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 § 102
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 - 10 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Newman, U.S. Patent Publication No. 2023/0239854.
Newman teaches:
[Claim 1] A model determination system, comprising one or more processors, the model determination system causing at least one of the one or more processors to execute:
a test data acquisition process of acquiring test data indicating a time series of actual result values of a plurality of types of performance index values related to a communication system (network performance data is acquired, [0204]);
a predicted value acquisition process of inputting, to each of a plurality of trained machine learning models to be used for a given prediction purpose related to the communication system, input data corresponding to the machine learning model and acquiring a predicted value for a prediction time point, the input data being a part of the test data, the input data indicating the actual result value for at least one of time points with respect to at least one of the plurality of types of performance index values, pieces of the input data to be input to the plurality of trained machine learning models being different from each other, and the prediction time point being later than any one of the time points (the AI model is operated to predict the future network operations or a network performance metric, [0204]);
a prediction accuracy evaluation process of evaluating, for each of the plurality of trained machine learning models, an accuracy of a prediction related to the given prediction purpose by the machine learning model based on the acquired predicted value and, of the test data, a part indicating the actual result value at the prediction time point of the at least one of the plurality of types corresponding to the predicted value; and a model determination process of determining at least one machine learning model among the plurality of trained machine learning models based on a result of the evaluation of the accuracy (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 2] The model determination system according to claim 1,
wherein the machine learning model outputs the predicted value of at least one of the plurality of types of performance index values (predict network performance metrics, [0204]), and
wherein a type of the actual result value indicated by the input data and a type of the predicted value are different (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 3] The model determination system according to claim 1,
wherein the machine learning model outputs the predicted value of at least one of the plurality of types of performance index values (predict network performance metrics, [0204]), and
wherein a type of the actual result value indicated by the input data and a type of the predicted value are the same (prediction can be deemed accurate, [0204], if a perfect match is deemed acceptable or within a few decimal points is acceptable is a matter of design choice and not given much patentable weight).
[Claim 4] The model determination system according to claim 1, wherein the model determination system causes the at least one of the one or more processors to execute a learning process of generating the plurality of trained machine learning models by executing learning which uses data that is different from the test data and that indicates the actual result values of the plurality of types of performance index values related to the communication system (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 5] The model determination system according to The model determination system according to wherein the model determination system causes the at least one of the one or more processors to execute:
a monitoring process of monitoring at least one type of performance index value related to the communication system (network performance data is acquired, [0204], Newman); and
an additional performance index value type identification process of identifying, for each of the plurality of trained machine learning models, an additional performance index value type which is a type of performance index value which is required to be added to targets of the monitoring in order to use the machine learning model (see Fig. 2, values are added and subtracted to adjust network performance), and
wherein, in the model determination process, the machine learning model is determined based on the result of the evaluation of the accuracy and the additional performance index value type (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 6] The model determination system according to claim 5, wherein the model determination system causes the at least one of the one or more processors to execute a monitoring target addition process of adding, to monitoring targets in the monitoring process, the performance index value of the additional performance index value type which is required to be added in order to use the determined machine learning model (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 7] The model determination system according to claim 1, wherein the model determination system is configured to cause the at least one of the one or more processors to execute:
a monitoring process of monitoring at least one type of performance index value related to the communication system (network performance data is acquired, [0204]); and
a monitoring target addition process of adding, to monitoring targets in the monitoring process, a type of performance index value which is required to be added in order to use the determined machine learning model (see Fig. 2, values are added and subtracted to adjust network performance).
[Claim 8] The model determination system according to claim 1, wherein the model determination system causes the at least one of the one or more processors to execute a prediction process of predicting the performance index value of the communication system by using the determined machine learning model (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
[Claim 9] The model determination system according to claim 1, wherein, in the model determination process, for each of a plurality of time slots, the machine learning model to be used in prediction in the time slot is determined (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204], it is inherent that time slot utilized for prediction would be known).
[Claim 10] A model determination method, comprising:
acquiring test data indicating a time series of actual result values of a plurality of types of performance index values related to a communication system (network performance data is acquired, [0204]);
inputting, to each of a plurality of trained machine learning models to be used for a given prediction purpose related to the communication system, input data corresponding to the machine learning model and acquiring a predicted value for a prediction time point, the input data being a part of the test data, the input data indicating the actual result value for at least one of time points with respect to at least one of the plurality of types of performance index values, pieces of the input data to be input to the plurality of trained machine learning models being different from each other, and the prediction time point being later than any one of the time points (the AI model is operated to predict the future network operations or a network performance metric, [0204]);
evaluating, for each of the plurality of trained machine learning models, an accuracy of a prediction related to the given prediction purpose by the machine learning model based on the acquired predicted value and, of the test data, a part indicating the actual result value at the prediction time point of the at least one of the plurality of types corresponding to the predicted value (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]); and
determining at least one machine learning model among the plurality of trained machine learning models based on a result of the evaluation of the accuracy (the accuracy of the predication is evaluated, if the prediction is determined to not be satisfactory, the model variables are adjusted to improve the predictive power, [0204]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER G SOLINSKY whose telephone number is (571)270-7216. The examiner can normally be reached M - Th, 6:30 A - 5:00 P.
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PETER G. SOLINSKY
Examiner
Art Unit 2463
/Peter G Solinsky/Primary Examiner, Art Unit 2463