CTFR 18/091,979 CTFR 79921 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 This is a Final Office Action of the instant application 18/091,979 (hereinafter the ‘979 application), responsive to the Amendment file 3/17/2026. The ‘979 application was filed on 12/30/2022. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nikopour et al., U.S. Publication No. 2024/0023028, hereinafter Nikopour, Bahnasy et al., U.S. Publication Patent No. 2023/0124663, hereinafter Bahnasy, and Ojo et al., U.S. Patent No. 12,093,322, hereinafter Ojo . With regard to claims 1, 8, and 15, which teach “A computer-implementable method for generalized flowchart for deployment recommendation of a hardware infrastructure configurations at a customer site comprising:” , Nikopour teaches a system and method for using AI on graph based network configuration data to deploy a recommendation for a hardware infrastructure configuration to a customer (see paragraph 121]. With regard to claims 1, 8, and 15, which teach converting a fabric diagram representing the hardware infrastructure, wherein hardware infrastructure is converted to a multigraph;” Nikopour teaches converting the network configuration into a multigraph (see paragraphs 52 and 71 and figures 2 and 3). With regard to claims 1, 8, and 15, which teach “wherein nodes correspond to hardware components and edges represent inter-component communication links;” Nikopour teaches nodes corresponding to hardware components and edges corresponding to inter-connections between nodes (see paragraphs 70 and 71 and figures 2 and 3) With regard to claims 1, 8, and 15, which teach creating an augmented matrix, A, from the multigraph;” Nikopour teaches creating an adjacency matrix from the multigraph (see paragraph 70). With regard to claims 1, 8, and 15, which teach deriving feature matrix, X, from the multigraph;” Nikopour teaches creating an feature matrix from the multigraph (see paragraphs 75, 86, and 100). With regard to claims 1, 8, and 15, which teach using a multi-layer graph convolution network (GCN), processing augmented matrix, A, and feature matrix, X, to determine a predicted score for the hardware infrastructure;” Nikopour teaches using a Graph Convolution Network (GCN), adjacency matrix, and feature matrix to predict a suitable network configuration (see paragraphs 17 and 121). With regard to claims 1, 8, and 15, which teach providing a recommendation based on a minimal acceptable score” , Nikopour teaches scoring the results (see paragraph 147) and determining optimization (see paragraph 86), but doesn’t specifically teach providing the recommendation based on a minimal acceptable score. Bahnasy teaches a similar system for using network fabrics (see figure 5), multigraphs (see figure 6), and resultant matrices to determine network configuration optimization (see paragraph 77-80, 105-109, and 122-123), but further goes on to describe only providing the result of the testing once a minimum amount of network requirements are satisfied (see paragraphs 139-142 and figure 9). It would have been obvious to one of ordinary skill in the art at the time of the invention to use the minimum score met test of Bahnasy in the network optimization system of Nikopour, to assure a sufficient result is reached prior to sharing configuration data. While Nikopour does teach use of normalization, in paragraphs 157 and 173, and discusses adjacency matrixes. Ojo better teaches the combination of the two concepts in a ‘normalized’ ‘adjacency matrix’ (see 16:1-14). Ojo teaches a system for providing recommendations to a user based on a graph, utilizing an adjacency matrix, a feature matrix, and a GNN/CNN to provide recommendations (see 8:64-9:10; 13:35-60; 1416-55; and 15:1-42 and in figures 4-8). Ojo further specifically teaches the use of normalization in adjacency matrixes (see 15:54-16:14). It would have been obvious to one of ordinary skill in the art at the time of the invention to use the normalized adjacency matrix of Ojo in the network optimization systems of Nikopour and Bahnasy, to condense values stabilizing training data. Furthermore, Graph Convolutional Networks are known in the art to utilize both node features and connectivity features (adjacency) to learn about the graph structure, while specifically using a normalized adjacency in the matrix to keep extreme values from overtaking the simulation. (see Graph Convolutional Networks (GCN) & Pooling by Jonathan Hui, published on Medium.com Graph Convolutional Networks (GCN) & Pooling | by Jonathan Hui | Medium and Learning Graph Normalization for Graph Neural Networks, by Chen et al., from Neurocomputing published on ScienceDirect Learning graph normalization for graph neural networks - ScienceDirect). With regard to claims 2, 9, and 16, which teach “wherein the fabric diagram is a spine leaf architecture , Nikopour teaches the fabric diagram of the network structure being of a spine leaf structure (see paragraph 52 and figure 2). With regard to claims 3, 10, and 17, which teach “wherein switching components having particular features are represented in the multigraph , Nikopour teaches the multigraph depicts all network components (switch, hub, bridge, or other network element) according to their defining feature in the multigraph (see paragraphs 27, 28, and 71 and figure 3). With regard to claims 4, 11, and 18, which teach “wherein the augmented matrix, A, is created based on degree of connectivity and functionality of nodes of the multigraph , Nikopour teaches an adjacency matrix constructed based upon the connectivity of each component and associated function (see paragraph 70). With regard to claims 5, 12, and 19, which teach “, wherein the feature matrix, X, includes feature vectors , Nikopour teaches creating an feature matrix from the defined feature vectors of network elements (see paragraphs 75, 86, and 100). With regard to claims 6, 13, and 20, which teach “wherein the multi-layer graph convolution network (GCN) is three layers , Nikopour teaches a three level GCN (see paragraph 86 and figure 5) With regard to claims 7 and 14, which teach “wherein the minimal acceptable score is predetermined , Nikopour teaches scoring the results (see paragraph 147) and determining optimization (see paragraph 86). Bahnasy further teaches providing the result of the testing once a minimum predetermined amount of network requirements are satisfied (see paragraphs 139-142 and figure 9). Response to Arguments Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely the same combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Summary Claims 1-20 are REJECTED. Conclusion 07-40 AIA 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 DENNIS G BONSHOCK whose telephone number is (571)272-4047. The examiner can normally be reached M-F 7:15 - 4:45. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Kosowski can be reached at (571) 272-3744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992 Application/Control Number: 18/091,979 Page 2 Art Unit: 3992 Application/Control Number: 18/091,979 Page 3 Art Unit: 3992 Application/Control Number: 18/091,979 Page 4 Art Unit: 3992 Application/Control Number: 18/091,979 Page 5 Art Unit: 3992 Application/Control Number: 18/091,979 Page 6 Art Unit: 3992 Application/Control Number: 18/091,979 Page 7 Art Unit: 3992 Application/Control Number: 18/091,979 Page 8 Art Unit: 3992