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 the communication filed on July 31, 2025.
Claims 1-20 are examined and are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 31, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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).
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Claim 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,399,954 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations. Especially, the U.S. Patent 12,399,954 B2 discloses more details in data lineage in plurality of datasets and identifying hops associated with datasets. Therefore, it would have been obvious to one of ordinary skill in the art to realize that claims 1-20 of the instant application is fully disclosed by the U.S. Patent 12,399,954 B2.
The following table shows the claims in Instant Application that are rejected by corresponding claim(s) in U.S. Patent No.12135756.
Instant Application: 19/287,275
Patent: 12,399,954 B2
1. A system, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to :receive information identifying a data lineage, wherein the data lineage includes, for a dataset of a plurality of datasets, information associated with a plurality of hops associated with a plurality of processes that the plurality of datasets is subjected to; evaluate the dataset to determine a hop, of the plurality of hops, based on one or more hop characteristics, wherein the hop corresponds to a transformation of the dataset, and wherein the transformed dataset corresponds to a data processing process; generate a plurality of data lineage metrics for the plurality of hops, wherein the plurality of data lineage metrics is related to data lineage accuracy for the plurality of processes, the plurality of datasets, and individual hops of the data lineage; and cause one or more actions to be performed based on an event corresponding to one or more of the plurality of data lineage metrics.
2. The system of claim 1, wherein the event is associated with a change in the data lineage.
3. The system of claim 1, wherein causing the one or more actions to be performed is based on: determining whether a threshold is met based on evaluating the event using the one or more of the plurality of data lineage metrics.
4. The system of claim 1, wherein the one or more processors are further configured to: evaluate the event using the one or more of the plurality of data lineage metrics.
5. The system of claim 1, wherein the one or more processors are further configured to: receive information identifying the event; and obtain data compliance information based on the information identifying the event.
6. The system of claim 5, wherein the data compliance information is associated with at least one of: a privacy rule, a risk management rule, or a data anonymization rule.
7. The system of claim 1, wherein the event is related to at least one of: assessing a downstream data impact, ensuring data quality, maintaining compliance, or supporting data governance.
8. A method, comprising: receiving, by a device, information identifying a data lineage; wherein the data lineage includes, for a dataset of a plurality of datasets, information associated with a plurality of hops associated with a plurality of processes that the plurality of datasets is subjected to; evaluating, by the device, the dataset to determine a hop, of the plurality of hops, based on one or more hop characteristics, wherein the hop corresponds to a transformation of the dataset, and wherein the transformed dataset corresponds to a data processing process; generating, by the device, a plurality of data lineage metrics for the plurality of hops, the plurality of data lineage metrics is related to data lineage accuracy for the plurality of processes, the plurality of datasets, and individual hops of the data lineage; and causing one or more actions to be performed based on an event corresponding to one or more of the plurality of data lineage metrics.
9. The method of claim 8, wherein the event is associated with a change in the data lineage.
10. The method of claim 8, further comprising: determining whether a threshold is met based on evaluating the event using the one or more of the plurality of data lineage metrics.
11. The method of claim 8, further comprising: evaluating the event using the one or more of the plurality of data lineage metrics.
12. The method of claim 8, further comprising: receiving information identifying the event; and obtaining data compliance information based on the information identifying the event.
13. The method of claim 12, wherein the data compliance information is associated with at least one of: a privacy rule, a risk management rule, or a data anonymization rule.
14. The method of claim 8, wherein the event is related to at least one of: assessing a downstream data impact, ensuring data quality, maintaining compliance, or supporting data governance.
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 information identifying a data lineage; wherein the data lineage includes, for a dataset of a plurality of datasets, information associated with a plurality of hops associated with a plurality of processes that the plurality of datasets is subjected to; evaluate the dataset to determine a hop, of the plurality of hops, based on one or more hop characteristics, wherein the hops corresponds to a transformation of the dataset, and wherein the transformed dataset corresponds to a data processing process; generate a plurality of data lineage metrics for the plurality of hops, wherein the plurality of data lineage metrics is related to data lineage accuracy for the plurality of processes, the plurality of datasets, and individual hops of the data lineage; and cause one or more actions to be performed based on an event corresponding to one or more of the plurality of data lineage metrics.
16. The non-transitory computer-readable medium of claim 15, wherein the event is associated with a change in the data lineage.
17. The non-transitory computer-readable medium of claim 15, wherein causing the one or more actions to be performed is based on: determining whether a threshold is met based on evaluating the event using the one or more of the plurality of data lineage metrics.
18. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the device to: evaluate the event using the one or more of the plurality of data lineage metrics.
19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, when executed by the one or more processors, further cause the device to: receive information identifying the event; and obtain data compliance information based on the information identifying the event.
20. The non-transitory computer-readable medium of claim 19, wherein the data compliance information is associated with at least one of: a privacy rule, a risk management rule, or a data anonymization rule.
1. A system for data processing, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: receive information identifying a data lineage for a plurality of datasets, the data lineage including, for a dataset of the plurality of datasets, information associated with one or more hops associated with the dataset; evaluate the dataset to determine a hop based on one or more hop characteristics, each hop, of the one or more hops, corresponding to a transformation of the dataset corresponding to a data processing process, and a first hop, of the one or more hops, corresponding to a first transformation of a first dataset and a first output from the first data processing process; generate a plurality of data lineage metrics for a plurality of hops associated with a plurality of data processing processes to which the plurality of datasets is subjected in association with the data lineage; generate an overall data lineage metric based on the plurality of data lineage metrics, the overall data lineage metric having a plurality of components; wherein the plurality of components are related to data lineage accuracy for processes of the data lineage, datasets of the data lineage and individual hops of the data lineage; receive information identifying an event associated with the data lineage; and perform a processing action based on the event and the overall data lineage metric.
2. The system of claim 1, wherein the one or more processors are further configured to: determine a new overall data lineage metric corresponding to the event associated with the data lineage; and determine that the new overall data lineage metric satisfies a threshold; and wherein the one or more processors, to perform the processing action, are configured to: approve the event based on determining that the new overall data lineage metric satisfies the threshold.
3. The system of claim 1, wherein the one or more processors are further configured to: determine a new overall data lineage metric corresponding to the event associated with the data lineage; and determine that the new overall data lineage metric does not satisfy a threshold; and wherein the one or more processors, to perform the processing action, are configured to: reject the event based on determining that the new overall data lineage metric does not satisfy the threshold.
4. The system of claim 1, wherein the one or more processors are further configured to: identify a compliance rule associated with the event; and wherein the one or more processors, to perform the processing action, are configured to: perform the processing action to satisfy the compliance rule.
5. The system of claim 1, wherein the one or more processors, to perform the processing action, are configured to: delete one or more datasets from the plurality of datasets.
6. The system of claim 1, wherein the one or more processors, to perform the processing action, are configured to: anonymize one or more datasets from the plurality of datasets.
7. The system of claim 1, wherein the one or more processors are further configured to: transmit information identifying the overall data lineage metric for display via a user interface.
8. A method, comprising: receiving, by device, information identifying a data lineage for a plurality of datasets, the data lineage including, for a dataset of the plurality of datasets, information associated with one or more hops associated with the dataset; evaluate the dataset to determine a hop based on one or more hop characteristics, each hop, of the one or more hops, corresponding to a transformation of the dataset corresponding to a data processing process, and a first hop, of the one or more hops, corresponding to a first transformation of a first dataset and a first output from the first data processing process; generating, by the device, a plurality of data lineage metrics for a plurality of hops associated with a plurality of data processing processes to which the plurality of datasets is subjected in association with the data lineage; generating, by the device, an overall data lineage metric based on the plurality of data lineage metrics, the overall data lineage metric having a plurality of components, wherein the plurality of components are related to data lineage accuracy for processes of the data lineage, datasets of the data lineage and individual hops of the data lineage; and transmitting, by the device, an output associated with the overall data lineage metric.
9. The method of claim 8, wherein the plurality of components includes at least one of: an accuracy component metric, a resolution component metric, a frequency component metric, or a completeness component metric.
10. The method of claim 8, further comprising: receiving program code; parsing the program code to determine one or more inputs of the program code and one or more outputs of the program code; and correlating the plurality of datasets to the one or more inputs of the program code and the one or more outputs of the program code; and wherein identifying the one or more hops comprises: identifying the one or more hops based on correlating the plurality of datasets to the one or more inputs of the program code and the one or more outputs of the program code.
11. The method of claim 8, further comprising: receiving information identifying a new dataset associated with the data lineage; determining a new overall data lineage metric based on a new one or more hops associated with the new dataset; and transmitting a new output associated with the new overall data lineage metric.
12. The method of claim 11, further comprising: determining that the new overall data lineage metric differs from the overall data lineage metric by a threshold amount; and wherein transmitting the new output comprises: transmitting an alert identifying the new dataset and indicating that the new overall data lineage metric differs from the overall data lineage metric by the threshold amount.
13. The method of claim 8, further comprising: receiving information identifying a new data processing process associated with the data lineage; determining a new overall data lineage metric based on a new one or more datasets associated with the new data processing process; and transmitting a new output associated with the new overall data lineage metric.
14. The method of claim 13, further comprising: determining that the new overall data lineage metric differs from the overall data lineage metric by a threshold amount; and wherein transmitting the new output comprises: transmitting an alert identifying the new data processing process and indicating that the new overall data lineage metric differs from the overall data lineage metric by the threshold amount.
15. The method of claim 8, further comprising: generating a user interface with which to display the output.
16. 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 system, cause the system to: receive information identifying a data lineage for a plurality of datasets, the data lineage including, for a dataset of the plurality of datasets, information associated with one or more hops associated with the dataset; evaluate the dataset to determine a hop based on one or more hop characteristics, each hop, of the one or more hops, corresponding to a transformation of the dataset corresponding to a data processing process, and a first hop, of the one or more hops, corresponding to a first transformation of a first dataset and a first output from the first data processing process; generate a plurality of data lineage metrics for a plurality of hops associated with a plurality of data processing processes to which the plurality of datasets are subjected in association with the data lineage; generate an overall data lineage metric based on the plurality of data lineage metrics, the overall data lineage metric having a plurality of components, wherein the plurality of components are related to data lineage accuracy for processes of the data lineage, datasets of the data lineage and individual hops of the data lineage; and transmit one or more alerts associated with the overall data lineage metric.
17. The non-transitory computer-readable medium of claim 16, wherein an alert, of the one or more alerts, classifies the overall data lineage metric into a class of a plurality of classes associated with a plurality of different ranges for the overall data lineage metric.
18. The non-transitory computer-readable medium of claim 16, wherein an alert, of the one or more alerts, indicates a compliance rule that is not satisfied by the data lineage corresponding to the overall data lineage metric.
19. The non-transitory computer-readable medium of claim 16, wherein an alert, of the one or more alerts, includes a recommendation for increasing the overall data lineage metric.
20. The non-transitory computer-readable medium of claim 16, wherein an alert, of the one or more alerts, includes information identifying one or more missing hops that affect the overall data lineage metric.
“Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Kaspa et al US 2022/0374442 A1 Extract, Transform and Load Monitoring Platform.
Fankhauser et al US 2011/0320460 A1 Efficient Representation of Data Lineage Information.
MacLeod et al US 6,434,295 B1 Data Lineage
Lefever et al US 10,025,878 B1 Data Lineage Analysis
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED R UDDIN whose telephone number is (571)270-3138. The examiner can normally be reached M-F: 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at (571) 272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED R UDDIN/Primary Examiner, Art Unit 2161
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