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 .
This communication is responsive to the application filed on April 9, 2025. Claims 1-20 are pending at the time of examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/30/25 was considered by the examiner. See attached PTO-form 1449.
Specification
Content of Specification
(b) CROSS-REFERENCES TO RELATED APPLICATIONS: See 37 CFR 1.78 and MPEP § 211 et seq.
The specification is objected because it fails to disclose the section (b) as indicated above since this application is claimed a CON of 18/524,131 filed on 11/30/2023, is now Patent No. 12298990.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 of the instant application are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,298,990. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 are directed to
Instant application Patent (‘990)
1.A computer-implemented method comprising:
segmenting a residual component and a trend component of a time-series dataset based on a time value;
classifying a first data point of the time-series dataset as an anomaly;
determining whether the first data point is within an outlier confidence interval;
responsive to a determination the first data point is within the outlier confidence interval, labelling the first data point as a non-outlier;
determining whether the first data point is within a level shift confidence interval for a residual segment containing the first data point;
responsive to a number of data points of the time-series dataset being labeled as non- outlier data points exceeding a threshold value, calculating a mean difference between a first trend segment corresponding to the residual segment and a second trend segment, which immediately precedes the first trend segment;
determining whether the mean difference between the first trend segment and the second trend segment is within the level shift confidence interval;
responsive to the mean difference being within the level shift confidence interval, computing a variance difference between the first trend segment and the second trend segment; and responsive to the variance difference being within a variance confidence interval, removing the anomaly classification of the first data point.
A computer-implemented method for anomaly adjustment in time-series prediction models comprising:
decomposing a time series data into a residual component, a trend component, and a seasonal component;
segmenting the residual component and the trend component, based on a time value;
classifying a first data point of the time-series data as an anomaly;
determining whether the first data point is inside of an outlier confidence interval range, responsive to a determination the first data point is inside the outlier confidence interval label the first data point as a non-outlier;
determining whether the first data point is inside of a level shift confidence interval range for the same residual segment as the first data point; responsive to a number of non-outlier data points being labeled as non-outliers is above a threshold value calculating a mean difference between a first trend segment corresponding to the residual segment containing the first data point, and a second trend segment which immediately precedes it;
determining whether the mean difference between the first trend segment and the second trend segment is within a level shift confidence interval range;
responsive to the mean difference being within the level shift confidence interval range computing the variance difference between the first trend segment and the second trend segment;
determining whether the variance difference is within a variance confidence interval range; responsive to determining the variance difference is within a variance confidence interval range removing the anomaly classification of the first data point.
8. A computer system comprising: a processor set; and a computer-readable storage medium having program instructions stored therein; wherein: the processor set executes the program instructions that cause the processor set to perform a method comprising:
segmenting a residual component and a trend component of a time-series dataset based on a time value;
classifying a first data point of the time-series dataset as an anomaly;
determining whether the first data point is within an outlier confidence interval;
responsive to a determination the first data point is within the outlier confidence interval, labelling the first data point as a non-outlier;
determining whether the first data point is within a level shift confidence interval for a residual segment containing the first data point;
responsive to a number of data points of the time-series dataset being labeled as non-outlier data points exceeding a threshold value, calculating a mean difference between a first trend segment corresponding to the residual segment and a second trend segment, which immediately precedes the first trend segment;
determining whether the mean difference between the first trend segment and the second trend segment is within the level shift confidence interval;
responsive to the mean difference being within the level shift confidence interval, computing a variance difference between the first trend segment and the second trend segment; and
responsive to the variance difference being within a variance confidence interval,
removing the anomaly classification of the first data point.
8. A computer system for anomaly adjustment in time-series prediction models comprising: a processor, a computer-readable memory, a computer-readable tangible storage device, and program instructions stored on the computer-readable storage device for execution by a processor via the computer-readable memory, wherein the computer system is configured to perform one or more operations, comprising operations to:
decompose a time series data into a residual component, a trend component, and a seasonal component; segment the residual component and the trend component, based on a time value; classify a first data point of the time-series data as an anomaly;
determine whether the first data point is inside of an outlier confidence interval range, responsive to a determination the first data point is inside the outlier confidence interval label the first data point as a non-outlier;
determine whether the first data point is inside of a level shift confidence interval range for the same residual segment as the first data point; responsive to a number of non-outlier data points being labeled as non-outliers is above a threshold value a mean difference between a first trend segment corresponding to the residual segment containing the first data point, and a second trend segment which immediately precedes it; determine whether the mean difference between the first trend segment and the second trend segment is within a level shift confidence interval range;
responsive to the mean difference being within the level shift confidence interval range compute the variance difference between the first trend segment and the second trend segment;
determine whether the variance difference is within a variance confidence interval range; responsive to a determination the variance difference is within a variance confidence interval range, remove the anomaly classification of the first data point.
15. A computer program product comprising a computer-readable storage medium having a set of instructions stored therein which, when executed by a processor, cause the processor to perform a method comprising:
segmenting a residual component and a trend component of a time-series dataset based on a time value;
classifying a first data point of the time-series dataset as an anomaly;
determining whether the first data point is within an outlier confidence interval;
responsive to a determination the first data point is within the outlier confidence interval, labelling the first data point as a non-outlier;
determining whether the first data point is within a level shift confidence interval for a residual segment containing the first data point;
responsive to a number of data points of the time-series dataset being labeled as non- outlier data points exceeding a threshold value, calculating a mean difference between a first trend segment corresponding to the residual segment and a second trend segment, which immediately precedes the first trend segment;
determining whether the mean difference between the first trend segment and the second trend segment is within a level shift confidence interval;
responsive to the mean difference being within the level shift confidence interval, computing a variance difference between the first trend segment and the second trend segment; and responsive to the variance difference being within a variance confidence interval, removing the anomaly classification of the first data point.
15. A computer program product for anomaly adjustment in time-series prediction models, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the program instructions are executable by a computer processor, the program instructions comprising: program instructions to decompose a time series data into a residual component, a trend component, and a seasonal component; program instructions to segment the residual component and the trend component, based on a time value; program instructions to classify a first data point of the time-series data as an anomaly; program instructions to determine whether the first data point is inside of an outlier confidence interval range, responsive to a determination the first data point is inside the outlier confidence interval program instructions to label the first data point as a non-outlier; program instructions to determine whether the first data point is inside of a level shift confidence interval range for the same residual segment as the first data point; responsive to a number of non-outlier data points being labeled as non-outliers is above a threshold value program instructions to calculate a mean difference between a first trend segment corresponding to the residual segment containing the first data point, and a second trend segment which immediately precedes it; program instructions to determine whether the mean difference between the first trend segment and the second trend segment is within a level shift confidence interval range; responsive to the mean difference being within the level shift confidence interval range program instructions to compute the variance difference between the first trend segment and the second trend segment; program instructions to determine whether the variance difference is within a variance confidence interval range; responsive to a determination the variance difference is within a variance confidence interval range program instructions to removing the anomaly classification of the first data point.
After analyzing the language claim of the claims, it is clear that claims 1-20 of the instant application are merely an obvious variation of claims 1-20 of U.S. Patent No. 12,298,990. While claims 1-20 of the instant application is slightly broader than claims 1-20 of U.S. Patent No. 12,298,990, this difference is not enough to distinguish the two instant application claims and the patent claims. With respect to the language and the disclosure of the instant application not only fail to distinguish it from the Patent No. 12,298,990, but indicate that it is merely a subset of the Patent No. 12,298,990. These differences are not sufficient to render the claims patentably distinct, and therefore, claims 1-20 of the instant application are valid.
A later patent/application claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim.
Conclusion
The prior art made of record, listed on form PTO-892, if any, is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBBIE M LE whose telephone number is (571)272-4111. The examiner can normally be reached 9:00-5:00.
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/DEBBIE M LE/Primary Examiner, Art Unit 2168 March 16, 2026