Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. Claims 1-20 are presented for examination.
3. This office action is in response to the REM filed 12/03/2025.
4. Claims 1, 10 and 19 are independent claims.
5. The office action is made Final.
Examiner Note
6. The Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 103
7. 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 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.
8. 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) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
9. Claims 1, 2, 4, 10, 11, 13 and 119-20 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) hereinafter as Li in view of Rossi et al (US 20200004888 A1) hereinafter as Rossi and further in view of Zou (US 20190213488 A1) hereinafter as Zou.
10. Regarding claim 10, Li teaches A method, comprising:
receiving a query graph ([0046], “receive a query graph”, Fig 6, step 601, [0113], “At step S601, a first graph 101 is received by one or more processors.”, [0123], “A graph may be provided as a query”, [0124], “receive a query graph and to generate a vector representation of the query graph using the one or more neural network”);
calculating one or more vectors for the query graph, wherein the one or more vectors each identifies a corresponding portion of the query graph (Fig 1, “vector representation of first graph 103”, Fig 2, “input graph 204 and vector representation of input graph 208”, [0046], “generate a vector representation of the query graph using the one or more neural networks”, [0072], “a vector representation of an input graph is obtained by processing an individual input graph, this is referred to as a graph embedding model.”, Fig 6, step 603, [0114], “A vector representation of the first graph is generated by one or more neural networks at step S603 Generating a vector representation of a graph may comprise processing the graph to generate a node state representation vector for each node of the graph and an edge representation vector for each edge of the graph by the one or more neural networks. The node state representation vectors and the edge representation vectors are then processed by the one or more neural networks to generate the vector representation of the graph. The operations for generating a vector representation of a graph may be carried out as described above with reference to FIGS. 2 to 4.”, [0124], “receive a query graph and to generate a vector representation of the query graph using the one or more neural network”);
identifying one or more graphs similar to the query graph including by comparing the calculated one or more vectors for the query graph with one or more previously-calculated vectors for a different set of graphs stored in a knowledge base (Fig 1, “similarity scorer between vector representation of first graph 103 and second graph 104”, [0039], “a knowledge graph (historical graph embeddings of other graphs / previously-calculated vectors for a different set of graphs)”, [0046], “for each record of the plurality of records, process the vector representation of the query graph and a vector representation associated with the respective graph associated with the record to determine a respective similarity score;”, [0047], “determine a set of candidate graphs based upon the determined similarity scores between the vector representations of the query graph and each respective graph associated with each of the plurality of records”, [0073-0074], “the graph embedding and graph matching models”, [0085], “A similarity score 106 for any two graphs may be determined based upon a comparison of the vector representations of the two respective graphs. For example, the comparison may be based upon any vector space metric such as a Euclidean distance, cosine similarity or Hamming distance.”, Fig 6, step 605, [0115], “At step S605, the one or more processors then determines a similarity score based upon the vector representations of the first and second graphs generated at steps S603 and S604.”, [0116], “the above processing is presented as being carried out in a particular order, it is not intended to limit to any particular ordering of steps and the above steps may be carried out in a different order. For example, the vector representation of the first graph may be determined prior to receipt of the second graph. It is also possible that steps are carried out in parallel rather than as a sequential process. For example, the generation of the first and second vector representations of the graph may be performed in parallel.”, [0120], “Using the neural network system described above, vector representations of the first and second control flow graphs may be obtained and a similarity score between them may be generated.”, [0124], “The system may further comprise a database comprising a plurality of records, each record of the plurality of records being associated with a respective graph (historical graph embeddings of other graphs / previously-calculated vectors for a different set of graphs), For each record of the plurality of records, the one or more processors may be configured to process the vector representation of the query graph and a vector representation associated with the respective graph associated with the record to determine a respective similarity score.”);
Li implicitly teaches outputting the identified one or more similar graphs ([0034], “similar graphs”, [0047], “outputting data associated with the record associated with a candidate graph based upon the determined similarity scores for each query graph and candidate graph pair.”, [0124], “output data associated with one or more records based upon the determined similarity score.”).
Li didn’t specifically teach updating the knowledge base including by: receiving transaction data; transforming the transaction data into a corresponding graph: obtaining a graph embedding for the corresponding graph; and calculating a representative graph for a cluster of vectors including the transaction data; and outputting the identified one or more similar graphs.
However, Rossi explicitly teaches outputting the identified one or more similar graphs (Fig 2, [0049], “Find Graphs with Similar Structure”, Fig 4, step 406, [0062], “The system orders the plurality of relevant graphs from a most relevant ranking to a least relevant ranking (operation 406).”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Rossi’s system into Li’s and by incorporating Rossi into Li because both systems are related to graph search engine would answer graph queries based on the structural properties of the graph of interest. This can result in an efficient system for data mining and searching the vast amount of digital (Rossi, [0004]).
Further Zou explicitly teaches updating the knowledge base ([0054], “construct or update the knowledge graph”, Fig 5, [0081], “process for constructing and/or updating a knowledge graph”) including by:
receiving transaction data ([0001], “semantic analysis based on a knowledge graph in finance”, [0042], “The semantic analysis method may include collecting the information”, Fig 3, [0061], “in step 301, the original information input by the user through various communication terminals may be received. The above-required information may include but is not limited to, various news, announcements, comments, research reports, blogs, messages, reports, notices, essays, journals, or the like, or any combination thereof.”);
transforming the transaction data into a corresponding graph ([0042], “The semantic analysis method may include collecting the information, identifying the semantic vector in the information, constructing the semantic vector library, constructing the knowledge graph, mapping the semantic vector to the knowledge graph, generating a semantic recognition result according to the knowledge graph and the relationships of the semantic vectors, etc.”, [0077], “The semantic vector construction unit 403 may construct a semantic vector based on the collected information.”, [0078] “The semantic vector mapping unit 404 may map the semantic vectors in the semantic vector library 402 (and/or the system storage module 207) into the knowledge graph 401.”):
obtaining a graph embedding for the corresponding graph ([0042], “identifying the semantic vector in the information, constructing the semantic vector library, constructing the knowledge graph, mapping the semantic vector to the knowledge graph (a graph embedding (vector representation))”); and
calculating a representative graph for a cluster of vectors including the transaction data ([0076], [0078-0079], “the knowledge graph management unit 405 may construct associations among semantic vectors in different clusters according to the clustered semantic vectors. The knowledge graph management unit 405 may update the knowledge graph 401 according to the clustered semantic vectors and the associations of semantic vectors in different classes.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Zou’s system into Li and Rossi combined system and by incorporating Zou into Li and Rossi combined system because all systems are related to graph search engine would provide a system that can intelligently identify the prediction information published by users (Zou, [0004]).
11. Regarding claim 11, Li, Rossi and Zou teach the invention as claimed in claim 10 above and Rossi further teaches determining a recommendation associated with the received query graph based on a comparison of the one or more calculated vectors for the query graph with one or more previously-calculated vectors of interest for the different set of graphs ([0059], “the recommendation of similar scientific graph data.”, [0060], “improvements and enhancements in many areas of technology, including: sub-graph detection; graph classification and categorization; anomaly detection; intelligent workflow automation; and recommendation of similar scientific graph data. These technological areas are related to the general idea of data mining, i.e.: to examine large databases in order to generate new information; to find anomalies, patterns, and correlations within large data set to predict outcomes; and to use the new information and predicted outcomes to improve the performance of a technological, business, or other system.” Fig 4, steps 406-410, [0062], “The system enhances the search for relevant graphs by allowing the graph search engine to take as an input the user-inputted graph and return as an output the relevant graphs (operation 410).”), also Li teaches the limitation at ([0039], [0052], “determine an estimated likelihood that the user will respond favorably to being recommended the content item based upon the determined similarity score”, [0073-0074], [0085], Fig 6).
12. Regarding claim 13, Li, Rossi and Zou teach the invention as claimed in claim 10 above and Rossi further teaches wherein outputting the identified one or more similar graphs includes outputting an ordered list of one or more vectors (Fig 4, step 406, [0062], “The system orders the plurality of relevant graphs from a most relevant ranking to a least relevant ranking (operation 406).”).
13. Regarding claims 1, 2 and 4, those claims recite a system performs the method of claims 10, 11 and 13respectively and are rejected under the same rationale.
14. Regarding claims 19 and 20, those claims recite a computer program product embodied in a non-transitory computer readable medium and comprising computer instructions performing the method of claims 10 and 11 respectively and are rejected under the same rationale.
15. Claims 3 and 12 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) in view of Rossi et al (US 20200004888 A1) and Zou (US 20190213488 A1) as claimed in claim 10 above and further in view of Topol et al (US 20220269859 A1) hereinafter as Topol.
16. Regarding claim 12, Li, Rossi and Zou teach the invention as claimed in claim 10 above, Li, Rossi and Zou did not specifically teach claim 12 limitations.
However, Topol teaches wherein identifying the one or more graphs similar to the query graph includes: calculating one or more distances of (i) each of the one or more calculated vectors for the query graph to (ii) each cluster center in a knowledge base; determining a closest cluster to the query graph based on the calculated one or more distances; assigning the closest cluster as a candidate cluster; calculating one or more distances of (i) each of the one or more calculated vectors for the query graph to (ii) all graphs that belong to the candidate cluster; and outputting the one or more similar graphs as an ordered list based on the calculated distances ([0043], “Dimensionality of vectors is typically determined empirically between 50 and 150, based on how well distances between vectors capture similarities between graphs. Graph embedder 366 maps similar graphs to vectors that are closer in space (e.g., the angle between vectors of similar graphs is smaller compared to the angle between vectors of less similar graphs).”, [0065], [0071-0073], Fig 12, [0077-0078]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Topol’s system into Li, Rossi and Zou combined system and by incorporating Topol into Li, Rossi and Zou combined system because all systems are related to graph search engine would Provide a natural language interface that will handle a conversation (Topol, [0003]).
17. Regarding claim 3, this claim recites a system performs the method of claim 12 and is rejected under the same rationale.
18. Claims 14-18 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) in view of Rossi et al (US 20200004888 A1) and Zou (US 20190213488 A1) as claimed in claim 10 above and further in view of Choudhury et al (US 20180329958 A1) hereinafter as Choudhury.
19. Regarding claim 14, Li, Rossi and Zou teach the invention as claimed in claim 10 above, Li, Rossi and Zou did not specifically teach claim 14 limitations.
However, Choudhury teaches wherein outputting the identified one or more similar graphs includes outputting at least one of the one or more similar graphs as a node-link diagram and at least one attribute of the one or more similar graphs (Fig 3, [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Choudhury’s system into Li, Rossi and Zou combined system and by incorporating Choudhury into Li, Rossi and Zou combined system because all systems are related to graph search engine would Provide an effective detection of matches of the query graph and its subgraphs (Choudhury, [0005]).
20. Regarding claim 15, Li, Rossi and Zou teach the invention as claimed in claim 10 above, Li, Rossi and Zou did not specifically teach claim 15 limitations.
However, Choudhury teaches wherein outputting the identified one or more similar graphs includes: outputting a card representing the query graph; outputting at least one of the one or more similar graphs on a card, wherein the card includes: node attributes common to the query graph and the at least one of the one or more similar graphs; and node attributes differing between the query graph and the at least one of the one or more similar graphs, wherein the common node attributes and the differing node attributes are visually distinguished from each other (Fig 16, [0230-0233]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Choudhury’s system into Li, Rossi and Zou combined system and by incorporating Choudhury into Li, Rossi and Zou combined system because all systems are related to graph search engine would Provide an effective detection of matches of the query graph and its subgraphs (Choudhury, [0005]).
21. Regarding claim 16, Li, Rossi and Zou teach the invention as claimed in claim 10 above, Li, Rossi and Zou did not specifically teach claim 16 limitations.
However, Choudhury teaches outputting a closest cluster to the query graph in an interactive scatter plot (Fig 2, [0072], Fig 6, [0104], [0203] and Fig 16, [0230-0233]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Choudhury’s system into Li, Rossi and Zou combined system and by incorporating Choudhury into Li, Rossi and Zou combined system because all systems are related to graph search engine would Provide an effective detection of matches of the query graph and its subgraphs (Choudhury, [0005]).
22. Regarding claim 17, Li, Rossi, Zou and Choudhury teach the invention as claimed in claim 16 above, Choudhury further teaches wherein the scatter plot includes a two-dimensional representation of each graph associated with a candidate cluster (Fig 2, [0072], Fig 6, [0104], [0203] and Fig 16, [0230-0233]).
23. Regarding claim 18, Li, Rossi, Zou and Choudhury teach the invention as claimed in claim 17 above and Rossi further teaches wherein the two-dimensional representation indicates a relationship of a respective graph to the query graph and relevant information about the respective graph (Fig 3, a table illustrating exemplary results from a graph search engine), also Choudhury further teaches the limitation at (Fig 2, [0072], Fig 6, [0104], [0203] and Fig 16, [0230-0233]).
24. Regarding claims 5-9, those claims recite a system performs the method of claims 14-18 respectively and are rejected under the same rationale.
Respond to Amendments and Arguments
25. Applicant has amended claims 1, 3, 10, 12, and 19 to recite new features, and argued that Li in view of Rossi fails to teach one or more features of amended claims. For a number of reasons, including but not limited to, the following.
Li does not disclose or suggest that its previously-calculated vectors are stored in a knowledge base and the knowledge base is updated as recited in the context of claim 1. An advantage of updating a knowledge base including by calculating a representative graph for a cluster of vectors including the transaction data is that this is more efficient than conventional methods and is a highly scalable and low latency solution for graph searching and exploration, a benefit not recognized by Li.
Examiner presents the following responses to Applicant’s arguments:
Applicant’s 35 U.S.C. § 103 arguments on claims 1-20 has been fully considered but are moot in view of the new ground of rejection necessitated by applicant’s amendment presented above, 35 USC § 103.
CONCLUSION
26. The prior art made of record and not relied upon is considered pertinent to applicant s disclosure.
Gramatica et al (US 10528871 B1)
Tarameshloo et al (US 20200267186 A1)
Sathish et al (US 20190073434 A1)
Schwarz et al (US 20190267133 A1)
Marin et al (US 20190034780 A1)
Sloan (WO 2019008394 A1)
The Applicant’s amendment necessitated a new ground of rejection. Therefore, THIS ACTION IS MADE FINAL. Applicants are reminded of the extension of time policy as set forth in 37 C.F.R. § 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 HICHAM SKHOUN whose telephone number is (571)272-9466. The examiner can normally be reached Normal schedule: Mon-Fri 10am-6:30pm.
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/HICHAM SKHOUN/Primary Examiner, Art Unit 2164