DETAILED ACTION
Application No. 19/258,230 filed on 07/02/2025 has been examined. In this Office Action, claims 1-21 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/04/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP § 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto- processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5-6, 8-12, 15-21, 23-24, 26-27 of U.S. Patent No. 12,354,061 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they are cover the same recitations, limitations that are combined or worded slightly differently from the original patented claims but in essence they convey the same subject matter. The instant application is a broader scope of recitation of the claims of the already patented application. The similarities and differences are highlighted in the tabulated comparison below.
Present Application 19/258230
US PAT. 12354061 B2
1. A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: receive, by the processor, a production network model, a first Boolean parameter; perform, by the processor, a topological sort of the production network model; address, by the processor, one or more cycles present in the production network model; in a first iteration: receive, by the processor, a set of multi-level search parameters; execute, by the processor, a multi-level search in parallel; determine, by the processor, a total number of iterations based on a limit parameter; in the first iteration and subsequent iterations: determine, by the processor, a first optimal solution across all iterations; fix, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: execute, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increase, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and select, by the processor, a second optimal solution.
2. The computing apparatus of claim 1, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start.
3. The computing apparatus of claim 1, wherein when addressing the one or more cycles, the apparatus is further configured to: apply, by the processor, a cycle-breaking transformation to the production network model.
4. The computing apparatus of claim 1, wherein after the first iteration, when fixing the one or more node flows, the apparatus is further configured to: re-evaluate, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
5. The computing apparatus of claim 1, wherein after the first iteration, when fixing the one or more node flows, the apparatus is further configured to: create, by the processor, a new production network model; eliminate, by the processor, fixed nodes that have a flow of zero; and add, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
6. The computing apparatus of claim 1, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
7. The computing apparatus of claim 1, wherein the apparatus is further configured to: construct, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generate, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
8. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: receive, by a processor, a production network model, a first Boolean parameter; perform, by the processor, a topological sort of the production network model; address, by the processor, one or more cycles present in the production network model; in a first iteration: receive, by the processor, a set of multi-level search parameters; execute, by the processor, a multi-level search in parallel; determine, by the processor, a total number of iterations based on a limit parameter; in the first iteration and subsequent iterations: determine, by the processor, a first optimal solution across all iterations; fix, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: execute, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increase, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and select, by the processor, a second optimal solution.
9. The non-transitory computer-readable storage medium of claim 8, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start.
10. The non-transitory computer-readable storage medium of claim 8, wherein when addressing the one or more cycles, the computer is further configured to: apply, by the processor, a cycle-breaking transformation to the production network model.
11. The non-transitory computer-readable storage medium of claim 8, wherein after the first iteration, when fixing the one or more node flows, the computer is further configured to: re-evaluate, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
12. The non-transitory computer-readable storage medium of claim 8, wherein after the first iteration, when fixing the one or more node flows, the computer is further configured to: create, by the processor, a new production network model; eliminate, by the processor, fixed nodes that have a flow of zero; and add, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
13. The non-transitory computer-readable storage medium of claim 8, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
14. The non-transitory computer-readable storage medium of claim 8, wherein the computer is further configured to: construct, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generate, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
15. A computer-implemented method for iterative supply cascading, the method comprising: receiving, by a processor, a production network model, a first Boolean parameter; performing, by the processor, a topological sort of the production network model; addressing, by the processor, one or more cycles present in the production network model; in a first iteration: receiving, by the processor, a set of multi-level search parameters; executing, by the processor, a multi-level search in parallel; determining, by the processor, a total number of iterations based on a limit parameter; in the first iteration and subsequent iterations: determining, by the processor, a first optimal solution across all iterations; fixing, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: executing, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increasing, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and selecting, by the processor, a second optimal solution.
16. The computer-implemented method of claim 15, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start.
17. The computer-implemented method of claim 15, wherein addressing the one or more cycles comprises: applying, by the processor, a cycle-breaking transformation to the production network model.
18. The computer-implemented method of claim 15, wherein after the first iteration, fixing the one or more node flows comprises: re-evaluating, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
19. The computer-implemented method of claim 15, wherein after the first iteration, fixing the one or more node flows comprises: creating, by the processor, a new production network model; eliminating, by the processor, fixed nodes that have a flow of zero; and adding, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
20. The computer-implemented method of claim 15, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
21. The computer-implemented method of claim 15, wherein the production network model is generated by: constructing, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generating, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
1. Computer-implemented method for iterative supply cascading, the method comprising: receiving, by a processor, a production network model, a first Boolean parameter, and a second Boolean parameter; converting, by the processor, the production network model to a linear program; determining, by the processor, a first optimal solution of the linear program; performing, by the processor, a topological sort of the production network model; addressing, by the processor, one or more cycles present in the production network model; in a first iteration: receiving, by the processor, a set of multi-level search parameters; executing, by the processor, a multi-level search in parallel; receiving, by the processor, a limit parameter; determining, by the processor, a total number of iterations; in the first iteration and subsequent iterations: determining, by the processor, a second optimal solution across all iterations; fixing, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: executing, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increasing, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and selecting, by the processor, a third optimal solution.
2. The computer-implemented method of claim 1, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start; and the second Boolean parameter specifies whether to use an optimal solution of a linear relaxation as an initial warm start.
3. The computer-implemented method of claim 1, wherein addressing the one or more cycles comprises: applying, by the processor, a cycle-breaking transformation to the production network model.
5. The computer-implemented method of claim 1, wherein after the first iteration, fixing the one or more node flows comprises: re-evaluating, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
6. The computer-implemented method of claim 1, wherein after the first iteration, fixing the one or more node flows comprises: creating, by the processor, a new production network model; eliminating, by the processor, fixed nodes that have a flow of zero; and adding, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
8. The computer-implemented method of claim 1, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
9. The computer-implemented method of claim 1, wherein the production network model is generated by: constructing, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generating, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
10. Computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: receive, by the processor, a production network model, a first Boolean parameter, and a second Boolean parameter; convert, by the processor, the production network model to a linear program; determine, by the processor, a first optimal solution of the linear program; perform, by the processor, a topological sort of the production network model; address, by the processor, one or more cycles present in the production network model; in a first iteration: receive, by the processor, a set of multi-level search parameters; execute, by the processor, a multi-level search in parallel; receive, by the processor, a limit parameter; determine, by the processor, a total number of iterations; in the first iteration and subsequent iterations: determine, by the processor, a second optimal solution across all iterations; fix, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: execute, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increase, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and select, by the processor, a third optimal solution.
11. He computing apparatus of claim 10, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start; and the second Boolean parameter specifies whether to use an optimal solution of a linear relaxation as an initial warm start.
12. He computing apparatus of claim 10, wherein when addressing the one or more cycles, the apparatus is further configured to: apply, by the processor, a cycle-breaking transformation to the production network model.
15. The computing apparatus of claim 10, wherein after the first iteration, when fixing the one or more node flows, the apparatus is further configured to: re-evaluate, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
16. The computing apparatus of claim 10, wherein after the first iteration, when fixing the one or more node flows, the apparatus is further configured to: create, by the processor, a new production network model; eliminate, by the processor, fixed nodes that have a flow of zero; and add, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
17. He computing apparatus of claim 10, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
18. The computing apparatus of claim 10, wherein the apparatus is further configured to: construct, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generate, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
19. Non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: receive, by a processor, a production network model, a first Boolean parameter, and a second Boolean parameter; convert, by the processor, the production network model to a linear program; determine, by the processor, a first optimal solution of the linear program; perform, by the processor, a topological sort of the production network model; address, by the processor, one or more cycles present in the production network model; in a first iteration: receive, by the processor, a set of multi-level search parameters; execute, by the processor, a multi-level search in parallel; receive, by the processor, a limit parameter; determine, by the processor, a total number of iterations; in the first iteration and subsequent iterations: determine, by the processor, a second optimal solution across all iterations; fix, by the processor, one or more node flows for a current iteration for a subset of production nodes in the production network model; iteratively: execute, by the processor, the multi-level search in parallel, using the fixed one or more node flows; and increase, by the processor, an iteration number; until either: an objective value is less than a threshold; or the total number of iterations is reached; or a time limit is reached; and select, by the processor, a third optimal solution.
20. The non-transitory computer-readable storage medium of claim 19, wherein: the first Boolean parameter specifies whether to fix supply quantities in an output solution, or fix the supply quantities in a warm start; and the second Boolean parameter specifies whether to use an optimal solution of a linear relaxation as an initial warm start.
21. The non-transitory computer-readable storage medium of claim 19, wherein when addressing the one or more cycles, the computer is further configured to: apply, by the processor, a cycle-breaking transformation to the production network model.
23. The non-transitory computer-readable storage medium of claim 19, wherein after the first iteration, when fixing the one or more node flows, the computer is further configured to: re-evaluate, by the processor, a linear relaxation using the one or more node flows that are fixed to a respective value in a most recent optimal solution.
24. The non-transitory computer-readable storage medium of claim 19, wherein after the first iteration, when fixing the one or more node flows, the computer is further configured to: create, by the processor, a new production network model; eliminate, by the processor, fixed nodes that have a flow of zero; and add, by the processor, one or more supply nodes to replace nodes that are fixed to a positive flow.
26. The non-transitory computer-readable storage medium of claim 19, wherein parametrization of the multi-level search comprises at least one of: a warm start solution, a demand sequence, and a pass count.
27. The non-transitory computer-readable storage medium of claim 19, wherein the computer is further configured to: construct, by the processor, a representation of a Mixed Integer Programming (MIP) problem by defining nodes and arcs that are parametrized independent of input data; and generate, by the processor, one or more flow nodes, one or more production nodes and one or more arcs in the production network model for specific instances of the MIP problem by mapping the input data to parameters, each production node incorporating variables that are in three or more constraints, with the production network model incorporating one or more directionality bits.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
Chen et al US 20250053828 A1 discloses Method for Solving Task, Involves Determining Target Parallel Mode, And Solving First Sub-Node and Second Sub-node In Parallel on First Device and Second Device According to Target Parallel Mode.
Baumgartner et al US 9280626 B2 discloses Method for Determining Boolean Satisfiability in Data Processing System I.e. Distributed Data Processing System, Involves Adding Constraint to List of Non-affecting Constraints, and Returning Satisfied Result.
Moll et al US 9396163 B2 discloses Method for Selecting Optimized Solution in Computerized Multiple-constraint Problem Space of E.g. Computer System, Involves Determining Solution to Quadratic Function and New Constraints Using Quadratic Programming Solver.
Zeng et al US 20160335223 A1 discloses METHODS AND SYSTEMS FOR COMPUTATION OF BILEVEL MIXED INTEGER PROGRAMMING PROBLEMS.
Di Cairano et al US 20230074148 A1 discloses Controller for Optimizing Motion Trajectory to Control Motion of One Or More Devices.
Narisetty et al US 20140122390 A1 discloses Systems and Methods for Conflict Resolution and Stabilizing Cut Generation in A Mixed Integer Program Solver.
Kamath et al US 8429035 B1 discloses System and Method of Solving Large Scale Supply Chain Planning Problems with Integer Constraints.
Weber et al US 20020156663 A1 discloses Shipping and Transportation Optimization System and Method.
Conclusion
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/Mohammad A Sana/Primary Examiner, Art Unit 2166
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