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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/5/2025 has been entered.
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 1, 8, 12, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuiyan, Alam (PGPUB Document No. 20220261406), hereafter referred as to “Bhuiyan”, in view of Rosa, Arthur et al(US Patent No. 12554759 ), hereafter, referred to as “Rosa”, in further view of Choi, Sung Hyeok et al(PGPUB Document No. 20240064381), hereafter, referred to as “Choi”
Regarding claim 1(Currently Amended), Bhuiyan teaches A system for query intent-aware search retrieval, the system comprising: a processor; and a computer-readable medium storing instructions that are operative upon execution by the processor to(Bhuiyan, Fig. 2 and para 0037-0038 disclose a system processors and storage media for storing instructions): obtain a generalized search query from a user device via a network, the generalized search query comprising a set of words corresponding to a category of item types (Bhuiyan, para 0019 discloses obtaining/receiving a query and identifying multiple query intents from the query “receive an input query from a user; detect a first intent of the input query via a natural language processor; and retrieve the second intent (or all the intents associated with the first intent) based upon its association with the first intent”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”);
identify, using vector embeddings, multiple intents associated with a plurality of sub-categories associated with the category(Bhuiyan, para 0021 discloses detecting multiple query intents from the query-intent graph embedding or vector space “The operations a first module for determining similar intents by accessing prior engagements including the prior queries and prior intents resultant from the prior queries; mapping the prior queries to the respective prior intents; …, applying a graph embedding methods over the bipartite graph to determine distance between prior intents, and grouping prior intents together into a plurality of similar intent groups based upon the respective distance between the prior intents and storing the intent groups”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”);
submit the plurality of specific-intent queries to a multi-query search engine(Bhuiyan, para 0019 discloses submitting multi-intent query for execution “The computing device also configured to create an extended query containing the first intent and the second intent (or all the intents associated with the first intent); query the database with the extended query”);
obtain a plurality of results responsive to the plurality of specific-intent queries from the multi-query search engine, the plurality of results comprising a plurality of types of items associated with the plurality of sub-categories(Bhuiyan, para 0019 discloses obtaining results by executing multi-intent queries “query the database with the extended query; and receive and transmit the extended query results from the database to the user in response to the input query”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”); wherein grouping multi-intent query results reduces processor cycles and network transmissions by eliminating repeated iterative queries by the user(Bhuiyan, para 0047 discloses identifying multi-intent query and grouping them together reduces the number of identification process; reduction in any computing process suggests reduction in processing cycles and any transmission overhead “The similar intents are grouped together 415 based on the vector distance between intents and the resultant groups are stored 416 to be used in the extended search. …. similarly the smaller the distance threshold to determining similar intents with result in higher confidence. Conversely the higher confidence may reduce the number of similar intent groups identified”).
But Bhuiyan does not explicitly teach generate, using a generative artificial-intelligence module for query embeddings hosted on a cloud platform, embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query and in candidate search results: generate, by a customized prompt generator using artificial intelligence capabilities and vector similarity searches, a plurality of specific-intent queries corresponding to the plurality of sub-categories; generate a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories, wherein the multi-intent query results page is presented to a user via a user interface (UI) device; and create a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group in the plurality of groups, the plurality of clickable tabs enabling non-linear navigation and real-time presentation of the grouped multi-intent query results via the UI device,
However, in the same field of endeavor of vector similarity search Rosa teaches generate, using a generative artificial-intelligence module for query embeddings(Rosa, Fig. 4 and col 13: 58-70 disclose translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”) hosted on a cloud platform(Rosa, col 17: 41-45 discloses the system can be hosed on a cloud computing environment “The machine can operate in the capacity of a server or a client machine in a client-server network environment,….. or as a server or a client machine in a cloud computing infrastructure or environment”), embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query (Rosa, Fig. 4 and col 13: 58-70 discloses translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”; col 7:42-45 further discloses using a deep learning model for generative language model “The generative language model 160 includes a deep learning model that is configured using artificial intelligence-based technologies to machine-generate natural language text……”) and in candidate search results(Rosa, Fig. 1 and col 5: 9-17 further disclose candidate search result or content items are getting vectorized by a vector generator (element 120) “Vector store generator 120 receives content items 102 from application software system 130 and generates vector store 104…..vector store generator 120 uses a large language model to generate an embedding based on the semantic meaning of the text in each content item category for each content item of content items 102”): generate, by a customized prompt generator using artificial intelligence capabilities and vector similarity searches(Rosa, col 20: 65-67~ col 21: 1-9 discloses generation of a prompt based on vector similarity search “perform a similarity search on the embedding and standardized content items,………. formulate a prompt including the supplemental text; apply a generative language model to the prompt; output, by the generative language model, based on the prompt”), a plurality of specific-intent queries corresponding to the plurality of sub-categories(Rosa, col 16:17-19 discloses queries are corresponding category “embedded user input 406 is a matrix where the rows of the matrix represent embedding vectors for the content item categories of user input 106”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of query and result set for searching of Rosa into searching by multiple query intents of Bhuiyan to produce an expected result of performing vector similarity search. The modification would be obvious because one of ordinary skill in the art would be motivated to use categories of preexisting taxonomy to prevent hallucination and/or irrelevant recommendation or answer generation(Rosa, col 3:31-37).
But Bhuiyan and Rosa don’t explicitly teach generate a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories, wherein the multi-intent query results page is presented to a user via a user interface (UI) device; and create a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group in the plurality of groups, the plurality of clickable tabs enabling non-linear navigation and real-time presentation of the grouped multi-intent query results via the UI device,
However, in the same field of endeavor of user searching and displaying results Choi teaches generate a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories, wherein the multi-intent query results page is presented to a user via a user interface (UI) device(Choi, Fig. 3 and para 0031 discloses displaying search results in group of categories/subcategories “search results for all categories such as TV channels, TV programs, and VOD are provided. In this case, since the search results are too verbose, and it may make it difficult for a user to select contents. Accordingly, in the present invention, the universal search results are classified and provided by attribute group again. A category corresponds to main category for contents, and an attribute group corresponds to sub-category for contents”; Bhuiyan in para 0019 discloses searching multi-intent queries and obtaining results);
and create a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group(Choi, Fig. 3 and para 0031 discloses creating multiple clickable tabs for result categories displaying “search results for all categories such as TV channels, TV programs, and VOD are provided……”), the plurality of clickable tabs enabling non-linear navigation and real-time presentation of the grouped multi-intent query results via the UI device(Choi, Fig. 3 and para 0012-0013 disclose navigating back and forth further negating along content hierarchy such group to sub-group etc. “providing thumbnail instant viewing for the highlighted live channel on the result screen of the universal contents search; and providing contents of the highlighted live channel to main display in response to user selection operation on the display of the thumbnail instant viewing ……The contents navigation method may further comprise: removing the thumbnail instant viewing in response to an arrow key operation of the user on a display screen to which the thumbnail instant viewing is provided; and changing the highlight of a live channel in response to the direction of the arrow key operation”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of displaying search results by their categories of Choi into searching by multiple query intents of Bhuiyan and Rosa to produce an expected result of displaying query result separately by their category/sub-category. The modification would be obvious because one of ordinary skill in the art would be motivated to display search results classified in group attribute so that users don’t have to select content item described too verbosely(Choi, para 0031).
Claim 5, Cancelled.
Regarding claim 8 (Currently Amended), Bhuiyan teaches A method for query intent-aware search retrieval, the method comprising: obtaining a generalized search query from a user device via a network, the generalized search query comprising a set of words corresponding to a category of item types, wherein the generalized search query comprises multiple intents associated with a plurality of sub-categories associated with the category(Bhuiyan, para 0019 discloses obtaining/receiving a query and identifying multiple query intents from the query “receive an input query from a user; detect a first intent of the input query via a natural language processor; and retrieve the second intent (or all the intents associated with the first intent) based upon its association with the first intent”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”); identifying, using vector embeddings, the multiple intents associated with the plurality of sub-categories corresponding to the category(Bhuiyan, para 0021 discloses detecting multiple query intents from the query-intent graph embedding or vector space “The operations a first module for determining similar intents by accessing prior engagements including the prior queries and prior intents resultant from the prior queries; mapping the prior queries to the respective prior intents; …, applying a graph embedding methods over the bipartite graph to determine distance between prior intents, and grouping prior intents together into a plurality of similar intent groups based upon the respective distance between the prior intents and storing the intent groups”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”);
submitting the plurality of specific-intent queries to a multi-query search engine; obtaining a plurality of results responsive to the plurality of specific-intent queries from the multi-query search engine, the plurality of results comprising a plurality of types of items associated with the plurality of sub-categories(Bhuiyan, para 0019 discloses obtaining results by executing multi-intent queries “query the database with the extended query; and receive and transmit the extended query results from the database to the user in response to the input query”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”); wherein grouping multi-intent query results reduces processor cycles and network transmissions by eliminating repeated iterative queries by the user(Bhuiyan, para 0047 discloses by identifying multi-intent query and grouping them together reduces the number of identification process; reduction in any computing process suggests reduction in processing cycles and any transmission overhead “The similar intents are grouped together 415 based on the vector distance between intents and the resultant groups are stored 416 to be used in the extended search. …. similarly the smaller the distance threshold to determining similar intents with result in higher confidence. Conversely the higher confidence may reduce the number of similar intent groups identified”).
But Bhuiyan does not explicitly teach generating, using a generative artificial-intelligence module for query embeddings hosted on a cloud platform, embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query and in candidate search results; generating, by a customized prompt generator employing artificial-intelligence capabilities and vector-similarity searching, a plurality of specific-intent queries corresponding to the plurality of sub-categories;
generating a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories; generating a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group in the plurality of groups; and presenting the multi-intent query results page to a user via a user interface (UI) device, the plurality of clickable tabs enabling presentation of the grouped multi-intent query results via the UI device,
However, in the same field of endeavor of vector similarity search Rosa teaches generating, using a generative artificial-intelligence module for query embeddings (Rosa, Fig. 4 and col 13: 58-70 disclose translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”) hosted on a cloud platform(Rosa, col 17: 41-45 discloses the system can be hosed on a cloud computing environment “The machine can operate in the capacity of a server or a client machine in a client-server network environment,….. or as a server or a client machine in a cloud computing infrastructure or environment”), embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query (Rosa, Fig. 4 and col 13: 58-70 discloses translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”; col 7:42-45 further discloses using a deep learning model for generative language model “The generative language model 160 includes a deep learning model that is configured using artificial intelligence-based technologies to machine-generate natural language text……”) and in candidate search results(Rosa, Fig. 1 and col 5: 9-17 further disclose candidate search result or content items are getting vectorized by a vector generator (element 120) “Vector store generator 120 receives content items 102 from application software system 130 and generates vector store 104…..vector store generator 120 uses a large language model to generate an embedding based on the semantic meaning of the text in each content item category for each content item of content items 102”); generating, by a customized prompt generator employing artificial-intelligence capabilities and vector-similarity searching(Rosa, col 20: 65-67~ col 21: 1-9 discloses generation of a prompt based on vector similarity search “perform a similarity search on the embedding and standardized content items,………. formulate a prompt including the supplemental text; apply a generative language model to the prompt; output, by the generative language model, based on the prompt”), a plurality of specific-intent queries corresponding to the plurality of sub-categories(Rosa, col 16:17-19 discloses queries are corresponding category “embedded user input 406 is a matrix where the rows of the matrix represent embedding vectors for the content item categories of user input 106”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of query and result set for searching of Rosa into searching by multiple query intents of Bhuiyan to produce an expected result of performing vector similarity search. The modification would be obvious because one of ordinary skill in the art would be motivated to use categories of preexisting taxonomy to prevent hallucination and/or irrelevant recommendation or answer generation(Rosa, col 3:31-37).
But Bhuiyan and Rosa don’t explicitly teach generating a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories; generating a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group in the plurality of groups; and presenting the multi-intent query results page to a user via a user interface (UI) device, the plurality of clickable tabs enabling presentation of the grouped multi-intent query results via the UI device,
However, in the same field of endeavor of user searching and displaying results Choi teaches generating a multi-intent query results page comprising grouped multi-intent query results, the grouped multi-intent query results comprising the plurality of results organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories(Choi, Fig. 3 and para 0031 discloses displaying search results in group of categories/subcategories “search results for all categories such as TV channels, TV programs, and VOD are provided. In this case, since the search results are too verbose, and it may make it difficult for a user to select contents. Accordingly, in the present invention, the universal search results are classified and provided by attribute group again. A category corresponds to main category for contents, and an attribute group corresponds to sub-category for contents”; Bhuiyan in para 0019 discloses searching multi-intent queries and obtaining results); generating a plurality of clickable tabs within the multi-intent query results page corresponding to identified multiple intents, each clickable tab linked to a group in the plurality of groups; and presenting the multi-intent query results page to a user via a user interface (UI) device, the plurality of clickable tabs enabling presentation of the grouped multi-intent query results via the UI device(Choi, Fig. 3 and para 0031 discloses creating multiple clickable tabs for result categories displaying “search results for all categories such as TV channels, TV programs, and VOD are provided……”; Fig. 3 and para 0012-0013 further disclose navigating back and forth further negating along content hierarchy such group to sub-group etc. “providing thumbnail instant viewing for the highlighted live channel on the result screen of the universal contents search; and providing contents of the highlighted live channel to main display in response to user selection operation on the display of the thumbnail instant viewing ……The contents navigation method may further comprise: removing the thumbnail instant viewing in response to an arrow key operation of the user on a display screen to which the thumbnail instant viewing is provided; and changing the highlight of a live channel in response to the direction of the arrow key operation”) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of displaying search results by their categories of Choi into searching by multiple query intents of Bhuiyan and Rosa to produce an expected result of displaying query result separately by their category/sub-category. The modification would be obvious because one of ordinary skill in the art would be motivated to display search results classified in group attribute so that users don’t have to select content item described too verbosely(Choi, para 0031).
Regarding claim 12 (Currently Amended), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 and Choi further teaches generating the plurality of clickable tabs within the multi-intent query results page corresponding to each group in the plurality of groups, wherein the user selects a clickable tab associated with a sub-category of types of items in the plurality of results to view a selected type of item in a sub-category results page(Choi, Fig. 3 and para 0035 discloses clickable tabs of sub-categories." A specific category item is selected in response to user selection on a specific tab among them, and an attribute group list as a sub-category of the selected category item is identified. For example, in the case illustrated in FIG. 3, the category of TV channels is selected, and the attribute group lists of English News and Entertainment are identified as the sub-category of the category of TV channels”).
Regarding claim 15(Currently Amended), Bhuiyan teaches One or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations comprising(Bhuiyan, Fig. 2 and para 0037-0038 disclose a system processors and storage media for storing instructions): obtaining a generalized search query from a user device via a network, the generalized search query comprising a set of words corresponding to a category of item types, wherein the generalized search query comprises multiple intents associated with a plurality of sub-categories associated with the category of item types(Bhuiyan, para 0019 discloses obtaining/receiving a query and identifying multiple query intents from the query “receive an input query from a user; detect a first intent of the input query via a natural language processor; and retrieve the second intent (or all the intents associated with the first intent) based upon its association with the first intent”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”);
identifying, using vector embeddings, the multiple intents associated with the plurality of sub-categories corresponding to the category(Bhuiyan, para 0021 discloses detecting multiple query intents from the query-intent graph embedding or vector space “The operations a first module for determining similar intents by accessing prior engagements including the prior queries and prior intents resultant from the prior queries; mapping the prior queries to the respective prior intents; …, applying a graph embedding methods over the bipartite graph to determine distance between prior intents, and grouping prior intents together into a plurality of similar intent groups based upon the respective distance between the prior intents and storing the intent groups”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”);
submitting the query set to a multi-query search engine(Bhuiyan, para 0019 discloses submitting multi-intent query for execution “The computing device also configured to create an extended query containing the first intent and the second intent (or all the intents associated with the first intent); query the database with the extended query”); obtaining a plurality of results responsive to the plurality of specific-intent queries from the multi-query search engine, the plurality of results comprising a plurality of types of items associated with the plurality of sub-categories(Bhuiyan, para 0019 discloses obtaining results by executing multi-intent queries “query the database with the extended query; and receive and transmit the extended query results from the database to the user in response to the input query”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”); wherein grouping multi-intent query results reduces processor cycles and network transmissions by eliminating repeated iterative queries by the user(Bhuiyan, para 0047 discloses by identifying multi-intent query and grouping them together reduces the number of identification process; reduction in any computing process suggests reduction in processing cycles and any transmission overhead “The similar intents are grouped together 415 based on the vector distance between intents and the resultant groups are stored 416 to be used in the extended search. …. similarly the smaller the distance threshold to determining similar intents with result in higher confidence. Conversely the higher confidence may reduce the number of similar intent groups identified”).
But Bhuiyan does not explicitly teach generating, using a generative artificial-intelligence module for query embeddings hosted on a cloud platform, embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query and in candidate search results; generating, by a customized prompt generator employing artificial-intelligence capabilities and vector-similarity searching, a plurality of specific-intent queries corresponding to the plurality of sub-categories;
generating a multi-intent query results page comprising grouped multi-intent query results and a plurality of clickable tabs linking to the plurality of results, wherein each clickable tab linked to a group in the plurality of clickable tabs corresponds to a sub-category in the plurality of sub-categories, and wherein a user selects a clickable tab in the plurality of clickable tabs to view a set of results returned in response to a specific-intent query in the plurality of specific-intent queries; and presenting the multi-intent query results page to the user via a user interface (UI) device, the plurality of clickable tabs enabling non-linear navigation and real-time presentation of the grouped multi-intent query results via the UI device,
However, in the same field of endeavor of vector similarity search Rosa teaches generating, using a generative artificial-intelligence module for query embeddings(Rosa, Fig. 4 and col 13: 58-70 disclose translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”) hosted on a cloud platform(Rosa, col 17: 41-45 discloses the system can be hosed on a cloud computing environment “The machine can operate in the capacity of a server or a client machine in a client-server network environment,….. or as a server or a client machine in a cloud computing infrastructure or environment”), embeddings of the generalized search query with a machine-learning or deep-learning embedding model that are used to generate vectors representing each word or the set of words in a query (Rosa, Fig. 4 and col 13: 58-70 discloses translating query/user input by AI model (404) into embeddings (406) “similarity search engine 425 receives embedded user input 406 from large language model 415”; col 7:42-45 further discloses using a deep learning model for generative language model “The generative language model 160 includes a deep learning model that is configured using artificial intelligence-based technologies to machine-generate natural language text……”) and in candidate search results(Rosa, Fig. 1 and col 5: 9-17 further disclose candidate search result or content items are getting vectorized by a vector generator (element 120) “Vector store generator 120 receives content items 102 from application software system 130 and generates vector store 104…..vector store generator 120 uses a large language model to generate an embedding based on the semantic meaning of the text in each content item category for each content item of content items 102”); generating, by a customized prompt generator employing artificial-intelligence capabilities and vector-similarity searching(Rosa, col 20: 65-67~ col 21: 1-9 discloses generation of a prompt based on vector similarity search “perform a similarity search on the embedding and standardized content items,………. formulate a prompt including the supplemental text; apply a generative language model to the prompt; output, by the generative language model, based on the prompt”), a plurality of specific-intent queries corresponding to the plurality of sub-categories(Rosa, col 16:17-19 discloses queries are corresponding category “embedded user input 406 is a matrix where the rows of the matrix represent embedding vectors for the content item categories of user input 106”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of vectorization of query and result set for searching of Rosa into searching by multiple query intents of Bhuiyan to produce an expected result of performing vector similarity search. The modification would be obvious because one of ordinary skill in the art would be motivated to use categories of preexisting taxonomy to prevent hallucination and/or irrelevant recommendation or answer generation(Rosa, col 3:31-37).
But Bhuiyan and Rosa don’t explicitly teach generating a multi-intent query results page comprising grouped multi-intent query results and a plurality of clickable tabs linking to the plurality of results, wherein each clickable tab linked to a group in the plurality of clickable tabs corresponds to a sub-category in the plurality of sub-categories(Choi, Fig. 3 and para 0031 discloses displaying search results in group of categories/subcategories “search results for all categories such as TV channels, TV programs, and VOD are provided. In this case, since the search results are too verbose, and it may make it difficult for a user to select contents. Accordingly, in the present invention, the universal search results are classified and provided by attribute group again. A category corresponds to main category for contents, and an attribute group corresponds to sub-category for contents”; Bhuiyan in para 0019 discloses searching multi-intent queries and obtaining results), and wherein a user selects a clickable tab in the plurality of clickable tabs to view a set of results returned in response to a specific-intent query in the plurality of specific-intent queries; and presenting the multi-intent query results page to the user via a user interface (UI) device(Choi, Fig. 3 and para 0031 discloses creating multiple clickable tabs for result categories displaying “search results for all categories such as TV channels, TV programs, and VOD are provided……”), the plurality of clickable tabs enabling non-linear navigation and real-time presentation of the grouped multi-intent query results via the UI device Choi, Fig. 3 and para 0012-0013 disclose navigating back and forth further negating along content hierarchy such group to sub-group etc. “providing thumbnail instant viewing for the highlighted live channel on the result screen of the universal contents search; and providing contents of the highlighted live channel to main display in response to user selection operation on the display of the thumbnail instant viewing ……The contents navigation method may further comprise: removing the thumbnail instant viewing in response to an arrow key operation of the user on a display screen to which the thumbnail instant viewing is provided; and changing the highlight of a live channel in response to the direction of the arrow key operation”),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of displaying search results by their categories of Choi into searching by multiple query intents of Bhuiyan and Rosa to produce an expected result of displaying query result separately by their category/sub-category. The modification would be obvious because one of ordinary skill in the art would be motivated to display search results classified in group attribute so that users don’t have to select content item described too verbosely(Choi, para 0031).
Regarding claim 16 (Currently Amended), Bhuiyan, Rosa and Choi teach all the limitations of claim 15 and Choi further teaches wherein the operations further comprise: presenting the plurality of types of items associated with the plurality of sub-categories in the plurality of results on the multi-intent query results page, the plurality of types of items organized into a plurality of groups corresponding to each sub-category in the plurality of sub-categories (Choi, Fig. 3 and para 0035 discloses clickable tabs of sub-categories." A specific category item is selected in response to user selection on a specific tab among them, and an attribute group list as a sub-category of the selected category item is identified. For example, in the case illustrated in FIG. 3, the category of TV channels is selected, and the attribute group lists of English News and Entertainment are identified as the sub-category of the category of TV channels”).
Claim 2, 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuiyan, Alam (PGPUB Document No. 20220261406), hereafter referred as to “Bhuiyan”, in view of Rosa, Arthur et al(US Patent No. 12554759 ), hereafter, referred to as “Rosa”, in further view of Choi, Sung Hyeok et al(PGPUB Document No. 20240064381), hereafter, referred to as “Choi”, in further view of Venkataraman, Sashikumar et al(PGPUB Document No. 20170364520), hereafter, referred to as “Venkataraman”.
Regarding claim 2 (Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 1 and Bhuiyan further teaches wherein the instructions are further operative to: receive a plurality of generalized search queries(Bhuiyan, para 0046 discloses a process for receiving multiple input query form users “An input query is received 302 and the intents are detected in 304 using natural language understanding technique, a determination is made as to whether the detected intent has a similar intent group…..…..”);
But Bhuiyan, Rosa and Choi don’t explicitly teach and filter a set of generalized search queries from the plurality of generalized search queries having at least one word in a set of prohibited words.
However, in the same field of endeavor of user searching and displaying results Venkataraman teaches and filter a set of generalized search queries from the plurality of generalized search queries having at least one word in a set of prohibited words(Venkataraman, para 0136 discloses having a filter mechanism to remove stop words from user queries “The media guidance application may create a modified query excluding the stop words by removing the stop words from the user's original query and keeping words in the user's original query that do not correspond to a stop words category”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of filtering stop words from user queries (initial query) of Venkataraman into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of executing users queries without stop-words. The modification would be obvious because one of ordinary skill in the art would be motivated to avoid wastage of resources by removing search terms which don’t get used for query result matching (Venkataraman, 0136).
Regarding claim 9(Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 and Bhuiyan further teaches further comprising: receiving a plurality of generalized search queries (Bhuiyan, para 0046 discloses a process for receiving multiple input query form users “An input query is received 302 and the intents are detected in 304 using natural language understanding technique, a determination is made as to whether the detected intent has a similar intent group…..…..”);
But Bhuiyan, Rosa and Choi don’t explicitly teach and filtering a set of generalized search queries from the plurality of generalized search queries having at least one word in a set of prohibited words.
However, in the same field of endeavor of user searching and displaying results Venkataraman teaches and filtering a set of generalized search queries from the plurality of generalized search queries having at least one word in a set of prohibited words(Venkataraman, para 0136 discloses having a filter mechanism to remove stop words from user queries “The media guidance application may create a modified query excluding the stop words by removing the stop words from the user's original query and keeping words in the user's original query that do not correspond to a stop words category”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of filtering stop words from user queries (initial query) of Venkataraman into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of executing users queries without stop-words. The modification would be obvious because one of ordinary skill in the art would be motivated to avoid wastage of resources by removing search terms which don’t get used for query result matching (Venkataraman, 0136).
Regarding claim 17(Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 15 but don’t explicitly teach wherein the operations further comprise: applying a customized profanity filter to a first generalized search query and a second generalized search query; filtering the first generalized search query responsive to detection of at least one word in a set of prohibited words within the first generalized search query; and generating a query set comprising customized specific-intent queries responsive to a failure to detect at least one word in the set of prohibited words within the second generalized search query.
However, in the same field of endeavor of user searching and displaying results Venkataraman teaches wherein the operations further comprise: applying a customized profanity filter to a first generalized search query and a second generalized search query; filtering the first generalized search query responsive to detection of at least one word in a set of prohibited words within the first generalized search query; and generating a query set comprising customized specific-intent queries responsive to a failure to detect at least one word in the set of prohibited words within the second generalized search query (Venkataraman, para 0136 discloses having a filter mechanism to remove stop/prohibited words from user queries and which can be applied for any search query such as first or second search queries “The media guidance application may create a modified query excluding the stop words by removing the stop words from the user's original query and keeping words in the user's original query that do not correspond to a stop words category”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of filtering stop words from user queries (initial query) of Venkataraman into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of executing users queries without stop-words. The modification would be obvious because one of ordinary skill in the art would be motivated to avoid wastage of resources by removing search terms which don’t get used for query result matching (Venkataraman, 0136).
Claim 3-4, 10-11 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuiyan, Alam (PGPUB Document No. 20220261406), hereafter referred as to “Bhuiyan”, in view of Rosa, Arthur et al(US Patent No. 12554759 ), hereafter, referred to as “Rosa”, in view of Choi, Sung Hyeok et al(PGPUB Document No. 20240064381), hereafter, referred to as “Choi”, in further view of Aksar, Burak et al(PGPUB Document No. 20230362107), hereafter, referred to as “Aksar”.
Regarding claim 3 (Previously Presented), Bhuiyan, Rosa and Choi teach all the limitations of claim 1 and Bhuiyan further teaches wherein the instructions are further operative to: receive a plurality of generalized search queries (Bhuiyan, para 0046 discloses a process for receiving multiple input query form users “An input query is received 302 and the intents are detected in 304 using natural language understanding technique, a determination is made as to whether the detected intent has a similar intent group…..…..”);
But Bhuiyan, Rosa and Choi don’t explicitly teach identify a set of single-intent queries in the plurality of generalized search queries having a single intent; identify a set of multi-intent queries in the plurality of generalized search queries; send the set of single-intent queries to a traditional search engine; and send the set of multi-intent queries to the multi-query search engine.
However, in the same field of endeavor of identifying query intents Aksar teaches identify a set of single-intent queries in the plurality of generalized search queries having a single intent; identify a set of multi-intent queries in the plurality of generalized search queries (Aksar, para 0027 discloses identifying if the query has single of multiple intents “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”); send the set of single-intent queries to a traditional search engine; and send the set of multi-intent queries to the multi-query search engine(Aksar, para 0030-0031 disclose sending query to different agent/query processor based on the intent “the statement/query may be a single intent query that is capable of being processed by a single computer agent such as a website or database ………. may connect the 2 parts within the statement/query whereby instead each part represents an intent (and consequently, the statement/query includes more than one intent) such that the statement/query may require multiple computer agents to process the statement/query”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Regarding claim 4(Currently Amended), Bhuiyan, Rosa and Choi teach all the limitations of claim 1 but don’t explicitly teach wherein the instructions are further operative to: analyze the generalize search query by a multi-use case query classifier; determine whether the generalized search query is a multi-intent query or a single-intent query; send the generalized search query to a traditional search engine responsive to a determination the generalized search query is the single-intent query; and send the generalized search query to the customized prompt generator responsive to the determination the generalized search query is the multi-intent query.
Using the broadest reasonable interpretation consistent with the specification (paragraph 0023) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “multi-use case query classifier” to mean at least a query analyzing mechanism which identifies single and multi-intent queries.
However, in the same field of endeavor of identifying query intents Aksar teaches further operative to: analyze the generalize search query by a multi-use case query classifier; determine whether the generalized search query is a multi-intent query or a single-intent query(Aksar, para 0027 discloses identifying if the query has single of multiple intents “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”); send the generalized search query to a traditional search engine responsive to a determination the generalized search query is the single-intent query; and send the generalized search query to the customized prompt generator responsive to the determination the generalized search query is the multi-intent query (Aksar, para 0030-0031 disclose sending query to different agent/query processor based on the intent “the statement/query may be a single intent query that is capable of being processed by a single computer agent such as a website or database ………. may connect the 2 parts within the statement/query whereby instead each part represents an intent (and consequently, the statement/query includes more than one intent) such that the statement/query may require multiple computer agents to process the statement/query”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Regarding claim 10(Previously Presented), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 and Bhuiyan further teaches further comprising: receiving a plurality of generalized search queries (Bhuiyan, para 0046 discloses a process for receiving multiple input query form users “An input query is received 302 and the intents are detected in 304 using natural language understanding technique, a determination is made as to whether the detected intent has a similar intent group…..…..”);
But Bhuiyan, Rosa and Choi don’t explicitly teach identifying a set of single-intent queries in the plurality of generalized search queries having a single intent; identifying a set of multi-intent queries in the plurality of generalized search queries; sending the set of single-intent queries to a traditional search engine; and sending the set of multi-intent queries to the multi-query search engine.
However, in the same field of endeavor of identifying query intents Aksar teaches identifying a set of single-intent queries in the plurality of generalized search queries having a single intent; identifying a set of multi-intent queries in the plurality of generalized search queries (Aksar, para 0027 discloses identifying if the query has single of multiple intents “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”); sending the set of single-intent queries to a traditional search engine; and sending the set of multi-intent queries to the multi-query search engine(Aksar, para 0030-0031 disclose sending query to different agent/query processor based on the intent “the statement/query may be a single intent query that is capable of being processed by a single computer agent such as a website or database ………. may connect the 2 parts within the statement/query whereby instead each part represents an intent (and consequently, the statement/query includes more than one intent) such that the statement/query may require multiple computer agents to process the statement/query”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Regarding claim 11(Currently Amended), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 but don’t explicitly teach further comprising: analyzing the generalize search query by a multi-use case query classifier; determining whether the generalized search query is a multi-intent query or a single-intent query; routing the generalized search query to a traditional search engine responsive to a determination the generalized search query is the single-intent query; and routing the generalized search query to the customized prompt generator responsive to the determination the generalized search query is the multi-intent query.
Using the broadest reasonable interpretation consistent with the specification (paragraph 0023) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “multi-use case query classifier” to mean at least a query analyzing mechanism which identifies single and multi-intent queries.
However, in the same field of endeavor of identifying query intents Aksar teaches further comprising: analyzing the generalize search query by a multi-use case query classifier; determining whether the generalized search query is a multi-intent query or a single-intent query(Aksar, para 0027 discloses identifying if the query has single of multiple intents “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”); routing the generalized search query to a traditional search engine responsive to a determination the generalized search query is the single-intent query; and routing the generalized search query to the customized prompt generator responsive to the determination the generalized search query is the multi-intent query (Aksar, para 0030-0031 disclose sending query to different agent/query processor based on the intent “the statement/query may be a single intent query that is capable of being processed by a single computer agent such as a website or database ………. may connect the 2 parts within the statement/query whereby instead each part represents an intent (and consequently, the statement/query includes more than one intent) such that the statement/query may require multiple computer agents to process the statement/query”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Regarding claim 18 (Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 15 but don’t explicitly teach wherein the operations further comprise: classifying a first set of generalized search queries as single-intent queries for handling by a traditional search engine; and classifying a second set of generalized search queries as multi-intent queries, wherein the second set of generalized search queries are handled by a multi-intent query search engine.
However, in the same field of endeavor of identifying query intents Aksar teaches wherein the operations further comprise: classifying a first set of generalized search queries as single-intent queries for handling by a traditional search engine; and classifying a second set of generalized search queries as multi-intent queries, wherein the second set of generalized search queries are handled by a multi-intent query search engine (Aksar, para 0027 discloses identifying if the query has single of multiple intents “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”; Aksar further in para 0030-0031 disclose sending query to different agent/query processor based on the intent “the statement/query may be a single intent query that is capable of being processed by a single computer agent such as a website or database ………. may connect the 2 parts within the statement/query whereby instead each part represents an intent (and consequently, the statement/query includes more than one intent) such that the statement/query may require multiple computer agents to process the statement/query”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Regarding claim 19 (Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 15 but don’t explicitly teach wherein the operations further comprise: differentiating between single-intent generalized search queries and multi-intent generalized search queries.
However, in the same field of endeavor of identifying query intents Aksar teaches wherein the operations further comprise: differentiating between single-intent generalized search queries and multi-intent generalized search queries (Aksar, para 0027 discloses identifying if the query has single of multiple intents differently “the intent detection program 108A, 108B may use the parser to identify the different parts of the statement/query as well as to determine whether the combination of the different parts represent a single intent or multiple intents”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying single or multi-intent query of Aksar into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the natural language understanding of question and answering system by incorporating identification of single and multiple query intents (Aksar, 0015).
Claim 6-7, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhuiyan, Alam (PGPUB Document No. 20220261406), hereafter referred as to “Bhuiyan”, in view of Rosa, Arthur et al(US Patent No. 12554759 ), hereafter, referred to as “Rosa”, in view of Choi, Sung Hyeok et al(PGPUB Document No. 20240064381), hereafter, referred to as “Choi”, in further view of Kamath, Pethri et al(PGPUB Document No. 20250014578), hereafter, referred to as “Kamath”.
Regarding claim 6 (Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 1 but Bhuiyan and Choi don’t explicitly teach wherein the instructions are further operative to: generate a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; generate a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query; and send the first customized specific-intent query and the second customized specific-intent query to the multi-query search engine.
However, in the same field of endeavor of identifying query intents Kamath teaches wherein the instructions are further operative to: generate a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; generate a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query(Kamath, para 0069 discloses determining/generating first and second intent’s prediction or probabilities “At S104, the method includes determining a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command. At S105, the method includes determining whether the probabilities exceed a threshold. At S106, the method includes determining a first intent associated with the first candidate substring and a second intent associated with the second candidate substring when the probabilities exceed the threshold. At S107”); and send the first customized specific-intent query and the second customized specific-intent query to the multi-query search engine(Kamath, Fig. 1 and para 0069 disclose performing first and second intents “…. the method includes performing operations based on the first intent and the second intent (also referred to herein as “performing the first intent” or “performing the second intent”). At S108, the method includes providing the user an acknowledgment associated with the first intent and the second intent”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of query intent prediction of Kamath into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to correct the received text by using a classifier that validates linguistic correctness (Kamath, 0113).
Regarding claim 7 (Original), Bhuiyan, Rosa, Choi and Kamath teach all the limitations of claim 6 and Kamath further teaches wherein the instructions are further operative to: receive a first set of results responsive to the first customized specific-intent query from the multi-query search engine; receive a second set of results responsive to the second customized specific-intent query from the multi-query search engine(Kamath, Fig. 1 and para 0069 disclose receiving result responses after identified intents got executed “the method includes determining a first intent associated with the first candidate substring and a second intent associated with the second candidate substring when the probabilities exceed the threshold. At S107, the method includes performing operations based on the first intent and the second intent (also referred to herein as “performing the first intent” or “performing the second intent”). At S108, the method includes providing the user an acknowledgment associated with the first intent and the second intent”);
Choi further teaches generate a first clickable tab in a plurality of clickable tabs in the multi-intent query results page; and generate a second clickable tab in the plurality of clickable tabs in the multi-intent query results page, wherein the user selects the first clickable tab to view the first set of results, and wherein the user selects the second clickable tab to view the second set of results(Choi, Fig. 3 and para 0031 discloses creating multiple clickable tabs for result categories displaying “search results for all categories such as TV channels, TV programs, and VOD are provided……”; Fig. 3 and para 0012-0013 further disclose navigating back and forth and further negating along content hierarchy such group to sub-group etc. by selecting “providing thumbnail instant viewing for the highlighted live channel on the result screen of the universal contents search; and providing contents of the highlighted live channel to main display in response to user selection operation on the display of the thumbnail instant viewing ……The contents navigation method may further comprise: removing the thumbnail instant viewing in response to an arrow key operation of the user on a display screen to which the thumbnail instant viewing is provided; and changing the highlight of a live channel in response to the direction of the arrow key operation”).
Regarding claim 13(Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 but don’t explicitly teach generating a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; generating a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query; and sending the first customized specific-intent query and the second customized specific-intent query to the multi-query search engine.
However, in the same field of endeavor of identifying query intents Kamath teaches generating a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; generating a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query (Kamath, para 0069 discloses determining/generating first and second intent’s prediction or probabilities “At S104, the method includes determining a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command. At S105, the method includes determining whether the probabilities exceed a threshold. At S106, the method includes determining a first intent associated with the first candidate substring and a second intent associated with the second candidate substring when the probabilities exceed the threshold. At S107”); and sending the first customized specific-intent query and the second customized specific-intent query to the multi-query search engine(Kamath, Fig. 1 and para 0069 disclose performing first and second intents “…. the method includes performing operations based on the first intent and the second intent (also referred to herein as “performing the first intent” or “performing the second intent”). At S108, the method includes providing the user an acknowledgment associated with the first intent and the second intent”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of query intent prediction of Kamath into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to correct the received text by using a classifier that validates linguistic correctness (Kamath, 0113).
Regarding claim 14(Currently Amended), Bhuiyan, Rosa and Choi teach all the limitations of claim 8 and Choi further teaches generating a first clickable tab in the plurality of clickable tabs in the multi-intent query results page; and generating a second clickable tab in the plurality of clickable tabs in the multi-intent query results page, wherein the user selects the first clickable tab to view the first set of results, and wherein the user selects the second clickable tab to view the second set of results (Choi, Fig. 3 and para 0031 discloses creating multiple clickable tabs for result categories displaying “search results for all categories such as TV channels, TV programs, and VOD are provided……”; Fig. 3 and para 0012-0013 further disclose navigating back and forth and further negating along content hierarchy such group to sub-group etc. by selecting “providing thumbnail instant viewing for the highlighted live channel on the result screen of the universal contents search; and providing contents of the highlighted live channel to main display in response to user selection operation on the display of the thumbnail instant viewing ……The contents navigation method may further comprise: removing the thumbnail instant viewing in response to an arrow key operation of the user on a display screen to which the thumbnail instant viewing is provided; and changing the highlight of a live channel in response to the direction of the arrow key operation”).
But Bhuiyan, Rosa and Choi don’t explicitly teach receiving a first set of results responsive to a first customized specific-intent query from the multi-query search engine; receiving a second set of results responsive to a second customized specific-intent query from the multi-query search engine.
However, in the same field of endeavor of identifying query intents Kamath teaches receiving a first set of results responsive to a first customized specific-intent query from the multi-query search engine; receiving a second set of results responsive to a second customized specific-intent query from the multi-query search engine(Kamath, Fig. 1 and para 0069 disclose receiving result responses after identified intents got executed “the method includes determining a first intent associated with the first candidate substring and a second intent associated with the second candidate substring when the probabilities exceed the threshold. At S107, the method includes performing operations based on the first intent and the second intent (also referred to herein as “performing the first intent” or “performing the second intent”). At S108, the method includes providing the user an acknowledgment associated with the first intent and the second intent”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of query intent prediction of Kamath into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to correct the received text by using a classifier that validates linguistic correctness (Kamath, 0113).
Regarding claim 20(Original), Bhuiyan, Rosa and Choi teach all the limitations of claim 15 and Bhuiyan further teaches wherein the first customized specific-intent query and the second customized specific-intent query are sent to the multi-query search engine (Bhuiyan, para 0019 discloses obtaining results by executing multi-intent queries “…query the database with the extended query; and receive and transmit the extended query results from the database to the user in response to the input query”; where claim 8 further discloses intents are related to category or product types “wherein the first and second intents are product types”).
But Bhuiyan, Rosa and Choi don’t explicitly teach wherein the operations further comprise: generating a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; and generating a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query.
However, in the same field of endeavor of identifying query intents Kamath teaches wherein the operations further comprise: generating a first customized specific-intent query corresponding to a first predicted intent associated with the generalized search query; and generating a second customized specific-intent query corresponding to a second predicted intent associated with the generalized search query (Kamath, para 0069 discloses determining/generating first and second intent’s prediction or probabilities “At S104, the method includes determining a first probability that the first candidate substring corresponds to a first actionable command and a second probability that the second candidate substring corresponds to a second actionable command. At S105, the method includes determining whether the probabilities exceed a threshold. At S106, the method includes determining a first intent associated with the first candidate substring and a second intent associated with the second candidate substring when the probabilities exceed the threshold. At S107”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of query intent prediction of Kamath into searching by multiple query intents of Bhuiyan, Rosa and Choi to produce an expected result of processing both single and multi-intent queries. The modification would be obvious because one of ordinary skill in the art would be motivated to correct the received text by using a classifier that validates linguistic correctness (Kamath, 0113).
Response to Arguments
I. 35 U.S.C §101 Abstract idea rejection
In light of independent claim amendments and argument consideration abstract idea rejection to claim 1-20 has been withdrawn.
II. 35 U.S.C §103
Applicant’s arguments filed on 12/5/2025 have been fully considered but are
moot because the independent claim 1, 8 and 15 have been amended with newly added
features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
Conclusion
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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164