DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4-10, 12-16, and 20 rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
As to claims 4, 12, and 20, Applicants failed to define every element of the formula, rendering the scope of the claim indefinite. It is also unclear how this formula is utilized in connection with any of the steps. In particular, it is unclear whether the step of “storing” relies on this formula to store the set of electronic network resource parameters in a particular way or the system is utilizing this formula in any of the subsequent steps. It appears that the claimed formula is a non-functional descriptive material because there is no linkage of usage of the formula anywhere in the claim itself of the corresponding independent claim. Therefore, no prior art can be reasonable provided to reject these claims until the claimed subject matter is clarified.
As to claims 5 and 13, Applicants failed to define every element of the formula, rendering the scope of the claim indefinite. The claim also fails to explain what the function represented by the formula actually is as an active step. In particular, it is unclear what is/are the active steps that are performed as a result of execution of the function defined by the formula, rendering the scope of the claim indefinite and preventing Examiner from applying a prior art reference.
As to claims 7 and 15, it is unclear how transmitting anything to an AI/ML model without getting a response from the model can assist in the “determining” step rendering the scope of the claim indefinite because “transmitting” does not further limit the step of determining without significantly more.
As to claim 8, further defining the AI/ML model amounts to a non-functional descriptive material because the model is not meaningfully linked to any of the claimed steps. Therefore, claim 8 fails to further limit the claim from which it depends.
As to claim 9, it fails to define N2 rendering the scope of the claim indefinite. No prior art can be reasonable provided to reject this claim until the claimed subject matter is clarified.
As to claims 10 and 16, Applicants failed to define every element of the formula, rendering the scope of the claim indefinite. The claim also fails to explain how utilizing the model to train a function bears any relation to determining a set of network configuration changes of independent claims. The claim fails to establish a meaningful connection between training a network resource usage efficiency cost function defined by the formula and “determining, by evaluating the first set of electronic network resource usage changes against the reconfiguration guide, a set of network configuration changes that improves the electronic network resource usage efficiency”. Therefore, no prior art can be reasonable provided to reject these claims until the claimed subject matter is clarified.
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 1-3, 7-8, 11, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hermoni et al. (US Patent 10,972,345 B1) in view of Mathews et al. (US 2025/0315448 A1).
As to claim 1, Hermoni teaches a method of implementing an electronic network reconfiguration tool [network management system] (Fig. 2A) that improves electronic network resource usage efficiency within an electronic network, the method comprising:
storing, as a reconfiguration dataset, electronic network resource parameters that describe electronic network resource usage attributes of the electronic network [logging event data and values for parameters relating to the performance of the communication network and network entities that is used for reconfiguring the network] (col. 6 lines 53-67; col. 8 lines 5-22);
generating a reconfiguration guide of the reconfiguration dataset, by considering a plurality of network configurations and a plurality of usage factors of the electronic network resource usage attributes [generating the run-time rules and deep-system rules for network management and reconfiguration] (col. 7 lines 13-24);
during a first state of operation that comprises a first electronic network resource usage state, receiving a first set of electronic network resource usage changes [demand changes and network situations are detected] (col. 3 lines 52-59; col. 8 lines 50-61);
based on the first state of operation, determining, by evaluating the first set of electronic network resource usage changes against the reconfiguration guide, a set of network configuration changes that improves the electronic network resource usage efficiency [determine optimal configuration settings based on load-change, network fault, preventative maintenance, cyber-attack, etc.] (col. 7 lines 25-51); and
based on the first state of operation and the first set of electronic network resource usage changes, improving the electronic network resource usage efficiency by implementing, within the electronic network, the set of network configuration changes [reconfiguring the network to optimize efficiency and cost] (col. 3 lines 14-34; col. 7 lines 45-51).
Hermoni fails to teach that generation of a reconfiguration guide is performed utilizing perturbation theory by perturbing a plurality of network configurations and a plurality of usage factors of the electronic network resource usage attributes.
Matthews is directed to evaluating explainable AI models and an architecture for an ensemble explainable model selection (abstract). In particular, Matthews teaches utilizing perturbation theory by perturbing input data and observing changes in predictions based on the perturbed input data (par. [0031], [0044]).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Hermoni by generating a reconfiguration guide utilizing perturbation theory by perturbing a plurality of network configurations and a plurality of usage factors of the electronic network resource usage attributes, in order to make predictions of machine learning models of Hermoni understandable to humans by breaking down the predictions into understandable contributions from each input feature (par. [0031] in Matthews).
As to claim 2, Hermoni in view of Matthews teaches that utilizing perturbation theory comprises: verifying, by implementing at least one from among a test set of network configuration changes and a test set of electronic network resource usage changes, the perturbing of the plurality of network configurations and the plurality of usage factors (col. 27 lines 1-10 in Hermoni; par. [0040], [0054] in Matthews).
As to claim 3, Hermoni teaches that the reconfiguration guide [rules] indicates how the electronic network resource usage attributes respond to electronic network resource usage changes with respect to the plurality of network configurations (col. 10 lines 5-33),
wherein the reconfiguration guide comprises a network resource usage response graph that correlates, to a respectively corresponding responsive state of operation including a set of respectively corresponding electronic network resource attribute responses, each network resource usage configuration change from among a range of network configuration changes [rules are implemented in the form of a file, a repository or a database which allows administrators to modify the rules, add/remove rules] (col. 10 line 40 to col. 11 line 4), and
wherein each network resource usage configuration change comprises at least one from among a respectively corresponding network setting change and a respectively corresponding electronic network resource usage change (col. 15 line 63 to col. 16 line 29).
As to claims 7 and 15, Hermoni teaches that the determining comprises: transmitting, at least one respectively corresponding group of network configuration changes from among a plurality of groups of network configuration changes that comprises each respectively corresponding group of network configuration changes that is generated, to at least one artificial intelligence and machine learning (AI/ML) model (col. 9 lines 48-65).
As to claim 8, Hermoni in view of Matthews teaches that the at least one AI/ML model comprises a large language model (LLM) (par. [0108] in Matthews).
As to claim 11, Hermoni in view of Matthews teaches a system of implementing an electronic network reconfiguration tool that improves electronic network resource usage efficiency within an electronic network (Figs. 2A-2C in Hermoni), the system comprising: a processor (Fig. 8 in Hermoni); and memory storing instructions (Fig. 8 in Hermoni) that, when executed by the processor, cause the processor to perform operations as discussed per claim 1 above.
As to claim 17, Hermoni in view of Matthews teaches a non-transitory computer-readable medium that implements an electronic network reconfiguration tool that improves electronic network resource usage efficiency within an electronic network, the computer-readable medium storing instructions (Fig. 8, col. 28 lines 14-47 in Hermoni) that, when executed by a processor, cause the processor to perform operations as discussed per claim 1 above.
As to claims 18-19, Hermoni in view of Matthews teaches all the elements, as discussed per corresponding method claims 2-3 above.
Allowable Subject Matter
Claims 6, 14, and 20 would be allowable if rewritten to overcome the rejection under 35 U.S.C. 112(b) set forth in this Office action and to include 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 OLEG SURVILLO whose telephone number is (571)272-9691. The examiner can normally be reached 9:00am - 5:00pm.
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/OLEG SURVILLO/Primary Examiner, Art Unit 2457
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