DETAILED ACTION
Introduction
This office action is in response to applicant’s amendment filed 2/18/2026. Claims 1-20 are currently pending and have been examined. Applicant’s IDS have been considered. There is no claim to foreign priority.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/11/2026 has been entered.
Response to Arguments
Applicant’s arguments, see remarks, filed 8/11/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC 103 have been fully considered and are not persuasive. More specifically, applicant argues, with respect to claims 1, 11 and 18, “Thus, in Baldua, the knowledge graph is merely a queried resource during execution, not a structure used to generate prompt elements. However, Baldua does not teach or suggest "generating, ... based on a query input, one or more prompt elements based on context data and the knowledge graph data object ... ," as recited in amended claims 1, 11, and 18. The remaining references, including Krishnan, Madhavan, Wang, and Man, fail to cure at least this deficiency of Baldua.”
However, the Examiner does not concur with the applicant’s assessment of Baldua, such that the knowledge graph of Baldua is not merely a queried resource during execution (although querying the knowledge graph, and returning an answer from the knowledge graph, is also part of Baldua, wherein the knowledge graph is a subgraph of his entity graph, and the knowledge graph). The Examiner notes Baldua explicitly teaches, in the previously cited paragraphs [0064, 0072, 0147, 0061-0073], that there is a query (as a prompt) that is generated, this particular query is generated as a prompt element, in his prompt generation plan and corresponding generated prompts, this particular query includes a knowledge graph data object. The applicant incorrectly cites, paragraph [0064], on page 13, wherein the cited paragraph refers to paragraph [0072], and furthermore, close inspection of that paragraph states that not only is the knowledge graph, that has been received, queried, but the actual query elements from that knowledge graph, thus the knowledge graph object, is used as a prompt/query element, into the data resources, which includes at the least, a knowledge/entity graph. The Examiner notes, this knowledge graph, structure, as shown in the cited figures, Fig. 6 items 632, 634, 630, Fig. 1B, items 144, clearly and explicitly comprises a structured data set, via nodes and edges, and the knowledge graph object, itself is used to generate a query, or modified query, as a prompt, in his prompt generation. And further queries the data sources (knowledge graph or entity graph) utilizing this new prompt, which contains contextual information and prompt elements or subgraph elements, which are extracted from the knowledge graph itself, in order to generate an answer (see paragraphs [0061, 0072, 0073]). Therefore, the premise that the knowledge graph is merely a queried resource, and not a structure used to generate prompt elements is not persuasive.
The Examiner notes, the previous arguments are still relevant and are maintained below.
Baldua explicitly teaches, generating a prompt, as this is not argued. Baldua further teaches wherein generating the prompt comprises context data, see paragraphs [0047, 0048]-see is prompt generation discussion, which includes generating the prompt based on input and context data. Therefore, these two elements of criteria with respect to the claim are met. Baldua further teaches in paragraphs [0048, 0080], as previously cited, in order to generate the prompt, the final prompt or query uses the input classification associated with the knowledge graph, as the data resources, thus as described and see in the illustration of Fig. 1, the input and context data as associated with the data resources, which contains the knowledge graph, and thus there is a clear associated between these elements, and the subsequently generated prompt is based on each of these respective elements. The Examiner notes the applicant’s argument that Baldua teaches outputting a query execution plan for executing a query to one or more of the data resources, 134, however, the applicant lacks taking not of the entire prompt generation element, which involves the teaching above, and the communication between elements in Fig. 1A item 110 and 134. Wherein the input, context and knowledge graph data as associated are all used in generating a prompt, which can then be used to query a knowledge data source in order to generate a response/answer. Therefore, the corresponding arguments are deemed non-persuasive, wherein the Examiner notes the applicant has appeared to dismiss the input, context and knowledge graph which are used to generate a prompt (zero or few shot prompts) which are then subsequently used as a query to data resources.
The Examiner further notes, based on the amendments to the claims and the Examiner’s further and updated search and consideration, the allowable subject matter noted in claim 5 is withdrawn and a new rejection is seen below. Wherein the Examiner notes that in the generation of a knowledge graph data object, which includes document-topic-entity relationship features is clearly taught, such that from a document, the topic and entity relationship features associated therewith, in vector form, are similarity compared to mention-context vectors in order to generate a more robust feature vector space for a data object, that can be used in downstream task, such as information retrieval or question answering (See LU, page 92072 section I.)
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.
Claim(s) 1, 6-8, 10, 11, 15, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baldua et al. (Baldua, US 2025/0110957) in view of Krishnan et al. (Krishnan, US 2025/0005050) and further in view of Madhavan et al. (Madhavan, US 2025/0245226).
As per claim 1, Baldua teaches a computer-implemented method comprising:
receiving, by one or more processors, a knowledge graph data object, wherein the knowledge graph data object comprises (i) a plurality of nodes associated with a plurality of topics, a plurality of entities, or a plurality of documents and (ii) a plurality of edges between the plurality of nodes (paragraphs [0072, 0064, 147]-his knowledge graph, Fig. 6 item 600, 634, 632, his topics, entities and concepts as part of his knowledge graph, see his edges between nodes, Fig. 8, his processing device, with respect to processor, hereinafter);
generating, by the one or more processors and based on a query input, one or more prompt elements based on context data and the knowledge graph data object (ibid-paragraph [0025, 0032-0034, 0061, 0064, 0078]-his prompt, the full scope of his prompt generation, and prompt planning comprising context data and specifically knowledge graph data object, as the data resource Fig. 1B item 144), wherein the context data is further associated with one or more query topics, one or more query entities, or one or more query documents (ibid, see also, paragraph [0025, 0033, 0034, 0047, 0048, 0064, 0078, 0080, 0143-0159]-his user history, data sources, associated context data, his query, name entity, type, including topics, or query entities or documents, application, etc.);
providing, by the one or more processors, a prompt comprising the one or more prompt elements to a natural language processing machine learning model to receive a set of one or more subgraph data [objects] from the natural language processing machine learning model (paragraphs [0027-0029]-his GAI is designed using a LLM, neural network and deep learning, for performing NLP tasks, and his explicit providing of a prompt to this GAI model in order to generate a response/answer, this answer/response is coming from the LLM, GAI configured to generate a response, based on training and knowledge graph data, in paragraph [0072, 0073], after the knowledge graph is used to generate a prompt/query, this explicit query, is deemed to have at least a portion of the knowledge graph from Fig. 1B, item 144, Fig. 6 items 632/634, as a set of subgraph data, paragraph [0064], “For example, entity graphs and knowledge graphs 144 can include one or more portions of entity graph 632 and/or knowledge graph 634, described below with reference to FIG. 6.”), wherein the set of one or more subgraph data objects comprises a portion of the knowledge graph data object that corresponds to the prompt (ibid-see his second query discussion, the NLP LLM, Fig. 1B item 116, and above discussed LLM, is used to generated this final second query, which does not comprise the entire knowledge graph data object, as seen in Fig. 1B item 144, which is also described as portions of the knowledge graph, as his one or more sets of data, which then queries the data resources to generate an answer, the data resources also comprising an entity graph); and
providing, by the one or more processors, one or more answer outputs based on the set of one or more subgraph data [objects] (ibid-his plan generation prompt, sent to the LLM, based on the subgraph data, Fig. 4-his prompt execution, and abstract, paragraphs [0004, 0027, 0042, 0047, 0061-0073, 0101-0111]-all detailing the response/answer outputs based on the subgraph data, Figs. 3-6-the response stemming from the subgraph dependency graph, which processes only graph elements relevant to subset of functions).
Baldua does not explicitly refer or teach subgraph data objects, as taught by Krishnan, his subgraph data objects (paragraph [0213, 0212], Figs. 2B and 6, and hereinafter the subgraph data as the subgraph data objects, as combined below).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua and Krishnan to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects as taught by Baldua with the subgraph data objects as taught by Krishnan as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be providing a result, response and answer based on traversing subgraph data objects, (ibid, Krishnan, see also abstract).
The above combination lacks explicitly teaching that which Madhavan teaches, providing, by the one or more processors, a prompt comprising the one or more prompt elements to a natural language processing machine learning model to receive a set of one or more subgraph data objects from the natural language processing machine learning model (paragraphs [0029, 0051-0064, 0151]-his knowledge graph construction system, including a natural language processing machine learning model, within his graph component, which generates the graph elements comprising subgraph data objects, and corresponding prompt, based on an input and contextual data, provided to the artificial intelligence machine learning system to receive from the natural language processing machine learning model subgraph data objects, wherein the machine learning models produce domain specific graphs, and return graph components as accessed, and used to generate an answer/output).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua and Krishnan and Madhavan to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects as taught by Baldua with the subgraph data objects as taught by Krishnan with receiving one or more subgraph data objects from a natural language processing machine learning model as taught by Madhavanas each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be providing a result, response and answer based on traversing subgraph data objects, wherein a natural language machine learning model is used to generate and construct a knowledge graph, therefore allowing the system to receive subgraph objects when prompted (ibid, Krishnan, see also abstract, ibid-Madhavan-see his nodes and edges, used to answer the prompts discussion, the nodes and edges coming from, generated by, the natural language processing machine learning model).
As per claim 6, Baldua further makes obvious the computer-implemented method of claim 1, wherein the natural language processing machine learning model comprises a transformer machine learning model (ibid, his transformers, Chat-GPT and BERT used in his NLP models, paragraph [0067]).
As per claims 7 and 15, Baldua further makes obvious the computer-implemented method of claim 1, wherein the one or more prompt elements further comprise (i) one or more historic queries, or (ii) one or more prompt templates (ibid-see claim 1, prompt discussion, see also paragraphs [0025, 0156, 0157]-see his query history utilized in prompt construction, and also his prompt templates).
As per claims 8 and 16, Baldua further makes obvious the computer-implemented method of claim 7, wherein generating the set of one or more [subgraph] data objects comprises:
generating the prompt based on one of the one or more prompt templates (ibid, paragraph [0101, 0099-0104]-his subgraph and corresponding data components, and generation prompt, prompt templates);
generating a question based on the one or more prompt templates and one or more of (a) the context data, (b) the knowledge graph data object, or (c) the one or more historic queries (ibid, his prompt and query generated based on the prompt templates, knowledge graph data, historic queries, and further input into LLM, Figs. 1A-Fig. 3); and
generating, using the natural language processing machine learning model, an answer based on the question (ibid-see claim 1, answer/response discussion, his transformer, GPT/BERT, NLP model, LLM and generated response based on the above, context data and generated query/prompt Fig. 7A items 702-718).
Baldua lacks explicitly teaching that which Madhavan teaches, generating the one or more subgraph data object (see claim 1, generating his knowledge graph discussion, his generated prompt, and knowledge graph data objects, and generated answer using the natural language processing machine learning model.
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua and Krishnan and Madhavan to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects as taught by Baldua with the subgraph data objects as taught by Krishnan with generating the one or more subgraph data objects, comprising generating the prompt, a question and answer, as described above, using the natural language processing machine learning model as taught by Madhavanas each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be providing a result, response and answer based on traversing subgraph data objects, wherein a natural language machine learning model is used to generate and construct a knowledge graph, therefore allowing the system to receive subgraph objects when prompted (ibid, Krishnan, see also abstract, ibid-Madhavan-see his nodes and edges, used to answer the prompts discussion, the nodes and edges coming from, generated by, the natural language processing machine learning model).
As per claim 10, Baldua further makes obvious the computer-implemented method of claim 1, wherein generating the one or more answer outputs comprises:
traversing the set of one or more subgraph data objects (ibid-see claim 1, subgraph discussion, Baldua, paragraph [0143, 0144]-his traversed graph structure discussion, see also Khrishnan, paragraphs [0212, 0213]-traversal of sub-graph data objects); and
identifying one or more entities or one or more documents from the set of one or more subgraph data objects that are relevant to the query input based on a traversal of the set of one or more subgraph data objects (ibid-the traversal to identify entities, via queries, thus relevant to queries, based on the traversal, Krishnan, as similarly motivated and combined above).
As per claim 11, claim 1 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the system is deemed to embody the method, such that Baldua with Krishnan with Madhavan make obvious a system comprising one or more processors and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (paragraphs [0213-0219]-see his system, computer readable media, processors, memory discussion):
receiving a knowledge graph data object, wherein the knowledge graph data object comprises (i) a plurality of nodes associated with a plurality of topics, a plurality of entities, or a plurality of documents and (ii) a plurality of edges between the plurality of nodes (ibid-see claim 1, corresponding and similar limitation); generating, based on a query input, one or more prompt elements based on context data and the knowledge graph data object, wherein the context data is further associated with one or more query topics, one or more query entities, or one or more query documents (ibid); providing a prompt comprising the one or more prompt elements to a natural language processing machine learning model to receive a set of one or more subgraph data objects from the natural language processing machine learning model (ibid), wherein the set of one or more subgraph data objects comprises a portion of the knowledge graph data object that corresponds to the prompt (ibid); and providing one or more answer outputs based on the one or more subgraph data objects (ibid).
As per claim 18, claim 18 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the non-transitory computer-readable media is deemed to embody the method, such that Baldua with Krishnan with Madhavan make obvious one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising (paragraphs [0213-0219]-see his system, processors, memory, and machine readable medium discussion): receiving knowledge graph data object, wherein the knowledge graph data object comprises (i) a plurality of nodes associated with a plurality of topics, a plurality of entities, or a plurality of documents and (ii) a plurality of edges between the plurality of nodes (ibid-see claim 1, corresponding and similar limitation); generating, based on a query input, one or more prompt elements based on context data and the knowledge graph data object, wherein the context data is further associated with one or more query topics, one or more query entities, or one or more query documents (ibid); providing a prompt comprising the one or more prompt elements to a natural language processing machine learning model to receive a set of one or more subgraph data objects from the natural language processing machine learning model (ibid),wherein the set of one or more subgraph data objects comprises a portion of the knowledge graph data object that corresponds to the prompt (ibid); and providing one or more answer outputs based on the set of one or more subgraph data objects (ibid).
Claim(s) 2-4, 12-14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baldua et al. (Baldua, US 2025/0110957) in view of Krishnan in view of Madhavan, as applied to claim 1 above, and further in view of Wang et al. (Wang, NERank+: a graph-based approach for entity ranking in document collections).
As per claims 2, 12 and 19, Baldua further makes obvious the computer-implemented method of claim 1, wherein: (i) the knowledge graph data object is generated based on one or more document-topic-entity relationship features that are associated with the plurality of topics, the plurality of entities, and the plurality of documents (ibid, paragraph [0147, 0064, 0072]-his knowledge graphs as previously generated and stored, and queried, with corresponding topics, entities, relationships between them and weights, similarity measurements and other scores to describe the relationships),
but lack explicitly teaching, that which Wang teaches, and (ii) the one or more document-topic-entity relationship features are generated by generating one or more of (a) a document-topic distribution matrix (see Wang, pages 507-512, section 4…This Fig. 2 Graph, and Entity Aware Topic Modeling discussion, including a Document-topic matrix and weights used to construct the graph, topic-entity common word matrix, topic-document weights, as his relationship/edge weights, entity-topic weights, as the relationship weights between entities and topics, topic random scoring, based on his quality metric, background and topic specificity scoring), (b) a topic- entity-common word matrix (ibid), (c) a plurality of topic-document weights (ibid), (d) a plurality of entity- topic weights (ibid), (e) a plurality of topic randomness scores (ibid), (f) a plurality of entity quality scores (ibid), or (g) a plurality of similarity scores associated with the plurality of entities, the plurality of topics, or the plurality of documents.
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua and Wang to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects and subgraph data objects as taught by Baldua with generating knowledge graph data objects as taught by Wang as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be assisting in retrieving ranked data based on a user query and knowledge data object relationships, weights, and objects (Wang, abstract).
As per claims 3 and 13, Baldua with Krishnan with Wang make obvious the computer-implemented method of claim 1, wherein: (i) the knowledge graph data object is generated based on one or more document-topic- entity relationship features that are associated with the plurality of topics, the plurality of entities, and the plurality of documents, and
(ii) the one or more document-topic-entity relationship features are generated by (ibid-see claim 2, corresponding document-topic-entity relationship discussion, Wang, Fig. 2, and matrix discussion):
(a) generating a document-topic distribution matrix that comprises a distribution of the plurality of topics with respect to the plurality of documents (ibid-Wang, previously cited sections and corresponding discussion, his document-topic distribution matrix);
(b) generating a plurality of topic-document weights for the plurality of topics with respect to the plurality of documents based on the document-topic distribution matrix (ibid-see his weights discussion, wrt the corresponding matrix); and
(c) generating a plurality of topic randomness scores for the plurality of topics based on the plurality of topic-document weights (ibid-see his quality metrics, 2 and 3, based on topic specificity, as the topic randomness score, as similarly motivated and combined as seen in claim 2).
As per claims 4 and 14, Baldua with Krishnan with Madhavan with Wang make obvious the computer-implemented method of claim 1, wherein: (i) the knowledge graph data object is generated based on one or more document-topic- entity relationship features that are associated with the plurality of topics, the plurality of entities, and the plurality of documents, and
(ii) the one or more document-topic-entity relationship features are generated by (ibid-see Wang, document topic entity relationship discussion, claims 2, 3):
(a) generating a topic-entity-common word matrix comprising a distribution of the plurality of entities within a plurality of common words that connect the plurality of topics with respect to the plurality of topics in the plurality of documents (ibid-see above topic-common word matrix discussion, Wang sections 3, 4, Fig. 2, Table 1, his collection of common words, and corresponding Topic-entity matrix based on “W”, as his common words),
(b) generating a plurality of entity-topic weights for the plurality of entities with respect to the plurality of topics based on the topic-entity-common word matrix (ibid-see his weights for the edges, R.sub.t.e. based on the topic-entity common word matrix and corresponding weights), and
(c) generating a plurality of entity quality scores for the plurality of entities based the plurality of entity-topic weights (ibid, page 509- section 5, including his quality metrics, and entity richness and quality, based on the entity-topic weights, the motivation to combine, similar and under the same rationale as claim 2).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baldua et al. (Baldua, US 2025/0110957) in view of Krishnan et al. (Krishnan, US 2025/0005050) and further in view of Madhavan et al. (Madhavan, US 2025/0245226), and further in view of Lu et al. (Lu, Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications, 2020).
As per claims 5, Baldua further makes obvious the computer-implemented method of claim 1, but lacks teaching that which Lu teaches, wherein:
(i) the knowledge graph data object is generated based on one or more document-topic-entity relationship features that are associated with the plurality of topics, the plurality of entities, and the plurality of documents (page 92077, section 4-his document topic model, entities, and embedding, vectorization thereof, vectors of pairs of entities, and relationships therebetween, via scoring), and
(ii) the one or more document-topic-entity relationship features are generated by generating a plurality of similarity scores for a plurality of mention-context vector and topic- entity-mention vector pairs (page 92074-92077, 92084-his mentions, and corresponding context, see Figs, 2, 3, and embedding and vectorization of the document-topic-entity and the mention-context vectors, his similarity between entities, including the above stated mentions, and similar context, as scored).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua and Krishnan and Madhavan and Lu to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects as taught by Baldua with the subgraph data objects as taught by Krishnan with generating the one or more subgraph data objects, comprising generating the prompt, a question and answer, as described above, using the natural language processing machine learning model as taught by Madhavanas with generating knowledge graph data objects based on document-topic-entity and mention-entity contexts, as taught by Lu as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be providing a result, response and answer based on traversing subgraph data objects, wherein a natural language machine learning model is used to generate and construct a knowledge graph, modeled from documents-topics-entities and mentions-context, therefore allowing the system to receive subgraph objects when prompted (ibid, Krishnan, see also abstract, ibid-Madhavan-see his nodes and edges, used to answer the prompts discussion, the nodes and edges coming from, generated by, the natural language processing machine learning model, ibid-Lu-see page 92072-sectin I-question answer discussion).
Claim(s) 9, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baldua et al. (Baldua, US 2025/0110957) in view of Krishnan, as applied to claim 1 above, and further in view of Man et al. (Man, US 2023/0394605).
As per claims 9, 17 and 20, Baldua teaches the computer-implemented method of claim 1, wherein generating the one or more answer outputs comprises:
determining that an amount of the set of one or more subgraph data objects is zero or exceeds a threshold representative of the query input comprising an ambiguity (paragraph [0032, 0081, 0082, 0188]-his zero results, from the query, as processed, in claim 1, Figs 1A-7A, using his subgraph data);
generating a follow-up question based on the query input, the set of one or more subgraph data objects, or the knowledge graph data object (ibid-his modified prompt, based on the initial prompt and query);
receiving a response to the follow-up question (ibid, his user feedback, see also paragraph [0128]-his feedback);
generating one or more follow-up prompt elements based on the response (ibid-his modified prompt as generated); and
[adding] one or more nodes or edges to the knowledge graph data object based on the one or more follow-up prompt elements (ibid, paragraph [0138, 0190]-his company names, weights, as applied to his knowledge graph, as his data source, for querying the LLM).
Baldua lacks explicitly teaching that which Man teaches, adding one or more nodes or edges to the one or more knowledge graph data objects based on the one or more follow-up prompt elements (paragraph [0080, 0011]-his node or connections added to the knowledge graph, based on prompt elements to the user, to clarify ambiguities).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Baldua, Krishnan and Man to combine the prior art element of providing answer outputs based on context data associated with knowledge graph data objects and subgraph data objects as taught by Baldua with disambiguating ambiguous queries and adding nodes and edges to a knowledge graph as taught by Wang as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be when prompting a user for data, in order to disambiguate and construct a knowledge graph having appropriate nodes and connections for use in answer an ambiguous query (Wang, abstract, paragraph [0011]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
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/LAMONT M SPOONER/Primary Examiner, Art Unit 2657
10/9/2026
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