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 .
REMARKS
On page 9, Applicant’s summary of the Interview, June 03, 2026, is acknowledged.
On pages 10-12, Applicant’s argument to overcome the 35 USC 102 rejection in view of Gates et al. (US 12,340,410 B1) as applied to claims 1, 5, 6, 9, 10, 15, 16, and 20 is persuasive. The 35 USC 102 rejection in view of Gates et al. (US 12,340,410 B1) as applied to claims 1, 5, 6, 9, 10, 15, 16, and 20 is withdrawn. The 35 USC 103 in view of Gates et al. (US 12,340,410 B1) and Smith (US 20210271699 A1) is withdrawn.
Non-Final Office Action
Claims 1-20, June 15, 2026, are examined on the merits.
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, 6, 9, 10, 15, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gates et al. (Gates hereafter, US 12,340,410 B1) in view of Rezaeian et al. (Rezaeian hereafter, US 20260064731 A1).
Claim 1, Gates discloses a method for generation of structured data for query execution, the method comprising:
identifying, by a server computer system, a query requesting information regarding a client system (column 6, lines 48-63, e.g. User interface 103, illustrated conceptually in FIG. 1, may be a graphical user interface (GUI) that is accessible from computing device 102…perform searches, and interact with various components of recommendation system 100, such as initiating queries for resale goods or accessing search results);
generating, with a first machine learning (ML) model executed by the server computer system, an embedding in a vector space based on the query (column 2, lines 1-13, e.g. image vector embeddings are generated using a machine learning (ML) image embedding model and text vector embeddings are generated using a ML text embedding model. Multiple ranked result sets are retrieved from a vector database of resale goods including a first result set generated from the image vector embeddings and a second result set generated from the text vector embeddings).
identifying, by the server computer system, a similar embedding in the vector space and located in a vector store (column 13, lines 30-50, e.g. A joint embedding space refers to a shared numerical space where both image and text features are mapped into embedding vectors with similar representations if they are semantically related. For example, an image of a red handbag and the text description “red leather handbag” would be placed close together in a joint embedding space. This alignment allows for efficient comparisons and searches across different modalities (i.e., visual and textual data)), wherein the vector store includes a plurality of embeddings generated based on data scraped from a website associated with the client system (column 11, lines 25-28, e.g. cloud serverless infrastructure 104 may extract and collect image URLs and associated metadata from accessed websites, such as names, descriptions, and pricing details associated with resale goods, column 30, lines 16-19, e.g. scraping service 722 scrapes relevant retail goods metadata from the webpage being viewed, including title, image URL, price, brand, etc.);
retrieving, from a database, content associated with the similar embedding (column 20, lines 46-60, e.g. When the search is initiated, application server 114 interacts with database 116, including its vector database components, to locate embeddings that are most similar to the query embeddings generated during embedding generation step 406. For an image-based search, query image embeddings, generated by the CLIP model within serverless ML inference API 446, are compared to resale goods image embeddings stored in vector databases 540 and 542);
transforming, by the server computer system, the response data into structured data corresponding to the information requested regarding the client system (column 16, lines 54-62, e.g. recommendation system 100 may perform LLM-based extraction to process unstructured website content such as free-form retail goods descriptions 528 that are not embedded as structured metadata. With reference to FIG. 5, recommendation system 100 may use an LLM-based extraction component 536 employing models such as Llama-7B to transform unstructured content such as free-form retail goods descriptions 528 into structured metadata 538).
However, Gates does not disclose generating, by the server computer system, a prompt based on the query and the content associated with the similar embedding; providing, by the server computer system, the prompt to a second ML model; identifying, by the server computer system, response data generated by the second ML model based on the prompt.
Rezaeian discloses generating, by the server computer system, a prompt based on the query ([0017], e.g. the system generates a prompt based on the initial user query and the selected subset of the merged results, and the content associated with the similar embedding ([0018], e.g. performs an embedding operation on the unstructured data to generate a corresponding vector, which the system stores in a vector data store… executes an embedding operation to generate a vector corresponding to the unstructured data, and stores the vector corresponding to the unstructured data in the vector data store);
providing, by the server computer system, the prompt to a second ML model ([0017], e.g. system then submits the prompt to a third LLM to generate a response to the initial user query);
identifying, by the server computer system, response data generated by the second ML model based on the prompt ([0017], e.g. system then submits the prompt to a third LLM to generate a response to the initial user query).
Rezaeian discloses a technique that helps LLMs to improve their logic and reasoning by evaluating, refining, and verifying their own outputs ([0024]). One of ordinary skill in the art at the time before the effective filing date of the claimed invention would have been motivated by Rezaeian to improve the method and system of Gates. Therefore, it would have been obvious for one of ordinary skill in the art to use the prompt generation of Rezaeian. The benefit would be to improve their logic and reasoning by evaluating, refining, and verifying their own outputs.
Claim 5, Gates as modified discloses providing the response to the second ML model comprises:
generating an application program interface (API) request comprising the prompt; and communicating the API request to the second ML model (column 16, line 65 to column 17, line 7, e.g. Instruction-based prompting guides the LLM with carefully crafted prompts tailored for metadata extraction... Cloud serverless infrastructure 104, via serverless ML inference API 446, facilitates the computationally intensive execution of these tasks, enabling scalable and efficient LLM inference, and Figure 5, e.g. ML image embedding model, ML text embedding model, and LLM-based extract (e.g. Llama).
Claim 6, Gates as modified discloses identifying the response data generated by the second ML model based on the prompt comprises receiving an API response corresponding to the API request (column 16, line 65 to column 17, line 7, e.g. Instruction-based prompting guides the LLM with carefully crafted prompts tailored for metadata extraction... Cloud serverless infrastructure 104, via serverless ML inference API 446, facilitates the computationally intensive execution of these tasks, enabling scalable and efficient LLM inference, and Figure 5, e.g. ML image embedding model, ML text embedding model, and LLM-based extract (e.g. Llama).
Claim 9, Gates as modified discloses configuring a service of the server computer system based on the structured data corresponding to the information requested regarding the client system (Figure 1).
Claims 10, 15, 16, and 20, Gates as modified discloses a server computer system, and a non-transitory computer readable storage medium (Figure 1) comprising the same steps as claims 1, 5, 6, and 9. These claims are similarly rejected under the same rationale as claims 1, 5, 6, and 9, supra.
Claim(s) 2, 4, 11, 13, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gates et al. (Gates hereafter, US 12,340,410 B1) in view of Rezaeian et al. (Rezaeian hereafter, US 20260064731 A1), as applied to claims 1, 5, 6, 9, 10, 15, 16, and 20 above, in further view of Smith (US 20210271699 A1).
Claim 2, Gates as modified discloses the claimed invention except for generating a plurality of content snapshots based on data obtained from scraping a website associated with the client system; and generating the plurality of embeddings in the vector store based on the plurality of content snapshots and the first ML model.
Smith discloses generating a plurality of content snapshots based on data obtained from scraping a website associated with the client system; and generating the plurality of embeddings in the vector store based on the plurality of content snapshots and the first ML model ([0079], e.g. the same embedding model used to index the outcome of a crawl is applied to the query image. This will result in a query vector that lives in the same space as those used in the indexes. If the user selects multiple images to use as a query (or selects a video snippet from which one can sample a representative set of individual frames), one simple strategy is to average the vectors associated with each individual query image. Although it is expected that few users will search by memory state (they must have a platform snapshot in hand), it is still useful to think of memory embedding vectors as possible (components of) query vectors).
Smith discloses the embedding module 120 maps the document 110 to a vector 122. In some embodiments, the dimension N of the vector is much less than a size S of the document, which offers the advantage of more efficient operations ([0034]). One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Smith to improve the method of Gates as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Gates as modified with the content snapshots of Smith. The benefit would be for more efficient operations.
Claim 4, Gates as modified discloses wherein each of the plurality of content snapshots comprises an HTML snapshot (Smith, [0066], e.g. Test data is obtained from BizHawk (found at domains tasvideos in superdomain org in file SNES.html in folder Bizhawk), which can emulate many different game platforms ranging from the Atari 2600 to the Nintendo 64 (based on a 32-bit processor connected to approximately 4 megabytes of working memory)).
Claims 11, 13, and 17, Gates discloses a server computer system, and a non-transitory computer readable storage medium (Gates, Figure 1) comprising the same steps as claims 2 and 4. These claims are similarly rejected under the same rationale as claims 2 and 4, supra.
Claim(s) 7, 8, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gates et al. (Gates hereafter, US 12,340,410 B1) in view of Rezaeian et al. (Rezaeian hereafter, US 20260064731 A1), as applied to claims 1, 5, 6, 9, 10, 15, 16, and 20 above, in further view of Morse et al. (Morse hereafter, US 20250191493 A1).
Claim 7, Gates as modified discloses the claimed invention except for the first ML model comprises a bi- encoder or a bi-direction encoder. Morse discloses the first ML model comprises a bi- encoder or a bi-direction encoder ([0056], e.g. generative transformer model may be a machine learning model in accordance with a transformer model (e.g., generative pre-trained model or bidirectional encoder representations from transformers)).
One of ordinary skill in the art at the time prior to the effective filing date of the instant invention would have been motivated by Morse to improve the method of Gates as modified. Therefore, it would have been obvious for one of ordinary skill in the art to use the method of Gates as modified with the bi-direction encode of Morse. The benefit would be to make generative transformer model may be a machine learning model in accordance with a transformer model.
Claim 8, Gates as modified updating a profile corresponding to the client system based on the structured data corresponding to the information requested regarding the client system (Morse, [0062], e.g. user profile 175 may be continuously updated by the application 125 and the session management service 105).
Claims 14 and 19, Gates discloses a server computer system, and a non-transitory computer readable storage medium (Gates, Figure 1) comprising the same steps as claims 7 and 8. These claims are similarly rejected under the same rationale as claims 7 and 8, supra.
Allowable Subject Matter
Claims 3, 12, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CONCLUSION
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
8/26/2026
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