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 .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ronan Collobert, “A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning,” Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008, pages 160-167 and Cetoli et al. (Publication No. 2026/0111671 filed March 17, 2025, continuation of application No. 18/961440 filed November 26, 2024, now Patent No. 12,254,272 which is a continuation-in-part of application 18/922,212 filed on October 21, 2024, now Patent No. 12, 222,992).
Regarding Claims 1 and 11, Collobert teaches determining one or more semantic boundaries (Abstract, semantic roles, Introduction semantic-role labeling) associated with a data asset based on a neural network model (Abstract, neural network architecture);
segmenting the data asset into a plurality of chunks (page 161, 2. LP Tasks - Chunking) based at least in part on the one or more semantic boundaries (page 166, see Table 2, A Deep Architecture for SRL (Semantic Role Labelling), e.g., SRL + Chunking); and
mapping the plurality of chunks (page 166, Result: POS and Chunking Training, also, Table 2, e.g., SRL + POS + Chunking), respectively, associated with the neural network model . (page 166, Result: POS and Chunking Training, also, Table 2, e.g., SRL + POS + Chunking).
Collobert teaches mapping a plurality of vector embeddings (page 161, 3.1. Transforming Indices into Vectors), but does not teach mapping the plurality of chunks to a plurality of vector embeddings.
Cetoli teaches mapping a plurality of chunks to a plurality of vector embeddings ([0050] For each particular chunk in the set of chunks, the intent-based data generation platform 104 can determine a distance metric value between the vector representation of the received output generation request and the vector representation of the particular chunk to measure the similarity/dissimilarity of the vectors.).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Collobert’s method with Cetoli’s method because Collobert’s method teaches a convolutional network architecture and its application of NLP tasks describing a part-of-speech tagging having chunking-named-entity recognition but does not teach mapping a plurality of chunks to a plurality of vector embeddings. Cetoli teaches generating context-aware responses using semantically chunked information, wherein partitioning a set of artifacts responsive to a query. Incorporating the method of Cetoli with the method of Collobert would improve Collobert’s method to enable a prompt for an artificial intelligence model into a set of continuous chunks of queried data and associate each continuous chunk with a knowledge graph consisting of nodes representing chunks and edges indication common attributes.
8. Regarding Claims 2 and 12, Cetoli teaches the neural network model comprises a large language model (LLM) (Abstract, a large language model).
9. Regarding Claims 3 and 13, Cetoli teaches the determining of the one or more semantic boundaries comprises:
inferring a semantic cell from the data asset using the LLM; and inferring a number (N) of learnings from the semantic cell using the LLM, each of the N learnings associated with a respective semantic boundary of the one or more semantic boundaries ([0002] When the model receives a new input, the model produces an output based on patterns determined from the data the model was trained on. A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs can be used for text generation, a form of generative AI (e.g., GenAI, Gen AI, or GAI), by taking an input text and repeatedly predicting the next token or word.).
10. Regarding Claims 4 and 14, generating a prompt for the LLM requesting a group of semantically related content; and receiving a completion from the LLM, responsive to the prompt, that includes the semantic cell ([0002] When the model receives a new input, the model produces an output based on patterns determined from the data the model was trained on. A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs can be used for text generation, a form of generative AI (e.g., GenAI, Gen AI, or GAI), by taking an input text and repeatedly predicting the next token or word.).
11. Regarding Claims 5 and 15, Cetoli teaches the inferring of the N learnings comprises: generating a prompt for the LLM requesting the number of learnings associated with the semantic cell; and
receiving a completion from the LLM, responsive to the prompt, that includes the N learnings (Abstract, generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes.).
12. Regarding Claims 6 and 16, Cetoli teaches the prompt further includes a request to order the N learnings based on the order in which they are conveyed in the semantic cell (Abstract, generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes.).
13. Regarding Claims 7 and 17, Cetoli teaches the segmenting of the data asset comprises: segmenting the semantic cell into N chunks of the plurality of chunks based at least in part on the N learnings (Abstract, generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes.).
14. Regarding Claims 8 and 18, Cetoli teaches the segmenting of the semantic cell comprises: inferring the N chunks from the semantic cell using the LLM so that each of the N chunks is associated with a respective learning of the N learnings (Abstract, generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes.).
15. Regarding Claims 9 and 19, Cetoli teaches the inferring of the N chunks comprises: generating a prompt for the LLM requesting a partitioning of the semantic cell along boundaries associated with the N learnings; and receiving a completion from the LLM, responsive to the prompt, that includes the N chunks (Abstract, generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes.).
16. Regarding Claims 10 and 20, Collobert teaches determining a size of each chunk of the plurality of chunks based on a dimension of each vector embedding of the plurality of vector embeddings (Lookup-Table Layer word vector size, Variations on Word Representations vector size); and determining a number of chunks included in the plurality of chunks based on the size of each chunk (Lookup-Table Layer word vector size, Variations on Word Representations vector size).
Conclusion
17. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERYL R LEWIS whose telephone number is (571)272-4113. The examiner can normally be reached Monday-Thursday, 8am-5pm, EST.
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/CHERYL LEWIS/Primary Examiner, Art Unit 2166 June 26, 2026
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