per the Abstract “analyzes the conversation and labels (e.g., “tags”) the text where the conversation associated with the label took place, such as, “An interest rate was provided.” The labels are customizable, so each client can define its own labels based on business needs”, therefore, the labels may be keywords or sentences not found in the conversation but instead are based on business needs).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Cole (directed towards a technique of suggesting labels, for a business, by AI models regarding information received about a conversations, e.g. business conversations) which is applicable to a known base device/method of Khosla (already directed towards use of AI models, specifically GAI models to provide answers to customer natural language queries, which may be conversations, e.g. received via audio) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Cole to the device/method of Khosla in order to perform the limitation in question because Khosla and Cole are analogous art in the same field of endeavor (at least G 06 N 20/00) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 13:
Although Khosla teaches the limitations upon which this claim depends, Khosla may not explicitly teach the nuance as recited below. However, regarding this feature, Khosla in view of Cole teaches the following:
The system of claim 10, wherein upon receipt of the labels the one or more service provider servers are configured to allow manual curation of the labels (Cole, see at least [0055]-[0060], e.g.: “…The user interface provides an option to annotate 328 (e.g., add labels) the conversation, such as to edit the suggested labels generated by the AI models, edit the transcript suggested by the NLP, tag the states, and validate values of identified parameters….”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Cole (directed towards a technique of suggesting labels, for a business, by AI models regarding information received about a conversations, e.g. business conversations) which is applicable to a known base device/method of Khosla (already directed towards use of AI models, specifically GAI models to provide answers to customer natural language queries, which may be conversations, e.g. received via audio) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Cole to the device/method of Khosla in order to perform the limitation in question because Khosla and Cole are analogous art in the same field of endeavor (at least G 06 N 20/00) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 14:
Khosla/Cole teach the limitations upon which this claim depends. Furthermore, Khosla in view of Cole teaches the following:
The system of claim 13, wherein the manual curation includes at least one of the addition of new labels, the amendment of labels generated by the GAI engine and deletion of labels generated by the GAI engine (Cole, see citations noted supra, again at least Figs. 4-5 and [0056]-[0060], e.g.: “..The user interface provides an option to annotate 328 (e.g., add labels) the conversation, such as to edit the suggested labels generated by the AI models, edit the transcript suggested by the NLP, tag the states, and validate values of identified parameters….”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Cole (directed towards a technique of manual curation of AI suggested labels, including adding labels) which is applicable to a known base device/method of Khosla (already directed towards a system/method by which a model using a GAI may tag [label] text for use in providing answers to customer questions) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Cole to the device/method of Khosla in order to perform the limitation in question because Khosla and Cole are analogous art in the same field of endeavor (at least G 06 N 20/00) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621