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 action is in response to Application filed on 07/11/2025.
Claims 1-20 are pending.
Priority
This application is claimed as a continuation of U.S. Patent Application No. 18/517,700 filed on 11/22/2023. The parent application provides sufficient support for the claimed invention of this application as requirements under 35 U.S.C. § 112(a) or (pre-AIA ) 35 U.S.C. § 112, first paragraph. Therefore, the effective filing date of this application is 11/22/2023.
Specification
The disclosure is objected to because of the following informalities:
Regarding paragraph [0001], U.S. Patent Application No. 18/517,700 has been patented, its information should be supplemented with its patent information (e.g., Patent No.).
Appropriate correction is required.
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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6 and 10-11 of U.S. Patent No. 12,411,860. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-4, 6 and 10-11 of the earlier patent teach, suggest and/or render obvious to all limitations of claims 1-20 of this instant application.
In particular, the mapping of the rejection is as follows:
Instant Application
Patent No. 12,411,860
1. A system for generating synthetic time series datasets having anomalies, the system comprising:
memory; and
one or more processors, coupled to the memory, configured to cause the system to:
1. A system for generating synthetic time series datasets having anomalies, the system comprising:
one or more processors and one or more non-transitory computer-readable media having computer-executable instructions stored thereon, the computer-executable instructions, when executed by the one or more processors, causing operations comprising:
receive a user input comprising a command to generate a synthetic time series dataset;
receiving a user input comprising a plurality of parameters for generating a synthetic time series data set, wherein the synthetic time series dataset comprises a plurality of data points for a plurality of equal time periods;
generate, for a plurality of time slots of the synthetic time series dataset, a first plurality of data points using a first function from a first plurality of available functions, a second plurality of data points using a second function from a second plurality of available functions, and a third plurality of data points using a third function from a third plurality of functions;
generating, for a plurality of time slots of the synthetic time series dataset, (1) a first plurality of data points using a first harmonic function from a plurality of available harmonic functions, (2) a second plurality of data points for the synthetic time series dataset using a first trend function from a plurality of available trend functions, and (3) a third plurality of data points using a first noise function of a plurality of noise-generating functions, wherein a relationship between the second plurality of data points and the third plurality of data points satisfies a ratio retrieved from the plurality of parameters, and wherein the plurality of noise- generating functions comprises Gaussian functions and auto-regressive functions;
determine a minimum anomaly variance and a maximum anomaly variance for generating one or more anomalies for the synthetic time series dataset, wherein the minimum anomaly variance defines a minimum change of an anomaly relative to a point variance of the third plurality of data points and the maximum anomaly variance defines a maximum change of the anomaly relative to the point variance of the third plurality of data points;
determining a point variance of the third plurality of data points, wherein the point variance comprises a measure of variance of the third plurality of data points;
determining, based on the plurality of parameters, a minimum anomaly variance and a maximum anomaly variance, wherein the minimum anomaly variance defines a minimum change of anomalies relative to the point variance and the maximum anomaly variance defines a maximum change of the anomalies relative to the point variance;
generate the one or more anomalies based on applying, to one or more data points in the third plurality of data points, a corresponding anomaly variance generated based on the minimum anomaly variance and the maximum anomaly variance; and
generating one or more anomalies based on applying corresponding anomaly variance to one or more data points in the third plurality of data points, wherein each corresponding anomaly variance is between the minimum anomaly variance and the maximum anomaly variance;
generate, according to the plurality of time slots, the synthetic time series dataset comprising the one or more anomalies by combining the first plurality of data points, the second plurality of data points, and the third plurality of data points into corresponding time slots of the plurality of time slots, wherein the synthetic time series dataset does not include original information included in authentic data.
storing the synthetic time series dataset comprising the one or more anomalies by combining the first plurality of data points, the second plurality of data points, and the third plurality of data points into corresponding time slots of the plurality of time slots, wherein the synthetic time series dataset does not include original information included in authentic data; and
training or benchmarking, using the synthetic time series dataset, a machine learning model used to identify anomalies.
Similarly,
Claim 2 rejected by Claim 2
Claims 3-10 rejected by Claim 2
Claims 11-12 rejected by Claim 3
Claim 13 rejected by Claim 4
Claim 14 rejected by Claim 6
Claim 15 rejected by Claim 10
Claims 16-17 rejected by Claim 11
Claim 18 rejected by Claim 1
Claims 19-20 rejected by Claim 1
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 2-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of generating a synthetic time series dataset and/or anomalies without significantly more.
The claims recite an abstract idea for generating a synthetic time series dataset and/or anomalies based on broadly recited steps of generating, determining, generating and generating, which are directed a combination of mathematical concepts and mental processes that can be performed in human mind and/or with the aid of pencil and paper. The claimed invention recites an abstract idea (e.g., mathematical concepts and/or mental processes) along with a generic
computer components and/or routine computer functions is directed to an abstract idea without significantly more.
Abstract idea analysis is as follows:
Step 1:
According to the first part of the analysis, in the instant claims, claims 2-17 are directed to a method (i.e., a process), and claims 18-20 are directed to one or more non-transitory, computer-readable media storing instructions (i.e., an article of manufacture). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture or composition of matter).
Step 2a Prong 1 (claims 2 and 18):
The following limitations recited in claims 2 and 18 are abstract ideas that fall under mental processes and/or mathematical concepts:
generating, for a plurality of time slots of a synthetic time series dataset, one or more of a first plurality of data points using a first function from a first plurality of available functions, a second plurality of data points using a second function from a second plurality of available functions, or a third plurality of data points using a third function from a third plurality of functions (this step of generating data points/values using a function as broadly recited is directed to a mathematical concepts grouping of abstract ideas, which can be performed in the human mind or with the aid of pencil and paper);
determining a first anomaly variance and a second anomaly variance for generating one or more anomalies for the synthetic time series dataset (this step of determining as broadly recited can be mentally performed in the human and/or with the aid of pencil and paper (e.g., thinking about the concept));
generating the one or more anomalies based on applying, to one or more data points, a corresponding anomaly variance generated based on the first anomaly variance and the second anomaly variance (this step of generating as broadly recited can be mentally performed in the human mind and/or with the aid of pencil and paper (e.g., thinking about the concept)); and
generating, according to the plurality of time slots, the synthetic time series dataset comprising the one or more anomalies based on one or more of the first plurality of data points, the second plurality of data points, or the third plurality of data points into corresponding time slots of the plurality of time slots (a step of generating a dataset by combining data points as broadly recited can be interpreted as being directed to mental processes of observing, evaluating and making a decision on combining, which can be performed in human mind).
All the limitations above are directed to an abstract idea including mathematical concepts and/or mental processes that can be performed in the human mind or by a human using pen and paper.
Step 2a Prong 2 (Claims 2 and 18):
The following limitations in claim 18 are additional elements:
one or more non-transitory, computer-readable media storing instructions, when executed by one or more processor, cause operations comprising (these elements are directed to generic computer components).
These are a generic computer and/or generic computer components used to perform generic computer functions such that they amount to no more than components used to execute mere instructions, see MPEP 2106.05(d)(II). Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2b (Claims 2 and 18):
The following limitations in claim 18 are additional elements:
one or more non-transitory, computer-readable media storing instructions, when executed by one or more processor, cause operations comprising (these elements are directed to generic computer components).
These are a generic computer and/or generic computer components used to perform generic computer functions such that they amount to no more than components used to execute mere instructions. These limitations simply recite generic computer and/or generic computer components as well as routine computer functions (e.g., inputting/outputting data) that does not amount to significant more than the abstract idea, see MPEP 2106.05(d)(II).
Regarding claim 3, claim 3 depends on claim 2. As such, claim 3 recites the abstract idea as presented in claim 2.
In addition, claim 3 recites additional elements:
wherein the first anomaly variance defines a minimum change of an anomaly relative to a point variance of the third plurality of data points (this element specifying the first anomaly variance, which is directed to additional descriptive data).
These additional elements are directed to additional descriptive data to implement an abstract idea (i.e., mental processes) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 4, claim 4 depends on claim 2. As such, claim 4 recites the abstract idea as presented in claim 2.
In addition, claim 4 recites additional elements:
wherein the second anomaly variance defines a maximum change of an anomaly relative to a point variance of the third plurality of data points (this element specifying the second anomaly variance, which is directed to additional descriptive data).
These additional elements are directed to additional descriptive data to implement an abstract idea (i.e., mental processes) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 5, claim 5 depends on claim 2. As such, claim 5 recites the abstract idea as presented in claim 2.
In addition, claim 5 recites additional elements:
wherein the third plurality of data points include the one or more data points (this element specifying the relation between the third plurality of data points and the one or more data points, which is directed to additional descriptive data).
These additional elements are directed to additional descriptive data to implement an abstract idea (i.e., mental processes) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 6, claim 6 depends on claim 2. As such, claim 6 recites the abstract idea as presented in claim 2.
In addition, claim 6 recites additional elements:
wherein generating one or more of the first plurality of data points, the second plurality of data points, or the third plurality of data points comprises:
generating the first plurality of data points, the second plurality of data points, and the third plurality of data points (this step of generating as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper).
These additional elements are directed to mental step (i.e., abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 7, claim 7 depends on claim 2. As such, claim 7 recites the abstract idea as presented in claim 2.
In addition, claim 7 recites additional elements:
wherein generating the synthetic time series dataset comprising the one or more anomalies by combining the first plurality of data points, the second plurality of data points, or the third plurality of data points (this step of generating by combining as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper).
These additional elements are directed to mental step (i.e., abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claims 8 and 19, claims 8 and 19 depends on claims 2 and 18 respectively. As such, claims 8 and 19 recite the abstract idea as presented in claims 2 and 18 respectively.
In addition, claims 8 and 19 recites additional elements:
wherein the synthetic time series dataset does not include original information included in authentic data (this element describes the synthetic time series dataset, which is directed to mere additional data).
These additional elements are directed to mere additional data for implementing the abstract idea and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 9, claim 9 depends on claim 2. As such, claim 9 recites the abstract idea as presented in claim 2.
In addition, claim 9 recites additional elements:
receiving a user input comprising a command to generate the synthetic time series dataset (this step of receiving user input as broadly recited being directed to routine function of a generic computer system).
These additional elements are directed to generic computer function for implementing the abstract idea and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 10, claim 10 depends on claim 2. As such, claim 10 recites the abstract idea as presented in claim 2.
In addition, claim 10 recites additional elements:
determining, based on a user input, a number of anomalies to generate within the synthetic time series dataset (this step of determining as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion).
These additional elements are directed mental process (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 11, claim 11 depends on claim 10. As such, claim 11 recites the abstract idea as presented in claim 10.
In addition, claim 11 recites additional elements:
determining one or more time slots of the one or more data points to update based on the number of anomalies such that the one or more time slots satisfy a minimum distribution for the one or more anomalies relative to each other (this step of determining as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion).
These additional elements are directed mental process (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 12, claim 12 depends on claim 11. As such, claim 12 recites the abstract idea as presented in claim 11.
In addition, claim 12 recites additional elements:
wherein the minimum distribution comprises a minimum number of time slots between any two anomalies within the synthetic time series dataset (this element specifying the minimum distribution, which is directed to additional descriptive data).
These additional elements are directed additional data for implementing the abstract idea and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 13, claim 13 depends on claim 10. As such, claim 13 recites the abstract idea as presented in claim 10.
In addition, claim 13 recites additional elements:
determining one or more random time slots of the one or more data points to update based on the number of anomalies (this step of determining as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion).
These additional elements are directed mental process (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 14, claim 14 depends on claim 2. As such, claim 14 recites the abstract idea as presented in claim 2.
In addition, claim 14 recites additional elements:
determining that a data point, of the third plurality of data points, has a value that exceeds the first anomaly variance (this step of determining as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion); and
replacing, based on determining that the data point has the value that exceeds the first anomaly variance, the value with a new value that is equal to the first anomaly variance (this step of replacing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper).
These additional elements are directed mental steps/processes (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 15, claim 15 depends on claim 2. As such, claim 15 recites the abstract idea as presented in claim 2.
In addition, claim 15 recites additional elements:
scaling the third plurality of data points such that a relationship between the second plurality of data points and the third plurality of data points satisfies a ratio retrieved from a user input (this step of scaling as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion).
These additional elements are directed mental steps/processes (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claims 16 and 20, claims 16 and 20 depend on claims 2 and 18 respectively. As such, claims 16 and 20 recite the abstract idea as presented in claims 2 and 18 respectively.
In addition, claims 16 and 20 recite additional elements:
wherein generating the one or more anomalies comprises: applying the corresponding anomaly variance to the one or more data points (see claim 16) (these steps of generating and/or applying as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion), or
wherein generating the one or more anomalies comprises: applying the corresponding anomaly variance to one or more data points in the third plurality of data points (these steps of generating and/or applying as broadly recited can be mentally performed in the human mind through mental processes such as observation, evaluation, judgment or opinion).
These additional elements are directed mental steps/processes (i.e., the abstract idea) and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Regarding claim 17, claim 17 depends on claim 2. As such, claim 17 recites the abstract idea as presented in claim 2.
In addition, claim 17 recites additional elements:
wherein the corresponding anomaly variance is evenly distributed between the first anomaly variance and the second anomaly variance (this element specifying or describing the corresponding anomaly variance, which is directed to mere additional data).
These additional elements are directed mere additional data for implementing the abstract idea and do not integrate the judicial exception into a practical application and do not amount to significant more, see MPEP 2106.05(d)(II).
Allowable Subject Matter
It should be noted that no prior art rejection of claims 1-20.
The following is a statement of reasons for the indication of allowable subject matter:
A closest prior art of record, Klopries et al. (“Synthetic time series dataset generation for unsupervised autoencoders”, 2022), discloses generating a synthetic time series dataset using different types of continuous functions for the specific patterns of seasonality, trend, events and noise (see Introduction section), wherein an event represents an uneven or abrupt change in the time series data and its presence can highlight significant phenomenons like anomalies or fault cases in industrial processes (see page 3, column 1 for definitions of Trend, Seasonality, Events, and Noise).
Another close prior art of record, Chao et al. (U.S. Publication No. 2024/0412098), teaches synthesizing realistic time series data with outliers by generating time series data using different types of waves/functions (e.g., a sinusoidal (sine) wave, square wave, triangular wave, sawtooth wave, or another periodic wave), adding noise to time series data, and adding anomalies to time series data (see [0044]-[0048]).
However, the prior art of record does not explicitly teach a feature of generating anomalies by determining a minimum anomaly variance and a maximum anomaly variance for generating one or more anomalies for the synthetic time series dataset, wherein the minimum anomaly variance defines a minimum change of an anomaly relative to a point variance of the third plurality of data points and the maximum anomaly variance defines a maximum change of the anomaly relative to the point variance of the third plurality of data points; AND generating the one or more anomalies based on applying, to one or more data points in the third plurality of data points, a corresponding anomaly variance generated based on the minimum anomaly variance and the maximum anomaly variance, as recited in independent claim 1 and/or broadly recited in independent claims 2 and 18.
Conclusion
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/Phuong Thao Cao/Primary Examiner, Art Unit 2164
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