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 .
Response to Amendment
This communication is in response to the amendment filed on 2 June 2026.
Claims 1, 10 and 19 are amended.
Claims 1-20 have been examined.
Response to Arguments
In response to Applicant’s remarks filed on 2 June 2026:
a. The terminal disclaimer filed 2 June 2026 has been approved. Accordingly, the provisional nonstatutory double patenting rejections made in the previous Office action are withdrawn.
b. Applicant's arguments with respect to the 35 U.S.C. 101 rejections of the pending claims have been fully considered but are not deemed persuasive.
On pages 9-14 of Applicant’s remarks, Applicant argues against the 35 U.S.C. 101 rejections of the pending claims. Applicant argues that claims 1, 10, and 19 do not recite an abstract idea under Step 2A, Prong One and/or do recite a practical application under Step 2A, Prong Two.
The Office respectfully disagrees with the above remarks. With regards to the analysis at Step 2A, Prong One; Applicant reproduces the limitations of claim 1 with emphasis on the words “by the computing system” recited multiple times in the claim. Applicant is advised that “Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible,” MPEP 2106.05(f) citing Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. Accordingly, “Claims can recite a mental process even if they are claimed as being performed on a computer” (MPEP § 2106.04(a)(2)(III)(C)). As detailed below in the claim rejections under 35 U.S.C. 101, claim 1 recites an abstract idea in the following limitations: a) claimed generating of a set of vector embeddings (mathematical concept and/or mental process), b) claimed generating a set of knowledge graphs (mental process), c) and claimed augmenting the user query (mental process). Accordingly, claim 1 does recite an abstract idea in the aforementioned limitations. Also as detailed below, the “computing system” recited in claim 1 is recited at a high level of generality and amounts to a generic computer performing generic computing functions. Mere instructions to apply an abstract idea on a general purpose computer cannot be deemed a practical application nor significantly more than the abstract idea. See MPEP 2106.05(f). Also as detailed below, the other additional elements of claim 1 are insignificant extra solution activity in the form of mere data gathering/outputting; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to significantly more than the abstract idea. These claims are not patent eligible.
With regards to the analysis at Step 2A, Prong Two; Applicant points out that the claimed invention solves a problem that exists in a question answering (QA) system that leverages “complex algorithms and vast databases of information” (remarks, paragraph spanning page 12-13). However, claim 1 does not recite any “complex” algorithm nor any “vast” database of information. Applicant is advised of the following:
“Claims in a pending application must be ‘given their broadest reasonable interpretation consistent with the specification.’” MPEP § 2111 citing Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005)..
With regards to subject matter eligibility analysis, “It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility. The BRI sets the boundaries of the coverage sought by the claim and will influence whether the claim seeks to cover subject matter that is beyond the four statutory categories or encompasses subject matter that falls within the exceptions.” MPEP 2106(II).
In the instant case, the BRI of claim 1 encompasses a simple case, as detailed below. Applicant has ignored the BRI of claim 1, resulting in a flawed analysis at Step 2A, Prong Two. Since claim 1 does not recite any “complex” algorithm nor any “vast” database of information, the purported practical application is not reflected in the claim. Furthermore, the purported improvement, i.e. semantic vector augmentation and knowledge graph augmentation (see Applicant’s remarks, page 13, first paragraph, citing para. 0013 of the instant specification), is based entirely on abstract ideas. As set forth above and as further detailed below, the semantic vector augmentation and knowledge graph augmentation recited in claim 1 are abstract ideas under the “Mathematical Concepts” and/or “Mental Processes” groupings. “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements…In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception.” MPEP 2106.05(a), (emphasis added). As detailed below, the additional elements of claim 1 amount to insignificant extra-solution activity and/or generic computer implementation, neither of which can be deemed a practical application. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Claims 10 and 19 recite limitations similar to those of claim 1 and are ineligible under 35 U.S.C. 101 for the same reasons that claim 1 is ineligible, as set forth above.
Claims 2-9, 11-18, and 20 are ineligible under 35 U.S.C. 101 for the same reasons that claims 1, 10, and 19 are ineligible, as set forth above, and for the additional reasons detailed below in the claim rejections under 35 U.S.C. 101.
c. Applicant's arguments with respect to the 35 U.S.C. 103 rejections of the pending claims have been fully considered but are not deemed persuasive.
On pages 14-16 of Applicant’s remarks, Applicant argues that the cited prior art fails to teach or suggest the limitations of claim 1. In support of this argument, Applicant attacks Crabtree as modified by Larson for allegedly failing to teach or suggest the following limitation of claim 1: “augmenting, by the computing system, the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings and one or more knowledge graph triplets from the set of knowledge graphs” (remarks, pages 15-16).
The Office respectfully disagrees with the above remarks. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 426 (CCPA 1981). Rather, the test for obviousness is whether the combination of references, taken as a whole, would have suggested the patentee's invention to a person having ordinary skill in the art. In re Merck & Co., Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986). See MPEP 2145(IV). In the instant case, Applicant’s arguments are unpersuasive because Applicant has failed to consider the combined teaching of the applied references. Crabtree teaches the majority of the features of claim 1, including teaching the majority of the “augmenting” limitation, as follows: augmenting the user query to generate an augmented prompt (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements) based at least in part on one or more vector embeddings from the set of vector embeddings and the set of knowledge graphs (Crabtree para. 0079 and Fig. 21: vectors/embeddings database 2128 and knowledge graph database 2129; and Crabtree para. 0100: retrieval augmented generation (RAG) to augment customer's input based on knowledge graph). All that’s missing from Crabtree is an explicit statement that the knowledge graphs are comprised of triplets. Larson teaches knowledge graphs comprised of triplets utilized for retrieval augmented generation of a prompt for an LLM, as follows: augmenting the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings (Larson para. 0010, 0052, and 0062: retrieval augmented generation (RAG) generates augmented prompt for a large language model (LLM) based on vector embeddings and knowledge graph) and one or more knowledge graph triplets from the set of knowledge graphs (Larson para. 0017: knowledge graph comprised of triplets). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Crabtree to include the teachings of Larson because it allows combining LLM based graph construction and inference, providing enhanced reasoning capabilities (Larson para. 0017). Therefore, Crabtree as modified by Larson teaches the features of claim 1 as claimed.
Claims 10 and 19 recite limitations similar to those of claim 1 and are unpatentable over the prior art for the same reasons that claim 1 is unpatentable, as set forth above.
Claims 2-9, 11-18, and 20 are unpatentable over the prior art for the same reasons that claims 1, 10 and 19 are unpatentable, as set forth above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 10, and 19, these claims recite a plurality of documents. The claims do not specify nor place any limits upon the number of documents, other than using the plural form of the word (i.e. “documents”). Under the broadest reasonable interpretation (BRI), the claims encompass a simple case of just two documents. In addition, the claims do not specify nor place any limits upon the length of these documents. The BRI encompasses a simple case of small documents (e.g. containing just a few words or sentences). These claims recite generating a set of vector embeddings based at least in part on the plurality of documents and a semantic vector augmentation pipeline. “Vector embeddings are numerical representations of data points that express different types of data, including nonmathematical data such as words or images, as an array of numbers that machine learning (ML) models can process1.” Hence, the claimed generating of a set of vector embeddings is calculating a certain set of numerical values, and this limitation amounts to no more than mathematical calculation(s). Accordingly, this limitation is an abstract idea under the “Mathematical Concepts” grouping. Furthermore, it is well known to those of ordinary skill in the art that “the specific features represented by the dimensions of vector embeddings can be established through manual feature engineering2.” Given that the BRI of the claims encompasses a simple case, as set forth above, a human could, with the aid of pencil and paper, mentally generate a set of vector embeddings, as claimed. Hence, this limitation may alternatively be deemed an abstract idea under the “Mental Processes” grouping.
These claims also recite generating a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline, wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets. These claims do not specify nor place any limits upon the number of knowledge graphs or the number of knowledge graph triplets, other than using the plural forms of these words (i.e. “graphs” and “triplets”). Under the BRI, this limitation encompasses a simple case of just two knowledge graphs, each one being a simple knowledge graph comprising just a few triplets. Given that the BRI of the claims encompasses such a simple case, a human could, with the aid of pencil and paper, mentally generate graphs in the manner claimed. For example, a human could draw out on a piece of paper two simple knowledge graphs that each comprise a few triplets, as claimed. Hence, this limitation is an abstract idea under the “Mental Processes” grouping.
These claims also recite augmenting the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings and one or more knowledge graph triplets from the set of knowledge graphs. Given that the BRI of the claims encompasses such a simple case, a human could, with the aid of pencil and paper, mentally perform the claimed augmenting as claimed. By looking at the vector embeddings and knowledge graph triplets, a human could augment the user query by adding to it appropriate keywords or context understood from the vector embeddings and/or knowledge graph triplets. Hence, this limitation is an abstract idea under the “Mental Processes” grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Other than the abstract idea, the claims recite the following:
a) “obtaining, by a computing system for data processing, a plurality of documents for input into a query response system associated with a large language model (LLM), the query response system included in the computing system;”
b) “obtaining, at the query response system, a user query;”
c) “providing, by the computing system, as an input to the LLM, the augmented
prompt, wherein the LLM outputs a response to the augmented prompt;”
d) “outputting, by the computing system, an indication of the response to the augmented prompt as an answer to the user query;”
e) one or more processors coupled with one or more memories; and
f) a non-transitory computer-readable medium storing code.
Limitations (a) and (b) amount to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). Limitation (c) is recited as a high level of generality and amounts to mere instructions to apply the abstract on a general purpose computer, which cannot provide a practical application. See MPEP 2106.05(f). Limitation (d) amounts to no more than merely outputting a result, which has been deemed by the courts to be insignificant extra-solution activity. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). See MPEP 2106.05(g). Limitations (e) and (f) are recited at a high level of generality, i.e. as generic computer components performing generic computing functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Limitations (a) and (b) amount to no more than mere data gathering, which has been deemed by the courts to be insignificant extra-solution activity. See MPEP 2106.05(g). In addition, the courts have deemed receiving data to be well-understood, routine, and conventional activity, as in the following cases: Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) (storing and retrieving information in memory). See MPEP 2106.05(d)(II). Hence, elements (a) and (b) cannot be deemed an inventive concept. Limitation (c) is recited as a high level of generality and amounts to mere instructions to apply the abstract on a general purpose computer, which cannot be deemed an inventive concept. See MPEP 2106.05(f). Limitation (d) amounts to no more than merely outputting a result, which has been deemed by the courts to be insignificant extra-solution activity. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). See MPEP 2106.05(g). Furthermore, Applicant’s specification provides few details about the claimed outputting an indication of the response or its functions (see para. 0077 of Applicant’s published specification). This indicates that this feature is well known in the art. Cf Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1384 (Fed. Cir. 1986) (explaining that "a patent need not teach, and preferably omits, what is well known in the art"). As a result, the written description adequately supports that additional element (d) is conventional and performs well-understood, routine, and conventional activities. See MPEP § 2106.07(a)(III)(A)3. As discussed above with respect to integration of the abstract idea into a practical application, additional elements (e) and (f) amount to no more than mere field of use limitations and instructions to apply the exception using generic computer components. Mere instructions to apply an exception using conventional computer components and functions cannot provide an inventive concept. Looking at the additional elements as a whole adds nothing beyond the additional elements considered individually—they still represent insignificant extra-solution activity; well-understood, routine, and conventional subject matter; and/or generic computer implementation. Hence, the claim as a whole, looking at the additional elements individually and in combination, does not amount to significantly more than the abstract idea. These claims are not patent eligible.
As to dependent claims 2, 11, and 20, these claims recite generating a set of graph embeddings and augmenting the user query. The former is an abstract idea under the “Mathematical Concepts” and/or “Mental Processes” groupings and the latter is an abstract idea under the “Mental Processes” grouping, for the same reasons set forth above with regards to the parent claims.
As to dependent claims 3 and 12, these claims recite generating the set of knowledge graphs by making determinations of named entities and relationships and generating a knowledge graph triplet accordingly. Given that the BRI of the claims encompasses a simple case, as set forth above in the parent claims, nothing in these claims goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, these claims are directed to an abstract idea under the “Mental Processes” grouping.
As to dependent claims 4 and 13, these claims recite performing coreference resolution to replace, in a document of the plurality of documents, a reference to a named entity with the named entity. Given that the BRI of the claims encompasses a simple case, as set forth above in the parent claims, nothing in these claims goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, these claims are directed to an abstract idea under the “Mental Processes” grouping.
As to dependent claims 5-6 and 14-15, these claims recite certain types of documents upon which to apply the invention. This amounts to generally linking the abstract idea to a particular field of use or technological environment, which cannot provide a practical application nor an inventive concept. See MPEP 2106.05(h).
As to dependent claims 7 and 16, these claims recite “obtaining a set of structured data; and extracting a set of entities from the set of structured data; and performing entity resolution for one or more entities in the knowledge graph augmentation pipeline based at least in part on the set of entities extracted from the set of structured data.” Given that the BRI of the claims encompasses a simple case, a human could, with the aid of pencil and paper, mentally perform the claimed obtaining, extracting, and performing entity resolution, as claimed. Hence, these limitations are abstract ideas under the “Mental Processes” grouping.
As to dependent claims 8-9 and 17-18, these claims recites the use of timestamps associated with node or edges of the knowledge graph to determine whether or not to augment the user query with the corresponding graph triplets. Given that the BRI of the claims encompasses a simple case, as set forth above in the parent claims, nothing in these claims goes beyond what a human could mentally perform with the aid of pencil and paper. Hence, these claims are directed to an abstract idea under the “Mental Processes” grouping.
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 of this title, 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 1-8, 10-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (U.S. Patent Application Publication No. 20250258852 A1, hereinafter referred to as Crabtree) in view of Larson et al. (U.S. Patent Application Publication No. 20250131289 A1, hereinafter referred to as Larson).
As to claim 1, Crabtree teaches a method for data processing, comprising:
obtaining, by a computing system for data processing, a plurality of documents for input (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) into a query response system; (Crabtree para. 0116 and 0174: the system is a query response system) associated with a large language model (LLM), the query response system included in the computing system (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response);
generating, by the computing system, a set of vector embeddings (Crabtree para. 0083: generating vector embeddings) based at least in part on the plurality of documents (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) and a semantic vector augmentation pipeline (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0125: vectors capture semantic information);
generating a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Crabtree para. 0085: the system populates knowledge graph database 2129; and Crabtree para. 0065 and 0080: knowledge graph is enhanced/augmented);
obtaining, at the query response system, a user query (Crabtree para. 0093 and 0156: the system receives a user’s query);
augmenting, by the computing system, the user query to generate an augmented prompt (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements) based at least in part on one or more vector embeddings from the set of vector embeddings and the set of knowledge graphs (Crabtree para. 0079 and Fig. 21: vectors/embeddings database 2128 and knowledge graph database 2129; and Crabtree para. 0100: retrieval augmented generation (RAG) to augment customer's input based on knowledge graph);
providing, by the computing system, as an input to the LLM, the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response); and
outputting, by the computing system, an indication of the response to the augmented prompt as an answer to the user query (Crabtree para. 0156: response of the LLM is sent to the user).
Crabtree does not appear to explicitly disclose wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets.
However, Larson teaches:
obtaining a plurality of documents for input into a query response system (Larson para. 0012: document retrieval);
generating a set of vector embeddings based at least in part on the plurality of documents and a semantic vector augmentation pipeline (Larson para. 0012 and 0014-0015: retrieval augmented generation (RAG) generates vector embeddings of documents);
generating a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Larson para. 0017: knowledge graph construction), wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets (Larson para. 0017: knowledge graph comprised of triplets);
obtaining, at the query response system, a user query (Larson para. 0012 and 0017: user query/question);
augmenting the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings (Larson para. 0010, 0052, and 0062: retrieval augmented generation (RAG) generates augmented prompt for a large language model (LLM) based on vector embeddings and knowledge graph) and one or more knowledge graph triplets from the set of knowledge graphs (Larson para. 0017: knowledge graph comprised of triplets);
providing, as an input to a large language model (LLM), the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Larson para. 0052: prompt is designed for an LLM to generate a response to the user’s ask (i.e. user’s question/query)); and
outputting an indication of the response to the augmented prompt as an answer to the user query (Larson para. 0012 and Fig. 1: response 110).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Crabtree to include the teachings of Larson because it allows combining LLM based graph construction and inference, providing enhanced reasoning capabilities (Larson para. 0017).
As to claim 2, Crabtree as modified by Larson teaches further comprising:
generating a set of graph embeddings based at least in part on the set of knowledge graphs, a set of structured data, or both (Crabtree para. 0083: generating vector embeddings based on graph data and Structured Query Language (SQL) data; and see Larson para. 0012 and 0014-0015: retrieval augmented generation (RAG) generates vector embeddings of documents); and
augmenting the user query based at least in part on one or more graph embeddings from the set of graph embeddings (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements; and see Larson para. 0010, 0052, and 0062: retrieval augmented generation (RAG) generates augmented prompt for a large language model (LLM) based on vector embeddings and knowledge graph).
As to claim 3, Crabtree as modified by Larson teaches wherein generating the set of knowledge graphs comprises:
determining a plurality of named entities from the plurality of documents (Crabtree para. 0085: named entity recognition performed on documents);
determining a relationship between a first named entity and a second named entity of the plurality of named entities based at least in part on the plurality of documents (Crabtree para. 0085: extraction of relations between named entities based on documents); and
generating a knowledge graph triplet that indicates the first named entity, the relationship, and the second named entity (Crabtree para. 0085: knowledge graph is populated based named entity recognition and relation extraction, and the knowledge graph is stored in a triple store; and see Larson para. 0017: knowledge graph triplets).
As to claim 4, Crabtree as modified by Larson teaches wherein determining the plurality of named entities comprises:
performing coreference resolution to replace, in a document of the plurality of documents, a reference to a named entity with the named entity (Crabtree para. 0085: co-reference resolution of named entities).
As to claim 5, this claim merely describes particular types of documents upon which to apply the invention, without any limiting of the claimed technique. Hence, this claim is merely an intended use of the claimed invention that has no patentable weight. However, assuming arguendo that the claim has patentable weight, prior art is cited.
Crabtree as modified by Larson teaches wherein the plurality of documents comprises one or more websites (Crabtree para. 0252: data sources include websites), one or more Really Simple Syndication (RSS) feed objects, one or more communication platform feeds (Crabtree para. 0252: data sources include social media), or a combination thereof.
As to claim 6, this claim merely describes particular types of documents upon which to apply the invention, without any limiting of the claimed technique. Hence, this claim is merely an intended use of the claimed invention that has no patentable weight. However, assuming arguendo that the claim has patentable weight, prior art is cited.
Crabtree as modified by Larson teaches wherein the plurality of documents comprises public unstructured data (Crabtree para. 0157: enterprise documents such as regulatory documents; and see Crabtree para. 0252: public unstructured data such as websites), private unstructured data (Crabtree para. 0157: enterprise documents such as those provided by customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, rules and policies databases, and transactional databases), or both.
As to claim 7, Crabtree as modified by Larson teaches further comprising:
obtaining a set of structured data (Crabtree para. 0252: input data is received from structured data sources);
extracting a set of entities from the set of structured data (Crabtree para. 0252: extracting entities); and
performing entity resolution for one or more entities in the knowledge graph augmentation pipeline based at least in part on the set of entities extracted from the set of structured data (Crabtree para. 0085 and 0252: named entity recognition and co-reference resolution are performed).
As to claim 8, Crabtree as modified by Larson teaches wherein the set of knowledge graphs comprises one or more nodes, one or more edges, or both that are associated with respective timestamps, and wherein augmenting the user query is further based at least in part on the respective timestamps (Crabtree para. 0065: knowledge graph has temporal representations based on age or Gregorian calendar; and see Crabtree para. 0089: knowledge graph is queried based on contextual factors including time; and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements).
As to claim 10, Crabtree teaches an apparatus for data processing, comprising:
one or more memories storing processor-executable code (Crabtree para. 0256 and Fig. 32: computing device 10 having one or more processors 20 coupled to system memory 30); and
one or more processors coupled with the one or more memories (Crabtree para. 0256 and Fig. 32: computing device 10 having one or more processors 20 coupled to system memory 30) and
individually or collectively operable to execute the code to cause the apparatus to:
obtaining, by a computing system for data processing, a plurality of documents for input (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) into a query response system (Crabtree para. 0116 and 0174: the system is a query response system) associated with a large language model (LLM), the query response system included in the computing system (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response);
generating, by the computing system, a set of vector embeddings (Crabtree para. 0083: generating vector embeddings) based at least in part on the plurality of documents (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) and a semantic vector augmentation pipeline (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0125: vectors capture semantic information);
generating, by the computing system, a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Crabtree para. 0085: the system populates knowledge graph database 2129; and Crabtree para. 0065 and 0080: knowledge graph is enhanced/augmented);
obtaining, at the query response system, a user query (Crabtree para. 0093 and 0156: the system receives a user’s query);
augmenting, by the computing system, the user query to generate an augmented prompt (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements) based at least in part on one or more vector embeddings from the set of vector embeddings and the set of knowledge graphs (Crabtree para. 0079 and Fig. 21: vectors/embeddings database 2128 and knowledge graph database 2129; and Crabtree para. 0100: retrieval augmented generation (RAG) to augment customer's input based on knowledge graph);
providing, by the computing system as an input to the LLM, the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response); and
outputting, by the computing system, an indication of the response to the augmented prompt as an answer to the user query (Crabtree para. 0156: response of the LLM is sent to the user).
Crabtree does not appear to explicitly disclose wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets.
However, Larson teaches:
obtaining a plurality of documents for input into a query response system (Larson para. 0012: document retrieval);
generating a set of vector embeddings based at least in part on the plurality of documents and a semantic vector augmentation pipeline (Larson para. 0012 and 0014-0015: retrieval augmented generation (RAG) generates vector embeddings of documents);
generating a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Larson para. 0017: knowledge graph construction), wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets (Larson para. 0017: knowledge graph comprised of triplets);
obtaining, at the query response system, a user query (Larson para. 0012 and 0017: user query/question);
augmenting the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings (Larson para. 0010, 0052, and 0062: retrieval augmented generation (RAG) generates augmented prompt for a large language model (LLM) based on vector embeddings and knowledge graph) and one or more knowledge graph triplets from the set of knowledge graphs (Larson para. 0017: knowledge graph comprised of triplets);
providing, as an input to a large language model (LLM), the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Larson para. 0052: prompt is designed for an LLM to generate a response to the user’s ask (i.e. user’s question/query)); and
outputting an indication of the response to the augmented prompt as an answer to the user query (Larson para. 0012 and Fig. 1: response 110).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Crabtree to include the teachings of Larson because it allows combining LLM based graph construction and inference, providing enhanced reasoning capabilities (Larson para. 0017).
As to claim 11, see the rejection of claim 2 above.
As to claim 12, see the rejection of claim 3 above.
As to claim 13, see the rejection of claim 4 above.
As to claim 14, see the rejection of claim 5 above.
As to claim 15, see the rejection of claim 6 above.
As to claim 16, see the rejection of claim 7 above.
As to claim 17, see the rejection of claim 8 above.
As to claim 19, Crabtree teaches a non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to (Crabtree para. 0256, 0258, and Fig. 32: computing device 10 having storage devices):
obtaining, by a computing system for data processing, a plurality of documents for input (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) into a query response system; (Crabtree para. 0116 and 0174: the system is a query response system) associated with a large language model (LLM), the query response system included in the computing system (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response);
generating, by the computing system, a set of vector embeddings (Crabtree para. 0083: generating vector embeddings) based at least in part on the plurality of documents (Crabtree 0157 and Fig. 1: Enterprise knowledge 111 comprises documents) and a semantic vector augmentation pipeline (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0125: vectors capture semantic information);
generating, by the computing system, a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Crabtree para. 0085: the system populates knowledge graph database 2129; and Crabtree para. 0065 and 0080: knowledge graph is enhanced/augmented);
obtaining, at the query response system, a user query (Crabtree para. 0093 and 0156: the system receives a user’s query);
augmenting, by the computing system, the user query to generate an augmented prompt (Crabtree para. 0100: Retrieval augmented generation (RAG); and Crabtree para. 0156: prompt is augmented to include additional contextual data/elements) based at least in part on one or more vector embeddings from the set of vector embeddings and the set of knowledge graphs (Crabtree para. 0079 and Fig. 21: vectors/embeddings database 2128 and knowledge graph database 2129; and Crabtree para. 0100: retrieval augmented generation (RAG) to augment customer's input based on knowledge graph);
providing, by the computing system as an input to the LLM, the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Crabtree para. 0156: augmented prompt is provided to a large language model (LLM) to produce a response); and
outputting an indication of the response to the augmented prompt as an answer to the user query (Crabtree para. 0156: response of the LLM is sent to the user).
Crabtree does not appear to explicitly disclose wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets.
However, Larson teaches:
obtaining a plurality of documents for input into a query response system (Larson para. 0012: document retrieval);
generating a set of vector embeddings based at least in part on the plurality of documents and a semantic vector augmentation pipeline (Larson para. 0012 and 0014-0015: retrieval augmented generation (RAG) generates vector embeddings of documents);
generating a set of knowledge graphs based at least in part on the plurality of documents and a knowledge graph augmentation pipeline (Larson para. 0017: knowledge graph construction), wherein a knowledge graph of the set of knowledge graphs comprises a respective plurality of knowledge graph triplets (Larson para. 0017: knowledge graph comprised of triplets);
obtaining, at the query response system, a user query (Larson para. 0012 and 0017: user query/question);
augmenting the user query to generate an augmented prompt based at least in part on one or more vector embeddings from the set of vector embeddings (Larson para. 0010, 0052, and 0062: retrieval augmented generation (RAG) generates augmented prompt for a large language model (LLM) based on vector embeddings and knowledge graph) and one or more knowledge graph triplets from the set of knowledge graphs (Larson para. 0017: knowledge graph comprised of triplets);
providing, as an input to a large language model (LLM), the augmented prompt, wherein the LLM outputs a response to the augmented prompt (Larson para. 0052: prompt is designed for an LLM to generate a response to the user’s ask (i.e. user’s question/query)); and
outputting an indication of the response to the augmented prompt as an answer to the user query (Larson para. 0012 and Fig. 1: response 110).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Crabtree to include the teachings of Larson because it allows combining LLM based graph construction and inference, providing enhanced reasoning capabilities (Larson para. 0017).
As to claim 20, see the rejection of claim 2 above.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree and Larson as applied to claims 8 and 17 above, and further in view of Britton et al. (U.S. Patent Application Publication No. 20020174126A1, hereinafter referred to as Britton).
As to claim 9, Crabtree as modified by Larson does not appear to explicitly disclose further comprising: determining that a timestamp indicates that a corresponding knowledge graph triplet is expired; and refraining from augmenting the user query with the corresponding knowledge graph triplet based at least in part on the timestamp.
However, Britton teaches further comprising:
determining that a timestamp indicates that a corresponding knowledge graph triplet is expired (Britton abstract and para. 0054: RDF graph triples have corresponding timestamps; and Britton para. 0057: triples that are expired are ignored or deleted); and
refraining from augmenting the user query with the corresponding knowledge graph triplet based at least in part on the timestamp (Britton para. 0054 and 0057: triples that are expired are ignored or deleted).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Crabtree as modified by Larson to include the teachings of Britton because it helps to prevent utilizing expired, stale, or outdated data (Britton para. 0054 and 0057), enhancing information integrity.
As to claim 18, see the rejection of claim 9 above.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/Umar Mian/
Primary Examiner, Art Unit 2163
1 Bergmann, David and Strykyer, Cole. "What is vector embedding?" Published 12 June 2024 by IBM. Accessed 28 August 2025 from https://www.ibm.com/think/topics/vector-embedding
2 Bergmann, David and Strykyer, Cole. "What is vector embedding?" Published 12 June 2024 by IBM. Accessed 28 August 2025 from https://www.ibm.com/think/topics/vector-embedding
3 MPEP § 2106.07(a)(III)(A) explains that a specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional elements as well-understood or routine or conventional ( or an equivalent term) or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a).
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