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 . This Office Action is in reply to communication filed on 03/21/2025. Claimed priority is granted from Provisional application filed on 02/22/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2025 was filed after the mailing date of the of the original application filed on 03/21/2025. The submission 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
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 1-8, 10-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al, US 2023034471 A1, in view of Kadioglu, US 20240232652 A1.
Regarding claim 1, Brown teaches the invention substantially as claimed. Brown discloses a computer-implemented method, comprising:
receiving declarations describing one or more components in a radio access network (RAN) (par. 0005, 0008, 0010, 0042, and 0048);
generating a declarative model of the RAN based on the declarations, the declarative model comprising a plurality of constraints (0005, 0013, 0051-0057);
performing an analysis on the declarative model including reasoning about the plurality of constraints (0014-0019, 0052-0058, 0067-0075);
identifying a solution for the RAN based on the analysis of the declarative model; and providing the solution to a user of the RAN (0049-0050, 0059-0061, 0065-0076).
Brown, however, does not expressly disclose reasoning about the plurality of constraints using a constraint-solving or constraint-satisfaction framework.
Kadioglo teaches performing reasoning over a plurality of constraints using a prescriptive reasoning model. Specifically, Kadioglu teaches a model configured with objectives, variables, and multiple constraints (par. 0035-0048); constraint satisfaction mechanisms including linking constraints, distribution constraints, and all-different constraints (0049-0053); search heuristics, variable ordering, and value ordering for evaluating candidate solutions (0054-0058); and a search process in which solutions are determined through variable-value assignments (0060). Kadioglu further teaches a reasoning model that solves a problem structure to satisfy constraints and preferences and selects preferred feasible solutions from among candidate solutions (0063-0065).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Brown’s reconciliation engine to employ the constraint-reasoning techniques taught by Kadioglu. Such a modification would have predictably improved Brown’s ability to automatically determine network configurations satisfying multiple declared intent reasoning and search techniques to identify feasible solutions that satisfy multiple competing constraints and objectives, thereby improving the accuracy, consistency, and automation of network configuration decisions.
As modified, Brown’s instant-based network management system would receive declarations, generate declarative models containing constraints, reason about those constraints using the constraints-solving techniques taught by Kadioglu, identify a solution satisfying the constraints, and provide the resulting solution, thereby meeting all limitations of claim 1. By this rationale, claim 1 is rejected.
Regarding claims 2-8, 10-17, 19 and 20, the combination Brown-Kadioglu teaches:
2. The computer-implemented method of claim 1, wherein the declarations include at least metadata on RAN functions, a description of dependencies between the RAN functions and flows for the one or more components, and hardware constraints comprising specifications on resources of the RAN (Brown, 0009, 0019).
3. The computer-implemented method of claim 1, wherein performing the analysis includes using a Boolean satisfiability problem (SAT) or Satisfiability Modulo Theories (SMT) solver (See data analysis module 106a and par. 0062). The Examiner takes official notice that using a SAT or SMT to perform data analysis in the context of the invention is well-known in the art.
4. The computer-implemented method of claim 1, further comprising receiving at least a subset of the plurality of constraints from the user of the RAN, the subset including deployment constraints (Kadioglu, par. 0065).
5. The computer-implemented method of claim 1, further comprising storing the plurality of constraints in a repository of known facts and constraints associated with the RAN (Brown, par. 0046, disclosure of claims 1 and 12).
6. The computer-implemented method of claim 1, further comprising: searching, using artificial intelligence (Al) reasoning, through a test space corresponding to the RAN to identify an example of a feasible solution; and providing the example of the feasible solution to the user of the RAN (Kadioglu, 0028, 0054-0058, 0060-0065).
7. The computer-implemented method of claim 1, wherein the solution includes (i) a pass or fail determination for a test for the one or more components of the RAN based on the plurality of constraints, (ii) one or more constraints that represent conditions under which the test will pass or fail, and (iii) reasons for the pass or failure of the test (Kadioglu, par. 0063).
8. The computer-implemented method of claim 1, further comprising: generating a configuration file implementable by the one or more components in the RAN; and deploying the configuration file in a test space associated with the RAN (Kadioglu, par. 0063; Brown par. 0043 and 0046).
10. A system, comprising: a processor; and a memory (Brown, fig. 5, items 502 and 504); comprising instructions stored thereon, which when executed by the processor, causes the processor to perform: receiving declarations describing one or more components in a radio access network (RAN) RAN) (Brown, par. 0005, 0008, 0010, 0042, and 0048);
generating a declarative model of the RAN based on the declarations, the declarative model comprising a plurality of constraints (Brown, 0005, 0013, 0051-0057); performing an analysis on the declarative model including reasoning about the plurality of constraints (Brown, 0014-0019, 0052-0058, 0067-0075); identifying a solution for the RAN based on the analysis of the declarative model; and providing the solution to a user of the RAN (Brown, 0049-0050, 0059-0061, 0065-0076).
Brown, however, does not expressly disclose reasoning about the plurality of constraints using a constraint-solving or constraint-satisfaction framework.
Kadioglo teaches performing reasoning over a plurality of constraints using a prescriptive reasoning model. Specifically, Kadioglu teaches a model configured with objectives, variables, and multiple constraints (par. 0035-0048); constraint satisfaction mechanisms including linking constraints, distribution constraints, and all-different constraints (0049-0053); search heuristics, variable ordering, and value ordering for evaluating candidate solutions (0054-0058); and a search process in which solutions are determined through variable-value assignments (0060). Kadioglu further teaches a reasoning model that solves a problem structure to satisfy constraints and preferences and selects preferred feasible solutions from among candidate solutions (0063-0065).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Brown’s reconciliation engine to employ the constraint-reasoning techniques taught by Kadioglu. Such a modification would have predictably improved Brown’s ability to automatically determine network configurations satisfying multiple declared intent reasoning and search techniques to identify feasible solutions that satisfy multiple competing constraints and objectives, thereby improving the accuracy, consistency, and automation of network configuration decisions.
As modified, Brown’s instant-based network management system would receive declarations, generate declarative models containing constraints, reason about those constraints using the constraints-solving techniques taught by Kadioglu, identify a solution satisfying the constraints, and provide the resulting solution, thereby meeting all limitations of claim 1. By this rationale, claim 10 is rejected.
11. The system of claim 10, wherein the declarations include at least metadata on RAN functions, a description of dependencies between the RAN functions and flows for the one or more components, and hardware constraints comprising specifications on resources of the RAN (Brown, 0009, 0019).
12. The system of claim 10, wherein performing the analysis includes using a Boolean satisfiability problem (SAT) or Satisfiability Modulo Theories (SMT) solver (See data analysis module 106a and par. 0062). The Examiner takes official notice that using a SAT or SMT to perform data analysis in the context of the invention is well-known in the art. (See data analysis module 106a and par. 0062).
13. The system of claim 10, further comprising stored sequences of instructions, which when executed by the processor, cause the processor to perform receiving at least a subset of the plurality of constraints from the user of the RAN, the subset including deployment constraints (Kadioglu, par. 0065).
14. The system of claim 10, further comprising stored sequences of instructions, which when executed by the processor, cause the processor to perform storing the plurality of constraints in a repository of known facts and constraints associated with the RAN (Brown, par. 0046, disclosure of claims 1 and 12).
15. The system of claim 10, further comprising stored sequences of instructions, which when executed by the processor, cause the processor to perform: searching, using artificial intelligence (Al) reasoning, through a test space corresponding to the RAN to identify an example of a feasible solution; and providing the example of the feasible solution to the user of the RAN (Kadioglu, 0028, 0054-0058, 0060-0065).
16. The system of claim 10, wherein the solution includes (i) a pass or fail determination for a test for the one or more components of the RAN based on the plurality of constraints, (ii) one or more constraints that represent conditions under which the test will pass or fail, and (iii) reasons for the pass or failure of the test (Kadioglu, par. 0063).
17. The system of claim 10, further comprising stored sequences of instructions, which when executed by the processor, cause the processor to perform: generating a configuration file implementable by the one or more components in the RAN; and deploying the configuration file in a test space associated with the RAN (Kadioglu, par. 0063; Brown par. 0043 and 0046).
19. A non-transitory computer-readable storage medium comprising instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising: receiving declarations describing one or more components in a radio access network (RAN) (Brown, par. 0005, 0008, 0010, 0042, and 0048);generating a declarative model of the RAN based on the declarations, the declarative model comprising a plurality of constraints (Brown, 0005, 0013, 0051-0057); performing an analysis on the declarative model including reasoning about the plurality of constraints (Brown, 0014-0019, 0052-0058, 0067-0075); identifying a solution for the RAN based on the analysis of the declarative model; and providing the solution to a user of the RAN (Brown, 0049-0050, 0059-0061, 0065-0076).
Brown, however, does not expressly disclose reasoning about the plurality of constraints using a constraint-solving or constraint-satisfaction framework.
Kadioglo teaches performing reasoning over a plurality of constraints using a prescriptive reasoning model. Specifically, Kadioglu teaches a model configured with objectives, variables, and multiple constraints (par. 0035-0048); constraint satisfaction mechanisms including linking constraints, distribution constraints, and all-different constraints (0049-0053); search heuristics, variable ordering, and value ordering for evaluating candidate solutions (0054-0058); and a search process in which solutions are determined through variable-value assignments (0060). Kadioglu further teaches a reasoning model that solves a problem structure to satisfy constraints and preferences and selects preferred feasible solutions from among candidate solutions (0063-0065).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Brown’s reconciliation engine to employ the constraint-reasoning techniques taught by Kadioglu. Such a modification would have predictably improved Brown’s ability to automatically determine network configurations satisfying multiple declared intent reasoning and search techniques to identify feasible solutions that satisfy multiple competing constraints and objectives, thereby improving the accuracy, consistency, and automation of network configuration decisions.
As modified, Brown’s instant-based network management system would receive declarations, generate declarative models containing constraints, reason about those constraints using the constraints-solving techniques taught by Kadioglu, identify a solution satisfying the constraints, and provide the resulting solution, thereby meeting all limitations of claim 1. By this rationale, claim 16 is rejected.
20. The non-transitory computer-readable storage medium of claim 19, further comprising stored instructions, which when executed by the processor, cause the processor to perform refining the declarative model based on results of deploying a configuration file in a test space, wherein refining the declarative model includes at least one of adding a new constraint about operations of the RAN to the plurality of constraints or updating one of the plurality of constraints (Brown, 0014-0019, 0052-0058, 0067-0075).
Allowable Subject Matter
Claims 9 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jude Jean-Gilles whose telephone number is 571-272-3914. The examiner can normally be reached on Mon-Fri, from 9:00AM-5:00PM.
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/JUDE JEAN GILLES/Primary Examiner, Art Unit 2459
June 24, 2026
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