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 08/17/2026.
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 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 1-4, 10-13 and 19-20 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) hereinafter as Li in view of Chen (US 11636486 B2) hereinafter as Chen and further in view of Hunter (US 20210073282 A1) hereinafter as Hunter.
10. Regarding claim 10, Li teaches A method, comprising:
receiving a query graph ([0046], “receive a query graph”, Fig 1, “First graph 101”, Fig 2, [0074], “process an input graph 204”, [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], [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)”, [0046-0047], “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], [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 (a knowledge graph) 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.”); and
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; adding the graph embedding to graph embeddings stored in a previous version of the knowledge base; clustering the graph embedding and the graph embeddings stored in the previous version of the knowledge base using a hierarchical agglomerative clustering model to generate a plurality of clusters; and calculating a representative graph for each of a plurality of clusters, wherein calculating the representative graph for each of the plurality of clusters comprises selecting a specific graph within the respective cluster having a lowest average distance to every other graph in the respective cluster.
However, Chen explicitly teaches updating the knowledge base including by:
receiving transaction data (Fig 3, step 602, para-32, Fig 6, para86, “At block 602, computer system 100 receives a first set of information set (e.g., transaction set 110) that describes a set of transactions between pairs of user accounts of a service.”);
transforming the transaction data into a corresponding graph (Fig 6, step 606, para-86, “At block 606, computer system 100 generates a graph model specifying nodes representing user accounts and the set of transactions as edges between pairs of nodes”);
obtaining a graph embedding for the corresponding graph (para-31, para-34);
clustering the graph embedding and the graph embeddings stored in the previous version of the knowledge base using a hierarchical agglomerative clustering model to generate a plurality of clusters (Fig 2, para-31, “attribute clustering can be applied to cluster attributed nodes (i.e., nodes representing user accounts for which attribute information is included in attribute values set 112) into a number of attribute clusters using clustering algorithms such as Agglomerative Hierarchical Clustering”); and
calculating a representative graph for each of a plurality of clusters, wherein calculating the representative graph for each of the plurality of clusters comprises selecting a specific graph within the respective cluster having a lowest average distance to every other graph in the respective cluster (para-33-37, “Euclidean distance can be used if a k-means algorithm is used to cluster attribute values.”, para-77, “Jaccard distance”).
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 Chen’s system into Li’s and by incorporating Chen into Li because both systems are related to graph search engine would analyzing transactions between user accounts of a service to determine subsets of user accounts of the service (Chen, para-1).
Further, Hunter explicitly teaches updating the knowledge base (([0085], “the system may update a knowledge set based on the event and smart contract state changes that occurred in response to the event. ”) including by:
receiving transaction data (Fig 1, step 104, “receive event messages”, [0053], [0102], “transaction between entities”, [0202], “transaction costs associated with transactions between entities.”);
transforming the transaction data into a corresponding graph ([0103], “call graph may be a privity graph”, [0110], [0186], “By using a symbolic AI model that models entity transactions using a directed graph with associated vertex categories”, [0250-0251], [0251-0254], “a transaction graph may be used to track score exchanges, asset transfers, or other transactions between multiple entities across a set of smart contract programs. For example, the vertices of the transaction graph (“transaction graph vertices”) may represent entities of the set of smart contract programs, and a set transaction graph edges may represent score changes, where the directions of the set of transaction graph edges may be used to represent a net transaction score change. The entity of a transaction graph may refer to a vertex of the transaction graph (“transaction graph vertex”) that is associated with the entity.”, see also [0257-0258]);
obtaining a graph embedding for the corresponding graph ([0361], “various other algorithms or methods may be used to determine embeddings for a graph. For example, some embodiments may use a graph factorization embedding algorithm, GraRep embedding algorithm, locally linear embedding algorithm, laplacian eigenmaps embedding algorithm, high-order proximity preserved embedding algorithm, deep network embedding for graph representation embedding algorithm, graph convolutional neural network embedding algorithm, graph2vec algorithm, or the like.”);
and further explicitly teaches adding the graph embedding to graph embeddings stored in a previous version of the knowledge base ([0085], “the system may update a knowledge set based on the event and smart contract state changes that occurred in response to the event. ”);
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 Hunter system into Topol and Chen combined system and by incorporating Hunter into Topol and Chen combined system because all systems are related to graph search engine would Provide a graph-manipulation based domain-specific execution environments (Hunter).
11. Regarding claim 11, Li, Chen and Hunetr teach the invention as claimed in claim 10 above and Li 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 ([0002], “retrieving data associated with graphs”, [0047], “outputting data (a recommendation) associated with the record associated with a candidate graph based upon the determined similarity scores for each query graph and candidate graph pair.”, [0124], “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 and to output data associated with (a recommendation) one or more records based upon the determined similarity score.”, [0130], “The system may then be used to compare different scenes on the basis of their semantic content, e.g., for identifying of classifying a scene or for identifying a group of objects defining an object or coherent part of a scene e.g., to facilitate object/scene manipulation (editing) or information extraction (interpretation).”).
12. Regarding claim 12, Li, Chen and Hunetr teach the invention as claimed in claim 10 above and Chen further 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 the 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 (Fig 2, para-31, “attribute clustering can be applied to cluster attributed nodes (i.e., nodes representing user accounts for which attribute information is included in attribute values set 112) into a number of attribute clusters using clustering algorithms such as Agglomerative Hierarchical Clustering” and para-33-37, “Euclidean distance can be used if a k-means algorithm is used to cluster attribute values.”, para-77, “Jaccard distance”).
13. Regarding claim 13, Li, Chen and Hunetr teach the invention as claimed in claim 10 above and Li further teaches wherein outputting the identified one or more similar graphs. includes outputting an ordered list of one or more vectors ([0031], [0037] “Euclidean distance”, [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”, [0107] [0124], “output data associated with one or more records based upon the determined similarity score.”).
14. Regarding claims 1-4, those claims recite a system that performs the method of claims 10-13 respectively and is rejected under the same rationale.
15. 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.
16. Claims 14-18 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) in view of Chen (US 11636486 B2) and Hunter (US 20210073282 A1) as claimed in claim 10 above and further in view of Choudhury et al (US 20180329958 A1) hereinafter as Choudhury.
17. Regarding claim 14, Li, and Topol teach the invention as claimed in claim 10 above, Li, and Topol 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, Chen and Hunter combined system and by incorporating Choudhury into Li, Chen and Hunter 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]).
18. Regarding claim 15, Li, and Topol teach the invention as claimed in claim 10 above, Li, and Topol 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, Chen and Hunter combined system and by incorporating Choudhury into Li, Chen and Hunter 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]).
19. Regarding claim 16, Li, and Topol teach the invention as claimed in claim 10 above, Li, and Topol 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, Chen and Hunter combined system and by incorporating Choudhury into Li, Chen and Hunter 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 17, Li, Topol 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]).
21. Regarding claim 18, Li, Topol and Choudhury teach the invention as claimed in claim 17 above and Li 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 ([0002], “retrieving data associated with graphs”, [0047], “outputting data (relevant information) associated with the record associated with a candidate graph based upon the determined similarity scores for each query graph and candidate graph pair.”, [0124], “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 and to output data associated with (relevant information) one or more records based upon the determined similarity score.”, [0130], “The system may then be used to compare different scenes on the basis of their semantic content, e.g., for identifying of classifying a scene or for identifying a group of objects defining an object or coherent part of a scene e.g., to facilitate object/scene manipulation (editing) or information extraction (interpretation).”).
22. 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
23. In the remark received 08/17/2026, Applicant amended claims 1 and 19-20 to clarify the subject matter regarded as the invention and argued that Li in view of Topol fail to teach one or more features of amended claims. For several reasons, including but not limited to, the following.
Neither reference teaches hierarchical agglomerative clustering.
The references do not teach "adding the graph embedding to graph embeddings stored in a previous version of the knowledge base; clustering the graph embedding and the graph embeddings stored in the previous version of the knowledge base using a hierarchical agglomerative clustering model to generate a plurality of clusters;" and calculating a representative graph for "each of a plurality of clusters", wherein calculating the representative graph "for each of the plurality of clusters" comprises selecting a specific graph within the "respective" cluster having a lowest average distance to every other graph in the "respective" cluster, as amended in the independent claims.
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. Claim 1-20 are rejected under 35 U.S.C.103 as being unpatentable over Li et al (US 20190354689 A1) in view of Chen (US 11636486 B2) and Hunter (US 20210073282 A1) and further in view of Choudhury et al (US 20180329958 A1).
CONCLUSION
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
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