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 .
Claim Interpretation
It is the position of the Examiner that the computer program product of Claims 15-20 is not directed to transitory propagating signals at least in view of paragraph [0065] of the Specification.
Response to Amendment
Applicant’s Amendment, filed December 11, 2025, has been fully considered and entered. Accordingly, Claims 1-21 are pending in this application. Claims 7 and 14 have been canceled. Claim 21 is new. Claims 1-6, 8-13, and 15-19 have been amended. Claim 1, 8, and 15 are independent claims.
In view of Applicant’s Amendment, the rejection of Claims 1-6, 8-13, and 15-20 under 35 U.S.C. 101 has been withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 5, 8, 9, 12, 15, 16, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal (PG Pub. No. 2024/0427747 A1), and further in view of OpenMetadata (“What is Tiering”, https://docs.open-metadata.org/latest/how-to-guides/data-governance/classification/tiers, December 2, 2023), Hazra (PG Pub. No. 2023/0393259 A1), and Sarangi (PG Pub. No. 2020/0175029 A1).
Regarding Claim 1, Agarwal discloses a computer-implemented method for modifying one or more of a plurality of data pipelines within a computer architecture that process data from a plurality of datasets, comprising:
ingesting real-time observability data regarding the plurality of data pipelines and the plurality of datasets, wherein the real-time observability data is selected from the group consisting of: data quality metrics of the plurality of datasets, data lineage of the plurality of datasets, and data historical issues of the plurality of datasets (see Agarwal, paragraph [0015], where metadata relating to nodes may be periodically collected by the platform; data quality metrics may be automatically generated for some or all nodes accessible by the platform; for example, in some implementations, each of the tables in a data warehouse may have data quality metrics generated).
Agarwal does not disclose:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage and the high reliability; and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines, wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture.
The combination of Agarwal, OpenMetadata, and Hazra discloses:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage (see OpenMetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality; the data insights in OpenMetadata helps identify the unused datasets as Tier 5; see also Table 1 where Tiers 1, 2, and 3 are “highly used”) and the high reliability (see Hazra, paragraph [0005], where the method includes … classifying the target I/Q data as high quality data or as low quality data using a first neural network); and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
Agarwal discloses analyzing data quality using observability. OpenMetadata contemplates classifying data assets into “tiers” with multiple classification elements, and with higher usage tiers becoming the focus of data quality improvement. Hazra discloses classifying data based on data quality. In addition, OpenMetadata contemplates modifying datasets (data quality improvement) based on usage while Hazra discloses modifying datasets based on data quality. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Agarwal, OpenMetadata, and Hazra for the benefit of focusing data quality improvement on highest usage data (see Openmetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality”).
Agarwal in view of OpenMetadata and Hazra does not disclose wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture. Sarangi discloses the recommendation is a computer data structure that causes one or more changes/modifications to a data/AI platform within the computer architecture (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal, OpenMetadata, and Hazra with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Regarding Claim 2, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset.
Sarangi discloses the dataset is classified as the low usage and the low reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset. Hazra discloses the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Hazra for the benefit of generating a compensated dataset from high quality data (see Hazra, Abstract).
Regarding Claim 5, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes reducing data quality actions on the dataset.
Sarangi discloses:
the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure), and
the recommendation includes reducing data quality actions on the dataset (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Regarding Claim 8, Agarwal discloses a computer hardware system for modifying one or more of a plurality of data pipelines within a computer architecture that process data from a plurality of datasets, comprising:
a hardware processor (see Agarwal, Fig. 2, for processor 200) configured to perform the following executable instructions:
ingesting real-time observability data regarding the plurality of data pipelines and the plurality of datasets, wherein the real-time observability data is selected from the group consisting of: data quality metrics of the plurality of datasets, data lineage of the plurality of datasets, and data historical issues of the plurality of datasets (see Agarwal, paragraph [0015], where metadata relating to nodes may be periodically collected by the platform; data quality metrics may be automatically generated for some or all nodes accessible by the platform; for example, in some implementations, each of the tables in a data warehouse may have data quality metrics generated).
Agarwal does not disclose:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage and the high reliability; and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines, wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture.
The combination of Agarwal, OpenMetadata, and Hazra discloses:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage (see OpenMetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality; the data insights in OpenMetadata helps identify the unused datasets as Tier 5; see also Table 1 where Tiers 1, 2, and 3 are “highly used”) and the high reliability (see Hazra, paragraph [0005], where the method includes … classifying the target I/Q data as high quality data or as low quality data using a first neural network); and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
Agarwal discloses analyzing data quality using observability. OpenMetadata contemplates classifying data assets into “tiers” with multiple classification elements, and with higher usage tiers becoming the focus of data quality improvement. Hazra discloses classifying data based on data quality. In addition, OpenMetadata contemplates modifying datasets (data quality improvement) based on usage while Hazra discloses modifying datasets based on data quality. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Agarwal, OpenMetadata, and Hazra for the benefit of focusing data quality improvement on highest usage data (see Openmetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality”).
Agarwal in view of OpenMetadata and Hazra does not disclose wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture. Sarangi discloses the recommendation is a computer data structure that causes one or more changes/modifications to a data/AI platform within the computer architecture (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal, OpenMetadata, and Hazra with Sarangi for the benefit of optimizing validations carried out for ETL workflow data (see Sarangi, Abstract).
Regarding Claim 9, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the system of Claim 8, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset.
Sarangi discloses the dataset is classified as the low usage and the low reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset. Hazra discloses the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Hazra for the benefit of generating a compensated dataset from high quality data (see Hazra, Abstract).
Regarding Claim 12, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the system of Claim 8, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes reducing data quality actions on the dataset.
Sarangi discloses:
the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure), and
the recommendation includes reducing data quality actions on the dataset (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Regarding Claim 15, Agarwal discloses a computer program product; comprising:
a computer readable storage medium having stored therein program code for modifying one or more of a plurality of data pipelines within a computer architecture that process data from a plurality of datasets (see Agarwal, paragraph [0057], where a non-transitory computer readable medium storing instructions may be provided), the program code, when executed by a computer hardware system, causes the computer hardware to perform:
ingesting real-time observability data regarding the plurality of data pipelines and the plurality of datasets, wherein the real-time observability data is selected from the group consisting of: data quality metrics of the plurality of datasets, data lineage of the plurality of datasets, and data historical issues of the plurality of datasets (see Agarwal, paragraph [0015], where metadata relating to nodes may be periodically collected by the platform; data quality metrics may be automatically generated for some or all nodes accessible by the platform; for example, in some implementations, each of the tables in a data warehouse may have data quality metrics generated).
Agarwal does not disclose:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage and the high reliability; and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines, wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture.
The combination of Agarwal, OpenMetadata, and Hazra discloses:
classifying a dataset from the plurality of datasets based on the real-time observability data to generate a classification of the dataset, wherein the classification of the dataset is selected from the group consisting of: a low usage and a low reliability, the low usage and a high reliability, a high usage and the low reliability, and the high usage (see OpenMetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality; the data insights in OpenMetadata helps identify the unused datasets as Tier 5; see also Table 1 where Tiers 1, 2, and 3 are “highly used”) and the high reliability (see Hazra, paragraph [0005], where the method includes … classifying the target I/Q data as high quality data or as low quality data using a first neural network); and
generating, based on the classification of the dataset, a recommendation to modify the one or more data pipelines (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
Agarwal discloses analyzing data quality using observability. OpenMetadata contemplates classifying data assets into “tiers” with multiple classification elements, and with higher usage tiers becoming the focus of data quality improvement. Hazra discloses classifying data based on data quality. In addition, OpenMetadata contemplates modifying datasets (data quality improvement) based on usage while Hazra discloses modifying datasets based on data quality. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Agarwal, OpenMetadata, and Hazra for the benefit of focusing data quality improvement on highest usage data (see Openmetadata, “in case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data; once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality”).
Agarwal in view of OpenMetadata and Hazra does not disclose wherein the recommendation is a computer data structure that causes one or more changes to a data/AI platform within the computer architecture. Sarangi discloses the recommendation is a computer data structure that causes one or more changes/modifications to a data/AI platform within the computer architecture (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal, OpenMetadata, and Hazra with Sarangi for the benefit of optimizing validations carried out for ETL workflow data (see Sarangi, Abstract).
Regarding Claim 16, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer program product of Claim 15, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset.
Sarangi discloses the dataset is classified as the low usage and the low reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset. Hazra discloses the recommendation includes replacing, for the one or more data pipelines of the plurality of data pipelines, the dataset with a higher-quality dataset (see Hazra, paragraph [0005], where the method includes … when the target I/Q data is classified as low quality data, discarding the target I/Q data, when the target I/Q data is classified as high quality data, performing ellipse fitting on the target I/Q data to generate compensated I/Q data, and generating the target displacement signal based on the compensated I/Q data [it is the position of the Examiner that compensated I/Q data is not patentably distinguishable from replacing a dataset with a higher-quality dataset]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Hazra for the benefit of generating a compensated dataset from high quality data (see Hazra, Abstract).
Regarding Claim 19, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer program product of Claim 15, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes reducing data quality actions on the dataset.
Sarangi discloses:
the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure), and
the recommendation includes reducing data quality actions on the dataset (see Sarangi, paragraph [0036] where the system is enabled to take decision for which one or more instances the validation can be turned off based on the calculated exposure value of execution. If the calculated exposure value is lesser than the predefined threshold value, the system will turn off the execution for the corresponding instance of validation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Regarding Claim 21, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
a data pipeline of the plurality of data pipelines corresponds to an architecture in which one or more datasets of the plurality of datasets are ingested and transferred to one or more data repositories (see Agarwal, paragraph [0050], where a data pipeline can include one or more tables from tools from the categories of data sources, ETL, data warehouse, and Analytics); and
the real-time observability data regarding the plurality of data pipelines is selected from the group consisting of: data pipeline operational lineage of the plurality of data pipelines, process quality metrics of the plurality of data pipelines, data pipeline historical issues of the plurality of data pipelines, and resource consumption metrics of the plurality of data pipelines (see Agarwal, paragraph [0013], where aspects of this disclosure relate to a data observation platform and system that monitors a data pipeline for errors. For example, a data pipeline may have nodes through which data is passed in the data pipeline. A node may be, for example, a data source (e.g., Salesforce), a ETL (exchange transfer load) tool (e.g., Airbyte), a data warehouse (e.g., Snowflake), and an analytics tool (e.g., Tableau). The data observation platform and system may operate on structured data (e.g., databases, and data warehouses). Implementations of the platform and system may detect and notify users regarding anomalous patterns on data along data quality metrics such as: data quality, data loading and usage costs, query and load performance).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of OpenMetadata, Hazra, and Sarangi as applied to Claims 1, 2, 5, 8, 9, 12, 15, 16, 19, and 21 above, and further in view of Donovan (PG Pub. No. 2021/0385122 A1).
Regarding Claim 3, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes moving a portion of the dataset into a lower cost data repository.
Donovan discloses:
the dataset is classified as the low usage (see Donovan, paragraph [0082], where data files which are frequently used are stored on higher cost storage medium (like cache discs) but are eventually migrated to lower cost storage medium (like tapes or networked storage) if the data files are not used for a certain period of time) and the low reliability (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors); and
the recommendation includes moving a portion of the dataset into a lower cost data repository (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Donovan for the benefit of reducing the cost to store low quality data or unused data (see Donovan, paragraph [0082]).
Regarding Claim 10, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the system of Claim 8, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes moving a portion of the dataset into a lower cost data repository.
Donovan discloses:
the dataset is classified as the low usage (see Donovan, paragraph [0082], where data files which are frequently used are stored on higher cost storage medium (like cache discs) but are eventually migrated to lower cost storage medium (like tapes or networked storage) if the data files are not used for a certain period of time) and the low reliability (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors); and
the recommendation includes moving a portion of the dataset into a lower cost data repository (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Donovan for the benefit of reducing the cost to store low quality data or unused data (see Donovan, paragraph [0082]).
Regarding Claim 17, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer program product of Claim 15, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the low reliability; and
the recommendation includes moving a portion of the dataset into a lower cost data repository.
Donovan discloses:
the dataset is classified as the low usage (see Donovan, paragraph [0082], where data files which are frequently used are stored on higher cost storage medium (like cache discs) but are eventually migrated to lower cost storage medium (like tapes or networked storage) if the data files are not used for a certain period of time) and the low reliability (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors); and
the recommendation includes moving a portion of the dataset into a lower cost data repository (see Donovan, paragraph [0029], where hierarchical storage manager is adapted to manage storage and cascade of data through the hierarchy of two or more data storage devices based at least on attribute data; see also paragraph [0023], where the attribute data comprises a quality of sensory data produced by the sensors).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Donovan for the benefit of reducing the cost to store low quality data or unused data (see Donovan, paragraph [0082]).
Claims 4, 6, 11, 13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal in view of OpenMetadata, Hazra, and Sarangi as applied to Claims 1, 2, 5, 8, 9, 12, 15, 16, 19, and 21 above, and further in view of Schierz (PG Pub. No. 2021/0390455 A1)
Regarding Claim 4, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset.
Sarangi discloses the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset. Schierz discloses the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Regarding Claim 6, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer-implemented method of Claim 1, wherein:
Agarwal does not disclose:
the dataset is classified as the high usage and the high reliability; and
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Sarangi discloses the dataset is classified as the high usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose:
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Schierz discloses a determination is made that usage of the dataset is decreasing (see Schierz, paragraph [0013], where the present disclosure relates to a computer-implemented method including: providing a machine learning model configured to predict … a drift metric for determining data drift [it is the position of the Examiner that data drift is not patentably distinguishable from decreasing usage of high quality data]), and the recommendation includes increasing usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Regarding Claim 11, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the system of Claim 8, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset.
Sarangi discloses the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset. Schierz discloses the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Regarding Claim 13, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the system of Claim 8, wherein:
Agarwal does not disclose:
the dataset is classified as the high usage and the high reliability; and
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Sarangi discloses the dataset is classified as the high usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose:
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Schierz discloses a determination is made that usage of the dataset is decreasing (see Schierz, paragraph [0013], where the present disclosure relates to a computer-implemented method including: providing a machine learning model configured to predict … a drift metric for determining data drift [it is the position of the Examiner that data drift is not patentably distinguishable from decreasing usage of high quality data]), and the recommendation includes increasing usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Regarding Claim 18, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer program product of Claim 15, wherein:
Agarwal does not disclose:
the dataset is classified as the low usage and the high reliability; and
the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset.
Sarangi discloses the dataset is classified as the low usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset. Schierz discloses the recommendation includes modifying the one or more data pipelines of the plurality of data pipelines to increase usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Regarding Claim 20, Agarwal in view of OpenMetadata, Hazra, and Sarangi discloses the computer program product of Claim 15, wherein:
Agarwal does not disclose:
the dataset is classified as the high usage and the high reliability; and
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Sarangi discloses the dataset is classified as the high usage and the high reliability (see Sarangi, paragraph [0006], where the method includes determining an usage of the selected at least one field of the warehouse to determine an impact of the received input data being incorrect, executing one or more instances of a validation of the input data to the at least one selected field of the warehouse, analyzing the output of the executed one or more instances of the validation to determine a probability of failure of each instance of validation using the recorded repository of historical results of execution of one or more instances of validation, calculating an exposure value for each executed instance of validation, wherein the calculated exposure value is product of the probability of failure and Impact the failure of each executed instance of validation, and finally optimizing the plurality of validations carried out for input data at a data warehouse by comparing the exposure value for each executed instance of validation with a predefined threshold value of exposure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal with Sarangi for the benefit of optimizing validations of input data at a data warehouse (see Sarangi, Abstract).
Agarwal in view of Sarangi does not disclose:
a determination is made that usage of the dataset is decreasing, and the recommendation includes increasing usage of the dataset.
Schierz discloses a determination is made that usage of the dataset is decreasing (see Schierz, paragraph [0013], where the present disclosure relates to a computer-implemented method including: providing a machine learning model configured to predict … a drift metric for determining data drift [it is the position of the Examiner that data drift is not patentably distinguishable from decreasing usage of high quality data]), and the recommendation includes increasing usage of the dataset (see Schierz, paragraph [0097], where model management module 112 can be used to refresh models with updated training data … for example, in response to alerts received from the drift identification module 108 or the drift monitoring module 110; refreshing a model (step 136) can involve the use of various data management techniques, for example, relace old training data with new training data and/or maintain the training data at a reasonable size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Agarwal and Sarangi with Schierz for the benefit of automatically refreshing data in response to decreasing data quality (see Schierz, Abstract, paragraph [0119]).
Response to Arguments
Applicant’s Arguments, filed December 11, 2025, have been fully considered, but they are not persuasive in view of the new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the Applicant’s disclosure:
Dalgaard (US Patent No. 12,321,250 B1), which concerns configurable telemetry data processing via observability pipelines.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
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/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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