CTFR 18/309,625 CTFR 87138 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 action is responsive to the Amendment filed on 3/19/2026. Claims 1, 8, and 15 have been amended. Claims 7 and 14 have been canceled. Claims 1-6, 8-13 and 15-20 are pending in the case. Claims 1, 8, and 15 are independent claims. Specification The title of the invention has been amended and considered. Information Disclosure Statement The information disclosure statement (IDS) submitted on 2/06/5026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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 “Exact Combinatorial Optimization with Graph Convolutional Neural Networks”, Gasse et al, 10/30/2019 in view of Challita et al (US 20230217264 A1) . Referring to claims 1, 8 and 15, Gasse system, comprising: an agent engine; an encoder; a general-purpose solver engine; an automated policy search engine ; an orchestrator coupled to the agent engine, the general-purpose solver engine, and the encoder ( the Specification does not give the agent engine, encoder, general-purpose solver engine and orchestrator any specific function besides that they can process data, under BRI, these are software components with in a computer processor that process data and evaluate data, on pages 2-3 of Gasse, the system is able to evaluate problems and follow branching rules to solve the problems ) wherein the orchestrator is configured to: receive a first problem instance corresponding to a learned policy; ( pages 4-5 of Gasse, where B&B episode is to solve a MILP instance/problem ) and provide the first problem instance to the general-purpose solver engine, ( pages 2-3 of Gasse, the system is able to evaluate problems and follow branching rules to solve the problems ) wherein the general-purpose solver engine is configured to execute based on the first problem instance to determine a solver state, wherein the orchestrator is configured to: extract, in response to providing the first problem instance to the general-purpose solver and from the general-purpose solver engine, ( page 4 of Gasse, “Consider the solver to be the environment, and the brancher the agent. At the t th decision the solver is in a state st, which comprises the B&B tree with all past branching decisions, the best integer solution found so far, the LP solution of each node, the currently focused leaf node, as well as any other solver statistics (such as, for example, the number of times every primal heuristic has been called). The brancher then selects a variable at among all fractional variables A(st) ⊆ {1, . . . , p} at the currently focused node, according to a policy π(at | st). The solver in turn extends the B&B tree, solves the two child LP relaxations, runs any internal heuristic, prunes the tree if warranted, and finally selects the next leaf node to split. We are then in a new state st+1, and the brancher is called again to take the next branching decision. This process, illustrated in Figure 1, continues until the instance is solved, i.e., until there are no leaf node left for branching.” ) the solver state, wherein the solver state comprises a number of fixed variables ; ( page 5 and Fig. 2 of Gasse, number of fixed variables, i.e., n=3 of a search tree ) and provide the solver state to the encoder, wherein the encoder generates a fixed length embedding of the solver state into an encoded solver state is configured to query the agent engine for a best action according to the learned policy and an encoded solver state, ( pages 3-5 of Gasse, the search tree helps find the best solutions based on policy for the variables of the problem. Further, page 5 and Fig. 2 of Gasse, encoder the nodes and edge features (G, C, E, V) into embeddings ) wherein the agent engine is configured to determine the best action according to the learned policy and the encoded solver state, “ via offline learning ” ( page 2 of Gasse, learning a branching rule offline on collection of similar data ) and wherein the orchestrator is configured to: receive the best action; and direct the general-purpose solver to implement the best action. ( page 3 of Gasse, “he branch-and-bound algorithm [52, Ch. II.4], in its simplest formulation, repeatedly performs this binary decomposition, giving rise to a search tree. By design, the best LP solution in the leaf nodes of the tree provides a lower bound to the original MILP, whereas the best integral LP solution (if any) provides an upper bound.” ) Even though the specification does not explain what is considered rollout data from the encoder and pages 2-3 of Gasse, the learning of branching rules can also be learned offline on a collection of similar instances, but Gasse fails to specifically disclose a method “that is based on auto reinforcement learning”, “ a depth of a search tree ” and “” and “ wherein the automated search policy engine is configured to: receive rollout data from the encoder; determine the learned policy using the rollout data; and provide the learned policy to the agent engine for use during an interference phase ”. However, Challita discloses a method “that is based on auto reinforcement learning” ( [0058]-[0060] of Challita ) “ a depth of a search tree ” ( [0107] of Challita, depth of sub-trees of the policy ) and “” and “ wherein the automated search policy engine is configured to: receive rollout data from the encoder; determine the learned policy using the rollout data; and provide the learned policy to the agent engine for use during an interference phase ”. ( [0061] of Challita, run simulated rollout data (for policy) ) Gasse and Challita are analogous art because both references concern reinforcement learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Gasse’s reinforcement policy learning using B&B trees with Monte Carol tree search to obtain policy vector as taught by Challita. The motivation for doing so would have been improving searching through vectors of a search tree for better future prediction. Referring to claims 2, 9 and 16, Gasse in view of Challita disclose the system of claim 1, wherein the best action corresponds to one or more branching policies. ( page 3 of Gasse, “he branch-and-bound algorithm [52, Ch. II.4], in its simplest formulation, repeatedly performs this binary decomposition, giving rise to a search tree. By design, the best LP solution in the leaf nodes of the tree provides a lower bound to the original MILP, whereas the best integral LP solution (if any) provides an upper bound.” ) Referring to claims 3, 10 and 17, Gasse in view of Challita disclose the system of claim 2, wherein the solver state comprises a number of fixed variables and a depth of a search tree. ( page 4 of Gasse, “Consider the solver to be the environment, and the brancher the agent. At the t th decision the solver is in a state st, which comprises the B&B tree with all past branching decisions, the best integer solution found so far, the LP solution of each node, the currently focused leaf node, as well as any other solver statistics (such as, for example, the number of times every primal heuristic has been called). The brancher then selects a variable at among all fractional variables A(st) ⊆ {1, . . . , p} at the currently focused node, according to a policy π(at | st). The solver in turn extends the B&B tree, solves the two child LP relaxations, runs any internal heuristic, prunes the tree if warranted, and finally selects the next leaf node to split. We are then in a new state st+1, and the brancher is called again to take the next branching decision. This process, illustrated in Figure 1, continues until the instance is solved, i.e., until there are no leaf node left for branching.” ) Referring to claims 4, 11 and 18, Gasse in view of Challita disclose the system of claim 3, wherein the learned policy is configured to use the number of fixed variables and the depth to establish one or more variables to branch on. ( Fig. 1 and page 4 of Gasse, “Consider the solver to be the environment, and the brancher the agent. At the t th decision the solver is in a state st, which comprises the B&B tree with all past branching decisions, the best integer solution found so far, the LP solution of each node, the currently focused leaf node, as well as any other solver statistics (such as, for example, the number of times every primal heuristic has been called). The brancher then selects a variable at among all fractional variables A(st) ⊆ {1, . . . , p} at the currently focused node, according to a policy π(at | st). The solver in turn extends the B&B tree, solves the two child LP relaxations, runs any internal heuristic, prunes the tree if warranted, and finally selects the next leaf node to split. We are then in a new state st+1, and the brancher is called again to take the next branching decision. This process, illustrated in Figure 1, continues until the instance is solved, i.e., until there are no leaf node left for branching.” ) Referring to claims 5, 12 and 19, Gasse in view of Challita disclose the system of claim 4, wherein the one or more branching policies comprise the one or more variables. ( Fig. 1 and page 4 of Gasse, branching policies are associated with variables) Referring to claims 6, 13 and 20, Gasse discloses the system of claim 5, wherein the orchestrator being configured to direct the general-purpose solver to implement the best action comprises the orchestrator being configured to direct the general-purpose solver to branch on the one or more variables. ( Fig. 1 and pages 3-4 of Gasse, branch on one or more variables to provide best action/solution ) Response to Arguments 101 rejections are withdrawn in light of the amendments. Applicant’s arguments with respect to claims 1-6, 8-13 and 15-20 have been considered and are persuasive. Previous rejection is withdrawn. A new reference is used and the current arguments do not apply to the newly cited reference that renders the claims obvious. 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure : Cao et al (CN 114819857 A): The invention claims a model training method, optimizing method of goods stacking, device and electronic device, relating to data processing field, especially deep learning to the technical field of deep learning The specific implementation scheme is as follows: model training method, applied to the cargo stacking optimization problem, the method comprising: pre-constructing I neural network based on the mixed integer programming solver, using the first training data, carrying out iterative training to I neural network model to obtain I target neural network model; wherein, the jth of the neural network parameter of the model is associated with the j-1 of neural network the model training, neural network model is used for the received function information in the process of mixing integer programming, determining the decision variables of each node solving the tree; the first training data comprises the label data of the target decision variable corresponding to each branch node in the solving tree. The invention can optimize the flow of goods stacking. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://;www.uspto.gov/patent/laws-and-regulations/interview-practice. Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e- mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II). 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 HAIMEI JIANG whose telephone number is (571)270-1590. The examiner can normally be reached M-F 9-5pm. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HAIMEI JIANG/Primary Examiner, Art Unit 2142 Application/Control Number: 18/309,625 Page 2 Art Unit: 2142 Application/Control Number: 18/309,625 Page 3 Art Unit: 2142 Application/Control Number: 18/309,625 Page 4 Art Unit: 2142 Application/Control Number: 18/309,625 Page 5 Art Unit: 2142 Application/Control Number: 18/309,625 Page 6 Art Unit: 2142 Application/Control Number: 18/309,625 Page 7 Art Unit: 2142 Application/Control Number: 18/309,625 Page 8 Art Unit: 2142 Application/Control Number: 18/309,625 Page 9 Art Unit: 2142 Application/Control Number: 18/309,625 Page 10 Art Unit: 2142 Application/Control Number: 18/309,625 Page 11 Art Unit: 2142
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