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
According to paper filed February 3rd 2026, claims 1-15 are pending for examination with a February 18th 2022 priority date under 35 USC §119(e).
By way of the present Amendment, claim 1 is amended. No claim is canceled or added. Claim rejections under 35 USC 101 are withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. §103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. §102(b)(2)(C) for any potential 35 U.S.C. §102(a)(2) prior art against the later invention.
Claims 1-2, 6, 10-12, and 14-15 are rejected under 35 U.S.C. §103 as being unpatentable over Stumpe et al. (US 2022/0261668), hereinafter Stumpe, and further in view of Mitra et al. (US 2023/0169361), hereinafter Mitra.
Claim 1
“A method for automated decision making in an artificial intelligence task by fact-relevant open information extraction and knowledge graph generation” Stumpe [0048][0072][0110] teaches an artificial intelligence engine for directed hypothesis generation and ranking, removal of a certain percentage of links, and providing a relevance-learning engine to integrate annotation sources and supporting querying individual samples, relational facts and taxonomic categories, the percentage of the relationships can be reconstructed based on knowledge graph representation embeddings in a link prediction setting, in one embodiment, the knowledge graph is constructed by using open information extraction;
“the method being implemented by one or more hardware processors” Mitra [0035] teaches computing resources including processors:
“obtaining a keyword query for performing the fact-relevant open information extraction; expanding the keyword query using keyword alias and query generation; performing the fact-relevant open information extraction to extract triples from a text which contains the keyword or the keyword alias” Mitra [0043] teaches an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query, wherein
the “features” of query is considered the claimed expanding the “keywords” of query;
“generating the knowledge graph using the extracted triples using an open knowledge graph (OpenKG) extractor that has been trained using keywords and aliases” Stumpe [0073] teaches identify highly ranked triples/quadruples guided by the knowledge graph topology and knowledge representation;
“performing supervised or unsupervised classification using the generated knowledge graph to make the automated decision in the artificial intelligence task” Stumpe [0031][0032] teaches “entity classification” and “graph classification” in the context of a knowledge graph refers to inferring properties of an entity based on similar entities utilizing learned knowledge graph representation.
Stumpe and Mitra disclose analogous art. Mitra is analogous art because it is in the field of deep learning models specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. Stumpe does not spell out the “keyword query” as recited above. Said feature is taught in Mitra. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Mitra (Mitra [0043]: an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query) into Stumpe to enhance its open information extraction functions.
Claim 2
“obtaining a context query, expanding the context query using context alias and query generation, and performing the fact-relevant open information extraction to extract the triples from the text which contain the context or the context alias, and the keyword or the keyword alias” Mitra [0043] teaches an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query. The keyword query is the
claimed context query.
Claim 6
“wherein the keyword query is obtained from a recommendation system” Stumpe [0073][0075][0122] teaches knowledge graph topology, which corrections can be converted to a database query representation, and the knowledge modeling including recommendation systems.
Claim 10
“wherein the automated decision includes one of adapting parameters of a device or digital display, or manufacturing or providing instructions for manufacturing of a product” Mitra [0064] teaches outputting a natural language response to the query for display on a computer screen.
Claims 11-12 & 14-15
Claims 11-12 and 14-15 are rejected for the similar rationale given for claims 1-2, 10, and 1 respectively.
Claim 3 is rejected under 35 U.S.C. §103 as being unpatentable over Stumpe et al. (US 2022/0261668), hereinafter Stumpe, and further in view of Mitra et al. (US 2023/0169361), hereinafter Mitra, and Wen et al. (US 2003/0144994), hereinafter Wen.
Claim 3
“displaying the aliases and queries to a user, and updating the aliases and/or the queries based on a user input” Wen [0113] teaches query similarity based on query composition used to cluster queries and user selection feedback is used to identify queries.
Stumpe, Mitra, and Wen disclose analogous art. Mitra is analogous art because it is in the field of deep learning models specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. Wen is analogous art because it is in the field of making query similarity determinations, wherein the queries are used in information retrieval operations. Stumpe does not spell out the “keyword query” as recited above. Said feature is taught in Mitra. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Mitra (Mitra [0043]: an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query) into Stumpe to enhance its open information extraction functions. Still, Stumpe does not spell out the “query alias” as recited above. Said feature is taught in Wen. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Wen (Wen [0113]: query similarity based on query composition used to cluster queries and user selection feedback is used to identify queries) into Stumpe to enhance its query search by including query alias to achieve intended results.
Claims 4 and 5 are rejected under 35 U.S.C. §103 as being unpatentable over Stumpe et al. (US 2022/0261668), hereinafter Stumpe, and further in view of Mitra et al. (US 2023/0169361), hereinafter Mitra, and Ding et al. (US 2020/0012733), hereinafter Ding.
Claim 4
“displaying the knowledge graph to a user, and pruning the knowledge graph based on a user input” Ding [0004] teaches identifying a strength of each of the relationships between adjacent ones of the entities in the knowledge graph including pruning a plurality of the edges from the knowledge chain using a threshold on weights corresponding to the edges; and Stumpe [0006][0150] teaches a knowledge graph representing structured data and a patient data store including patient genetic/molecular sequencing, which inherently teaches the claimed “user input” feature.
Stumpe, Mitra, and Ding disclose analogous art. Mitra is analogous art because it is in the field of deep learning models specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. Ding is analogous art because it is in the field of augmenting a knowledge graph including the knowledge graph including entities and relationships between the entities defining respective edges. Stumpe does not spell out the “keyword query” as recited above. Said feature is taught in Mitra. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Mitra (Mitra [0043]: an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query) into Stumpe to enhance its open information extraction functions. Still, Stumpe does not spell out the “displaying and pruning knowledge graph” as recited above. Said feature is taught in Ding. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Ding (Ding [0004]: identifying a strength of each of the relationships between adjacent ones of the entities in the knowledge graph including pruning a plurality of the edges from the knowledge chain) into Stumpe to enhance its query search by pruning the knowledge graph.
Claim 5
“pruning the generated knowledge graph by at least one of temporal, location or triple pruning” Ding [0004] teaches pruning a plurality of the edges from the knowledge chain, and Stumpe [0013] teaches a
temporal knowledge graph.
Claim 8 is rejected under 35 U.S.C. §103 as being unpatentable over Stumpe et al. (US 2022/0261668), hereinafter Stumpe, and further in view of Mitra et al. (US 2023/0169361), hereinafter Mitra, and Karen et al. (US 2021/0248624), hereinafter Karen.
Claim 8
“wherein the unsupervised classification is performed using a relational page rank algorithm” Karen [0320] teaches analysis of websites found in the web crawling process includes a page rank algorithm.
Stumpe, Mitra, and Karen disclose analogous art. Mitra is analogous art because it is in the field of deep learning models specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. Karen is analogous art because it is in the field of collecting content of websites and generating a relevance score indicating the relative popularity of the website. Stumpe does not spell out the “keyword query” as recited above. Said feature is taught in Mitra. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Mitra (Mitra [0043]: an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query) into Stumpe to enhance its open information extraction functions. Still, Stumpe does not spell out the “relational page rank algorithm” as recited above. Said feature is taught in Karen. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Karen (Karen [0320]: analysis of websites found in the web crawling process includes a page rank algorithm) into Stumpe to enhance its automated decision making function by way of the knowledge graph.
Claims 9 and 13 are rejected under 35 U.S.C. §103 as being unpatentable over Stumpe et al. (US 2022/0261668), hereinafter Stumpe, and further in view of Mitra et al. (US 2023/0169361), hereinafter Mitra, and Dudkiewicz et al. (US 2022/0365976), hereinafter Dudkiewicz.
Claim 9
“wherein the OpenKG extractor has been trained using different keywords and context from a different source text, wherein each of the keywords and the respective context are combined at nodes in the knowledge graph” Dudkiewicz abstract teaches creating a list of nodes from the graph based on a relevancy of the subject of each of the nodes with respect to the keyword combination.
Stumpe, Mitra, and Dudkiewicz disclose analogous art. Mitra is analogous art because it is in the
field of deep learning models specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. Dudkiewicz is analogous art because it is in the field of a free-form query text that extracts from the free-form one or more keyword combinations and one or more logic terms. Stumpe does not spell out the “keyword query” as recited above. Said feature is taught in Mitra. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Mitra (Mitra [0043]: an encoder layer including a bi-directional long short-term memory (LSTM), the encoder layer provides one or more answers as output, each answer being characterized by specific features/keywords of query) into Stumpe to enhance its open information extraction functions. Still, Stumpe does not spell out the “each of the keywords combined at the nodes in the knowledge graph” as recited above. Said feature is taught in Dudkiewicz. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Dudkiewicz (Dudkiewicz abstract: creating a list of nodes from the graph based on a relevancy of the subject of each of the nodes with respect to the keyword combination) into Stumpe to enhance its OpenKG extractor’s extracting function.
Claim 13
Claim 13 is rejected for the similar rationale given for claim 9.
Allowable Subject Matter
Claim 7 is allowed over the cited prior art.
Response to Arguments
Applicant's arguments filed February 3rd 2026 have been fully considered but they are not persuasive.
Applicant cites Figure 2 of the present application and argues that “Mitra does not disclose or suggest to obtain a keyword query for performing fact-relevant open information extraction or to perform the fact-relevant open information extraction to extract triples from a text which contains the keyword or keywork alias. Paragraph [0043] of Mitra that is cited as allegedly teaching these steps merely describes that an encoder layer, which is used for learning use cases, might receive an input from a convolution layer and provide an answer that is characterized by features/keywords of the query. The answer from the encoder layer is not used for fact-relevant open information extraction to extract triples from a text.” Said argument is not persuasive.
In paragraph [0072] of Stumpe, “search is supporting querying individual samples, …, relational facts” clearly discloses the argued feature of fact-relevant open information. Although the “relational facts” is not identical to the phrase used in the claim recitation, “fact-relevant” or “fact-relevant open” information, applicant is invited to provide the differences between “relational facts” and “fact-relevant open”. Applicant is hereby advised to review the cited reference in its entirety not only to review the cited paragraphs, which are provided as exemplary excerpts. Accordingly, claim rejection citations are amended in the present Office action.
Further, applicant argues that “the prior art is completely silent with respect to expanding a keyword query, much less doing so using keyword aliases. Paragraph [0043] of Mitra is again cited as allegedly teaching this feature but is, in fact, completely silent as to aliases or synonyms of keywords, much less expanding a keyword query using an alias or synonym, … . Nor does Mitra or any of the other cited prior art disclose or suggest an open knowledge graph extractor that has be trained using keywords and aliases, … . Paragraph [0073] of Stumpe cited in the Office Action merely describes identifying highly ranked triples in a knowledge graph and not generating a knowledge graph, much less doing so using an open knowledge graph extractor that has been trained using keywords and aliases.” Said argument is not persuasive either.
In paragraphs [0023][0033] of Mitra, “a knowledge graph can be represented by triples that each represent two entities in order, … . Each entity and each relationship can be … included in multiple triples” and “query graph generation module receives embedded KG data and extracts relations from the graph, as well as in capturing paraphrases, and similarity in the KG” clearly disclose the argued feature. Applicant is again reminded to review the cited references in their entirety.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/Ruay Ho/Primary Patent Examiner, Art Unit 2142