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 .
DETAILED ACTION
This action is issued in response to Application filed July 24, 2024.
Claims 1-20 are pending.
Claims 1-17 are rejected.
Claims 18-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on February 17, 2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 24, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1, 5-9, and 13-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brenner (U.S. Patent Application No. 2025/0165463) in view of Qadrud-Din (U.S. Patent Application No. 2024/0289561).
Regarding Claim 1, Brenner discloses a method, comprising:
receiving a query regarding a source document that comprises one or more fields (par [0047], Brenner – retriever model receives a user query, wherein the retriever model 314 is used to identify document chunks 308 from a corpus that are relevant to each user query 318);
determining a field of the one or more fields referenced by the query (par [0047], [0062], Brenner – retriever model receives a user query, wherein the retriever model 314 is used to identify document chunks 308 from a corpus that are relevant to each user query 318);
retrieving first contextual metadata for the field (par [0048], [062-0063], Brenner);
generating an enriched query by adding the first contextual metadata to query text of the query (par [0048], Brenner - both the user query 318 and the context 322 can be provided as inputs to the generative model 316. Collectively, the user query 318 and the context 322 can be said to form at least part of a prompt for the generative model 316);
generating a query embedding from the enriched query (par [0051], Brenner - ranker generates independent embeddings (embedding vectors) for input queries 318 and document chunks 308, and the embeddings are used to compute similarities between the input queries 318 and the document chunks 308);
determining similarity scores between the query embedding and passage embeddings, wherein the passage embeddings are based on passage text from one or more resource documents (par [0051], [0072], [0079], Brenner – the ranker generates independent embeddings (embedding vectors) for input queries and document chunks, and the embeddings are used to compute similarities between the input queries and the document chunks… the ranker 402 is configured to receive and process a set 502 of document chunks 308 or embedding vectors associated with the set 502 of document chunks 308. The ranker 402 can determine which of the document chunks 308 appear to be most relevant to an associated user query 318, for example, by determining a similarity score between each document chunk 308 and the associated query); and
identifying one or more passages based on the similarity scores that satisfy a threshold (par [0079], Brenner - The identified information chunks are provided to the generative model at step 608, and the generative model is used to process the identified information chunks and generate a response to the input query at step 610. This may include providing the query 318 and a context 322 (the top K document chunks 404) to the generative model 316).
While Brenner teaches all of the claimed subject matter as stated above. However, Brenner is not as detailed with respect to passage in one or more resource document.
On the other hand, Qadrud-Din discloses passage in one or more resource document (par [0183], Qadrud-Din – find any passage(s) in the document that will help answer the query, wherein the system extracts passages from the document and assigns a score to each passage based on how the passage relates to the query).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Qadrud-Din’s teachings into Brenner’s RAG system optimization. A skilled artisan would have been motivated to combine in order to provide an improved natural language processing environment that seamlessly interacts with large companies to better evaluate documents against particular criteria.
Regarding Claim 5, the combination of Brenner in view of Qadrud-Din, disclose the method of claim 1, further comprising ranking the one or more passages with a large language model based on the query, the field, and the first contextual metadata for the field (par [0051], [0072], Brenner – the ranker generates independent embeddings (embedding vectors) for input queries and document chunks, and the embeddings are used to compute similarities between the input queries and the document chunks… the ranker 402 is configured to receive and process a set 502 of document chunks 308 or embedding vectors associated with the set 502 of document chunks 308. The ranker 402 can determine which of the document chunks 308 appear to be most relevant to an associated user query 318, for example, by determining a similarity score between each document chunk 308 and the associated query… par [0005], Brenner – RAG system optimization that includes a plurality of rankers such as a bi-encoder, a cross-encoder, and a large language model (LLM)-ranker).
Regarding Claim 6, the combination of Brenner in view of Qadrud-Din, disclose the method of claim 5, further comprising: prompting the large language model to generate a response to the query based on one or more respective rankings of the one or more passages; and receiving the response from the large language model (par [0007-0008], Brenner – obtain an input query at a retriever model which includes rankers, then processing information chunks from the retriever model to a generative model to create a response to the input query wherein the ranker can be an LLM ranker… method also includes generating a prompt for the generative model).
Regarding Claim 7, the combination of Brenner in view of Qadrud-Din, disclose the method of claim 1, wherein the source document is a tax form and the field is a tax form field (par [0030], [0033], [0053], Qadrud-Din – reference discusses domain-specific contexts such as law, wherein text segmentation occurs that indicates different legal columns/fields and the LLM generates legal documents; however, it is obvious to allow for other domain-specific categories as well, such as tax).
Regarding Claim 8, the combination of Brenner in view of Qadrud-Din, disclose the method of claim 1, wherein at least one of the one or more resource documents comprises instructions for completing the source document (par [0225], [0332-0333], Qadrud-Din).
Claims 9 and 13-17 contain similar subject matter as claims 1 and 5-8 above; and are rejected under the same rationale.
Claim(s) 2-4 and 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brenner in view of Qadrud-Din, further in view Jonassen (U.S. Patent Application No. 2020/0293528).
Regarding Claim 2, the combination of Brenner in view of Qadrud-Din, disclose identifying document chunks containing specified fields of information for use by a generative model, wherein each individual field of information that is to be extracted from a document chunk (see par [0062-0063], [0066], Brenner); and Qadrud-Din teaches text segmentation (see par [0029-0030], [0068], Qadrud-Din). It is obvious that structural elements are determined within the system. However, Brenner and Qadrud-Din are not as explicitly detailed as the examiner would like.
On the other hand, Jonassen discloses determining structural elements from the source document (par [0005-0006], [0023], Jonassen); identifying the field in the source document based on the structural elements (par [0031], [0051], Jonassen - structural rules module may be configured to facilitate configuration of structural rules for populating fields of the structured reports); and determining the first contextual metadata associated with the field (par [0031-0032], Jonassen - one structural rule may apply to various fields of a structured report, and may define what information is to be included in the various fields).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jonassen’s teachings into the Brenner and Qadrud-Din system. A skilled artisan would have been motivated to combine in order to provide easily digestible documents to all the system to perform at a more efficient and user friendly manner.
Regarding Claim 3, the combination of Brenner in view of Qadrud-Din, further in view of Jonassen, disclose the method of claim 2, further comprising executing a machine learning model to identify the field and determine the first contextual metadata (par [0030], [0035], Jonassen - classification algorithms may include any combination of machine learning algorithms, statistical algorithms, and/or any algorithm configured to identify correlations and/or relationships within and between data, and to train a model such that the model may be used to classify data according to the training).
Regarding Claim 4, the combination of Brenner in view of Qadrud-Din, further in view of Jonassen, disclose the method of claim 1, further comprising identifying structural elements in a resource document of the one or more resource documents (par [0005-0006], [0023], Jonassen); segmenting the resource document into passages of text based on the structural elements (par [0029-0030], [0068], [0183], Qadrud-Din – text segmentation… find any passage(s) in the document that will help answer the query, wherein the system extracts passages from the document and assigns a score to each passage based on how the passage relates to the query); and determining second contextual metadata for a respective passage of the passages of text based on the structural elements and respective passage text of the respective passage wherein a passage embedding, of the passage embeddings, of the respective passage is based on the respective passage text and the second contextual metadata (par [0031-0032], Jonassen… par [0029-0030], [0068], [0183], Qadrud-Din).
Claims 10-12 contain similar subject matter as claims 2-4 above; and are rejected under the same rationale.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. 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, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Chelcie Daye
Patent Examiner
Technology Center 2100
April 3, 2026
/CHELCIE L DAYE/Primary Examiner, Art Unit 2161