DETAIL ACTION
This Office Action is with regard to the most recent papers filed 2/3/2026.
Response to Arguments
Applicant's arguments filed 2/3/2026 have been fully considered but they are not persuasive.
On pages 9-11, Applicant argues the application of Singla to claim 29. Applicant provides a citation for the specification on page 10, arguing that this would support that a “favorability status” is some kind of indication of how good or favorable a particular prediction or decision of the ML was.
However, it is unclear why a favorability status, itself, would require an indication of “how” good or favorable the prediction is. In fact, the cited portion of the specification appears to provide that the status may be “favorable” or “not favorable,” which does not provide for the term “how” argued by applicant (which would provide a magnitude of some sort, such as a score). The passage proceeds to provide such a score, but this is an alternative to just providing “favorable” or “not favorable”. This does not, contrary to Applicant’s assertion, support an interpretation of a metric or parameter that is indicative of how good a decision or predication made was. Further, such a favorability status is not limited even by the cited passage of the instant specification. Such a status would be any status associated with a favorability (note that a similar term is used in the claim to denote a desired state in line 10, where rules are derived for producing a favorable status, where such a status would clearly be a desired state).
It is noted that although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). If Applicant intends for a “favorability status” to have specific meaning, the instant claim should be amended to clearly reflect this.
Based on the broad nature of the “favorability status,” while the disclosure of Singla may not align with Applicant’s asserted scope of the term, such a scope is not fully supported by the specification (specifically the term “how” argued by Applicant), nor does the cited passage of the specification serve to limit the instant claim. It should also be noted that the instant claim does not recite that the predictions were made by the machine learning algorithm, but instead they are “of” the machine learning algorithm, which would cover both past and future predictions/decisions. Again, if Applicant intends for the claim to have a requirement, the instant claim should be amended to clearly reflect this (e.g. recite that the favorability status specifies how favorable decisions previously made by the machine learning algorithm were for achieving an intended state).
Pages 12-14 Applicant argues the application of Mermoud. However, many of these arguments appear to rely on the overly narrow interpretation of “favorability status” argued by Applicant, as addressed above. Further, Applicant argues that Mermoud does not generate a counter-factual algorithm or generate rules. With regard to generating the algorithm, in reviewing the instant specification, such a generating appears allow for providing parameters to arrive at the decision, such as populating a decision tree, using the favorability statuses. Further, the “rules” are not well-defined in the instant claim, where policies may be generated to arrive at the favorable state (e.g. avoid SLA violations, as in Mermoud, or optimize the network, as in Singla). Again, if Applicant intends for the claim to have specific requirements, the instant claim should be amended to reflect these requirements.
On page 14, Applicant argues the rejection of claims 31, 38, and 45. Initially, Applicant admits that tree-based algorithms are well-known in the art. Then, Applicant argues the application of such to Singla in view of Mermoud. As in the rejection, the desired end state would be an optimized network (as addressed in Singla), which would correspond to the favorability status. The rules, as known for tree-based counter-factual algorithms, would define the branch traversals to find changes needed to optimize the network, with the optimal path being the path with the least number of changes that would be made. It is noted that if the independent claims were amended to better reflect what Applicant intends to be the invention, the instant claims would be addressed in a different manner, assuming that the term “favorability status” would no longer be able to be interpreted as providing a favorable state, but instead would specify how favorable a past decision was to arriving at an intended state.
With regard to claims 33, 40, and 47, Applicant argues that the Office stated that the SLAs, themselves, were the favorability statuses. The favorability status would be the SLA, in that it would provide that the SLA is met (or not violated), which would be a binary value, as the desired state is that the SLA is met (corresponding to a binary value of met versus not met).
With regard to claims 35 and 42, Applicant argues that the Office’s addressing of the claim makes no sense, as nothing in the cited passages suggests that SLAs are computed. However, the term “compute” would be similar to determining, such as by a computer, where the favorability status (desired end state) would be determined based on a target value for a performance metric (as defined for the SLA). Further, it should be noted that Mermoud is modifying Singla, where Singla is optimizing the network to have, for example, minimum interference.
Accordingly, the instant claims stand rejected as presented below.
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
THIS ACTION IS MADE FINAL. 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.
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SCOTT B. CHRISTENSEN
Examiner
Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444
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