DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In response to communications filed on 30 July 2025, claims 1-20 are presently pending in the application, of which, claims 1, 19 and 20 are presented in independent form.
Drawings
The drawings, filed 30 July 2026, have been reviewed and accepted by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01 August 2025 and 14 October 2025, respectively are 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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No.12,998,884 (known hereinafter as ‘884). Although the claims at issue are not identical, they are not patentably distinct from each other because all of the claims in ‘884 recite the limitations that anticipate all of the limitations of the instant claims.
Instant Application 19/285,415
U.S. Patent 12,998,884
Claim 1:
A method, comprising:
Claim 8:
A computer-implemented method for generating dataset zones, the method comprising the steps of:
receiving a plurality of datasets from a plurality of sources, the datasets including a plurality of characteristics;
storing the plurality of datasets into a data catalog in a storage device;
a plurality of zones in a storage device, the plurality of zones comprising: (i) a first zone, (ii) a second zone, (iii) a third zone, and (iv) a fourth zone
generating a first zone comprising a transient zone, wherein the transient zone is a first container in the storage device configured to store data from the plurality of datasets, and wherein the data in the transient zone has not been ingested or processed according to a processing pipeline;
storing, by the processor, data in the first zone, wherein the data in the first zone has not been ingested or processed according to a processing pipeline;
storing the data in the first zone; generating a second zone comprising a raw zone, wherein the raw zone is a second container in the storage device configured to store raw data generated from the data, wherein the raw data in the raw zone is in an original format;
processing, by the processor based on the processing pipeline, the data in the first zone to ingest and organize the data, thereby generating raw data;
processing, based on the processing pipeline, the data in the first zone to ingest and organize the data in the first zone, thereby generating the raw data;
storing, by the processor, the raw data in the second zone;
storing the raw data in the second zone;
generating a third zone comprising a trusted zone, wherein the trusted zone is a third container in the storage device configured to store standardized data;
processing, by the processor based on one or more policies of the processing pipeline, the data in the second zone to standardize the data in the second zone, thereby generating standardized data;
processing, based on one or more policies of the processing pipeline, the data in the second zone to standardize the data in the second zone, thereby generating the standardized data;
storing, by the processor, the standardized data in the third zone;
storing the standardized data in the third zone;
generating a fourth zone comprising a refined zone, wherein the refined zone is a fourth container configured to store business-specific data;
processing, by the processor based on the processing pipeline, the standardized data in the third zone to associate the standardized data with one or more lines of business, thereby generating business-specific data;
processing, based on the processing pipeline, the standardized data in the third zone to associate the standardized data with one or more lines of business, thereby generating the business-specific data;
storing, by the processor, the business-specific data in the fourth zone;
storing the business-specific data in the fourth zone; and
outputting, by the processor on a display, a graphical user interface comprising indications of the first zone, the second zone, the third zone, and the fourth zone.
displaying, via a graphical user interface, a representation comprising indications of the first zone, the second zone, the third zone, and the fourth zone
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable by Reynolds, Shad, et al (U.S. 2018/0210936 and known hereinafter as Reynolds).
As per claim 1, Reynolds teaches a method, comprising:
generating, by a processor, a plurality of zones in a storage device, the plurality of zones comprising: (i) a first zone, (ii) a second zone, (iii) a third zone, and (iv) a fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.);
storing, by the processor, data in the first zone (e.g. Reynolds, see paragraphs [0070-0077], which discloses a collaborative data consolidation system that includes a dataset query engine a collaborative data repository and a data repository that stores data from the community of users.), wherein the data in the first zone has not been ingested or processed according to a processing pipeline (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, dataset ingestion controller may be configured to receive one or more collaborative datasets, where a collaborative data set may be configured to, but need not be required to, format data in converted dataset (e.g. ingested).);
processing, by the processor based on the processing pipeline, the data in the first zone to ingest and organize the data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are form data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point.), thereby generating raw data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.);
storing, by the processor, the raw data in the second zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on one or more policies of the processing pipeline, the data in the second zone to standardize the data in the second zone, thereby generating standardized data (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.);
storing, by the processor, the standardized data in the third zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on the processing pipeline, the standardized data in the third zone to associate the standardized data with one or more lines of business (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.), thereby generating business-specific data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.);
storing, by the processor, the business-specific data in the fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.); and
outputting, by the processor on a display, a graphical user interface comprising indications of the first zone, the second zone, the third zone, and the fourth zone (e.g. Reynolds, see Figure 22, which discloses a display using a graphical user interface depicting the collaborative datasets.).
As per claim 19, Reynolds teaches an apparatus, comprising:
a processor (Reynolds, see Figure 34, which discloses a processor); and
a memory storing instructions that, when executed by the processor (Reynolds, see Figure 34, which discloses a processor coupled to memory.), cause the processor to:
generating, by a processor, a plurality of zones in a storage device, the plurality of zones comprising: (i) a first zone, (ii) a second zone, (iii) a third zone, and (iv) a fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.);
storing, by the processor, data in the first zone (e.g. Reynolds, see paragraphs [0070-0077], which discloses a collaborative data consolidation system that includes a dataset query engine a collaborative data repository and a data repository that stores data from the community of users.), wherein the data in the first zone has not been ingested or processed according to a processing pipeline (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, dataset ingestion controller may be configured to receive one or more collaborative datasets, where a collaborative data set may be configured to, but need not be required to, format data in converted dataset (e.g. ingested).);
processing, by the processor based on the processing pipeline, the data in the first zone to ingest and organize the data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are form data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point.), thereby generating raw data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.);
storing, by the processor, the raw data in the second zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on one or more policies of the processing pipeline, the data in the second zone to standardize the data in the second zone, thereby generating standardized data (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.);
storing, by the processor, the standardized data in the third zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on the processing pipeline, the standardized data in the third zone to associate the standardized data with one or more lines of business (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.), thereby generating business-specific data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.);
storing, by the processor, the business-specific data in the fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.); and
outputting, by the processor on a display, a graphical user interface comprising indications of the first zone, the second zone, the third zone, and the fourth zone (e.g. Reynolds, see Figure 22, which discloses a display using a graphical user interface depicting the collaborative datasets.).
As per claim 20, Reynolds teaches a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor (Reynolds, see Figure 34, which discloses a processor coupled to memory.), cause the processor to:
generating, by a processor, a plurality of zones in a storage device, the plurality of zones comprising: (i) a first zone, (ii) a second zone, (iii) a third zone, and (iv) a fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.);
storing, by the processor, data in the first zone (e.g. Reynolds, see paragraphs [0070-0077], which discloses a collaborative data consolidation system that includes a dataset query engine a collaborative data repository and a data repository that stores data from the community of users.), wherein the data in the first zone has not been ingested or processed according to a processing pipeline (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, dataset ingestion controller may be configured to receive one or more collaborative datasets, where a collaborative data set may be configured to, but need not be required to, format data in converted dataset (e.g. ingested).);
processing, by the processor based on the processing pipeline, the data in the first zone to ingest and organize the data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are form data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point.), thereby generating raw data (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.);
storing, by the processor, the raw data in the second zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on one or more policies of the processing pipeline, the data in the second zone to standardize the data in the second zone, thereby generating standardized data (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.);
storing, by the processor, the standardized data in the third zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.);
processing, by the processor based on the processing pipeline, the standardized data in the third zone to associate the standardized data with one or more lines of business (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.), thereby generating business-specific data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.);
storing, by the processor, the business-specific data in the fourth zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0075], which discloses a collaborative dataset consolidation system that stores the datasets from the community of users.); and
outputting, by the processor on a display, a graphical user interface comprising indications of the first zone, the second zone, the third zone, and the fourth zone (e.g. Reynolds, see Figure 22, which discloses a display using a graphical user interface depicting the collaborative datasets.).
As per claim 2, Reynolds teaches the method of claim 1, wherein the first zone, the second zone, the third zone, and the fourth zone are implemented as a first container, a second container, a third container, and a fourth container, respectively, in the storage device (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.).
As per claim 3, Reynolds teaches the method of claim 1, wherein the data comprises a plurality of datasets received from a plurality of data sources (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
As per claim 4, Reynolds teaches the method of claim 1, wherein the first zone, the second zone, the third zone, and the fourth zone each comprise: (i) a respective set of permissions, and (ii) a respective set of role-based access controls (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.).
As per claim 5, Reynolds teaches the method of claim 1, wherein the data moves through the first zone, the second zone, the third zone, and the fourth zone through based on the processing pipeline (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.).
As per claim 6, Reynolds teaches the method of claim 5, wherein the processing pipeline comprises a data quality check to ensure that the data is moved through the first zone, the second zone, the third zone, and the fourth zone according to one or more predetermined policies (e.g. Reynolds, see Figure 4, paragraph [0070], which discloses according to some embodiments, atomized datasets are format data in converted dataset, when the data is ingested by the dataset ingestion controller, where the atomized datasets may include a data arrangement (e.g. organized) in which data is stored as an atomized data point and further paragraph [0095], where the dataset may be raw data items.).
As per claim 7, Reynolds teaches the method of claim 1, further comprising:
receiving, by the processor, a request to generate a fifth zone comprising an analytical workspace zone (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.); and
generating, by the processor, the fifth zone in a container in the storage device (e.g. Reynolds, see paragraph [0139], which discloses creating datasets that initiates the creation of an atomized dataset based on the set of data, which may be raw data in data file.).
As per claim 8, Reynolds teaches the method of claim 7, wherein the analytical workspace zone is configured for storing data to be validated without altering the data in the first, second, third, and fourth zones (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.).
As per claim 9, Reynolds teaches the method of claim 8, wherein the analytical workspace zone comprises an experimental zone for ad-hoc use cases (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.).
As per claim 10, Reynolds teaches the method of claim 1, wherein the standardized data is further generated based on cleansing and validating the raw data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
As per claim 11, Reynolds teaches the method of claim 10, wherein cleansing and validating the raw data comprises one or more of data quality checks, masking, tokenization, removing personal information, removing personally identifiable information, removing sensitive information, or removing protected personal information (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
As per claim 12, Reynolds teaches the method of claim 1, wherein the first zone comprises a transient zone to store the data (e.g. Reynolds, see Figure 4 and paragraphs [0069-0072], which discloses receiving a plurality of collaborative datasets, where the dataset may be associated with data configured to establish one or more associations among subsets of dataset attribute data for datasets, where attribute data may be used to determine correlations among the collaborative datasets.).
As per claim 13, Reynolds teaches the method of claim 1, wherein the second zone comprises a raw zone to store the raw data (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.).
As per claim 14, Reynolds teaches the method of claim 13, wherein the raw data in the raw zone is in an original format (e.g. Reynolds, see paragraphs [0115-0125], which discloses raw data files may be ingested using a dataset ingestion controller, where the raw datasets are used to generate collaborative datasets which are stored in the dataset repository.).
As per claim 15, Reynolds teaches the method of claim 1, wherein the third zone comprises a trusted zone to store the standardized data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
As per claim 16, Reynolds teaches the method of claim 1, wherein the fourth zone comprises a trusted zone to store the business-specific data (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
As per claim 17, Reynolds teaches the method of claim 1, wherein the graphical user interface (e.g. Reynolds, see Figure 22, which discloses a display using a graphical user interface depicting the collaborative datasets.) comprises one or more of a governance graph, a report, a lineage, or a glossary (e.g. Reynolds, see Figure 22, which discloses a display using a graphical user interface depicting the collaborative datasets.).
As per claim 18, Reynolds teaches the method of claim 1, wherein each of the one or more lines of business is associated with a respective set of predetermined policies (e.g. Reynolds, see paragraphs [0140-0150], which discloses a dataset attribute manager configured to generate data to enhance datasets, which allows the it to be configured to correlate, identify, analyze, and summarize datasets and dataset interactions and are fed via a dataset activity feed to disseminate dataset-related information via a computing device.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 July 22, 2026
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