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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/13/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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)(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.
1: Claim(s) 1, 4 and 6 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2026/0142035 A1 Shelton, IV et al.
2: As for Claim 1, Claim 1 is rejected for reasons discussed related to Claim 6.
3: As for Claim 4, Shelton teaches in Paragraph [0238 and 0243] wherein the index value is a median of the distribution of the prediction errors.
4: As for Claim 6, Shelton teaches an outlier removal device that removes an outlier included in training data (Shelton teaches in Paragraph [0295] The data outliers (e.g., if considered/used during data analysis) may produce inaccurate results and/or conclusions. Removing the data outliers may allow for more accurate analysis.) The ML model may identify and remove outlier data during data reduction that comprises data of an explanatory variable and an objective variable used for machine learning (Paragraph [0237 and 0281]), the device comprising: a prediction error calculation processing unit that repeats, a predetermined number of times, division of the training data into teaching data (training data) and test data, creation of a regression model representing a correlation between the explanatory variable and the objective variable using the teaching data (Paragraph [0261]), and calculation of a prediction error (determine if the value is within an acceptable level) using the test data on the created regression model (Paragraph [0319]); a distribution calculation processing unit (probability distribution of calculated) that extracts, for each data included in the training data, prediction errors (the distribution is used to determine if an outlier should be removed from the data set) when using that data as the test data from prediction errors obtained by the prediction error calculation processing unit (Paragraphs [0243 and 0780]), and obtains, for each data included in the training data, an index value characterizing a distribution of the extracted prediction errors (Paragraph [0281] teaches the training data points are then classified as being outliers or inliers based on how big the prediction error between the model output and the training point. The process is then repeated with a separate sample of points until a certain proportion of points are classified as inliers (i.e. 70%) or certain number of loops of the process have occurred.); an outlier determination processing unit that determines whether each data is an outlier based on the index value of each data obtained by the distribution calculation processing unit (training data points are then classified as being outliers or inliers based on how big the prediction error between the model output and the training point); and an outlier removal processing unit that removes data determined to be an outlier by the outlier determination processing unit (Paragraph [0295]).
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.
5: Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2026/0142035 A1 Shelton, IV et al in view of CN 113469368 A Kubota.
6: As for Claim 5, Shelton teaches a machine learning system that removes outliers from training data in order to improve a regression model and ML model. Furthermore, Shelton teaches a method of determining outlier data. However, does not teach the determination can utilize at least one of mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) and one of mean percentage error (MPE), mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are used as the prediction error, and wherein data, in which the index value of any one of the mean error (ME), the mean absolute error (MAE) and the root mean square error (RMSE) is not less than a preset first criterion value and also the index value of any one of the mean percentage error (MPE), the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) to determine if the data point is an outlier value.
Kubota teaches a machine learning model for determining and classifying errors and teaches the performance of learning model 12a can be represented by F value, or represented by F value/(calculation time of learning process), or represented by the value of the first loss function. In addition, the F value is the value calculated by 2 PR/ (P + R) when the accuracy rate (precision) is expressed as P, and the recall rate (recal1) is expressed as R. In addition, the performance of the learning model 12a can also be used such as ME (average error), MAE (average absolute error), RMSE (root mean square error), MPE (average percentage error), MAPE (average absolute percentage error), RMSPE (root-mean-square percentage error); ROC (Receiver Operational Characteristic: recipient operating characteristics) curve and AUC (Area Under the Curve: curve area), Gini Norm, Kolmogorov-Smirnov or Precision/Recall and so on. calculating part 12 according to the learning model 12a machine learning, calculating the performance of learning model 12a represented by the F value and asserts that using a plurality of algorithms is advantageous to make the value of the first loss function becomes small and therefore, improve the accuracy of the ML model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the plurality of algorithms for determining errors of Kubota to classify the errors and identify the outliers in the training data of Shelton in order to make the value of the first loss function becomes small and therefore, improve the accuracy of the ML model.
Allowable Subject Matter
Claims 2 and 3 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M HANNETT whose telephone number is (571)272-7309. The examiner can normally be reached 8:00 AM-5:00 PM Monday thru Thursday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Twyler Haskins can be reached at 571-272-7406 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M HANNETT/Primary Examiner, Art Unit 2639
JMH
August 21, 2026
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