NON-FINAL OFFICE ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/31/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7-12, 14, 15, 20, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2018/0330253 to Gottschlich et al. (hereinafter Gottschlich) in view of U.S. Patent Pub. No. 2024/0028499 to Amador et al. (hereinafter Amador) and further in view of U.S. Patent Pub. No. 2004/0107386 to Burdick et al. (hereinafter Burdick).
Gottschlich discloses:
1. A system for data anomaly generation, the system comprising:
one or more memories (paras. [0060], [0065], and Fig. 1, anomaly data input storage 142 and nominal data input storage 140); and
one or more processors, communicatively coupled to the one or more memories (para. [0016] and Fig. 1, processor cores 150), configured to:
receive a data anomaly configuration (paras. [0018], [0024], [0025], and Fig. 1, user-defined generation functions and Fig. 2, generation specifications 208);
receive an input dataset (paras. [0018], [0023], and Fig. 1, anomaly data input 142 and nominal data input 140); and
output, based on the data anomaly configuration and the input dataset, an output test dataset comprising one or more data anomalies (paras. [0018], [0055] and Fig. 1, anomaly detection dataset 130).
Gottschlich does not disclose expressly receiving a data chaos configuration from a repository, the data chaos configuration including instructions for random, weighted, or user-defined selection of one or more feature modules for introducing data anomalies.
Amador teaches receiving a data chaos configuration from a repository, the data chaos configuration including instructions for random, weighted, or user-defined selection of one or more feature modules for introducing data anomalies (abstract and paras. [0050], [0051], [0066]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gottschlich to receive a data chaos configuration, as taught by Amador. A person of ordinary skill in the art would have been motivated to do so in order to allow the user to inject appropriate failures, as discussed by Amador (para. [0014]).
Gottschlich further does not disclose expressly wherein the data chaos configuration is used to select the one or more feature modules for introducing at least one of schema drift, partition key changes, or dirty data location anomalies into the output test dataset.
Burdick teaches wherein the data chaos configuration is used to select the one or more feature modules for introducing at least one of schema drift, partition key changes, or dirty data location anomalies into the output test dataset (abstract and paras. [0008], [0050]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gottschlich to introduce at least one of schema drift, partition key changes, or dirty data location anomalies into the output test dataset, as taught by Burdick. A person of ordinary skill in the art would have been motivated to do so in order to evaluate how well an application detects and corrects errors, as discussed by Burdick (paras. [0005], [0010]).
Modified Gottschlich discloses:
7. A method of data anomaly generation, comprising:
receiving a data anomaly configuration (Gottschlich - paras. [0018], [0024], [0025], and Fig. 1, user-defined generation functions and Fig. 2, generation specifications 208) and a data chaos configuration from a repository, the data chaos configuration including instructions for random, weighted, or user-defined selection of one or more feature modules for introducing data anomalies (Amador - abstract and paras. [0050], [0051], [0066]); and
outputting, based on the data anomaly configuration and the data chaos configuration, an output dataset comprising one or more data anomalies (Gottschlich - paras. [0018], [0055] and Fig. 1, anomaly detection dataset 130),
wherein the data chaos configuration is used to select the one or more feature modules for introducing at least one of schema drift, partition key changes, or dirty data location anomalies into the output test dataset (Burdick - abstract and paras. [0008], [0050]).
8. The method of claim 7, further comprising:
receiving an input dataset, wherein outputting the output dataset includes outputting the output dataset based on the input dataset (Gottschlich - paras. [0018], [0023], and Fig. 1, anomaly data input 142 and nominal data input 140).
9. The method of claim 7, wherein outputting the output dataset includes generating the output dataset (Gottschlich - paras. [0018], [0055]).
10. The method of claim 7, wherein the data anomaly configuration is based on one or more generative artificial intelligence prompts (Gottschlich - paras. [0018], [0022]).
11. The method of claim 7, wherein the data anomaly configuration comprises a data anomaly selection configuration, and wherein outputting the output dataset includes outputting the output dataset based on the data anomaly selection configuration (Gottschlich - paras. [0025], [0029]).
12. The method of claim 7, wherein outputting the output dataset includes outputting the output dataset based on a generative artificial intelligence model (Gottschlich - paras. [0018], [0022]).
14. The method of claim 7, wherein the output dataset comprises an output test dataset (Gottschlich - paras. [0018], [0022]).
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
receive a data anomaly configuration (Gottschlich - paras. [0018], [0024], [0025], and Fig. 1, user-defined generation functions and Fig. 2, generation specifications 208) and a data chaos configuration from a repository, the data chaos configuration including instructions for random, weighted, or user-defined selection of one or more feature modules for introducing data anomalies (Amador - abstract and paras. [0050], [0051], [0066]); and
output, based on the data anomaly configuration and the data chaos configuration, an output test dataset comprising one or more data anomalies (Gottschlich - paras. [0018], [0055] and Fig. 1, anomaly detection dataset 130),
wherein the data chaos configuration is used to select the one or more feature modules for introducing at least one of schema drift, partition key changes, or dirty data location anomalies into the output test dataset (Burdick - abstract and paras. [0008], [0050]).
20. The non-transitory computer-readable medium of claim 15, wherein the data anomaly configuration configures one or more data anomaly parameters of the output test dataset (Gottschlich - paras. [0018], [0024], [0025]).
22. The system of claim 1, further comprising a data anomaly generator that uses the data anomaly configuration to select the one or more feature modules for introducing the one or more data anomalies, wherein the data anomaly generator includes a data anomaly library that is containerized or application-pluggable (Amador – paras. [0096], [0097]).
23. The method of claim 7, further comprising selecting the one or more feature modules for introducing the one or more data anomalies via a data anomaly generator and using the data anomaly configuration, wherein the data anomaly generator includes a data anomaly library that is containerized or application-pluggable (Burdick – paras. [0050]-[0053]).
Claims 2-5, 13, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Gottschlich in view of Amador and Burdick and further in view of U.S. Patent Pub. No. 2025/0265140 to Hanebutte et al. (hereinafter Hanebutte).
Gottschlich does not disclose expressly:
2. The system of claim 1, wherein the one or more data anomalies include one or more data type anomalies.
Hanebutte teaches a system for testing for anomalies wherein the one or more data anomalies include one or more data type anomalies (paras. [0036], [0038]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gottschlich to test for one or more data type anomalies, as taught by Hanebutte. A person of ordinary skill in the art would have been motivated to do so in order to identify a classification of error, as described by Hanebutte (para. [0036]), and also desired by Gottschlich (para. [0015]). Therefore, it would have been obvious to a person of ordinary skill in the art to combine Gottschlich with Hanebutte to achieve the invention as recited in claim 2.
Modified Gottschilich discloses:
3. The system of claim 1, wherein the one or more data anomalies include one or more data padding anomalies (Hanebutte – paras. [0036], [0038]).
4. The system of claim 1, wherein the one or more data anomalies are associated with one or more enumerated values (Gottschlich – para. [0015], Hanebutte – paras. [0036], [0038]).
5. The system of claim 1, wherein the one or more data anomalies are associated with one or more value ranges (Hanebutte – paras. [0037], [0039]).
13. The method of claim 7, wherein the one or more data anomalies include one or more of:
one or more data type anomalies,
one or more data padding anomalies,
one or more data anomalies associated with one or more enumerated values,
one or more data anomalies associated with one or more value ranges, or
one or more time zone data anomalies (Gottschlich – para. [0015], Hanebutte – paras. [0036], [0038]).
16. The non-transitory computer-readable medium of claim 15, wherein the one or more data anomalies include one or more data type anomalies (Hanebutte - paras. [0036], [0038]).
17. The non-transitory computer-readable medium of claim 15, wherein the one or more data anomalies include one or more data padding anomalies (Hanebutte – paras. [0036], [0038]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Gottschlich in view of Amador and Burdick and further in view of U.S. Patent Pub. No. 2007/0174713 to Rossi et al. (hereinafter Rossi).
Gottschlich does not disclose expressly:
6. The system of claim 1, wherein the one or more data anomalies include one or more time zone data anomalies.
Rossi teaches testing for anomalies wherein the one or more data anomalies include one or more time zone data anomalies (para. [0024] and following table).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gottschlich by testing for time zone anomalies, as taught by Rossi. A person of ordinary skill in the art would have bene motivated to do so because date data is one of a finite set of data types, as shown by the properties in Rossi (para. [0024] and following table). Therefore, it would have been obvious to try all the types of properties as described by Rossi, including time zone anomalies.
Conclusion
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/PHILIP GUYTON/Primary Examiner, Art Unit 2113
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