DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 8/30/2024. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/583,172 filed on 9/15/2023 and Provisional Application 63/583,154 filed on 9/15/2023. An interview was conducted on 9/9/2026 and Applicant’s representative, Stephen Walder (reg. no. 41534), elected to prosecute Invention 1, claims 1-6 and 12-17, based on a requirement for restriction. Claims 7-11 and 18-22 will be withdrawn from further consideration.
Election/Restrictions
Restriction to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-6 and 12-17, drawn to a method comprising: receiving a user query; determining a categorization of the user query; selecting one of a plurality of machine learning models based at least in part on the categorization of the user query; generating a prompt based at least in part on the user query; and generating, using the one of the plurality of machine learning models, a response to the user query based at least in part on the prompt; wherein the method is performed by one or more computing devices, best classified in G06N3/0475.
II. Claims 7-11 and 18-22, drawn to a method comprising: obtaining a digital artifact; selecting one of a plurality of embeddings models; generating an embedding for the digital artifact using the one of the plurality of embeddings models; generating metadata for the digital artifact; and storing the embedding in a vector store in association with the metadata; wherein the method is performed by one or more computing devices, classified in G06N3/0455.
The inventions are independent or distinct, each from the other because:
Inventions I and II are directed to related processes. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed have a materially different design, mode of operation, function, or effect because Invention I required (and Invention II does not require) a method comprising: receiving a user query; determining a categorization of the user query; selecting one of a plurality of machine learning models based at least in part on the categorization of the user query; generating a prompt based at least in part on the user query; and generating, using the one of the plurality of machine learning models, a response to the user query based at least in part on the prompt; wherein the method is performed by one or more computing devices. Furthermore, Invention II requires (and Invention I does not require) a method comprising: obtaining a digital artifact; selecting one of a plurality of embeddings models; generating an embedding for the digital artifact using the one of the plurality of embeddings models; generating metadata for the digital artifact; and storing the embedding in a vector store in association with the metadata; wherein the method is performed by one or more computing devices. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply: the groupings of patentably distinct inventions require a different field of search (e.g., searching different CPC groups or subgroups, or employing different search strategies or search queries). It is additionally pointed out that the examination burden is not limited exclusively to a prior art search but also includes the effort required to apply the art by making and discussing all appropriate grounds of rejection. Multiple inventions, such as those in the present application, normally require additional reference material and further discussion for each additional invention examined. Concurrent examination of multiple inventions would thus typically involve a significant burden even if all searches were coextensive. The searches of the different inventions are indeed different and burdensome.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
During a telephone conversation with Stephen Walder (Reg. No. 41534) on 9/9/2026, a provisional election was made without traverse to prosecute Invention I, claims 1-6 and 12-17. Affirmation of this election must be made by applicant in replying to this Office action. Claims 7-11 and 18-22 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected 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.
Claims 3 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3 and 14 recite the limitation “the second response being a more accurate to the user query than the first response” (emphasis added). The phrase “more accurate” is unclear. There is no ascertainable standard to determine how a second response is “more accurate” than a first response. Neither the claim language nor specification provides a standard for making such a determination. Further, the phrasing of this statement is grammatically awkward. Is the intention for this phrase “more accurate than” or “more accurate response to”?. For examination purposes, the limitation will be interpreted to mean “the second response being [[a]] more accurate than the first response with respect to the user query based on a confidence score” (emphasis added). Appropriate 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 1-6 and 12-17 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
determining a categorization of the user query: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a category of a question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally determine the topic or category of a question.
selecting one of a plurality of machine learning models based at least in part on the categorization of the user query: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of selecting a model based on a category of a question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally select a model based on a category of question.
generating a prompt based at least in part on the user query: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a prompt based on a query or question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally determine the topic or category of a question.
generating, …, a response to the user query based at least in part on the prompt: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a response to a question or query based on a prompt, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally answer a question.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “receiving a user query”, “using the one of the plurality of machine learning models”, and “wherein the method is performed by one or more computing devices”.
The additional elements of “using the one of the plurality of machine learning models” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic plurality of ML models are broadly used to generate a response to a query or question. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving a user query” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
The additional element of “wherein the method is performed by one or more computing devices” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “using the one of the plurality of machine learning models” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic plurality of ML models are broadly used to generate a response to a query or question. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving a user query” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
The additional element of “wherein the method is performed by one or more computing devices” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the categorization of the user query is determined using a micro machine learning model” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generating, …, a second response to the user query based at least in part on the prompt, the second response being a more accurate to the user query than the first response: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a response to a question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally determine the topic or category of a question.
Step 2A Prong 2, Step 2B: The additional element of “using the one of the plurality of machine learning models” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic plurality of ML models are broadly used to generate a response to a query or question. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
performing a similarity search of a vector store using the prompt: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of performing a search of vectors using a prompt, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generating an enhanced prompt based on the data relevant to the prompt: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a better prompt based on certain data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generating, using the one of the plurality of machine learning models, the response based on the enhanced prompt: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a response to a better prompt, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “using the one of the plurality of machine learning models” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic plurality of ML models are broadly used to generate a response to a prompt. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional element “retrieving data relevant to the prompt from the vector store” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
performing the similarity search of a portion of the vector store corresponding to the categorization of the user query: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of performing a search of vectors corresponding to a question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one of the plurality of machine learning models is selected using a zero-shot classifier” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 12-17
Claims 12-17 recite one or more non-transitory storage media (step 1: a manufacture) using a computing device to perform the steps of claims 1-6, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-6, respectively.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (Chen et al., “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance”, May 9, 2023, arXiv:2305.05176v1, pp. 1-13, hereinafter “Chen”).
Regarding claim 1, Chen discloses [a] method comprising: (Abstract; “we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy”, which discloses a particular method or technique for learning or selecting an LLM for a specific query)
receiving a user query; (§2; “we concentrate on the standard natural language query answering task, where the objective is to answer a query q sampled from a natural language query distribution Q.”, which discloses receiving a query q to produce an answer or response)
determining a categorization of the user query; (§3, Strategy 3; “The generation scoring function, denoted by g(·,·) : Q × A → [0,1], generates a reliability score given a query and an answer produced by an LLM API … The scoring function can be obtained by training a simple regression model that learns whether a generation is correct from the query and a generated answer”, which discloses a DistilliBERT-based scorer that classifies each query and response pair against threshold values, which, under a broadest reasonable interpretation of the claim language, is a “categorization” of the query used to determine the routing decision)
selecting one of a plurality of machine learning models based at least in part on the categorization of the user query; (§3, Strategy 3; “LLM cascade sends a query to a list of LLM APIs sequentially. If one LLM API’s response is reliable, then its response is returned, and no further LLMs in the list are needed. The remaining LLM APIs are queried only if the previous APIs’ generations are deemed insufficiently reliable … The LLM router selects m LLM APIs to include in the list”, which discloses selecting one of a plurality of ML models or LLMs based on a category of a query; and §2; “K different LLM APIs, denoted by {fi(·)}K i=1.”, wherein {fi(·)}K i=1 is the set of LLMs or ML models from which one is selected based on a score or threshold categorization)
generating a prompt based at least in part on the user query; and (§2; “Note that to use LLM APIs, one has to convert each query q to some corresponding prompt first”, which discloses generating a prompt; and §3, Strategy 1, “Prompt Adaptation”; the section discloses generating a prompt from queries; and Figure 2)
generating, using the one of the plurality of machine learning models, a response to the user query based at least in part on the prompt; (§2; each API is defined as “Each fi(·) : P → A is a function that, given a prompt p from the prompt space P, generates an answer from the answer distribution A”; and §3; “it iteratively invokes the ith API in the list to obtain an answer fLi (q)”; and Figure 2)
wherein the method is performed by one or more computing devices (§4; the section discloses the experimental study that inherently uses computing devices to implement the experiments).
Regarding claim 12, it is a non-transitory storage media claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claims 2 and 13, the rejection of claims 1 and 12 are incorporated and Chen further discloses wherein the categorization of the user query is determined using a micro machine learning model (§4, “A Case Study”; “We employ a DistilBERT [SDCW19] tailored to regression as the scoring function. It is important to note that DistilBERT is considerably smaller and therefore less expensive than all LLMs considered here”, which discloses under BRI a micro machine learning model).
Regarding claims 3 and 14, the rejection of claims 1 and 12 are incorporated and Chen further discloses generating, using the one of the plurality of machine learning models, a second response to the user query based at least in part on the prompt, the second response being a more accurate to the user query than the first response (§4, “A Case Study”; “For any given query, it first extracts an answer from GPT-J. If the score of this answer is greater than 0.96, the answer is accepted as the final response. Otherwise, J1-L is queried. J1-L’s answer is accepted as the final response if its score is greater than 0.37; otherwise, GPT-4 is invoked to obtain the final answer”; and Figure 3 Caption; “Overall, we observe that FrugalGPT reduces the cost by 80%, while improves the accuracy by 1.5% compared to GPT-4”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4 and 15 are rejected under 35 USC § 103 as being obvious over Chen in view of Lewis et al. (Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, Apr. 12, 2021, arXiv:2005.11401v4, pp. 1-19, hereinafter “Lewis”).
Regarding claims 4 and 15, the rejection of claims 1 and 12 are incorporated and Chen fails to explicitly disclose but Lewis discloses performing a similarity search of a vector store using the prompt; (Abstract; “We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever”, where the retriever performs an approximate similarity using an encoding of the input)
retrieving data relevant to the prompt from the vector store; (§1; “we combine these components in a probabilistic model trained end-to-end (Fig. 1). The retriever (Dense Passage Retriever [26], henceforth DPR) provides latent documents conditioned on the input”)
generating an enhanced prompt based on the data relevant to the prompt; (§1; “the seq2seq model (BART [32]) then conditions on these latent documents together with the input to generate the output”, wherein the seq2seq generator produces the output conditioned on the input and retrieved documents, which are interpreted as the “enhanced prompt”)
generating, using the one of the plurality of machine learning models, the response based on the enhanced prompt (§1; “the seq2seq model (BART [32]) then conditions on these latent documents together with the input to generate the output”, wherein the seq2seq generator produces the output conditioned on the input and retrieved documents, which are interpreted as the “enhanced prompt”).
Chen and Lewis are analogous art because both are concerned with language models and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in large language modeling to combine the similarity search of Lewis and the method of Chen to yield to the predictable result of performing a similarity search of a vector store using the prompt; retrieving data relevant to the prompt from the vector store; generating an enhanced prompt based on the data relevant to the prompt; generating, using the one of the plurality of machine learning models, the response based on the enhanced prompt. The motivation for doing so would be to combine pre-trained parametric and non-parametric memory for language generation (Lewis; Abstract).
Claims 5 and 16 are rejected under 35 USC § 103 as being obvious over Chen in view of Lewis and further in view of Gollapudi et al. (Gollapudi et al., “Filtered − DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters”, May 4, 2023, WWW ’23, pp. 3406-3416, hereinafter “Gollapudi”).
Regarding claims 5 and 16, the rejection of claims 1, 4, 12, and 15 are incorporated and Chen fails to explicitly disclose but Gollapudi discloses performing the similarity search of a portion of the vector store corresponding to the categorization of the user query (§1.1; “In this setting, for every data point (a.k.a. vector) � ∈ �, we have an associated set of labels �� ⊆ F , where F is a finite universe of labels. A query to the index now comprises of the vector �� , the target number of nearest neighbors �, and a label filter � ∈ F . The ANNS index is required to find the closest neighbors of �� from �� = {� ∈ � : � ∈ �� }, i.e., points in the index that have the label � associated with them”; and Abstract; “Filtered ANNS queries ask for the nearest neighbors of a query’s embedding from the points in the index that match the query’s labels such as date, price range, language”).
Chen, Lewis, and Gollapudi are analogous art because all are concerned with language models and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in large language modeling to combine the similarity search and vector store of Gollapudi and the method of Chen and Lewis to yield to the predictable result of performing the similarity search of a portion of the vector store corresponding to the categorization of the user query. The motivation for doing so would be to use label metadata associated with vector data to build efficient indices for filtered ANNS queries (Gollapudi; Abstract).
Claims 6 and 17 are rejected under 35 USC § 103 as being obvious over Chen in view of Yin et al. (Yin et al., “Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach”, Aug. 31, 2019, arXiv:1909.00161v1, pp. 1-10, hereinafter “Yin”).
Regarding claims 6 and 17, the rejection of claims 1 and 12 are incorporated and Chen fails to explicitly disclose but Yin discloses wherein the one of the plurality of machine learning models is selected using a zero-shot classifier (§1; “In this work, we benchmark the datasets and evaluation setups of 0SHOT-TC. Furthermore, we propose a textual entailment approach to handle the 0SHOT-TC problem of diverse aspects in a unified paradigm”; and Abstract; “This work benchmarks the 0SHOT-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines”; and §5).
Chen and Yin are analogous art because both are concerned with language models and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in large language modeling to combine the zero-shot classifier of Yin and the method of Chen to yield to the predictable result of wherein the one of the plurality of machine learning models is selected using a zero-shot classifier. The motivation for doing so would be to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (Yin; Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brent Hoover whose telephone number is (303)297-4403. The examiner can normally be reached Monday - Friday 9-5 MST.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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