DETAILED ACTION
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 § 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gibson et al (US20250156460)
Gibson discloses:
1. A computer-implemented method for genealogical research assistance, comprising: receiving a user query at a user interface ([0005] One example aspect of the present disclosure is directed to a computing system. The computing system includes one or more processors and one or more tangible, non-transitory, computer readable media that store instructions that are executable by the one or more processors to cause the computing system to perform operations. The operations include receiving input data comprising a user query and query context data; ); classifying the user query using a classification large language model (LLM) ([0024] The system may classify the user queries into specific content clusters derived from the previously created training set. By linking user queries to the appropriate clusters, the system can streamline the response generation process. This classification can allow the LLM to identify which content is most relevant to the user's query in an efficient manner.); refining the classified user query using a refinement LLM ([0025] The system generates a workflow data structure to store these content clusters and the correlations between user queries and their corresponding responses. This structure serves as a repository of information that the LLM can reference when generating responses. In some cases, the LLM may employ a Retrieval-Augmented Generation (RAG) architecture when generating the query responses. When the LLM accesses the workflow data structure, it may employ RAG to retrieve relevant correlations between user inquiries and previous responses. Using this, the LLM can reference a curated set of information that directly addresses the user query. This allows the model to generate contextually relevant query responses that are grounded in corpus of documents.); vectorizing the refined, classified user query using an embedding model ([0008] - ; classifying, using a large language model (LLM), the user query to at least one content cluster of a plurality of content clusters based on the query context data; constructing, using the LLM, a workflow data structure as a function of the classifying; and generating, using the LLM, a query response as a function of the user query, the query context data, and the workflow data structure.); retrieving, from a vector database, a plurality of results based on the vectorized, refined, classified user query ([0053] With continued reference to FIG. 2, processing the corpus of documents 106 can include producing one or more embeddings 206 for each segment of the plurality of segments 202. Embeddings are mathematical representations of words or phrases in a continuous vector space where semantically similar items are located closer together. With respect to the plurality of segments 202, the plurality of embeddings 206 can be used to represent textual data using vectors. These embeddings 206 are configured to capture the semantic meanings and relationships between words within each segment. For instance, two segments discussing a maintenance procedure for a piece of equipment might generate embeddings that are closely aligned in the vector space, while segments on unrelated topics would be positioned farther apart. The spatial relationship between the embeddings can be used to generate training data for the LLM.); generating, using a response-generating LLM, a response to the vectorized, refined, classified user query based on the retrieved plurality of results; and causing to display, at the user interface, the generated response.( [0102] The LLM 110 utilizes the workflow data structure 118 to tailor the query response 120 to the specific context of the instant user query 112. By analyzing the user query 112 in conjunction with the structured data of the workflow data structure 118, the LLM 110 can tailor its output to better fit the individual's needs. For instance, if a user has previously engaged with the system about a particular software application, the LLM 110 can reference information that is specific to that application, offering insights and solutions that are more relevant and tailored to the user's ongoing challenges. The LLM 110 can be configured to adjust the query response 120 based on various factors, such as prior interactions or the nuances of the current inquiry.)
2. The computer-implemented method of claim 1, further comprising: assessing, using a response-validation LLM, the generated response prior to displaying the response at the user interface.( [0102] The LLM 110 utilizes the workflow data structure 118 to tailor the query response 120 to the specific context of the instant user query 112. By analyzing the user query 112 in conjunction with the structured data of the workflow data structure 118, the LLM 110 can tailor its output to better fit the individual's needs. For instance, if a user has previously engaged with the system about a particular software application, the LLM 110 can reference information that is specific to that application, offering insights and solutions that are more relevant and tailored to the user's ongoing challenges. The LLM 110 can be configured to adjust the query response 120 based on various factors, such as prior interactions or the nuances of the current inquiry.)
3. The computer-implemented method of claim 1, wherein the response-generating LLM includes a transformer architecture. ([0056] The processor 102 can generate one or more embeddings 206 for each segment of the plurality of segments 202 from the tokenized text. This will allow for the transformation of the textual data of the plurality of segments 202 into a numerical format that can be utilized by machine-learning models or other computer-based processes. Based on the tokenized text, the processor 102 can select a method for generating the embedding. These methods can include, but are not limited to, Word2Vec, Global Vectors for Word Representation (GloVe), transformer-based models (i.e., BERT and GPT), and similar approaches.)
4. The computer-implemented method of claim 1, wherein the classification LLM, the refinement LLM, and the response-generating LLM utilize distinct large-language models.
5. The computer-implemented method of claim 1, further comprising: generating the vector database using the embedding model, wherein the embedding model generates vectors from a plurality of genealogical-research content. ([0053] With continued reference to FIG. 2, processing the corpus of documents 106 can include producing one or more embeddings 206 for each segment of the plurality of segments 202. Embeddings are mathematical representations of words or phrases in a continuous vector space where semantically similar items are located closer together. With respect to the plurality of segments 202, the plurality of embeddings 206 can be used to represent textual data using vectors. These embeddings 206 are configured to capture the semantic meanings and relationships between words within each segment. For instance, two segments discussing a maintenance procedure for a piece of equipment might generate embeddings that are closely aligned in the vector space, while segments on unrelated topics would be positioned farther apart. The spatial relationship between the embeddings can be used to generate training data for the LLM.)
6. The computer-implemented method of claim 1, further comprising: modifying the vector database to include the vectorized, refined, classified user query. ([0111] In some cases, the method further includes generating, by the LLM, one or more contextual inquiries based on the user query and the query context data; receiving, by the LLM, a second user query from a user based on the one or more contextual inquiries; and updating, by the LLM, the query context data based on the second user query.)
7. The computer-implemented method of claim 1, further comprising: determining, using the classification LLM, that the user query requires clarification; generating, using the refinement LLM, a follow-up prompt; and causing to display, at the user interface, the follow-up prompt. ([0122] In a non-limiting example, assume the computing device receives user query such as “When is the next time Machine A should be serviced?” To analyze the user query, the LLM can classify the user query into a content cluster focused on Machine A's maintenance. After classifying the query, the LLM interacts with the workflow data structure to access known training examples concerning Machine A's service history. In an embodiment, the workforce data structure might contain information about protocols for maintenance, historical service intervals, and best practices derived from past repairs. Based on this information, the LLM generates a query response that might state, “Machine A was last serviced on Jan. 7, 2024, and is due for service every six months or 4000 machine hours.”)
8. The computer-implemented method of claim 1, further comprising: receiving a follow-up user query in response to the follow-up prompt; and wherein refining the classified user query using the refinement LLM comprises using the user query, the follow-up prompt and the follow-up user query to generate the classified user query. ([0122] In a non-limiting example, assume the computing device receives user query such as “When is the next time Machine A should be serviced?” To analyze the user query, the LLM can classify the user query into a content cluster focused on Machine A's maintenance. After classifying the query, the LLM interacts with the workflow data structure to access known training examples concerning Machine A's service history. In an embodiment, the workforce data structure might contain information about protocols for maintenance, historical service intervals, and best practices derived from past repairs. Based on this information, the LLM generates a query response that might state, “Machine A was last serviced on Jan. 7, 2024, and is due for service every six months or 4000 machine hours.”)
9. The computer-implemented method of claim 1, further comprising: receiving a follow-up user query in response to the follow-up prompt; and wherein vectorizing the refined, classified user query using the embedding model comprises vectorizing the user query, the follow-up prompt and the follow-up user query and conducting sematic search using the vectorized, refined, classified user query. ([0118] Classifying the user query to at least one content cluster of a plurality of content clusters can include using one or more natural language processing techniques to identify key words and phrases within the user query. These key words may be compared against a predefined set of content clusters, wherein each content cluster represents a distinct subject matter. The LLM may evaluate which content cluster aligns best with the user's query based on a semantic analysis. Once a suitable match is identified, the LLM links the user query to the relevant content cluster.)
10. The computer-implemented method of claim 1, wherein retrieving, from a vector database, a plurality of results based on the vectorized, refined, classified user query comprises: performing a semantic search of the vector database using the vectorized, refined, classified user query. ([0118] Classifying the user query to at least one content cluster of a plurality of content clusters can include using one or more natural language processing techniques to identify key words and phrases within the user query. These key words may be compared against a predefined set of content clusters, wherein each content cluster represents a distinct subject matter. The LLM may evaluate which content cluster aligns best with the user's query based on a semantic analysis. Once a suitable match is identified, the LLM links the user query to the relevant content cluster.)
11. The computer-implemented method of claim 10, further comprising: wherein the plurality of results comprises top five closest matches to the vectorized, refined, classified user query identified from the semantic search.( [0117] At step 508, the method includes classifying, using the LLM operating on the one or more processors, the user query to at least one content cluster of a plurality of content clusters based on the query context data.
[0118] Classifying the user query to at least one content cluster of a plurality of content clusters can include using one or more natural language processing techniques to identify key words and phrases within the user query. These key words may be compared against a predefined set of content clusters, wherein each content cluster represents a distinct subject matter. The LLM may evaluate which content cluster aligns best with the user's query based on a semantic analysis. Once a suitable match is identified, the LLM links the user query to the relevant content cluster.
[0119] In some embodiment, The LLM can classify portions of the corpus of documents into respective content clusters using the same or substantially similar process for categorizing the user query. As the LLM processes each document, it can use the labeled training data associated with the corpus of documents to organize the corpus of documents into the appropriate clusters.
)
Claims 12-20 are rejected using similar reasoning seen in the rejection of claims 1-11 above due to reciting similar limitations but directed towards different statutory categories.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20260050615 teaches limitations seen in the independent claims.
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/S.C.S./Examiner, Art Unit 2165
/ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
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