DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 4/10/2023 and the Remarks and Amendments filed on 3/2/2026. Acknowledgment is made with respect to a claim of priority to PCT Application PCT/IN2020/050877 filed on 10/12/2020.
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.
Claims 1-3, 12-14, 17, and 26 are rejected under 35 U.S.C. § 103 as being obvious over Shaydulin et al. (Shaydulin et al., “Network community detection on small quantum computers”, May 13, 2019, arXiv:1810.12484v4, pp. 1-26, hereinafter “Shaydulin”) in view of Blondel et al. (Blondel et al., “Fast unfolding of communities in large networks”, Jul. 25, 2008, arXiv:0803.0476v2, pp. 1-12, hereinafter “Blondel”) and Lin et al. (Lin et al., “Modularity-Based User-Centric Clustering and Resource Allocation for Ultra Dense Networks”, Oct. 18, 2018, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 67, NO. 12, 12457-12461, hereinafter “Lin”).
Regarding claim 1, Shaydulin discloses [a] method of using a quantum computing device to detect a group of interconnected nodes in a communication network, the method comprising: (Pages 9-10, §3; “To address the challenges outlined above, we introduce the QLS algorithm. QLS is a hybrid quantum-classical local-search approach, inspired by numerous existing local-search heuristics. QLS is motivated by the successful application of local-search heuristics to a variety of optimization problems. The novelty of QLS is that it can utilize both quantum annealers and universal quantum computers. In this work, we apply QLS to the problem of 2-community detection on graphs, but the success and versatility of local-search heuristics make us confident that QLS can be extended to other optimization problems. In QLS for community detection, the local search starts with a random assignment of communities to vertices and attempts to iteratively optimize the current community assignment of a subset of vertices with the goal of increasing modularity”, which discloses a method of using quantum computing device or annealer to detect a group of interconnected nodes (community detection) in a communication network; and Abstract; and Algorithm 1)
maximising modularity using the quantum computing device (Page 10, §3; “In QLS for community detection, the local search starts with a random assignment of communities to vertices and attempts to iteratively optimize the current community assignment of a subset of vertices with the goal of increasing modularity”)
Shaydulin fails to explicitly disclose but Blondel discloses determining initial groups of adjacent nodes based on maximising modularity; (Page 4, §2; “First, we assign a different community to each node of the network. So, in this initial partition there are as many communities as there are nodes. Then, for each node i we consider the neighbours j of i and we evaluate the gain of modularity that would take place by removing i from its community and by placing it in the community of j. The node i is then placed in the community for which this gain is maximum (in case of a tie we use a breaking rule), but only if this gain is positive. If no positive gain is possible, i stays in its original community. This process is applied repeatedly and sequentially for all nodes until no further improvement can be achieved and the first phase is then complete”, which discloses determining initial groups of adjacent nodes I and j based on maximizing modularity or having a maximum gain)
detecting a group of interconnected nodes by grouping the determined initial groups of adjacent nodes based on maximising modularity (Page 4, §2; “First, we assign a different community to each node of the network. So, in this initial partition there are as many communities as there are nodes. Then, for each node i we consider the neighbours j of i and we evaluate the gain of modularity that would take place by removing i from its community and by placing it in the community of j. The node i is then placed in the community for which this gain is maximum (in case of a tie we use a breaking rule), but only if this gain is positive. If no positive gain is possible, i stays in its original community. This process is applied repeatedly and sequentially for all nodes until no further improvement can be achieved and the first phase is then complete … The second phase of the algorithm consists in building a new network whose nodes are now the communities found during the first phase”, which discloses detecting a group of interconnected nodes by grouping the determined initial groups of adjacent nodes based on maximizing modularity).
Shaydulin and Blondel are analogous art because both are concerned with community detection methods. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in community detection to combine the determining of adjacent and interconnected nodes of Blondel with the method and quantum computing device of Shaydulin to yield to the predictable result of determining initial groups of adjacent nodes based on maximising modularity using the quantum computing device; detecting a group of interconnected nodes by grouping the determined initial groups of adjacent nodes based on maximising modularity. The motivation for doing so would be to provide a simple method to extract the community structure of large networks (Blondel; Abstract).
Shaydulin fails to explicitly disclose but Lin discloses configuring resources of the communication network based on the detected group of interconnected nodes with maximized modularity (Page 12459, Column 2; “Based upon the above graph G, we consider the problem of maximising the modularity Q of the network by iteratively evaluating the gain in modularity, denoted by ΔQ”, which discloses maximizing modularity of a communication network based on a group of connected nodes or resource blocks; and Page 12460, Column 1; “The whole process is iterated until there is no further improvement and a maximum value of Q is attained … Having stored the MUC clustering results in Y , the re source allocation becomes vital in order to deal with both the intra-MUC cluster interference and the inter-MUC cluster interference … To this end, below we design a heuristic resource allocation solution. To be specific, by confining the maximum size of the MUC clusters, i.e, |Uc|≤N, ∀c, we sequentially assign orthogonal RBs to the UEs in Uc from the RB set Nc = {1,...,|Uc|}, and finally the actual number of RBs in use becomes max{|Uc|},∀c ∈ [1,C]”; and Algorithm 1; the algorithm discloses configuring resources of a communication network (“sequentially assigning RBs from Nc”) based on a group of interconnected nodes or resource blocks (RBs) with a maximized modularity “Repeat step 6 and 8 until max{Q} attained”, where Q is the modularity metric).
Shaydulin, Blondel, and Lin are analogous art because both are concerned with community detection in networks. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in community detection to combine the resource allocation of Lin with the method and quantum computing device of Shaydulin and Blondel to yield to the predictable result of configuring resources of the communication network based on the detected group of interconnected nodes with maximized modularity. The motivation for doing so would be to improve the exploitation of orthogonal resource blocks in ultra dense networks (Lin; Page 12457, Column 2).
Regarding claim 12, it is an apparatus claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 26, it is a computer storage medium claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claims 2 and 13, the rejection of claims 1 and 12 are incorporated and Shaydulin fails to explicitly disclose but Blondel discloses wherein detecting the group of interconnected nodes comprises grouping the determined initial groups of adjacent nodes where modularity is increased (Page 4, §2; “First, we assign a different community to each node of the network. So, in this initial partition there are as many communities as there are nodes. Then, for each node i we consider the neighbours j of i and we evaluate the gain of modularity that would take place by removing i from its community and by placing it in the community of j. The node i is then placed in the community for which this gain is maximum (in case of a tie we use a breaking rule), but only if this gain is positive. If no positive gain is possible, i stays in its original community. This process is applied repeatedly and sequentially for all nodes until no further improvement can be achieved and the first phase is then complete”).
The motivation to combine Shaydulin and Blondel is the same as discussed above with respect to claim 1.
Regarding claims 3 and 14, the rejection of claims 1, 2, 12, and 13 are incorporated and Shaydulin fails to explicitly disclose but Blondel discloses iteratively grouping the determined initial groups of nodes until modularity no longer increases (Page 4, §2; “First, we assign a different community to each node of the network. So, in this initial partition there are as many communities as there are nodes. Then, for each node i we consider the neighbours j of i and we evaluate the gain of modularity that would take place by removing i from its community and by placing it in the community of j. The node i is then placed in the community for which this gain is maximum (in case of a tie we use a breaking rule), but only if this gain is positive. If no positive gain is possible, i stays in its original community. This process is applied repeatedly and sequentially for all nodes until no further improvement can be achieved and the first phase is then complete”).
The motivation to combine Shaydulin and Blondel is the same as discussed above with respect to claim 1.
Regarding claim 17, the rejection of claims 12-14 are incorporated and Shaydulin further discloses wherein the oracle comprises an adjacency matrix of the network and a second matrix dependent on the degrees of the nodes (Page 7, §1; “where the variables si ∈ {−1, +1} indicate community assignment of vertex i (si = −1 meaning vertex i is assigned to the first community and sj = +1 meaning that vertex j is assigned to the second community), ki is a degree of i ∈ V , and A is the adjacency matrix of G. In this work, we focus on clustering the network into two communities”).
Response to Arguments
Applicant’s arguments and amendments, filed on 3/2/2026, with respect to the objection to the specification have been fully considered and are persuasive. The objection to the specification is withdrawn.
Applicant’s arguments and amendments, filed on 3/2/2026, with respect to the objection to claims 6, 7, and 17-20 have been fully considered and are persuasive. The objection to claims 6, 7, and 17-20 is withdrawn.
Applicant’s arguments and amendments, filed on 3/2/2026, with respect to the 35 USC § 112(b) rejection of claims 5-8, 17-23, and 26 have been fully considered and are persuasive. The 35 USC § 112(b) rejection of claims 5-8, 17-23, and 26 is withdrawn.
Applicant’s arguments and amendments, filed on 3/2/2026, with respect to the 35 USC § 101 rejection of the pending claims have been fully considered and are persuasive. The 35 USC § 101 rejection of the pending claims is withdrawn.
Applicant’s arguments and amendments, filed on 3/2/2026, with respect to the 35 USC § 103 rejection of the pending claims have been fully considered but are moot because the arguments do not apply to any of the references used to presently reject the independent claims. Shaydulin, Blondel, and Lin are now being used to render the independent claims obvious under 35 USC § 103.
Conclusion
Claims 5-8, 16, and 18-23 have been searched, but no prior art was uncovered.
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.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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