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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 3, 4, 8, 9, 10 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea (mental process) without significantly more.
Claim 1 recites “An information processing device comprising: a memory; and at least one processor coupled to the memory, the processor performing operations, the operations comprising: acquiring at least one condition for evaluation data of a machine learning model; calculating a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition; and calculating a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated.”
The steps of acquiring, calculating and calculating can all be performed via mental process using routine mathematical calculating using variables.
This judicial exception is not integrated into a practical application because the claim only recites additional generic processing hardware in a memory and a processor. The claimed memory and processor are recited at a high-level of generality. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional claimed elements in the claim perform generic computing functions and do not amount to significantly more than the judicial.
Regarding claim 2, this claims recites the step of calculating which be performed via mental process using routine mathematical calculating using variables. The claim includes no additional elements to amount to significantly more than the judicial exception.
Regarding claim 3, this claims recites the step of outputting which can be performed by generic computer processing. The claim includes no additional elements to amount to significantly more than the judicial exception.
Regarding claim 4, this claims recites the step of outputting which can be performed by generic computer processing. The claim includes no additional elements to amount to significantly more than the judicial exception.
Regarding claim 8, this claims recites the step of predicting which be performed via mental process using routine mathematical calculating using variables. The claim includes no additional elements to amount to significantly more than the judicial exception.
Regarding claim 9, this claims recites the step of predicting, acquiring and calculating which be performed via mental process using routine mathematical calculating using variables. The claim includes no additional elements to amount to significantly more than the judicial exception.
Claim 10 recites “An information processing method comprising: acquiring at least one condition for evaluation data of a machine learning model; calculating a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition; and calculating a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated.”
The steps of acquiring, calculating and calculating can all be performed via mental process using routine mathematical calculating using variables.
This judicial exception is not integrated into a practical application because the claim does not require any other components/elements.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the claim does not require any other components/elements.
Claim 11 recites “ A non-transitory computer-readable recording medium recording a program for causing a computer to execute: acquiring at least one condition for evaluation data of a machine learning model; calculating a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition; and calculating a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated.
The steps of acquiring, calculating and calculating can all be performed via mental process using routine mathematical calculating using variables.
This judicial exception is not integrated into a practical application because the claim only recites an additional reference to a generic computer. The claimed computer recited at a high-level of generality. Accordingly, the additional element do not integrate the abstract idea into a practical application because it do not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional claimed element in the claim performs generic computing function and do not amount to significantly more than the judicial.
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 1-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yusuke (JP-2021039491-A).
Regarding claim 1, Yusuke discloses an information processing device (Figures 1 and 3) comprising: a memory (Figure 2, Element 22); and at least one processor coupled to the memory (Figure 2, Element 23), the processor performing operations, the operations comprising: acquiring at least one condition for evaluation data of a machine learning model (Figure 2, Element 231 and Pages 3-4 where the collection function device collects model validation data from the medical information storage device 3. The model validation data includes various data used in the model performance evaluation process); calculating a performance index of the machine learning model and a performance index of the machine learning model after being updated using a data set specified for each of the at least one condition (Figure 2, Element 234 and Pages 3, 4, 7, 8, 13, 14, 15 and 16 where a performance index of the machine learning model is calculated using the model validation data related to the health of the user. The performance index is repeatedly determined for a pre-detection model and an updated model); and calculating a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated (Figure 2, Element 234 and Pages 13, 15 and 16 where a deterioration index of the machine learning model is calculated. When the deterioration is larger than a threshold, the model is updated. The deterioration of the model is continuously determined before and after model updates).
Regarding claim 2, Yusuke discloses wherein the operations further comprise: calculating the deterioration index based on a difference between the performance index of the machine learning model and the performance index of the machine learning model after being updated (Figure 2, Element 234 and Pages 12, 13, 15 and 16 where a deterioration index of the machine learning model is calculated. When the deterioration is larger than a threshold, the model is updated. The deterioration of the model is continuously determined before and after model updates and the differences are used to determine the deterioration index).
Regarding claim 3, Yusuke discloses wherein the operations further comprise: outputting the machine learning model after being updated, the deterioration index, and an overall performance of the machine learning model after being updated (Figure 2, Elements 234, 235 and Pages 3, 4, 7, 8, 13, 14, 15 and 16 where the generation function device outputs the machine learning model after being updated along with the deterioration index and a performance measure of the model).
Regarding claim 4, Yusuke discloses wherein the operations further comprise: outputting the machine learning model, the machine learning model after being updated, data for which the machine learning model is correct, data for which the machine learning model after being updated is correct, a performance for each of the at least one condition, and the overall performance of the machine learning model after being updated (Figure 2, Elements 234, 235 and Pages 3, 4, 7, 8, 13, 14, 15 and 16 where the generation function device outputs the machine learning model after being updated along with the deterioration index and a performance measure of the model).
Regarding claim 5, Yusuke discloses wherein the operations further comprise: causing the machine learning model to perform relearning based on the performance index (Figure 2, Element 234 and Pages 13, 15 and 16 where a deterioration index of the machine learning model is calculated. When the deterioration is larger than a threshold, the model is updated. The deterioration of the model is continuously determined before and after model updates).
Regarding claim 6, Yusuke discloses causing the machine learning model to perform relearning by using a weight set based on the performance index of the machine learning model for the data set (Figure 2, Elements 234, 235 and Pages 3, 4, 7, 8, 13, 14, 15 and 16 where the model is updated using weighted variables based on the performance index and deterioration index).
Regarding claim 7, Yusuke discloses wherein the operations further comprise: calculating a performance index of the machine learning model after being updated, calculating a deterioration index of a performance of the machine learning model based on the performance indexes before and after the machine learning model is updated, and causing the machine learning model to perform relearning optimizing a loss function related to the deterioration index of the machine learning model after being updated (Figure 2, Element 234 and Pages 13, 15 and 16 where a deterioration index of the machine learning model is calculated. When the deterioration is larger than a threshold, the model is updated. The deterioration of the model is continuously determined before and after model updates).
Regarding claim 8, Yusuke discloses wherein the machine learning model is a model that predicts a health condition of a user with respect to health data of the user acquired from a terminal of the user (Figure 1 and Pages 3, 4, and 7 where the machine learning model is a model that predicts an adverse medical/health condition of the user).
Regarding claim 9, Yusuke discloses wherein the machine learning model is a model that predicts a health condition of a user with respect to health data of the user acquired from a terminal of the user, and the operations further comprise: acquiring an evaluation result of the user for a prediction result as evaluation data of the machine learning model, and calculating the deterioration index by using the evaluation result of the user for the prediction result (Figure 1 and Pages, 3, 4, 7, 13, 15 and 16 where a deterioration index of the machine learning model is calculated. When the deterioration is larger than a threshold, the model is updated. The deterioration of the model is continuously determined before and after model updates).
Method claim 10 is drawn to the method of using the corresponding apparatus claimed in claim 1. Therefore method claim 10 corresponds to apparatus claim 1 and is rejected for the same reasons of anticipation as used above.
Regarding claim 11, see rejection for claim 1 which discloses all of the claimed limitations.
Conclusion
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/THOMAS D ALUNKAL/Primary Examiner, Art Unit 2686