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
1. This action is response to application filed on 04/17/2025. Claims 1-20 are pending.
FOREIGN APPLICATION
2. The Document(s) of claimed Foreign Application INDIA 202511007734 is not submitted yet.
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.
3. Claims 1, 3, 8-9, 11, 14, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shernan et al. (US 20260067119) in view of Elsayyard et al. (US 20260203328)
Regarding claim 1:
A system, comprising: one or more processors, coupled with memory, to:
receive, via a chatbot, a user query: (querying an artificial intelligence chatbot based on meeting discussions: Shernan, abstract);
generate, using an artificial intelligence model, a vector representation based on a combination of the user query, one or more historical queries, and one or more responses corresponding to the one or more historical queries: (the AI model is trained to generate meeting summaries of virtual meetings using training data comprising media streams associated with historical virtual meetings. The training data includes a set of training inputs and a set of target outputs collected more participants of historical virtual meetings: Shernan [0006], [0055]-[0056]);
identify a cached response corresponding to the user query in a semantic cache by execution of a semantic search operation using the vector representation: (the training set generator can generate input/output mappings based on the received input (e.g., media streams, meeting transcripts, manually determined context of portions of the media streams and/or meeting transcripts) and a corresponding output (e.g., summaries of historical virtual meetings based on the received input: Shernan [0057]).
However, Shernan does not teach executing a hybrid search operation associated with the user query.
In similar art, Elsayyad teaches a hybrid search engine may comprise both keyword search function of the keyword search engine, and semantic search function of the semantic search engine, see (Elsayyad [0089]),
the hybrid search operation comprising: retrieval of first results having a first accuracy value by execution of a first search process of the hybrid search operation on a first data source using the vector representation (the search engine module may comprise one or more search engines. Each search engine in the plurality of search engines processes a search query determined by a corresponding search transformer to generate one or more search results. For example, the keyword search engine may utilize a keyword search query generated by the keyword search transformer, which may consist of relevant keywords extracted from the filtered preprocessed query. The keyword search engine compares these keywords with entries in the keyword search database stored in the database to retrieve matching resources (e.g., articles or documents): Elsayyad [0089]); and
retrieval of second results having a second accuracy value by execution of a second search process of the hybrid search operation on a second data source using data corresponding to the vector representation: (the semantic search engine may process a semantic search query, which may be represented as a vector generated by the semantic search transformer. This vector may capture the contextual and semantic meaning of the original query. The semantic search engine may compare this vector against vector embeddings of resources (e.g., articles or documents) stored in the semantic search database to identify the resources that are semantically similar. For instance, if the semantic search query vector represents “effective treatments for colds,” the semantic search engine may retrieve resources such as “home remedies for common colds,” “over-the-counter medications for colds,” or “best practices for cold recovery.” These results may be determined based on their proximity in the vector space, reflecting conceptual similarities: Elsayyad [0089]);
select one of the first results or the second results based on modeling the first accuracy value and the second accuracy value: (upon determining the ranked plurality of search results, the search server may transmit the initial input (e.g., the user's query) and the ranked plurality of search results to a large language model hosted on the language model server. The large language model may generate a search response to the initial input based on the ranked plurality of search results by selecting one or more search results among the plurality of search results based on the plurality of search results may be a generalizable function that other language models can also perform: Elsayyad [0100]);
display, responsive to the user query, an output corresponding to the selected one of the first results or the second results: (displaying the search response in a chat interface: Elsayyad [0185]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 3:
In addition to the rejection claim 1, Shernan-Elsayyad further teaches issue one or more automated requests to a plurality of internal sources comprising verified content; generate a plurality of vector representations corresponding to verified data extracted from the verified content; and store the plurality of vector representations in an index accessible to the first data source: (the resources may be transformed by a plurality of search transformers to generate a plurality of search data, with each search data tailored for storage in a respective search engine database. A keyword search transformer may process the resources to extract relevant keywords, which are then stored in a keyword search database. Similarly, a semantic search transformer may analyze the resources to create vector embeddings that represent the semantic meaning of the content, which are subsequently stored in the semantic search database . By transforming and distributing the resources into these specialized search engine databases, the search server ensures that each search engine can efficiently find relevant search results: Elsayyad [0120]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Sheran’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 8:
In addition to the rejection claim 1, Shernan-Elsayyad further teaches determine to execute the hybrid search operation based on determining that the cached response fails to satisfy at least one threshold: (the keyword search engine fails to produce the expected results: Elsayyad [0101]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 9:
In addition to the rejection claim 1, Shernan-Elsayyad further teaches categorize the user query in a query category based on a predefined taxonomy of query types; and select search parameters for the hybrid search operation based on the query category: (categorize queries according to structured attributes (e.g., domain-specific categories, price ranges, user-defined tags) to systematically narrow results: Elsayyad [0124]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 11:
In addition to the rejection claim 1, Shernan-Elsayyad further teaches determine the first accuracy value and the second accuracy value based on a subject of the user query: (the inference module may utilize the large language model to generate the search response by selecting one or more search results among the ranked plurality of articles based on chat history: Elsayyad [0074]); and adjust the first accuracy value or the second accuracy value in response to receiving a new query: (feedback loop may allow the next best agent to learn from user behavior, such as click-through rates or time spent on suggested content, enhancing the accuracy and personalization of future suggestions: Elsayyad [0158]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 14:
A method, comprising:
receiving, by one or more processors coupled with memory, via a chatbot, a user query: (querying an artificial intelligence chatbot based on meeting discussions: Sheran, abstract);
generating, by the one or more processors, using an artificial intelligence model, a vector representation based on a combination of the user query, one or more historical queries, and one or more responses corresponding to the one or more historical queries: (the AI model is trained to generate meeting summaries of virtual meetings using training data comprising media streams associated with historical virtual meetings. The training data includes a set of training inputs and a set of target outputs collected more participants of historical virtual meetings: Shernan [0006], [0055]-[0056]);
identifying, by the one or more processors, a cached response corresponding to the user query in a semantic cache by execution of a semantic search operation using the vector representation: (the training set generator can generate input/output mappings based on the received input (e.g., media streams, meeting transcripts, manually determined context of portions of the media streams and/or meeting transcripts) and a corresponding output (e.g., summaries of historical virtual meetings based on the received input: Shernan [0057]).
However, Shernan does not explicitly teach executing a hybrid search operation associated with the user query.
In similar art, Elsayyad teaches a hybrid search engine may comprise both keyword search function of the keyword search engine, and semantic search function of the semantic search engine, see (Elsayyad [0089]),
the hybrid search operation comprises: retrieving, by the one or more processors, first results having a first accuracy value by execution of a first search process of the hybrid search operation on a first data source using the vector representation: (the search engine module may comprise one or more search engines. Each search engine in the plurality of search engines processes a search query determined by a corresponding search transformer to generate one or more search results. For example, the keyword search engine may utilize a keyword search query generated by the keyword search transformer, which may consist of relevant keywords extracted from the filtered preprocessed query. The keyword search engine compares these keywords with entries in the keyword search database stored in the database to retrieve matching resources (e.g., articles or documents): Elsayyad [0089]); and
retrieving, by the one or more processors, second results having a second accuracy value by execution of a second search process of the hybrid search operation on a second data source using data corresponding to the vector representation: (the semantic search engine may process a semantic search query, which may be represented as a vector generated by the semantic search transformer. This vector may capture the contextual and semantic meaning of the original query. The semantic search engine may compare this vector against vector embeddings of resources (e.g., articles or documents) stored in the semantic search database to identify the resources that are semantically similar. For instance, if the semantic search query vector represents “effective treatments for colds,” the semantic search engine may retrieve resources such as “home remedies for common colds,” “over-the-counter medications for colds,” or “best practices for cold recovery.” These results may be determined based on their proximity in the vector space, reflecting conceptual similarities: Elsayyad [0089]);
selecting, by the one or more processors, one of the first results or the second results based on modeling the first accuracy value and the second accuracy value: (upon determining the ranked plurality of search results, the search server may transmit the initial input (e.g., the user's query) and the ranked plurality of search results to a large language model hosted on the language model server. The large language model may generate a search response to the initial input based on the ranked plurality of search results by selecting one or more search results among the plurality of search results based on the plurality of search results may be a generalizable function that other language models can also perform: Elsayyad [0100]);
displaying, by the one or more processors, responsive to the user query, an output corresponding to the selected one of the first results or the second results: (displaying the search response in a chat interface: Elsayyad [0185]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 16:
In addition to the rejection claim 14, Shernan-Elsayyad further teaches issue one or more automated requests to a plurality of internal sources comprising verified content; generate a plurality of vector representations corresponding to verified data extracted from the verified content; and store the plurality of vector representations in an index accessible to the first data source: (the resources may be transformed by a plurality of search transformers to generate a plurality of search data, with each search data tailored for storage in a respective search engine database. A keyword search transformer may process the resources to extract relevant keywords, which are then stored in a keyword search database. Similarly, a semantic search transformer may analyze the resources to create vector embeddings that represent the semantic meaning of the content, which are subsequently stored in the semantic search database . By transforming and distributing the resources into these specialized search engine databases, the search server ensures that each search engine can efficiently find relevant search results: Elsayyad [0120]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Regarding claim 20:
A non-transitory computer-readable storage device having instructions stored thereon on that, when executed by one or more processors, cause the one or more processors to:
receive, via a chatbot, a user query: (querying an artificial intelligence chatbot based on meeting discussions: Shernan, abstract);
generate, using an artificial intelligence model, a vector representation based on a combination of the user query, one or more historical queries, and one or more responses corresponding to the one or more historical queries: (the AI model is trained to generate meeting summaries of virtual meetings using training data comprising media streams associated with historical virtual meetings. The training data includes a set of training inputs and a set of target outputs collected more participants of historical virtual meetings: Shernan [0006], [0055]-[0056]);
identify a cached response corresponding to the user query in a semantic cache by execution of a semantic search operation using the vector representation: (the training set generator can generate input/output mappings based on the received input (e.g., media streams, meeting transcripts, manually determined context of portions of the media streams and/or meeting transcripts) and a corresponding output (e.g., summaries of historical virtual meetings based on the received input: Shernan [0057]).
However, Shernan does not teach executing a hybrid search operation associated with the user query.
In similar art, Elsayyad teaches a hybrid search engine may comprise both keyword search function of the keyword search engine, and semantic search function of the semantic search engine, see (Elsayyad [0089]),
the hybrid search operation comprising: retrieval of first results having a first accuracy value by execution of a first search process of the hybrid search operation on a first data source using the vector representation (the search engine module may comprise one or more search engines. Each search engine in the plurality of search engines processes a search query determined by a corresponding search transformer to generate one or more search results. For example, the keyword search engine may utilize a keyword search query generated by the keyword search transformer, which may consist of relevant keywords extracted from the filtered preprocessed query. The keyword search engine compares these keywords with entries in the keyword search database stored in the database to retrieve matching resources (e.g., articles or documents): Elsayyad [0089]); and
retrieval of second results having a second accuracy value by execution of a second search process of the hybrid search operation on a second data source using data corresponding to the vector representation: (the semantic search engine may process a semantic search query, which may be represented as a vector generated by the semantic search transformer. This vector may capture the contextual and semantic meaning of the original query. The semantic search engine may compare this vector against vector embeddings of resources (e.g., articles or documents) stored in the semantic search database to identify the resources that are semantically similar. For instance, if the semantic search query vector represents “effective treatments for colds,” the semantic search engine may retrieve resources such as “home remedies for common colds,” “over-the-counter medications for colds,” or “best practices for cold recovery.” These results may be determined based on their proximity in the vector space, reflecting conceptual similarities: Elsayyad [0089]);
select one of the first results or the second results based on modeling the first accuracy value and the second accuracy value: (upon determining the ranked plurality of search results, the search server may transmit the initial input (e.g., the user's query) and the ranked plurality of search results to a large language model hosted on the language model server. The large language model may generate a search response to the initial input based on the ranked plurality of search results by selecting one or more search results among the plurality of search results based on the plurality of search results may be a generalizable function that other language models can also perform: Elsayyad [0100]);
display, responsive to the user query, an output corresponding to the selected one of the first results or the second results: (displaying the search response in a chat interface: Elsayyad [0185]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
4. Claim 2 is rejected under 35 U.S.C 103 as being un-patentable over Shernan-Elsayyad in view of Higgins et al. (US 20260004110)
Regarding claim 2:
In addition to the rejection claim 1, Shernan-Elsayyad further teaches provide the first results comprising data of the plurality of documents in response to determining at least one score of the one or more scores satisfies at least one threshold corresponding to the first accuracy value: (upon determining the ranked plurality of search results, the search server may transmit the initial input (e.g., the user's query) and the ranked plurality of search results to a large language model hosted on the language model server. The large language model may generate a search response to the initial input based on the ranked plurality of search results by selecting one or more search results among the plurality of search results based on the plurality of search results may be a generalizable function that other language models can also perform: Elsayyad [0100]).
However, Shernan-Elsayyad does not explicitly teach generate an index comprising a plurality of vector representations of content based on scanning a plurality of documents.
In similar art, Higgins teaches the question and answer (Q and A) conversational chat requires the documents to be uploaded and indexed into the tool and make them available for Question and Answer. Generate test cases for indexed in vector the already existing Active database *Higgins, [0106]);
determine one or more scores representing similarities between the plurality of vector representations of the content and the vector representation: (perform similarity search on selected fields within selected fields an index: Higgins [0106]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Higgins’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Higgins’s ideas into Shernan-Elsayyad’s system.
5. Claims 4-7, 10, 17-19 are rejected under 35 U.S.C 103 as being un-patentable over Shernan-Elsayyad in view of Krakover et al. (US 20260212273)
Regarding claim 4:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 1, Sheran-Elsayyad but does not explicitly teach knowledge graph comprising a plurality of nodes and edges connecting the plurality of nodes, and wherein the data corresponding to the vector representation comprises a structured query corresponding to a schema of the at least one knowledge graph.
In similar art, Krakover teaches receiving an analysis data set, generating elements, and determining whether a first subset of elements is similar to an entry of a knowledge graph. Knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search) (see, Krakover [0071]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
Regarding claim 5:
In addition to the rejection claim 4, Shernan-Elsayyad-Krakover further teaches map one or more portions of the vector representation to at least one node type, node attribute, or edge type corresponding to the plurality of nodes and edges based on the schema; and generate the structured query comprising the at least one node type, node attribute, or edge type: (initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search): Krakover [0071]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
Regarding claim 6:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 1, Sheran-Elsayyad but does not explicitly teach identify a subject of the user query corresponding to at least one location; determine the at least one location corresponds to multiple locations or is incomplete; disambiguate the at least one location; and receive, responsive to the disambiguation, a plurality of candidate locations corresponding to the at least one location.
In similar art, Krakover teaches initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search) (see, Krakover [0071]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
Regarding claim 7:
In addition to the rejection claim 6, Shernan-Elsayyad-Krakover further teaches determine the plurality of candidate locations are dissociated with the subject; provide a prompt comprising one or more location queries corresponding to the at least one location, the multiple locations, or the plurality of candidate locations; receive an input responsive to the prompt; and update the subject of the user query based on the input: (initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search). Similarity is based on one or more matching substrings and/or a set of characters within a proximity threshold (such as a set of letters that appear in the same word but in a different order due to a spelling error). Data is converted into vector representations (embeddings) and the distance between the vectors is calculated using a similarity metric such as cosine similarity. Next a graph traversal algorithm is used to explore relationships and validate entity links based on the depth of connections and contextual relevance. The more links and context to an entry are found in the data, the higher confidence that the entities are a match: Krakover [0071]).
Regarding claim 10:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 1, Shernan-Elsayyad but does not explicitly teach aggregate the first results and the second results to generate aggregated results; rank the aggregated results based on at least one of the first accuracy value, the second accuracy value, or a third accuracy value generated by the artificial intelligence model; select, using the artificial intelligence model, a subset of the aggregated results satisfying at least one threshold; and display, using the chatbot, the output comprising the subset in response to the user query.
In similar art, Krakover teaches initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search). The knowledge graph enhances results, produces an accurate confidence score for potential matches, and reduces false positives and false negatives: [0073]; [0078]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
Regarding claim 17:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 16, Shernan-Elsayyad but does not explicitly teach knowledge graph comprising a plurality of nodes and edges connecting the plurality of nodes, and wherein the data corresponding to the vector representation comprises a structured query corresponding to a schema of the at least one knowledge graph.
In similar art, Krakover teaches receiving an analysis data set, generating elements, and determining whether a first subset of elements is similar to an entry of a knowledge graph. Knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search) (see, Krakover [0071]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
Regarding claim 18:
In addition to the rejection claim 17, Shernan-Elsayyad-Krakover further teaches map one or more portions of the vector representation to at least one node type, node attribute, or edge type corresponding to the plurality of nodes and edges based on the schema; and generate the structured query comprising the at least one node type, node attribute, or edge type: (initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search): Krakover [0071]).
Regarding claim 19:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 14, Shernan-Elsayyad but does not explicitly teach identify a subject of the user query corresponding to at least one location; determine the at least one location corresponds to multiple locations or is incomplete; disambiguate the at least one location; and receive, responsive to the disambiguation, a plurality of candidate locations corresponding to the at least one location.
In similar art, Krakover teaches initial mappings are created between identified entities and knowledge graph nodes using a combination of direct term matching (such as using text similarity ranking algorithms and indirect matching using an embeddings vector search) (see, Krakover [0071]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Krakover’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Krakover’s ideas into Shernan-Elsayyad’s system.
6. Claims 12-13 are rejected under 35 U.S.C 103 as being un-patentable over Shernan-Elsayyad in view of Fu (GB 2521422 A)
Regarding claim 12:
Shernan-Elsayyad discloses the invention substantially as disclosed in claim 11, but does not explicitly teach determine the first results failing the first accuracy value and the second results failing the second accuracy value; and initiate a third search of one or more third data sources, the one or more third data sources corresponding to a plurality of external links which direct to a plurality of external content.
In similar art, Fu teaches the user may desire to condition utilization of an external search engine upon failure to determine that the search term correlates with a search index entry. For example, if the apparatus fails to determine that the search term correlates with the search index entry, the apparatus may send a search query to the external search engine (Fu page 21).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Fu’s ideas into Shernan-Elsayyad’s system in order to save resources and development time by implying Fu’s ideas into Shernan-Elsayyad’s system.
Regarding claim 13:
In addition to the rejection claim 12, Shernan-Elsayyad- Fu further teaches retrieve one or more links of the plurality of external links relevant to the user query based on a content relevance score associated with the plurality of external content; and provide a prompt comprising the one or more links via the chatbot in response to the user query: (the search server may interact with the external server to generate an improved search response to the initial query. The external server may be a third-party server that implements a chatbot or similar interface hosted by the search server while maintaining the capability to store and manage user profile data (via database). The chat interface module may provide a chatbot or a chat interface to one or more client devices, allowing users to input queries and view search responses. The chat interface may display the user's chat history for the current session and, in some cases, may incorporate user profile information, including past chat history retrieved from an external server. The search server may utilize the plurality of search engines to process the plurality of search queries along with the chat history data when determining the search results. Similarly, the large language model may reference the chat history data when generating the search response, ensuring that the search response is contextually relevant and tailored to the ongoing interaction. This integration of chat history enhances continuity and personalization in the user experience: Elsayyad [0051]; [0110]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention was made to modify Elsayyad’s ideas into Shernan-Fu’s system in order to provide an efficient system/architectures leveraging machine learning to improving search response generation utilizing multiple search engines and machine learning techniques (see Elsayyad, [0002]).
Conclusions
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/LAN DAI T TRUONG/ Primary Examiner, Art Unit 2444
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