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 § 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) 1 – 22 are rejected under 35 U.S.C. 103 as being unpatentable over Smith, publication number: US 2021/0064431 in view of Bhatia, publication number: 2024/0146746.
As per claim 1, Smith teaches a computer implemented method comprising:
predicting, based on a previous usage of a cloud-based computing resource by a plurality of users of the cloud-based computing resource, a future usage of the cloud-based computing resource (forecasting usage, [0013][0068][0069]);
predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource (patterns differing from expectations, [0008][0069]);
identifying a top contributing user from the plurality of users that is responsible for the anomaly event at the cloud-based computing resource (Abnormal allocation of resources, [0225][0227]);
throttling an access of the top contributing user to the cloud-based computing resource (restricting resource availability, [0227]);
evaluating a speed of data requests received at the cloud-based computing resource from the top contributing user after the throttling, and a utilization level of the cloud-based computing resource (user tracking, [0006][0164][0167][0194]);
dynamically controlling the speed of data requests received at the cloud-based computing resource, based on the evaluation of the utilization level of the cloud-based computing resource, and additionally based on a controlling speed of data request recommended by a first artificial intelligence model monitoring the previous usage of the cloud-based computing resource and the future usage of the cloud-based computing resource (dynamically controlling usage, [0068], machine learning, [0069], load redistribution [0228-0229]), and
maintaining the utilization level of the cloud-based computing resource within a predetermined target range (Budget, [0259], Tracking usage [0260][0291][0321]).
Smith does not teach the recommended controlling speed of data request being validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive or non-intuitive recommendation of the first artificial intelligence model.
In an analogous art, Bhatia teaches the recommended controlling speed of data request being validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive or non-intuitive recommendation of the first artificial intelligence model (Reducing false positives by using a second machine learning model trained on outputs of a first machine learning model, [0037][0041][0043-0044]);
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Smith’s monitoring system to include a second machine learning model as described in Bhatia’s alert tracking system for the advantage of contextualizing identified events and improving the system’s response.
As per claim 2, the combination teaches wherein the dynamically controlling the speed of data requests comprises dynamically controlling the speed of data requests based on the recommendations from the first artificial intelligence model when the human reasoning based model validates that the controlling speed of data request recommended by the first artificial intelligence model is not counter-intuitive or non-intuitive (Bhatia: true positives, [0044]).
As per claim 3, the combination teaches wherein the dynamically controlling the speed of data requests comprises dynamically controlling the speed of data requests based on an alternate controlling speed of data request recommended by the human reasoning based model when the human reasoning based model validates that the controlling speed of data request recommended by the first artificial intelligence model is counter-intuitive or non-intuitive (Bhatia: False positives, [0044]).
As per claim 4, the combination teaches wherein the dynamically controlling the speed of data requests based on an alternate controlling speed of data request recommended by the human reasoning based model comprises generating the alternate controlling speed of data request using a second artificial intelligence model trained on a human domain knowledge relevant for mitigating the anomaly event (Bhatia: Second model, [0043], first model, [0037]).
As per claim 5, the combination teaches wherein the predetermined target range comprises 60% to 70% of a maximum utilization level of the cloud-based computing resource (Smith: Target range, [0260][0291][0321]).
As per claim 6, the combination teaches wherein the anomaly event comprises a deviation from an expected pattern or a normal operational parameter related to a security or performance aspect of the cloud-based computing resource (Smith: anomaly, [0014][0225]).
As per claim 7, the combination teaches wherein the deviation from the expected pattern comprises an overuse of the cloud-based computing resource by at least one of the plurality of users (Smith: abnormal patterns, [0225]).
Claims 8 – 14 and 15 – 21 are rejected based on claims 1 – 7
As per claim 22, Smith teaches a computer implemented method comprising:
predicting, based on a previous usage of a cloud-based computing resource by a plurality of users of the cloud-based computing resource, a future usage of the cloud-based computing resource (forecasting usage, [0013][0068][0069]);
predicting, based on the predicted future usage of the cloud-based computing resource, an anomaly event at the cloud-based computing resource (patterns differing from expectations, [0008][0069]);
identifying a top contributing user from the plurality of users that is responsible for the anomaly event at the cloud-based computing resource (Abnormal allocation of resources, [0225][0227]);
throttling an access of the top contributing user to the cloud-based computing resource (restricting resource availability, [0227]);
evaluating a speed of data requests received at the cloud-based computing resource from the top contributing user after the throttling, and a utilization level of the cloud-based computing resource (user tracking, [0006][0164][0167][0194]);
dynamically controlling the speed of data requests received at the cloud-based computing resource, based on the evaluation of the utilization level of the cloud-based computing resource, and additionally based on a controlling speed of data request recommended by a first artificial intelligence model monitoring the previous usage of the cloud-based computing resource and the future usage of the cloud-based computing resource (dynamically controlling usage, [0068], machine learning, [0069], load redistribution [0228-0229]),
and
maintaining the utilization level of the cloud-based computing resource within a predetermined target range comprising 60% to 70% of a maximum utilization level of the cloud-based computing resource (Budget, [0259], Tracking usage [0260][0291][0321], Target range, [0260][0291][0321]).
Smith does not teach the recommended controlling speed of data request being validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive or non-intuitive recommendation of the first artificial intelligence model;
in response to the human reasoning based model validating that the controlling speed of data request recommended by the first artificial intelligence model is not counter-intuitive or non-intuitive, dynamically controlling the speed of data requests based on the recommendations from the first artificial intelligence model;
in response to the human reasoning based model validating that the controlling speed of data request recommended by the first artificial intelligence model is counter-intuitive or non-intuitive, dynamically controlling the speed of data requests based on an alternate controlling speed of data request recommended by the human reasoning based model,
wherein the alternate controlling speed of data request is generated using a second artificial intelligence model trained on a human domain knowledge relevant for mitigating the anomaly event;
In an analogous art, Bhatia teaches Smith does not teach the recommended controlling speed of data request being validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive or non-intuitive recommendation of the first artificial intelligence model (Reducing false positives by using a second machine learning model trained on outputs of a first machine learning model, [0037][0041][0043-0044]);
in response to the human reasoning based model validating that the controlling speed of data request recommended by the first artificial intelligence model is not counter-intuitive or non-intuitive, dynamically controlling the speed of data requests based on the recommendations from the first artificial intelligence model (true positives, [0044]);
in response to the human reasoning based model validating that the controlling speed of data request recommended by the first artificial intelligence model is counter-intuitive or non-intuitive, dynamically controlling the speed of data requests based on an alternate controlling speed of data request recommended by the human reasoning based model (False positives, [0044]),
wherein the alternate controlling speed of data request is generated using a second artificial intelligence model trained on a human domain knowledge relevant for mitigating the anomaly event (Second model, [0043], first model, [0037]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Smith’s monitoring system to include a second machine learning model as described in Bhatia’s alert tracking system for the advantage of contextualizing identified events and improving the system’s response.
Conclusion
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494