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 .
Applicant’s Response
In Applicant’s Response dated 6/2/25, the Applicant amended Claims 1, 7-10, 14, 15, 17, added Claim 22 and argued previously rejected claims in the Non-Final Rejection dated 6/9/25. Claims 1-3, 7-10, 14-17 and 20-22 are pending examination.
In light of the Applicant’s amendments and remarks, the 35 USC 101 claims have been withdrawn.
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 1-3, 7-10, 14-17 and 20-22 are rejected under 35 U.S.C. 103 as being obvious over Gupta et al. United States Patent Publication 2014/0280307, in view of Sonmez et al., United States Patent Publication 20150095303 (hereinafter “Sonmez”), in further view of Yuan et al., CN108052547 (hereinafter “Yuan”) and Sharma et al., United States Patent Publication 2020/0159867 (hereinafter “Sharma”).
Claim 1:
Gupta discloses:
A method implemented by a computer-based natural language question-answering (QA) system that includes a processor and a memory accessible by the processor (see paragraph [0002]), the method comprising:
receiving a natural language question submitted to a question-answering (QA) system (see paragraph [0025]). Gupta teaches receiving a natural language question submitted for an answer.
determining a set of candidate answers and passages that correspond to the question (see paragraphs [0025], [0026] and [0068]). Gupta teaches receiving a set of answers (or responses from users) with corresponding confidence scores from the QA system;
selecting an original entity from an original knowledge graph from the passage knowledge graphs, wherein the original knowledge graph is associated with a collection of interlinked descriptions of entities in the original knowledge graph that are connected to each other by relationships and the original knowledge graph is a knowledge graph of a question submitted to a question-answering (QA) system (see paragraph [0002], [0022], [0045] and [0065]). Gupta teaches selecting an original entity from an original knowledge graph, The knowledge graph is related to the question submitted to the QA system. Gupta also teaches the graph is about relationships between entities and concepts interlinked.;
expanding the original knowledge graph by retrieving a data source external to the original knowledge graph, wherein the data source is an unstructured collection of natural language (see paragraphs [0032] and [0049]). Gupta teaches accessing a data source external to the original knowledge graph can be manually input data or data from a website;
wherein the data source includes reference texts and/or organization documents and/or newswire reports and/or online encyclopedias (see paragraph [0036]). Gupta teaches organizational documents.
using the original knowledge graph to initialize an expanded knowledge graph (see paragraph [0049]). Gupta teaches using the original knowledge graph and updating it.
for the entities in the original knowledge graph:
searching the data source for the respective entities in the original knowledge graph (see paragraph [0049]). Gupta teaches searching the data source for the original entity and related information;
generating the expanded knowledge graph formed by merging the new relation with the new entity and the respective one of the entities in the original knowledge graph (see paragraphs [0049] and [0055]). Gupta teaches generating an expanded knowledge graph by adding the new entities and relationships to the original knowledge graph.
Gupta fails to explicitly disclose identifying a relationship between the original entity and a new entity and expanding the knowledge graph by adding the new entity.
Sonmez discloses:
based at least in part on the searching, identifying a relation in the data source referencing a new entity from the respective one of the entities in the original knowledge graph, (see paragraph [0029]). Sonmez teaches based on the searching of the internal and external databases, identifying relationships between existing entity and new entities. Sonmez also teaches the entities can be identical but different formats whereas the new entities can be absent from the original knowledge graph;
in response to determining the new entity is absent from the original knowledge graph, generating an expanded knowledge graph by merging the new relation with the new entity and the respective one of the entities in the original knowledge graph (see paragraphs [0029]). Sonmez teaches generating an expanded knowledge graph by adding the new entities and relationships to the original knowledge graph.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta to include searching an external database for matching entities and updating the graph with the new entities for the purpose of efficiently collecting, organizing and utilizing knowledge graphs, as taught by Sonmez.
Gupta and Sonmez fail to expressly disclose generating an expanded knowledge graph by
Yuan discloses:
creating a plurality of passage knowledge graphs, wherein each of the passage knowledge graphs correspond to the respective passages (see pages 2-3 and 4-5). Yuan teaches for each node (i.e. sentence/passage), constructing new graphs by traversing each nodes as the new center of the graph and collecting data based on node to create passage nodes; and
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta and Sonmez to include creating passage graphs for the purpose providing the most accurate candidate answers to the input question including passages, as taught by Yuan.
Gupta, Sonmez and Yuan fail to expressly disclose computing candidate scores based on similarities.
Sharma discloses:
computing candidate answer scores based on similarities between the expanded knowledge graphs containing the new entity and the passage knowledge graphs (see paragraphs [0026], [0036] and [0037]). Sharma teaches computing similarity scores based on relevance of the content between the new knowledge graph and other knowledge graphs.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta, Sonmez and Yuan to include computing scores for similar information for the purpose the most accurate, relevant information regarding new entities in knowledge graphs, as taught by Sharma.
Claim 2:
Gupta and Yuan fail to expressly disclose including the new relation in the expanded knowledge graph.
Sonmez discloses:
including, in the expanded knowledge graph, a new relation between the original entity and the new entity (see paragraphs [0045] and [0046]). Gupta teaches adding the new entities and relationships to the original knowledge graph.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta and Yuan to include searching an external database for matching entities and updating the graph with the new entities for the purpose of efficiently collecting, organizing and utilizing knowledge graphs, as taught by Sonmez.
Claim 7:
Gupta and Sonmez fails to expressly disclose using a passages and scores of the knowledge system to identify candidate answers.
Yuan discloses:
comparing the expanded knowledge graph corresponding to the original knowledge graph to each of the expanded knowledge graph corresponding to the passages, generating a passage score pertaining to each of the passage knowledge graphs, and an identification of one or more candidate answers (see page 4). Yuan teaches determining the matching and similarity scores of the knowledge of each passage to the question to identify the best candidate answers;
selecting one or more of the candidate answers based on candidate answer scores corresponding to the selected candidate answers (see page 5-6). Yuan teaches selecting one or more candidate answer based on the candidate answer scores; and
providing the selected candidate answers to a requestor of the question. (see page 6). Yuan teaches providing the selected answers to the requestor.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta and Sonmez to include creating passage graphs for the purpose providing the most accurate candidate answers to the input question including passages, as taught by Yuan.
Claims 8, 9, 14:
Although Claims 8, 9 and 14 are system claims, they are interpreted and rejected for the same reasons as the method in Claims 1, 2 and 7, respectively.
Claims 15, 16, 20:
Although Claims 15, 16, 20 are computer program product claims, they are interpreted and rejected for the same reasons as the method in Claims 1, 2, 7, respectively.
Claim 21:
Gupta, Sonmez and Yuan fail to expressly disclose a vector space model.
Sharma discloses:
wherein the similarities between the expanded knowledge graph and the passage knowledge graphs are compared using a vector space model (see paragraph [0011]). Sharma teaches computing similarity scores based on between knowledge graphs.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta, Sonmez and Yuan to include using a vector space model to compare content and knowledge graphs for the purpose enable a self-evolving knowledge graph to be updated automatically to encompass information found in new documents in the knowledge graph's domain of study, thereby eliminating the need for researchers to manually discover and add entity associations to the knowledge graph, as taught by Sharma.
Claim 22:
Gupta discloses:
in response to receiving the question, building question knowledge graphs, wherein the question knowledge graphs corresponds to the respective candidate answers in the set of candidate answers (see paragraphs [0022], [0025], [0026], [0045], and [0068]). Gupta teaches receiving a set of answers (or responses from users) with corresponding confidence scores from the QA system. Gupta also teaches the knowledge graph is related to the question submitted to the QA system. Gupta also teaches the graph is about relationships between entities and concepts interlinked;
retrieving a secondary data source external to the question knowledge graphs, wherein the second data source is an unstructured collection of natural language, wherein the second data source includes reference texts and/or organization documents and/or newswire reports and/or online encyclopedias (see paragraphs [0032], [0036] and [0049]). Gupta teaches accessing data from multiple data sources external to the original knowledge graph can be manually input data or data from organizational documents; and
using the secondary data source to expand the question knowledge graphs and form expanded question knowledge graphs (see paragraph [0049]). Gupta teaches expanding the knowledge graph by updating the knowledge to a new knowledge by adding the data from the external sources.
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being obvious over Gupta, Sonmez, Yuan and Sharma, in further view of Sabah, et. al., United States Patent Publication 2016/0180402 (hereinafter “Sabah”).
Claim 3:
Gupta, Sonmez, Yuan and Sharma fail to expressly disclose an encyclopedia.
Sabah discloses:
wherein the data source is an online encyclopedia (see paragraph [0038]). Sabah teaches the data source is an encyclopedia.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention to modify the method disclosed by Gupta, Sonmez, Yuan and Sharma to include the external data source being an encyclopedia for the purpose of being more knowledgeable having encyclopedia information, as taught by Sabah.
Claim 10:
Although Claim 10 is a system claim, it is interpreted and rejected for the same reasons as the method in Claim 3.
Claim 17:
Although Claim 17 is a computer program product claim, it is interpreted and rejected for the same reasons as the method in Claim 3.
Response to Arguments
Applicant’s arguments, see REM, filed 9/9/25 with respect to Claims 1-3, 7-10, 14-17, 20, 21 have been fully considered and are persuasive. The 35 USC 101 rejections have been withdrawn.
All arguments with regard to the 35 USC 101 rejections are moot.
Applicant's arguments filed 9/9/25 with respect to rejections under 35 USC 103 have been fully considered but they are not persuasive.
35 USC 103 Rejections
Applicant argues While Applicant in no way concedes to this assertion made in the rejection, Applicant agrees that Gupta is unable to meet limitations which involve identifying relationships between an original entity and a new entity, much less expanding a knowledge graph as claimed. Following the rejection’s own admissions, Gupta thereby falls short of what is claimed.
The Examiner disagrees.
Gupta teaches determining if entities are related and also determining if entities are not relating, creating a link, reference or relationship between the entities by finding documents or data externally to create a relationship between and original entity and a new entity (see paragraphs [0032] and [0036]). A knowledge can be expanded by adding information to a knowledge graph that was not originally there such as adding information from an external data source. Thus, Gupta does teach expanding a knowledge graph and Gupta also teaches determining relationships between a new entity and an original entity.
Applicant argues However, Sonmez’s paragraph 0029 falls short of the claimed limitations as well. For instance, the cited section only mentions that “the knowledge base linking unit 242 may discover relationships between existing and new entities, and may update the knowledge base accordingly.” In other words, Sonmez is only concerned with updating a centralized knowledge base to reflect “new entities”.
The Examiner disagrees.
Sonmez recites “may merge the extracted data for entities and map relationships between entities to form an entity-centric knowledge base. The data reconciliation module 240 may use a Hadoop data processing (e.g., Map-Reduce) framework to handle big data via parallel computing on server clusters. The data reconciliation module 240 may comprises a unification unit 241 and a knowledge base linking unit 242. The unification unit 241 may handle the unification of extracted data from various sources. For example, different formats of an identical field (e.g., an address or movie title) retrieved from different sources may be unified to remove duplication. In addition, the knowledge base linking unit 242 may discover relationships between existing and new entities, and may update the knowledge base accordingly”. Sonmez teaches generating new knowledges, updating a knowledge graph by merging new extracted entities (see paragraph [0029]).
No, Sonmez does not create 3 different knowledge graphs. It is unclear if the claims are generating three knowledge graphs because the claims recite “passage knowledge graph”, “original knowledge graph” and “expanded knowledge graph”. It is unclear if the “passage knowledge graph” is the “original knowledge graph”. Thus, further defining the knowledge graphs will make the claims clearer to understand.
Applicant argues Applicant also submits that independent claims 8 and 15 recite similar limitations and therefore are believed to overcome the art of record for similar reasons as those discussed above with respect to claim 1.
The Examiner disagrees.
For the same reasons as the response to arguments with respect to Claim 1, the claims remain rejected.
Applicant argues The rejection of claims 3, 10, and 17 applies Gupta, Sonmez, Yuan, and Sharma as for respective parent claims 1, 8, and 15. Claims 3, 10, and 17 depend from claims 1, 8, and 15 respectively, and therefore the rejection suffers from the same deficiencies as set forth above with respect to claims 1, 8, and 15. Because Sabah has merely been added to allegedly show the limitation of the dependent claims, claims 3, 10, and 17 are believed to be allowable over the combination proposed by the Examiner. Withdrawal of the instant rejection and reconsideration of claims 3, 10, and 17 is therefore respectfully requested.
The Examiner disagrees.
For the same reasons as the response to arguments with respect to Claims 1, 8 and 15, the remaining claims remain rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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/TIONNA M BURKE/Examiner, Art Unit 2178 11/25/25
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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