DETAILED ACTION
Final 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 .
Claims 1, and 3-13 are pending.
Claims 1, and 3-13 are rejected below.
Claim Rejections - 35 USC § 103
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 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, 4-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kimura (U.S. PG Pub. 2021/0216914) in view of Yatabe (U.S. PG Pub. 2016/0291577).
As to claims 1, 10 and 11 Kimura teaches an information processing apparatus comprising: hardware processors configured to: store, in a memory, pieces of history information identification information of a model [0093] and a history of updating the model[0116 examiner notes that the historical data is what the model is updated based on and the claims does not require the history of the data to occur each time of updating], the model being configured to receive a piece of input data including variables and output a piece of output data[0041, 0047], the variables each being a variable for which a rate of influence on the output data is calculated[0099, 0132], the model having been updated by using pieces of first input data[0066]; predict the output data by using the second input data[0119], the output data being predicted for each of models identified by their respective identification information included in the pieces of history information[0120]; calculate, for each of the models, an evaluation value indicating accuracy of prediction on the basis of the output data[0120 most similar], select a target model to be updated by using the second input data, the target model being a model whose evaluation value indicates that the corresponding model has higher accuracy of prediction than the other models [0119-0120 since it is also the one training model this always occurs]; and update the target model by performing transfer learning in which updated parameters are estimated by using the second input data[0121, abstract].
Kimura teaches most of the claimed invention, but fails to teach all of the invention. However, this is an obvious variation as taught by Yatabe as follows:
As to claims 1, 10, and 11 Yatabe teaches storing pieces of history information, each of the pieces of history information including one of pieces of identification information of a model and a history of updating the model each identifying one of models( abstract [0051]).
Therefore, it would have been obvious to one of ordinary skill in the art to include the teachings of Yatabe into the system and methods of Kimura. The motivation to combine is that Yatabe teaches The anomaly determiner 15 determines a possibility of the abnormality of the plant by using the data managed by the data manager 13 and the model information (the normal model M1 and the abnormal model M2) managed by the model manager 14. For example, the anomaly determiner 15 calculates a correlation value by obtaining various types of information (for example, the pressure data, the image data, and the sound data which are shown in FIG. 4A) from the data manager 13. The anomaly determiner 15 determines the possibility of the abnormality of the plant by comparing the calculated correlation value with the normal model M1 and the abnormal model M2 which are obtained from the model manager 14. The anomaly determiner 15 is also capable of determining the possibility of the abnormality of the plant based on whether a number of turning points (or a magnitude of a deviation) of the data, which is obtained from the data manager 13 within a fixed period of time, is settled in a predetermined acceptable value [0053].
As to claim 4, Kimura teaches wherein, when the number of pieces of the history information exceeds a threshold value, the hardware processors delete part of the pieces of history information stored in the memory[0059]. The historical data can also include portions of the model.
As to claim 5, Kimura teaches wherein the hardware processors are configured to: generate attribute information representing attributes of a specified model being a model identified by a piece of identification information included in a specified piece of the pieces of the history information (fig. 1); and visualize the attribute information[0127].
As to claim 6, Kimura teaches wherein the hardware processors generate the rates of influence as the attribute information[0132].
As to claim 7, Kimura teaches wherein the hardware processors generate the attribute information indicating one of parameters of the specified model[0134], the one of parameters being a parameter having changed from a parameter of the target model selected when the specified model is updated[0134].
As to claim 8, Kimura teaches wherein the pieces of history information further include information indicating periods in which the corresponding pieces of first input data used for updating the specified model are acquired(fig. 8 – date), and the hardware processors generate the attribute information indicating the periods (other data in fig. 8).
As to claim 9, Kimura teaches wherein the pieces of history information further include information indicating periods in which the corresponding pieces of first input data used for updating the specified model are acquired (fig. 8 – date), and the hardware processors generate, on the basis of the history information, the attribute information indicating an inapplicable period in which the first input data is not used for updating the specified model [0135 – when the data is not used for updating].
As to claim 12, Kimura teaches wherein pieces of first input data used to update the models are acquired at mutually different data periods[0121].
As to claim 13, Kimura teaches wherein the hardware processors are configured to: store, in the memory, parameters each of which corresponds to one of the models; and update the target model by performing transfer learning by using, as an initial parameter, a parameter of the target model among the parameters stored in the memory[0121].
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kimura (U.S. PG Pub. 2021/0216914) in view of Yatabe in view of Vandikas (U.S. PG Pub. 2024/0256973).
Kimura in view of Yatabe teach most of the claimed invention, but fails to teach all of the invention. However, this is an obvious variation as taught by Vandikas as follows:
As to claim 3, Vandikas teaches wherein the models are each a regression model to which a piece of input data is input and from which a piece of output data is output, the piece of input data including a plurality of explanatory variables, the piece of output data being an objective variable, and the evaluation value is a mean square error, a coefficient of determination, or a mean absolute error[0037, 0064].
Therefore, it would have been obvious to one of ordinary skill in the art to include the teachings of Vandikas into the system and methods of Kimura in view Yatabe. The motivation to combine is that Vandikas teaches In some embodiments, the method 200 may be for use in training a machine learning model to predict actions that should be performed in a safety critical system. In such cases, the integrity of the machine learning model is critical, and the method may be used to quickly and accurately pinpoint and quarantine computing nodes that may be contributing malicious or otherwise inaccurate updates [0037].
Response to Arguments
Applicant’s arguments, see remarks on page 10, filed 2-5-26, with respect to the rejection(s) of claim(s) 1, 10, 11 under 35 USC 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Yatabe.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN L LAUGHLIN whose telephone number is (571)270-1042. The examiner can normally be reached Monday-Friday 8AM-4PM.
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/NATHAN L LAUGHLIN/Primary Examiner, Art Unit 2119
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