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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, and 5-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vashisht et al. (US 11,227,047 B1).
Regarding claim 1 and 7, Vashisht discloses a model determination system (abstract) and method, comprising one or more processors (125), the model determination system causing 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 a communication system (col. 12, lines 39-58, illustrating monitoring different types of performance metrics); an additional performance index value type identification process of identifying, for each of a plurality of machine learning models to be used for a given prediction purpose related to the communication system (col. 5, lines 30-50; col. 8, line 51-col. 9, line 6; col. 9, line 60-col. 10, line 2; col. 11, line 35-col. 12, line 23, describing the machine learning models are to generate accurate, best, and/or strong prediction), an additional performance index value type which is a type of performance index value (col. 13, lines 7-30; col. 11, lines 1-34; col. 12, lines 39-58; col. 13, line 42-col. 14, line 20; col. 11, line 54-col. 12, line 23, explaining identifying, determining, and/or selecting other different types of performance metrics for machine learning models for the best, accurate, and/or strong predication) required to be added to a target of the monitoring in order to use the machine learning model (col. 5, lines 30-38; col. 6, lines 26-57; col. 7, line 64-col. 8, line 50; col. 12, lines 24-38; col. 12, line 59-col. 13, line 58; col. 11, lines 1-34; col. 11, line 54-col. 12, line 4; col. 12, line 39-col. 13, line 30; col. 13, line 59-col. 14, line 20; and so on, describing that the other different types of performance metrics are collected and monitored in order to use the machine learning model); and a model determination process of determining at least one machine learning model among the plurality of machine learning models based on the additional performance index value type identified for each of the plurality of machine learning models (col. 12, line 39-col. 13, line 30; col. 13, line 59-col. 14, line 20; col. 1, lines 40-54; col. 7, lines 38-63; col. 8, line 28-col. 9, line 22; col. 11, line 20-col. 12, line 4; col. 13, line 59-col. 14, line 20, illustrating the selection of machine learning model among plurality of machine learning models based on the different types of performance metrics or characteristics such as superior performance metrics and so on).
Regarding claim 3, Vashisht discloses wherein, in the model determination process, at least one machine learning model among the plurality of machine learning models is determined based on the number of the additional performance index value types (col. 12, line 39-col. 13, line 30; col. 13, line 59-col. 14, line 20; col. 1, lines 40-54; col. 7, lines 38-63; col. 8, line 28-col. 9, line 22; col. 11, line 20-col. 12, line 4; col. 13, line 59-col. 14, line 20).
Regarding claim 5, Vashisht discloses 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 (col. 5, lines 30-38; col. 6, lines 26-57; col. 7, line 64-col. 8, line 50; col. 12, lines 24-38; col. 12, line 59-col. 13, line 58; col. 11, lines 1-34; col. 11, line 54-col. 12, line 4; col. 12, line 39-col. 13, line 30; col. 13, line 59-col. 14, line 20; and so on).
Regarding claim 6, Vashisht discloses 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 (col. 5, lines 30-50; col. 8, line 51-col. 9, line 6; col. 9, line 60-col. 10, line 2; col. 11, line 35-col. 12, line 23).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is are rejected under 35 U.S.C. 103 as being unpatentable over Vashisht in view of Lee et al. (US 2023/0222043 A1).
Regarding claim 2, as applied above, Vashisht discloses wherein, in the additional performance index value type identification process, for each of the plurality of machine learning models, a type of performance index value. However, Vashisht does not disclose the performance index value is not included in the target of the monitoring among types of performance index values which are inputs to the machine learning model is identified as the additional performance index value type.
Lee teaches the performance index value is not included in the target of the monitoring among types of performance index values which are inputs to the machine learning model is identified as the additional performance index value type (paragraph [0073]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the performance index value is not included in the target of the monitoring among types of performance index values which are inputs to the machine learning model is identified as the additional performance index value type as taught by Lee into Vashisht in order to reduce congestion.
Claim 4 is are rejected under 35 U.S.C. 103 as being unpatentable over Vashisht in view of Saxena et al. (US 2023/0419130 A1).
Regarding claim 4, as applied above, Vashisht discloses wherein, in the model determination process, at least one machine learning model among the plurality of machine learning models is determined based on performance index value type. Vashisht does not disclose the machine learning model is based on a ratio of the number of the additional performance index value types to the number of types of performance index values to be input to the machine learning model.
Saxena teaches the machine learning model is based on a ratio of the number of the additional performance index value types to the number of types of performance index values to be input to the machine learning model (paragraph [0059]-[0072]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the machine learning model is based on a ratio of the number of the additional performance index value types to the number of types of performance index values to be input to the machine learning model as taught by Saxena into Vashisht in order to enable better and simplified interpretations and predictions.
Conclusion
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/KIBROM T HAILU/Primary Examiner, Art Unit 2461