DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/26/2026 has been entered.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the 101 rejection, the AI and LLM are recited at a high-level of generality and fail to provide meaningful significance that go beyond generally linking the use of an abstract idea to a particular technological environment. Also, the “electronic communication” is merely for the purpose of data gathering and/or insignificant extra-solution activity that amount to no more than mere instructions to apply the exception using a generic computer component. For these reasons, examiner maintains the previous 101 rejection.
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 69-88 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 69, 84, and 87 recite “modifying the natural language query …”, “providing the raw response to a plurality of guardrail Ais …”, “forwarding a version of the raw response to the human user”, and “acting on the domain-specific evaluations”. These limitations, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “processor”. For example, but for the “processor” language, these steps in the context of this claim encompasses the user manually modifying a text query, providing a response to guardrail AIs, forwarding a response to a user, acting on the instructions described in the response. All of these steps can be performed in the mind and/or using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements - using a processor to perform these steps. The use of a processor is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements/steps of AIs and steps of “receiving” are merely for the purpose of data gathering and/or insignificant extra-solution activity that amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Similar to independent claims above, the steps in dependent claims 70-83, 85-86, and 88 under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. In the context of this claim encompasses the user manually performing these steps. All of these steps can be performed in the mind and/or using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 69-88 are rejected under 35 U.S.C. 103 as being unpatentable over Whalen et al. (USPG 2025/0200392, hereinafter Whalen) in view of Namer et al. (USPG 2025/0209308, hereinafter Namer).
Regarding claims 69, 84, and 87, Whalen discloses a computer-implemented method, non-transitory CRM, and orchestrator apparatus, comprising: a hardware platform, comprising a processor circuit and a memory (figures 10-11); and instructions encoded within the memory to instruct the processor circuit to (figures 10-11):
receiving, from a human user via an electronic communication channel, a natural language query (figure 1, customer submits a query in step 120 and/or paragraph 30);
receiving, from the general-purpose Al, a raw response to the modified query, including requiring the general purpose AI to limit the raw response to information from an approved dataset (figure 1, Vanilla LLM 102 provides responses; also see paragraph 30);
providing the raw response to a plurality of guardrail Als, wherein the guardrail Als are to provide domain-specific evaluations of the raw response (figure 1, responses are forwarded to Multi-tenant provider network 104 to check accuracy; these AIs are domain-specific as discussed in paragraphs 28 and 58 that models in various fields such as “healthcare, law, finance, and any other field”; also see paragraphs 28 and 42-43, “The automated aspect is facilitated by advanced algorithms and logical models within the LLM verifier system 100, which cross-check the vanilla LLM 102's outputs against a robust database of verified information and domain-specific knowledge”);
receiving, from the plurality of guardrail Als, the domain-specific evaluations (process in figure 1, the answer verifier 106 perform cross check and returns results to customer; paragraphs 26, 30, and 50);
forwarding a version of the raw response to the human user (process in figure 1, the answer verifier 106 perform cross check the Vanilla LMM’s responses and returns a result to the customer; paragraphs 26, 30, and 50); and
acting on the domain-specific evaluations (process in figure 1, the answer verifier 106 perform cross check and returns results to customer; paragraphs 26, 30, and 50).
Whalen fails to explicitly disclose, however, Namer teaches modifying the natural language query (a modified query) and posting the modified query to a general-purpose artificial intelligence (Al) (figure 2, Pre-Prompt Processor 224 and/or paragraphs 71 and 74; processing prompt before submitted to LLM); and based on the domain-specific evaluations, modifying or conditioning the raw response to create a conditioned response (figure 2, Response Processor 226 and/or process in figure 5 as discussed in paragraphs 81-95, particularly paragraph 93, The query association data and the response association data for a query and response pair can be aggregated to determine whether to provide a response including the generative content or a response including filtered content”; “conditioning the raw response” is equated to “filtered content”).
Since Whalen and Namer are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of processing query and filtering response content. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 70-73, 85-86, and 88, the combination of Whalen and Addanki further discloses wherein modifying the natural language query comprises building a multi-constraint prompt for the general-purpose Al, and providing the multi-constraint prompt as part of the modified query (Addanki: paragraph 67, modifying the query based on context would put constraints on the query); wherein the multi-constraint prompt comprises three or more constraints (Addanki: paragraph 67, modifying the query to make it more specific based on context would put any number of constraints on the query depending on specificity level); wherein the multi-constraint prompt comprises constraints that correlate to the domain-specific evaluations of the guardrail Als (Addanki: paragraphs 67-70; adding or removing words from the query can make the query specific to a particular domain or fields; Whale: paragraph 58 that models in various fields such as “healthcare, law, finance, and any other field”).
Since Whalen and Addanki are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of modifying the query before submitting the a general-purposed AI. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 74-83, Whalen further discloses the method of claim 69, wherein modifying the natural language query comprises providing a document from a prepared enterprise data set, and instructing the general-purpose Al to answer the natural language query according to the document (Whalen: figure 1, Vanilla LLM 102 provides responses; also see paragraph 30); wherein the general-purpose Al is a large language model (LLM) (figure 1, Vanilla LLM 102); wherein the LLM is a third-party LLM (figure 1, Vanilla LLM 102 can be a third party LLM); wherein the guardrail Als are domain-specific LLMs (figure 1, responses are forwarded to Multi-tenant provider network 104 to check accuracy; these AIs are domain-specific as discussed in paragraph 58 that models in various fields such as “healthcare, law, finance, and any other field”; also see paragraphs 28 and 42-43, “The automated aspect is facilitated by advanced algorithms and logical models within the LLM verifier system 100, which cross-check the vanilla LLM 102's outputs against a robust database of verified information and domain-specific knowledge”); wherein the version of the raw response is the raw response (process in figure 1, the answer verifier 106 perform cross check the Vanilla LMM’s responses and returns a result to the customer; paragraphs 26, 30, and 50; if the response is a valid response after the check, returning to the user); wherein the version of the raw response is modified from the raw response (process in figure 1, the answer verifier 106 perform cross check the Vanilla LMM’s responses and making correction before returning to the customer; paragraphs 26, 30, and 50; also see paragraphs 159-161); wherein acting on the domain-specific evaluations comprises forwarding the version of the raw response only after determining that the raw response passed the domain-specific evaluations (process in figure 1, the answer verifier 106 perform cross check the Vanilla LMM’s responses and returns a result to the customer; paragraphs 26, 30, and 50; if the response is a valid response after the check, returning to the user); further comprising forwarding the version of the raw response while the domain-specific evaluations are ongoing (within scope of process in figure 1 already discussed above), urther comprising interrupting the version of the raw response if the raw response failed at least one domain-specific evaluation (within scope of process in figure 1 already discussed above); wherein acting on the domain-specific evaluations comprises warning the human user if at least one domain-specific evaluation failed (within scope of process in figure 1, human expert 112 is notified to make modification when evaluation failed; see paragraphs 28, 33, and 36, human experts makes modification); wherein warning the human user comprises providing information about which domain-specific evaluation or evaluations failed (within scope of process in figure 1, human expert 112 is notified to make modification when evaluation failed; see paragraphs 28, 33, and 36, human experts makes modification).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nagaraju et al. (USPG 2024/0185001) teaches a method for modifying query before submitting to LLM for search and retrieval process. Ramachandra et al. (USPG 2019/0163785) teach modifying a query to submit to LLM to obtain response to forward to an AI to check for accuracy before presenting. These references are considered pertinent to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUYEN X VO whose telephone number is (571)272-7631. The examiner can normally be reached M-F, 8-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HUYEN X VO/Primary Examiner, Art Unit 2656
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