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 § 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.
Claim(s) 29-48 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0272317 (Singla) in view of US 2022/0231939 (Mermoud).
With regard to claim 29, Singla discloses a method for improving communication network performance, the method comprising:
identifying a favorability status of individual predictions and/or decisions of a plurality of predictions and/or decisions of a machine-learning algorithm acting on at least a portion of the communication network, and providing said favorability statuses along with corresponding values of network parameters used as features in the machine-learning algorithm (Singla: Abstract, Paragraphs [0062] and [0242] to [0252], and Figure 25. Singla teaches the use of machine learning techniques to optimize Wi-Fi networks, where a favorability status (e.g. optimized Wi-Fi networks, such as, for example, networks with minimal interference.) is at least recognized for the optimizations (e.g. minimal interference).
Singla fails to disclose expressly, but Mermoud teaches:
that the favorability statuses are stored; generating a counterfactual algorithm based on the stored favorability statuses and corresponding values of network parameters, to derive rules for producing a favorable status, based on one or more of the network parameters; identifying a proposed recourse action comprising a change in at least one of the network parameters, based on the rules derived in the counterfactual algorithm; generating a decision network and determining a confidence level estimating a reliability of achieving a favorable status by changing the at least one network parameter; and determining whether to implement the proposed recourse action on the communication network, based on the confidence level (Mermoud: Abstract, Paragraphs [0034] and [0070], and Figure 7. Mermoud teaches the use of counterfactual explanations to make predictions as to whether an SLA (stored favorability status) would be violated, and can cause actions to be performed responsive to the likelihoods of violations (confidence levels).).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to utilize counterfactual algorithms (an algorithm that involves counterfactuals, such as counterfactual explanations) to perform the optimizations of Singla based on specific requirements/goals, such as an SLA (favorability status) to ensure that the configuration meets any requirements of the users of the networks while performing the least amount of change needed to realize any optimizations of the network (Singla: Abstract, Paragraphs [0062] and [0242] to [0252], and Figure 25 and Mermoud: Paragraphs [0070] and [0081]).
With regard to claim 30, Singla in view of Mermoud teaches implementing the change in the at least one network parameter, in response to determining that the confidence level equals or exceeds a threshold (Mermoud: Paragraph [0081] and Singla: Figure 25).
With regard to claim 31, Singla fails to teach, but knowledge possessed by one of ordinary skill in the art at the time of filing teaches generating the counterfactual algorithm comprises generating a tree-based classification algorithm, based on the stored favorability statuses and corresponding values of network parameters, and wherein the derived rules correspond to branches in the tree-based classification algorithm (More specifically, Official Notice is taken that the use of tree-based classification algorithms with rules corresponding to branches were well-known to one of ordinary skill in the art at the time of filing. Accordingly, it would have been obvious to one of ordinary skill in the art at the time of filing to generate a tree-based classification algorithm with the branches corresponding to the derived rules for the algorithm of Singla in view of Mermoud to leverage the simplicity and efficiency of such algorithms (e.g. decision trees) to arrive at the minimal number of changes to arrive at the desired conclusion, realizing well-known benefits in the efficiency, diversity of data sets, speed, etc.
With regard to claim 32, Singla in view of Mermoud teaches counterfactual algorithm comprises one or more of any of the following: a combinatorial optimization algorithm; an evolutionary algorithm; a random search algorithm; a support-vector machine algorithm; Pearl's causal model; a variational autoencoder; a shortest path algorithm on a graph; and an integer programming technique (Mermoud: Paragraph [0036]. Mermoud at least teaches the use of support vector machines, where the language “one or more” provides that only one item from the listing is required to teach the instant claim subject matter.).
With regard to claim 33, Singla in view of Mermoud teaches that each of one or more of the favorability statuses is: represented as a binary value; or a numerical score representing a degree of favorability (Mermoud: Abstract, Paragraphs [0034] and [0070], and Figure 7. Mermoud would at least present that whether the SLA is violated or not, which would be a binary value.).
With regard to claim 34, Singla fails to teach, but knowledge possessed by one of ordinary skill in the art teaches wherein identifying the favorability status of individual predictions and/or decisions of the plurality of predictions and/or decisions comprises collecting at least one favorability status from a user or operator of the communication system (More specifically, Official Notice is taken that the providing of requirements, such as SLA or other requirements, by a user was well-known to one of ordinary skill in the art at the time of filing.). Accordingly, it would have been obvious to one of ordinary skill in the art to collect at least one favorability status from a user or operator to provide the user has an opportunity to define such requirements for the network, thus ensuring that any requirements of the user are recognized and used in the decision process.
With regard to claim 35, Singla in view of Mermoud teaches identifying the favorability status of individual predictions and/or decisions of the plurality of predictions and/or decisions comprises computing at least one favorability status based on at least one threshold value and/or at least one target value for a performance metric (Mermoud: Paragraph [0002]).
With regard to claims 36-48, the instant claims are similar to claims 29-35, and are rejected for similar reasons.
Conclusion
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SCOTT B. CHRISTENSEN
Examiner
Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444