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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/06/2025 has been entered.
Status of the Claims
Claims 1, 6, 10, 15, 19, 22 have been amended. Claims 1-22 are pending.
Priority
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/520,275, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for the limitation “performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus” in the independent claims and thereof do not receive the benefit of the priority filing date. Co-pending applications must provide written description support for the claimed invention under 35 U.S.C. 112(a) (e.g., must provide support to show both possession and enablement of the claimed subject matter such as an algorithm, process, flowchart, or the like for the limitation in order for the earlier priority date to be recognized for the limitation noted above.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims recite limitation – “performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus.” This limitation is not supported by the specification as originally filed.
(1) There is not disclosure of the limitation “a resource-intensive semantic search.” It is also not clear what functionality is actually required by such resource-intensive semantic search. No details explaining how one of ordinary skill in the art would implement such a limitation were provided in the specification. Although the specification in the background of the invention discloses - “Semantic searching is more time-consuming and computationally expensive than lexical searching” [0009] it is not clear if such definition is applicable to any semantic search. It seems the applicant is interpreting the specification. However, making assumptions in view of the specification does not provide a proper disclose.
(2) There is not disclosure of the limitation “search within boundaries defined by the limited set of first documents.” Although the specification in [0091]-[0092] states that the search can be limited to a scope of 8,788 documents, such number indicates a volume and not the claimed boundaries (which require limits, edges, or at least two points of references that defines where the object ends and its surroundings begin etc.). The number/volume of the limited set of first documents is fundamentally different in mathematical or physical concept from the claimed “boundaries defined by the limited set of first documents.”
(3) There is not disclosure of the limitation – “to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus.” First, “accuracy and precision relative to performing the semantic search on the entire document corpus” requires a specific mathematical formula that calculates such “accuracy and precision” for both the limited search and the entire search and then performing a comparison between two values. The specification does not disclose such calculations. The specification is silent on the claimed “accuracy and precision” and calculating such accuracy and precision relative to accuracy and precision for the entire document search. Making assumptions in view of the specification does not provide a proper disclose.
Second, “to achieve increased accuracy and precision” shows an intended assumption of performing a search on the limited set. Although such functionality might be a reasonable assumption, it is still not an original disclosure.
Thus, there is also no teaching of the limitation – “performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus.”
The dependent claims further carry the same deficiency and likewise rejected.
Claim Objections
Claims 1, 10, 19 are objected to because of the following informalities:
Claims recite limitation "the entire". There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
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.
Claims 1-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al. (US 20240362286) in view of Mukherjee et al. (US 20240354436) and in further view of Sommer et al. (US 6847966).
Regarding claim 1, He teaches a computer-implemented method for searching electronic documents, comprising:
defining a search scope for semantic searching ([0143] “substructure returned from a semantic query”, [0183] “search results 146 may comprise a subset of candidate document vectors from the set of candidate document vectors 718”, [0215] “generating a semantic similarity score for each candidate document … and selecting the subset of candidate document vectors”, [0221]) by:
receiving a non-semantic search query from a user to search a document corpus ([0119], [0131], [0133], [0227], F5:508, F7:508) and
executing a non-semantic search according to the non-semantic search query to generate a first search result that identifies a limited set of first documents ([0076], [0119] “use the lexical search generator to generate a first set of lexical search results 146”, wherein lexical search results 146 is a limited set of first documents) from the document corpus ([0131] “user performs a search for "climate change", the search engine can use the inverted index to quickly retrieve a list of all the articles that contain that term”, [0143] “lexical search generator to perform a lexical full-text search to produce and rank a first set of search results”);
receiving a natural language query from the user ([0172], [0185] “client may … perform subsequent operations, such as requesting more information about the candidate document … in the search results … a subsequent search query”)(see NOTE I);
servicing the natural language query to generate a response to the user ([0177], [0183]),
servicing the natural language query comprising:
executing a semantic search scoped to the limited set of first documents to generate a semantic search result that identifies semantically relevant content that is semantically relevant to the natural language query ([0133] “receive a search query to search for information within an electronic document”) to generate a semantic search result that identifies semantically relevant content that is semantically relevant to the natural language query ([0136], [0143] “then use the semantic search … to perform a semantic search that does a semantic re-ranking, which uses the context or semantic meaning of a search query … extracts and returns captions and answers in the response”, [0146]),
wherein executing the semantic search comprises performing a resource-intensive ([0121] “search operations as desired for a given set of design constraints, such as search speed, size of data sets, number of electronic documents, compute resources, memory resources, network resources, device resource”, [0135], [0212] “search index … will depend on … desired query performance, and the available system resources”) semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result ([0119] “search manager may use the lexical search generator to generate a first set of lexical search results 146, and the semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146”; [0135] “search query 144 may be modified or expanded … allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146”; [0143] “query execution pipeline in two ways. First, it adds secondary ranking over an initial result set … the search manager may use the lexical search generator to perform a lexical full-text search to produce and rank a first set of search results 146”; “then use the semantic search generator to perform a semantic search that does a semantic re-ranking, which uses the context or semantic meaning of a search query 144 to compute a new relevance score over the first set of search results 146”; [0144] “Using those results as the document corpus, semantic ranking re-scores those results based on the semantic strength of the match”)(NOTE II)
to achieve increased accuracy and precision ([0033] “An accurate summary of the sections/parts of the document relevant to a search query”; [0037] “provide more accurate and relevant search results … deliver more precise and personalized results”; [0084] “achieve higher accuracy”, [0091] “accuracy, precision, recall, and Fl score”) NOTE III);
generating an input to a Large Language Model (LLM), the input comprising the natural language query and the semantically relevant content from the semantic search result ([0122] “prepare a prompt with both the search query and some or all of the search results (e.g., the top k sections) from the electronic document and send it to the generative AI model”, [0147]); and
receiving generative text generated by the LLM to respond to the natural language query based on the semantically relevant content ([0147]-[0148], [0205], F17:1706-1712).
He does not explicitly teach, however Sommer discloses defining a search scope for semantic searching by:
performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus (C12L5-22, C13L18-20, 35-40, C14L21-30, C15L10-19, C16L27-50, C23L10-40).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of He to include a search scope defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus as disclosed by Sommer. Doing so provides an efficient and significant search for achieving overall optimized results (Sommer C19L31-35).
NOTE I He teaches receiving subsequent queries on the received search results. Given that the initial query is a natural language query, it is reasonable to conclude that the subsequent queries are natural language queries as well.
However, to merely obviate such reasoning, Mukherjee discloses receiving a natural language query from the user ([0146], [0150], [0154]); and servicing the natural language query to generate a response to the user ([0146], [0151]-[0152]).
Mukherjee further discloses generating an input to a Large Language Model (LLM), the input comprising the natural language query and the semantically relevant content from the semantic search result ([0046], [0113], [0138], [0146], [0154]-[0155]); and receiving generative text generated by the LLM to respond to the natural language query based on the semantically relevant content (F7-11).
NOTE III Mukherjee further discloses performing a resource-intensive semantic search within boundaries defined by the limited set of first documents ([0050] “respond to the user query may increase but the output of the LLM may be more accurate”) identified by the non- semantic search result to achieve increased accuracy and precision ([0041]-[0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of He to include receiving a natural language query from the user as disclosed by Mukherjee. Doing so would provide significant cognitive and ergonomic efficiencies and advantages over previous systems enabled by innovations in efficient interactions between the user interfaces and underlying systems and components (Mukherjee [0012]-[0013]).
NOTE II He teaches “lexical search results 146” can be returned in response to a lexical search – where “semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146” in response to a natural language from the user.
See example in [0131] – a user is searching for “climate change.” A lexical, exact search is performed, such as by user of inverted index and Boolean queries. The set of lexical search results is returned – “listing the article or articles where that term appears. The entry might look something like this: "climate change": article!, article2, article5”. Such results, can reasonably and obviously correspond to “a set of electronic documents 706 to create a set of contextualized embeddings … for document content contained within each electronic document 706” [0123], “encode a set of electronic documents 706 to create a set of contextualized embeddings” [0129]; “receive a search query 144, … to retrieve search results 146 with semantically similar document content within an electronic document 706” [0132].
Given that the user can – “perform lexical searching, semantic searching, or a combination of both” [0036], [0070], it is reasonable to conclude that when the user retrieves a first set of documents by lexical search, as shown in [0131] and then search withing such documents by a semantic search and natural query - “receive a search query to search for information within an electronic document … query may comprise any free form text in a natural language” ([0133]), “client may interact with the GUI view to perform subsequent operations, such as requesting more information about the candidate document vectors in the search results 146, presenting portions of the electronic document 706 containing the candidate document vectors, a subsequent search query 144” [0185]; “perform a semantic search for document content contained within au electronic document 706, and generate a set of search results 146 relevant to the search query” [0200].
He teaches performing “lexical searching, semantic searching, or a combination of both” a single set of search results 146, which can be returned by either lexical or semantic searching – “semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146”; “use the lexical search … to perform lexical searching in response to a search query 144 … may use the semantic search … to perform semantic searching in response to a search query 144.”
Thus, there is only a single set or search results – 146 and a single query 144. However, a query 144, which can include subsequent queries 144 (see [0185] “a subsequent search query 144”), can be either one - semantic or lexical (defined by the same number 144), which produces either semantic or lexical search results (defined by the same number 146). It is reasonable and obvious to conclude that the subsequent second query 144 (semantic or lexical) which “iterate over the first set of lexical search results 146” can be either one – semantic or lexical, given that the searching can be “a combination of both.” I.e. the search query 144 (which can be semantic or lexical) iterating, searching over search results 146 (which can be semantic or lexical) obviously and reasonably produces all possible combinations of searchings’ – lexical search over the semantic results and a semantic search over the lexical results.
Therefore, it is reasonable and obvious to conclude that given that the semantic query and the lexical query are defined by the same number 144 and a subsequent search query is defined by the same number 144 (which means the subsequent search query 144 can also be semantic or lexical) and the semantic results and the lexical results are defined by the same number 146, produces all possible combinations of searching and is limiting such searching to only search result 146 (which can be semantic or lexical) and satisfies the limitation – “lexical search is constrained to operate only within the first documents identified in the first search result.”
Claim 10 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 2 and 11, He as modified teaches the method and the medium, wherein the non-semantic search is a lexical search (He [0029], [0070], [0117], [0119], Mukherjee [0042], [0088] “search based on purely literal matching”, [0111]).
Regarding claims 3 and 12, He as modified teaches the method and the medium, further comprising:
providing the first search result to the user in a graphical user interface; and receiving, via user interaction with the graphical user interface, an indication to scope the semantic search to the first documents (He [0146], [0148], [0228], F15-16, Mukherjee F8-11).
Regarding claims 4 and 13, He as modified teaches the method and the medium, further comprising automatically scoping the semantic search to the first documents (He [0094], [0119], Mukherjee [0010], [0053]).
Regarding claims 5 and 14, He as modified teaches the method and the medium, wherein the natural language query is a query to an artificial intelligence search assistant (He F15: 1510, 1512, Mukherjee F8, [0058], [0060]).
Regarding claims 6 and 15, He as modified teaches the method and the medium, wherein servicing the natural language query comprises: providing the generative text to the user in response to the natural language query (He F17:1706-1712, Mukherjee F7-11).
Regarding claims 7 and 16, He as modified teaches the method and the medium, wherein the input to the large language model includes the natural language query as a prompt and the semantically relevant content as a context for responding to the prompt (He [0073], [0122], [0147]-[0148], [0205], Mukherjee [0044], [0047], [0056], [0064], [0081]).
Regarding claims 8 and 17, He as modified teaches the method and the medium, wherein the semantically relevant content comprises semantically relevant text chunks from the first documents (He [0029], [0031], [0033], Mukherjee [0046], [0113], [0138], [0146], [0154]-[0155]).
Regarding claims 9 and 18, He as modified teaches the method and the medium, wherein the semantically relevant content comprises semantically relevant documents from the first documents (He [0029], [0031], [0033], Mukherjee [0046], [0113], [0138], [0146], [0154]-[0155]).
Regarding claim 19, He teaches a computer system providing enhanced search, the computer system comprising:
storage storing: a plurality of snippets, each of the plurality of snippets comprising snippet text extracted from a document in a document corpus and a reference to the document from which the snippet text of that snippet was extracted ([0129]);
an embedding store comprising a vector index of the plurality of snippets ([0041] see “searching a document index of contextualized embeddings for the electronic document with the search vector, where each contextualized embedding comprises a vector representation of a sequence of words in the electronic document that includes contextual information”, [0071], [0116] “document vectors may be indexed and stored as a document index to facilitate search and retrieval operations”, [0130]);
a processor (F20); a non-semantic search engine that is executable to search the document corpus ([0131] “user performs a search for "climate change", the search engine can use the inverted index to quickly retrieve a list of all the articles that contain that term”, [0143] “lexical search generator to perform a lexical full-text search to produce and rank a first set of search results”);
a semantic search engine that is executable to perform semantic searching of the document corpus using the vector index ([0136], [0143] “then use the semantic search … to perform a semantic search that does a semantic re-ranking, which uses the context or semantic meaning of a search query … extracts and returns captions and answers in the response”, [0146]); and
memory storing instructions to:
define a search scope for semantic searching by executing the non-semantic search to generate a first search result that identifies a limited set of first documents from the document corpus ([0143] “substructure returned from a semantic query”, [0183] “search results 146 may comprise a subset of candidate document vectors from the set of candidate document vectors 718”, [0215] “generating a semantic similarity score for each candidate document … and selecting the subset of candidate document vectors”, [0221]);
service a natural language query received from a user to generate a response to the user ([0177], [0183]), the servicing comprising:
executing a semantic search scoped to the limited set of first documents to generate a semantic search result that identifies semantically relevant content that is semantically relevant to the natural language query ([0133], [0136], [0143] “then use the semantic search … to perform a semantic search that does a semantic re-ranking, which uses the context or semantic meaning of a search query … extracts and returns captions and answers in the response”, [0146]),
([0121] “search operations as desired for a given set of design constraints, such as search speed, size of data sets, number of electronic documents, compute resources, memory resources, network resources, device resource”, [0135], [0212] “search index … will depend on … desired query performance, and the available system resources”) semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result ([0119] “search manager may use the lexical search generator to generate a first set of lexical search results 146, and the semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146”; [0135] “search query 144 may be modified or expanded … allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146”; [0143] “query execution pipeline in two ways. First, it adds secondary ranking over an initial result set … the search manager may use the lexical search generator to perform a lexical full-text search to produce and rank a first set of search results 146”; “then use the semantic search generator to perform a semantic search that does a semantic re-ranking, which uses the context or semantic meaning of a search query 144 to compute a new relevance score over the first set of search results 146”; [0144] “Using those results as the document corpus, semantic ranking re-scores those results based on the semantic strength of the match”)(NOTE II)
to achieve increased accuracy and precision ([0033] “An accurate summary of the sections/parts of the document relevant to a search query”; [0037] “provide more accurate and relevant search results … deliver more precise and personalized results”; [0084] “achieve higher accuracy”, [0091] “accuracy, precision, recall, and Fl score”) NOTE III);
generating an input to a Large Language Model (LLM), the input comprising the natural language query and the semantically relevant content from the semantic search result ([0122] “prepare a prompt with both the search query and some or all of the search results (e.g., the top k sections) from the electronic document and send it to the generative AI model”, [0147]); and
receiving generative text generated by the LLM to respond to the natural language query based on the semantically relevant content ([0147]-[0148], [0205], F17:1706-1712).
He does not explicitly teach, however Sommer discloses defining a search scope for semantic searching by:
performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus (C12L5-22, C13L18-20, C16L27-50, C23L10-40).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of He to include a search scope defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus as disclosed by Sommer. Doing so provides an efficient and significant search for achieving overall optimized results (Sommer C19L31-35).
He does not explicitly teach, however Sommer discloses define a search scope for semantic searching by executing the non-semantic search to generate a first search result that identifies a limited set of first documents from the document corpus and performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus (C12L5-22, C13L18-20, 35-40, C14L21-30, C15L10-19, C16L27-50, C23L10-40).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of He to include a search scope defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus as disclosed by Sommer. Doing so provides an efficient and significant search for achieving overall optimized results (Sommer C19L31-35).
NOTE I He teaches receiving subsequent queries on the received search results. Given that the initial query is a natural language query, it is reasonable to conclude that the subsequent queries are natural language queries as well.
However, to merely obviate such reasoning, Mukherjee discloses receiving a natural language query from the user ([0146], [0150], [0154]); and servicing the natural language query to generate a response to the user ([0146], [0151]-[0152]).
Mukherjee further discloses generating an input to a Large Language Model (LLM), the input comprising the natural language query and the semantically relevant content from the semantic search result ([0046], [0113], [0138], [0146], [0154]-[0155]); and receiving generative text generated by the LLM to respond to the natural language query based on the semantically relevant content (F7-11).
NOTE III Mukherjee further discloses performing a resource-intensive semantic search within boundaries defined by the limited set of first documents ([0050] “respond to the user query may increase but the output of the LLM may be more accurate”) identified by the non- semantic search result to achieve increased accuracy and precision ([0041]-[0042]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of He to include receiving a natural language query from the user as disclosed by Mukherjee. Doing so would provide significant cognitive and ergonomic efficiencies and advantages over previous systems enabled by innovations in efficient interactions between the user interfaces and underlying systems and components (Mukherjee [0012]-[0013]).
NOTE II He teaches “lexical search results 146” can be returned in response to a lexical search – where “semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146” in response to a natural language from the user.
See example in [0131] – a user is searching for “climate change.” A lexical, exact search is performed, such as by user of inverted index and Boolean queries. The set of lexical search results is returned – “listing the article or articles where that term appears. The entry might look something like this: "climate change": article!, article2, article5”. Such results, can reasonably and obviously correspond to “a set of electronic documents 706 to create a set of contextualized embeddings … for document content contained within each electronic document 706” [0123], “encode a set of electronic documents 706 to create a set of contextualized embeddings” [0129]; “receive a search query 144, … to retrieve search results 146 with semantically similar document content within an electronic document 706” [0132].
Given that the user can – “perform lexical searching, semantic searching, or a combination of both” [0036], [0070], it is reasonable to conclude that when the user retrieves a first set of documents by lexical search, as shown in [0131] and then search withing such documents by a semantic search and natural query - “receive a search query to search for information within an electronic document … query may comprise any free form text in a natural language” ([0133]), “client may interact with the GUI view to perform subsequent operations, such as requesting more information about the candidate document vectors in the search results 146, presenting portions of the electronic document 706 containing the candidate document vectors, a subsequent search query 144” [0185]; “perform a semantic search for document content contained within au electronic document 706, and generate a set of search results 146 relevant to the search query” [0200].
He teaches performing “lexical searching, semantic searching, or a combination of both” a single set of search results 146, which can be returned by either lexical or semantic searching – “semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146”; “use the lexical search … to perform lexical searching in response to a search query 144 … may use the semantic search … to perform semantic searching in response to a search query 144.”
Thus, there is only a single set or search results – 146 and a single query 144. However, a query 144, which can include subsequent queries 144 (see [0185] “a subsequent search query 144”), can be either one - semantic or lexical (defined by the same number 144), which produces either semantic or lexical search results (defined by the same number 146). It is reasonable and obvious to conclude that the subsequent second query 144 (semantic or lexical) which “iterate over the first set of lexical search results 146” can be either one – semantic or lexical, given that the searching can be “a combination of both.” I.e. the search query 144 (which can be semantic or lexical) iterating, searching over search results 146 (which can be semantic or lexical) obviously and reasonably produces all possible combinations of searchings’ – lexical search over the semantic results and a semantic search over the lexical results.
Therefore, it is reasonable and obvious to conclude that given that the semantic query and the lexical query are defined by the same number 144 and a subsequent search query is defined by the same number 144 (which means the subsequent search query 144 can also be semantic or lexical) and the semantic results and the lexical results are defined by the same number 146, produces all possible combinations of searching and is limiting such searching to only search result 146 (which can be semantic or lexical) and satisfies the limitation – “lexical search is constrained to operate only within the first documents identified in the first search result.”
Regarding claim 20, He as modified teaches the computer system of Claim 19, wherein the non-semantic search engine is a lexical search engine (He [0029], [0070], [0117], [0119).
Regarding claim 21, He as modified teaches the computer system of Claim 19, wherein the memory further stores instructions executable to:
receive a non-semantic search result from the non-semantic search engine the non-semantic search results comprising document identifiers for first documents from the document corpus (Mukherjee [0038], [0052] “data object can represent a document … Each data object may be associated with a unique identifier that uniquely identifies the data object within the database”, [0094]);
store the document identifiers from the non-semantic search results as query parameters for a subsequent search (Mukherjee [0053] “session data object may include data objects or may be linked to data object(s) that represent documents … ( e.g., when one or more user queries of the user are received and/or responded)…that are associated with the user session”; “queries of the user … can be stored by the system using the session data object”, He [0182], [0185]);
subsequent to receiving the non-semantic search result, receive a natural language query that includes a query string from a user (Mukherjee [0146], [0150], [0154], HE [0145], [0172]); and
generate a request to the semantic search engine that includes the query string and the document identifiers that were stored as query parameters (Mukherjee [0095]-[0097], HE [0143], F17), wherein the semantic search engine is executable to:
receive the query string and the document identifiers (Mukherjee [0098]-[0099]); and
execute a corresponding semantic search scoped to the first documents to generate a semantic search result that identifies semantically relevant content that is semantically relevant to the query string (Mukherjee [0099], [0101], He F17, [0144], [0179]).
Regarding claim 22, He as modified teaches the computer system of Claim 21, wherein the memory further stores instructions executable to:
display the generative text to the user (He F17:1706-1712, Mukherjee F7-11).
Claims 21-22 is/are alternatively and additionally rejected under 35 U.S.C. 103 as being unpatentable over He as modified and in further view of Larimore et al. (US 20080114730).
Regarding claim 21, He as modified teaches the computer system of Claim 19, wherein the memory further stores instructions executable to:
receive a non-semantic search result from the non-semantic search engine the non-semantic search results comprising document identifiers for first documents from the document corpus (Mukherjee [0038], [0052] “data object can represent a document … Each data object may be associated with a unique identifier that uniquely identifies the data object within the database”, [0094]);
store the document identifiers from the non-semantic search results as query parameters for a subsequent search (Mukherjee [0053] “session data object may include data objects or may be linked to data object(s) that represent documents … ( e.g., when one or more user queries of the user are received and/or responded)…that are associated with the user session”; “queries of the user … can be stored by the system using the session data object”);
subsequent to receiving the non-semantic search result, receive a natural language query that includes a query string from a user (Mukherjee [0146], [0150], [0154]); and generate a request to the semantic search engine that includes the query string and the document identifiers that were stored as query parameters ([0095]-[0097]), wherein the semantic search engine is executable to: receive the query string and the document identifiers ([0098]-[0099]); and execute a corresponding semantic search scoped to the first documents to generate a semantic search result that identifies semantically relevant content that is semantically relevant to the query string ([0099], [0101]).
Both Mukherjee and HE disclose searching within the results received by the first query by performing a subsequent search on the results. Such searching is performed by matching vectorized segments of document to a query parameters. Wherein, indexing such vectors matching a next query to a document ID. Thus, given that the subsequent search is performed within the returned results, it is reasonable and obvious to conclude that the document ID is included as a search parameter in the query.
However, to further obviate such reasoning, Larimore discloses
store the document identifiers from the non-semantic search results as query parameters for a subsequent search; and generate a request to the semantic search engine that includes the query string and the document identifiers that were stored as query parameters ([0060] FIGS. 15-17) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of He to include document identifiers as query parameters as disclosed by Larimore. Doing so would reduce the network communications traffic involved in executing the search to identify relevant documents and other data (Larimore [0003]).
Regarding claim 22, He as modified teaches the computer system of Claim 21, wherein the memory further stores instructions executable to: display the generative text to the user (He F17:1706-1712, Mukherjee F7-11).
◊ Claims 1, 10 and 19 is/are alternatively rejected under 35 U.S.C. 103 as being unpatentable over He et al. (US 20240362286) in view of Mukherjee et al. (US 20240354436) and in further view of PATHAK et al. (US 20250139136).
Alternatively, with respect to the limitation “wherein executing the semantic search comprises performing the semantic search only on the first documents identified by the non-semantic search result”.
IF He does not explicitly teach, PATHAK discloses defining a search scope for semantic searching by performing a resource-intensive semantic search within boundaries defined by the limited set of first documents identified by the non- semantic search result to achieve increased accuracy and precision relative to performing the semantic search on the entire document corpus [0087].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of He to include semantic search only on the first documents identified by the non-semantic search result as disclosed by PATHAK. Doing so would enable efficiently and accurately process component queries because its focus of attention is restricted to a consideration of only some and not entire data (PATHAK [0088]).
Response to Arguments
Applicant's arguments, filed 11/06/2025, with respect to the previous rejections under 35 U.S.C. 101 have been fully considered and are persuasive in light of the amendments to independent claims.
Applicant's arguments with respect to the previous rejections under 35 U.S.C. 103, have been fully considered but they are not persuasive. With respect to the amended claims and the reference of He, the applicant argues –
“He teaches receiving subsequent queries to perform follow-up operations, which is generally analogous to refining a search by adding filters, not dynamically creating a necessary sub- corpus to solve performance and accuracy issues related to embedding vectors in semantic search”.
The arguments are not persuasive. Defining “a search scope” is fully analogous to filtering, which is evidence by the applicant’s own specification (see [0060] “semantic search engine 106 applies a search scope filter 220 to filter the identified semantically relevant content”, [0068] “apply a filter to limit the results”). He clearly discloses –
“lexical search generator to generate a first set of lexical search results 146, and the semantic search generator to iterate over the first set of lexical search results 146 to generate a second set of semantic search results 146” [0119].
The lexical search results 146 is the limited set of the documents from an entire corpus of documents – F5:508, F7:508. Iterating over the first set of lexical search results 146 to generate a second set of semantic search results scopes the semantic search to the limited number or the search results 146, as required by the claim.
Applicant's remaining arguments, in regard to the presently amended claims, are addressed in the updated rejections to the claims above. NOTE also see alternative rejection to the independent claims immediately above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/ Primary Examiner, Art Unit 2165 January 12, 2026