DETAILED ACTION
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 Office Action is in response to the communication filed on 08/22/2025.
Claims 1-20 are pending for consideration.
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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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, 6, 10, 13, 16, and 20 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 13 recite the limitation "the aliases" in line 1. There is insufficient antecedent basis for this limitation in the claim.
The term “actually matches” in claims 6, 10, 16, and 20 is a relative term which renders the claim indefinite. The term “actually matches” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner recommends the removal of the term “actually” or addition of a threshold to define a comparison threshold, if applicable.
The term “with appropriate ones” in claims 6, 10, 16, and 20 is a relative term which renders the claim indefinite. The term “appropriate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Niddam et al. (WO 2025/210632)(hereinafter Niddam).
Regarding claims 1 and 11, Niddam teaches storing documents in a vector database in a plurality of distinct indices with each of the distinct indices associated with a distinct access level based upon at least one of a security level and an appropriate geographic location (Niddam: see Page 1 Background, "Vector databases have emerged as a widely utilized technology for supporting the rapid growth of data-driven applications, particularly in areas such as artificial intelligence (Al), machine learning (ML), computer vision, natural language processing (NLP), and similar domains that require efficient handling of high-dimensional data vectors. Unlike traditional relational databases that store data in tables with rows and columns, vector databases are designed to store and manage data represented in the form of vectors, i.e., arrays of numbers, known as embeddings, each representing complex data points (such as words, sentences, images, or any other kind of data) in a high-dimensional space");
receiving a query from a user for information, and identifying at least one of a security level and a geographic location of the user (Niddam: see Page 6, "(iii). Wherein the search query is received from a first user having a predefined access level, and wherein generating the response is based on the access level");
and matching the identified at least one of the security level and the geographic location with at least one of a plurality of aliases, with each of the plurality of aliases providing access to particular ones of the indices in the vector database (Niddam: see Page 7, "(xvi). Wherein retrieving the data items comprises retrieving a subset of the data items for which a matching degree between the matching vectors to the embedding vector is above a predefined matching threshold, and processing the subset of the data items to generate the response");
accessing the vector database to match the appropriate indices to the identified alias (Niddam: see Page 5, "accessing a vector database storing a plurality of vectors wherein each vector represents a data item stored in at least one native application; searching the vector database to identify matching vectors of the plurality of vectors to the embedding vector"); and
displaying appropriate information from the appropriate indices (Niddam: see Page 5, "and providing a response based on the processed data items").
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 2-4, 7, 12-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Niddam, as applied to claims 1 and 11 above, in view of Pishehvar et al. (U.S. 2026/0220085)(hereinafter Pishehvar).
Regarding claims 2 and 12, Niddam teach the invention detailed above.
However, Niddam does not teach the vector database communicates to and from a retrieval augmented generation (RAG) pipeline.
Nevertheless, Pishehvar-which is in the same field of endeavor- teaches the vector database communicates to and from a retrieval augmented generation (RAG) pipeline (Pishehvar: see Page 4 paragraph 0042, "Typically, a RAG system stores this contextual information in a vector database for quick retrieval using semantic searching").
Niddam and Pishehvar are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to utilize the vector database metadata filter method of Niddam to the RAG pipeline refinement system of Pishehvar. The suggestion/motivation for doing so would be to prevent hallucinations and enhance the output of the data being provided in response to the user’s query.
Regarding claims 3 and 13, Niddam and Pishehvar teach a module for matching the aliases also communicates with the RAG pipeline and to the vector database (Pishehvar: see Page 4 paragraph 0043, "As noted above, RAG can help avoid hallucinations and enhance the output of LLMs or other generative AI models. Such enhancements are obtained by dynamically adding contextual information to the prompt for input to the model. This is usually done by retrieving contextual information from a vector database, which was previously fed with the relevant information"). Motivation to combine Niddam and Pishehvar in the instant claims, is the same as that in claims 2 and 12.
Regarding claims 4, 7, 14, and 17, Niddam and Pishehvar teach the RAG pipeline communicates with a large language module that generates a response for the information to be displayed, and is associated with the displayed appropriate information (Niddam: see Page 4, "Turning to generating a response to the user's query, with the immersive use of advanced Al systems, such as Large Language Models (LLMs) including GPT, BERT, T5, and others, trained on extensive datasets to understand, generate, and interact with human language, users wish to operate in an environment implementing a tailored data retrieval interaction that advances the search experience"; Pishehvar: see Page 5 paragraph 0060, "In some implementations, multimodal RAG system 514 may also augment input query 506 based on media 524 and/or a recommendation 510. In turn, multimodal LLM 508 may generate an output (e.g., an intermediate response to input query 506)"). Motivation to combine Niddam and Pishehvar in the instant claims, is the same as that in claims 2 and 12.
Claims 5-6, 8-10, 15-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Niddam and Pishehvar, as applied to claims 2-4, 7, 12-14, and 17 above, and in further view of Kumar et al. (U.S. 12,670,140)(hereinafter Kumar).
Regarding claims 5, 8, 9, 15, 18, and 19, Niddam and Pishehvar teach the invention detailed above.
However, Niddam and Pishehvar do not teach documents are ingested into an input folder system, and identified with a folder that is associated with each of the vector database indices.
Nevertheless, Kumar-which is in the same field of endeavor- teaches documents are ingested into an input folder system, and identified with a folder that is associated with each of the vector database indices (Kumar: see Fig. 2; Col 6 lines 23-24, "In step 204, the processing system may acquire data from at least one data source of an enterprise"; Col 6 lines 51-54, "In step 206, the processing system may detect a plurality of characteristics of the data. In one example, the automatically detected characteristics may include content of the data"; Col 8 lines 14-16, " In step 218, the processing system may apply an indexing algorithm to the plurality of vector embeddings to facilitate storage of the plurality of vector embeddings").
Niddam, Pishehvar, and Kumar are analogous art because they are from the same field of endeavor. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the indexing algorithm for filtering vector embeddings from the user’s query of Kumar to the RAG refinement system of Niddam and Pishehvar. The suggestion/motivation for doing so would be to store classified data to ensure that only applicable contextual information is being provided to the large language model which improves the accuracy of the data being returned to the user.
Regarding claims 6, 10, 16, and 20, Niddam, Pishehvar, and Kumar teach the ingested documents pass through a document class and folder extraction step to determine if the document actually matches with appropriate ones of the vector database indices, and if not then ingestion is stopped, and if there is a match then the document is passed to the vector database (Kumar: see Col 8 lines 14-26, " In step 218, the processing system may apply an indexing algorithm to the plurality of vector embeddings to facilitate storage of the plurality of vector embeddings. In one example, the indexing algorithm may create different indices for different data sets (e.g., text data, image data, video data, or the like) within the enterprise's data sources. This allows the vector embeddings to be stored in a manner that enhances scalability and accessibility for efficient retrieval (e.g., by an inferencing process such as that described below in connection with FIG. 3). In one example, multi-indexing and indexing algorithms may be used in step 218 to efficiently organize the diverse data sets acquired and processed by the method 200. The method 200 may end in step 220"). Motivation to combine Niddam, Pishehvar, and Kumar in the instant claims, is the same as that in claims 5, 8, 9, 15, 18, and 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dubey et al. (U.S. 2026/0187372) teaches a method for accessing and storing a set of document chunks in a hierarchical representation with a document retrieval index.
Michel et al. (U.S. 2026/0127211) teaches a document ingestion and analysis method, including the storage, retrieval, and categorization of documents, to produce summary reports.
Nethi et al. (U.S. 2026/0052153) teaches a fine-grained role based access control for retrieval augmented generation based large language models in enterprise systems.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KELAH JANAE MCFARLAND-BARNES whose telephone number is (571)272-5953. The examiner can normally be reached Monday through Friday 8:00am until 4:00pm Central Time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn D Feild can be reached at 571-272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KELAH JANAE MCFARLAND-BARNES/Examiner, Art Unit 2431
/LYNN D FEILD/Supervisory Patent Examiner, Art Unit 2431
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