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 Remarks and Amendments filed 4/30/2026.
Claims 1, 14, 15, 20 have been amended.
Claims 25-34 were withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim.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.
(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.
Official Notice / Admitted Prior Art
With regard to claim 19, the common knowledge declared to be well-known in the art (i.e. 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) is hereby taken to be Applicant admitted prior art because the Applicant failed to traverse the Examiner’s assertion of Official Notice. To adequately traverse such a finding, an applicant must specifically point out the supposed errors in the Examiner’s action, which would include stating why the noticed fact is not considered to be common knowledge or well-known in the art. See 37 CFR 1.111(b). See also Chevenard, 139 F.2d at 713, 60 USPQ at 241 (“[I]n the absence of any demand by Applicant for the examiner to produce authority for his statement, we will not consider this contention.”). A general allegation that the claims define a patentable invention without any reference to the Examiner’s assertion of Official Notice is inadequate. Support for the Applicant’s assertion should be included. Because Applicant failed to traverse Examiner’s Official Notice, the common knowledge or well-known in the art statement is taken to be admitted prior art. See MPEP 2144.03(C).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 15, 20 have been amended to recite limitations directed towards the following:
a generative artificial intelligence (GAI) engine trained on answering customer queries;… the GAI engine being configured to automatically identify facts [from] unstructured text data and generate a plurality of labels representing the identified facts, the plurality of labels forming [a] knowledgebase;.. generate a prompt comprising (i) the customer query, (ii) at least a portion of the knowledgebase including the plurality of labels, and (iii) a set of boundary instructions;… provide the prompt to the GAI engine, wherein the set of boundary instructions constrains the GAI engine to generate a response to the customer query using only information contained within the knowledgebase…”
These limitations recite functionality attributed to a generic “GAI engine”; i.e. a generic generative artificial intelligence engine. However, the broadly claimed functions (i.e. the steps as claimed) are not general functions that any generic “GAI engine” can perform without special programming. Nonetheless, Applicant asserts that these broadly claimed functions/steps are his invention. However, the Applicant does not demonstrate possession of a special GAI model capable of the recited functionality of such GAI model. The Specification does not provide sufficient written description support for the recited functionality of this GAI model – there is not sufficient description of steps necessary to create or otherwise train a generic GAI model such that it is capable of performing the functionality now ascribed to the GAI model as claimed.
Note the Applicant’s Specification itself actually declares the GAI model relied upon is any known model such as per Specification at [0024] stating: “…GAI engine 120 may be an existing generative neural network, such as ChatGPT-3, ChatGPT-4, or other known models. These models have been trained on extensive datasets and possess the ability to generate coherent and contextually relevant text based on provided input.” Therefore, it is clear that Applicant has not invented a special GAI model but relies upon a generic model such as ChatGPT-3 but Applicant fails to provide sufficient disclosure as to how to either train or prompt such generic GAI model to now provide the functionality as claimed. The Applicant’s invention as claimed is deficient.
This deficiency further indicates that the Specification describes only "a mere wish or plan for" achieving the functionality, as now claimed, by some generic “GAI model”. See Eli Lilly, 119 F .3d at 1566 (citation omitted). As such, the broadly recited limitation "merely recite[s] a description of the problem to be solved," and leaves to future inventors to "complete an unfinished invention." See Ariad, 598 F.3d at 1353. In this case, without the Specification describing any particular algorithm to achieve the claimed function of generating a de-duplicated advertising campaign, one of ordinary skill in the art would not have reasonably concluded that the inventors invented the claimed invention or that they possessed the claimed subject matter at the time of filing of the application. See Vasudevan, 782 F.3d at 683; see also Regents, 119 F.3d at 1566; § 112 Guidance at 61.
To satisfy the written description requirement of 35 U.S.C. § 112, first paragraph, the Specification must reasonably convey to an artisan of ordinary skill that Appellant had possession of the claimed invention at the time the application was filed. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682 (Fed. Cir. 2015) (citing Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc)). Functional claim language that merely describes an intended result and fails to support the scope of the claimed invention is insufficient to show possession, even when the claim recitations are found word-for-word in the Specification. Vasudevan, 782 F.3d at 682 ("[t]he written description requirement is not met if the specification merely describes a 'desired result"') (citing Ariad, 598 F.3d at 1349); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968 (Fed. Cir. 2002) ("[t]he appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy" the written description requirement). The Specification must explain, for example, how Appellant intended to achieve the claimed function to satisfy the written description requirement. Vasudevan, 782 F.3d at 683. While "[t]here is no rigid requirement that the disclosure contain 'either examples or an actual reduction to practice,"' due to the written description requirement, the Specification must set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing." Allergan, Inc. v. Sandoz Inc., 796 F.3d 1293, 1308 (Fed. Cir. 2015) (citing Ariad, 598 F.3d at 1352); see also Examining Computer-Implemented Functional Claim Limitations for Compliance with 35 US.C. 112, 84 F.R. 57, 61- 62 (January 7, 2019) ("112 Guidance").
Here, the Specification does not sufficiently support any particular “GAI model” nor training regimen necessary to convert a general GAI model (e.g. ChatGPT-3 as noted supra per Specification at [0024]) into one which is capable of performing the recited functionality as recited in the aforementioned limitations; i.e. as recited as noted per claims 1, 15, and 20.
The specification does not set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing.". Instead, the Specification at [0024]-[0033] merely generically describes the GAI model may be a generic third-party model or, alternatively may be a customized generative neural network. However, the applicant only mentions, at a very high level of generality, general training and developing steps for such a GAI. This general reference to training is not “an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing."
The remainder of the Specification is notably void of any particular discussion or claim to a novel or inventive GAI which is capable of performing the steps now attributed to it.
Under these circumstances, the Examiner has determined the Specification merely states a wish that machine learning could be used to perform the claimed steps as recited in the claims, but the Specification does not demonstrate the Applicant was in possession of such a capable machine learning nor how applicant intended to create such machine learning capable of performing the steps now claimed. The Specification does not describe the claimed invention in sufficient detail such that an ordinarily skilled artisan would understand that the inventor had made the invention at the time of filing. Thus, the Examiner rejects claims 1, 15, 20 under 35 U.S.C. § 112(a) for a lack of written description support.
How to Overcome This Rejection: Examiner notes Applicant may amend to include only subject matter for which there exists “an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing."
Dependent claims 2-14, 16-19, and 21-24 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
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, exemplified in limitations of claim 1:
a generative artificial intelligence (GAI) engine trained on answering customer queries;… the GAI engine being configured to automatically identify facts from the unstructured text data and generate a plurality of labels representing the identified facts, the plurality of labels forming the
knowledgebase;.. generate a prompt comprising (i) the customer query, (ii) at least a portion of the knowledgebase including the plurality of labels, and (iii) a set of boundary instructions;… provide the prompt to the GAI engine, wherein the set of boundary instructions constrains the GAI engine to generate a response to the customer query using only information contained 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 pertaining to 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)).
Note the Applicant’s Specification itself actually declares his GAI model to be any known model such as per Specification at [0024] stating: “…GAI engine 120 may be an existing generative neural network, such as ChatGPT-3, ChatGPT-4, or other known models. These models have been trained on extensive datasets and possess the ability to generate coherent and contextually relevant text based on provided input.” Therefore, it is clear that Applicant has not invented a special GAI model but relies upon a generic model such as ChatGPT-3. However, applicant does not provide a technical solution by which to train such generic GAI model to perform the specific functionality as claimed.
Therefore, the claims are seen as nothing more than a business decision to include certain information in a “prompt” to a generic GAI model with a hope or wish of receiving an desired answer which is wholly a business decision and one which is only recited at the highest of levels of generality.
Thus, the claims are found to 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 1: “A system for automated answering of customer queries, comprising: one or more service provider servers configured to receive the customer queries;… wherein the one or more service provider servers are further configured to: receive unstructured text data relating to a merchant business; provide the unstructured text data to the GAi engine…; receive a customer query…; and provide the prompt to the GAi engine…”
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 regardless of whether it is unstructured data or data such as a customer query, nor is applicant’s invention a technique or protocol for providing a prompt (i.e. merely data transfer). 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 generic computing environment or, link them to a field of use or, are insignificant extra-solution activity (i.e. data-gathering, transfer, storage, display, etc…) 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.
How to Overcome This Rejection: Respectfully, Examiner does not find subject matter in the Specification regarding the abstract ideas being claimed which if included in the claims would appear to overcome the current rejection.
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 § 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.
Claims 1-10, 12, 15-18, 20-24 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 Pauli et al. (U.S. US 2024/0338532 A1; hereinafter, "Pauli").
Claims 1, 15, 20:
Pertaining to claims 1, 15, 20 exemplified in the limitations of 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 are further configured to:
receive unstructured text data relating to a merchant business (Khosla, see citations noted supra, including also at least [0037], e.g.: teaching the system receives screen scrapes [unstructured text] from webpages [e.g. related to a merchant business], and per at least [0046] teaches use of DPR techniques by which the system “…retrieve relevant passages from a large corpus of unstructured text, etc….”);
Although Khosla teaches the above features including receiving unstructured text data related to a merchant business and teaches, e.g. per [0019] his search systems “may store passages and question and answer pairs (QA pairs) [labeled data] regarding a particular network-based service (e.g., questions and answer pairs for a specific network-based storage product) or computing domain (e.g., website of a clothing designer)….”; and Khosla teaches e.g. per [0024] his “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)…” which implies his GAI model can generate labels of data to enable such teachings, and teaches per [0034] his system has a module which determines which documents are relevant to the natural language question [query] e.g. via a “surface-level similarities” technique [a labeling technique], such as identifying the presence of surface level similar keywords like "network-based service" [a type of label], and Khosla teaches per [0046] use of DPRs, i.e. dense passage retrieval techniques, to “precompute dense vector representations” of unstructured text [automatically identify facts from the unstructured text data] and teaches he “store them in a search index” [knowledgebase of labeled facts], Khosla may not explicitly teach, at least not in a single embodiment, that his retrieved unstructured text is provided to his GAI model and his GAI model is configured to generate labels even though such label generation is apparently implied by virtue of the aforementioned teachings. Nonetheless, Khosla in view of Pauli are found to teach the combination of features as recited in the following limitation:
provide the unstructured text data to the GAI engine1, the GAI engine being configured to automatically identify facts from the unstructured text data and generate a plurality of labels representing the identified facts, the plurality of labels forming the knowledgebase (Pauli, see at least [0001], teaching: “Many organizations have large amounts of unstructured data (e.g., text, images, video, audio), but the data may need to be categorized before it can generate actionable insights. Labeling of unstructured data for machine learning applications is important for building efficient and accurate machine learning models….” And per [0003], e.g.: “…generating soft labels for the plurality of training samples using a large language machine learning model (LLM)…”; and per [0030], “…large generative language model (LLM) 120, such as GPT-3, Davinci, Babbage, or the like… initially uses the LLM 120 to generate a suggested label 122 for each particular sample (e.g., a category)… The LLM 120 may also be used to generate soft labels 126 for some samples, where soft labels 126 represent automatically-generated initial categorization guesses for those samples that may be used to train 112 model 130…”);
In view of these teachings, the Examiner understands that there is both motivation provided by Pauli to generate labels [labels representing identified facts] of unstructured data by a generative AI model and motivation to apply Pauli’s techniques to methods/systems which receive and process unstructured data, such as Khosla’s system/method. Therefore, the Examiner finds the limitation in question is merely applying a known technique of Pauli (directed towards use of a generative AI model to identify facts from unstructured data and generate labels of such unstructured data) which is applicable to a known base device/method of Khosla (already directed towards use of generative AI models and also to receiving unstructured text as well as generation and creation of a search index [knowledgebase of labeled facts]”) 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 Pauli to the device/method of Khosla in order to perform the limitation in question whereby Khosla’s system/method makes use of Pauli’s technique of generation of labeled data, by a GAI engine such as GPT-3, from unstructured text, which then may be stored in a search index of Khosla because Pauli is pertinent to the data analysis and processing steps of Khosla 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.
Furthermore, Khosla/Pauli is found to teach the following:
receive a customer query and generate a prompt (Khosla, see citations noted supra, including again at least [0012]: “…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 [received query], to modify, update, or supplement the natural language question, or the like, and produce a prompt [and generate a prompt]…”) comprising (i) the customer query, (ii) at least a portion of the knowledgebase including the plurality of labels, and (iii) a set of boundary instructions (Khosla, see citations noted supra, again per e.g. [0012]: “…For purposes of the present application, the prompt can correspond to [comprising] a few selected passages (e.g., from all the passages retrieved) [(iii) a set of boundary instructions] and the QA pairs [plurality of labels] of the selected passages [(ii) at least a portion of the knowledgebase], along with the natural language question [(i) the customer query] (e.g., or a form of the question where question may be a prompt or a command)….”; Examiner notes that the face of the claim does not require the prompt to include “the plurality of labels” but instead only “at least a portion of the knowledgebase”; the knowledgebase is understood to be described as including the plurality of labels. The Examiner understands the claim feature does not mean the entire “knowledgebase including the plurality of labels” is included in the prompt.);
provide the prompt to the GAI engine, wherein the set of boundary instructions constrains the GAI engine to generate a response to the customer query using only information contained within the knowledgebase (Examiner notes the 112(b) rejection guiding claim interpretation. Khosla, see citations noted supra, e.g. [0062] “As stated herein, the LLM component 106 may be a generative AI model that uses LLM techniques such as a RAG….”; per [0012]-[0013]: “…the natural language question answer service can utilize a trained large language model (LLM) [GAI engine] to use the prompt [prompt is provided to the GAI engine] and generate an answer to the natural language question…”; note again per [0012], Khosla teaches: “…the prompt can correspond to [comprising] a few selected passages (e.g., from all the passages retrieved) [i.e. the (iii) a set of boundary instructions]…” Examiner finds that the intended use of Khosla’s prompt is to constrain the answers generated by his LLM [GAI engine]. Because Khosla teaches the prompt includes these few selected passages, they are also intended to constrain the LLM [GAI engine]. The intended use of such constraint may be to generate a response using only information contained within Khosla’s index [knowledgebase of labeled data], e.g. as noted per [0046]: “…Given a question or query, the aggregator component 104 may use DPR techniques to retrieve relevant passages from an index [knowledge base of labeled data] based on the similarity between their representations and the representation of a query or question…”; Therefore there is at least motivation given from Khosla’s disclosure to perform Khosla’s provision of his prompt to his GAI model for the intended purpose of generating a response to the customer question [query] using only information from his index [the knowledgebase] 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 or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine 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.)
Claim 2:
Khosla/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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/Pauli 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 16:
Khosla/Pauli 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/Pauli 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/Pauli 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 21:
Khosla/Pauli 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/Pauli 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/Pauli 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/Pauli 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 19 is rejected under 35 U.S.C. 103 as obvious over Khosla in view of Pauli in view of Applicant Admitted Prior Art.
Claim 19:
Although Khosla/Pauli teaches the limitations upon which this claim depends, Khosla may not explicitly teach all of the below recited nuances. However, Khosla in view of Applicant Admitted Prior Art 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, per Applicant Admitted Prior Art Facts: 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 in view of Pauli in view of Cole et al. (U.S. 2022/0210268 A1; hereinafter, "Cole").
Claim 11:
Although Khosla/Pauli 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/PAuli 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/Pauli/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.
Response to Arguments
Applicant amended claims 1, 15, 20 on 04/30/2026. Applicant's arguments (hereinafter “Remarks”) also filed 04/30/2026, have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 101, 112, and 103 rejections with Khosla in view of Pauli teaching applicant’s amended and argued features of the independent claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
The following prior art is made of record although not relied upon as it is considered pertinent to applicant's disclosure:
Mukherjee, Anupam and Kamath, Nala Prasad, "INTELLIGENT AUTO-LEARNING GENERATIVE KNOWLEDGE FINDER FOR PROACTIVELY AIDING A VIRTUAL AGENT", Technical Disclosure Commons, (July 17, 2023) https://www.tdcommons.org/dpubs_series/6059 ; This reference is of particular interest to the GAI features of applicant’s claims; e.g. Techniques are presented herein that support an intelligent, proactive, auto-learning generative knowledge finder (KF) component which can be leveraged by a VA to automatically improve its competency level even if an organization's KBs are not current. Such a KF component may extract information from past caller-HA interactions and enrich itself with the help of an N-shot learning paradigm. Thus, without any manual intervention, a KF component, powered by generative AI, can enhance itself and, in turn, a VA's competency level beyond the VA's original intelligence (which may have been acquired through training and knowledge management system (KMS) access), etc…
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
1 Specification: GAI engine may be GPT-3 or the like.; e.g. Specification at [0024]: “…GAi engine 120 may be an existing generative neural network, such as Chat GPT-3, ChatGPT-4, or other known models…”
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