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 . Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
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.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
“Cloud Migration and Inter-Cloud Mobility Aspects” by Mikhail Gloukhovtsev (published in 2016, hereinafter “Gloukhovtsev”) in view of
“Integration Patterns for Open Subsurface Data Universe (OSDU)” by Sachin Kumar Patidar (published in October 2023, hereinafter “Patidar”).
Gloukhovtsev teaches:
1. A method for migrating bulk data from a first data platform to a second data platform [Gloukhovtsev, page 37 and Figure 10], the method comprising:
providing a virtual machine (VM) on a cloud service provider subscription [Gloukhovtsev, page 37 and Figure 10].
Gloukhovtsev does not explicitly teach, but Patidar teaches:
migrating access control list (ACL) information, one or more legal tags, and reference data from the first data platform to the second data platform, using the VM to control the migrating [Patidar, page 7, Authentication and Authorization; page 7, Metadata Management];
analyzing data types of the bulk data [Patidar, page 7, Data Standards; page 7, Metadata Management];
generating a file-generic data migration pipeline from the first data platform to the second data platform, using the VM [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
fetching storage records for the bulk data from the first data platform using the VM [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
migrating the bulk data from the first data platform to the second data platform, using the VM to control the migrating [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
validating the migrated bulk data in the second data platform [Patidar, page 5, API-based Integration; page 8, Testing and Validation]; and
synchronizing the migrated bulk data in the second data platform with any changes made to the bulk data in the first data platform since the migrating the bulk data began [Patidar, page 6, Data Replication and Caching].
Gloukhovtsev and Patidar are analogous art because they are in the same field of endeavor, cloud data management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the virtual machine inter-cloud data migration techniques of Gloukhovtsev with the Open Subsurface Data Universe (OSDU) cloud data management techniques to achieve the predictable result of a full-featured data transformation process.
The combination of Gloukhovtsev and Patidar teaches:
2. The method of claim 1, wherein the migrating the access control list (ACL) information, one or more legal tags, and reference data from the first data platform to the second data platform comprises:
determining access to available ACL groups in the first data platform [Patidar, page 7, Authentication and Authorization];
creating new ACL groups in the second data platform [Patidar, page 7, Authentication and Authorization];
determining whether the one or more legal tags are different between the first data platform and the second data platform [Patidar, page 7, Metadata Management];
in response to the one or more legal tags being determined to be different, mapping a change in the one or more legal tags determined to be different between the first data platform and the second data platform tags [Patidar, page 7, Metadata Management];
adding the one or more legal tags to the second data platform [Patidar, page 7, Metadata Management];
determining whether reference data is added or modified between the first data platform and the second data platform [Patidar, page 7, Metadata Management]; and
generating a query for determining which reference data is added or modified between the first data platform and the second data platform [Patidar, page 7, Metadata Management].
3. The method of claim 2, wherein the analyzing data types of the bulk data comprises:
generating a hierarchical data model of the data types to determine an order of ingestion of the bulk data [Patidar, page 7, Data Standards]; and
determining a scope of ancestry migration based on the hierarchical data model [Patidar, page 7, Metadata Management].
4. The method of claim 3, wherein the generating the file-generic data migration pipeline comprises:
downloading files for a given set of record identifications (IDs) of the first data platform and ingesting the files into the second data platform [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
during the downloading and ingesting of the files, populating a migration database with ID mapping information about the record IDs and version information from the first data platform and the second data platform [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps]; and
using the ID mapping information during migration of records from a storage service associated with the first data platform [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps].
5. The method of claim 4, wherein the fetching the storage records for the bulk data comprises:
generating one or more schema for custom data types from the first data platform to the second data platform [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
running the file-generic data migration pipeline to migrate sample records corresponding to the custom one or more schema for the custom data types [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
determining an order of ingestion for the data types based on the hierarchical data model [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
selecting one data type at a time for ingestion [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
fetching the record IDs and iterating a list of sample Work Product Component (WPC) record IDs for the records from the storage service [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying];
transforming the records from a first data format compatible with the first data platform to a second data format compatible with the second data platform using the ACL groups, any changed legal tags, and record IDs [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying]; and
ingesting the records [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 6, Federated Querying].
6. The method of claim 5, wherein the transforming the records comprises:
changing each ACL based on a new tenant ID for each ACL [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 7, Authentication and Authorization];
changing any of the one or more legal tags determined to be different [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 7, Authentication and Authorization];
in response to a record ID including a version ID, removing the version ID from the record ID including the version ID [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 7, Authentication and Authorization];
retaining ancestry records based on the determined scope of the ancestry migration [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 7, Authentication and Authorization]; and
providing a version ID for the ancestry records [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps; page 7, Authentication and Authorization].
7. The method of claim 6, wherein the migrating the bulk data from the first data platform to the second data platform comprises:
iterating the list of sample WPC record IDs to fetch the bulk data associated with the list [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
ingesting the bulk data into the second data platform in an order based on the list [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
determining whether match and merge rules are used for a Well Known Entity (WKE) generation service in the first data platform [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
in response to determining that match and merge rules are used for the WKE generation service in the first data platform, checking and migrating the bulk data [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps];
analyzing mapping of Well-Known Schemas (WKS) associated with the bulk data [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps]; and
changing a bulk data record ID of at least one associated WPC record using a mapping created at the time of creation of the bulk data [Patidar, page 5, Data Ingestion and ETL; page 5, ETL Ingestion Involves the Following Steps].
8. The method of claim 7, wherein the validating the migrated bulk data in the second data platform comprises:
implementing search service application programming interfaces (APIs) to fetch record counts of all data types in both the first data platform and the second data platform [Patidar, page 5, API-based Integration; page 8, Testing and Validation];
comparing the record counts of all data types in the first data platform to the record counts of all data types in the second data platform [Patidar, page 5, API-based Integration; page 8, Testing and Validation];
comparing an attribute count, a name, and a value for a selected record in both the first data platform and the second data platform [Patidar, page 5, API-based Integration; page 8, Testing and Validation]; and
providing a visual validation of randomly selected migrated data in an application project pointing to the second data platform [Patidar, page 5, API-based Integration; page 8, Testing and Validation].
9. The method of claim 8, wherein the synchronizing the migrated bulk data comprises:
synchronizing the migrated bulk data in the second data platform with the bulk data in the first data platform for a predetermined period of time [Patidar, page 6, Data Replication and Caching]; and
automatic retrieval of failures from a previous cycle [Patidar, page 6, Data Replication and Caching].
10. The method of claim 9, wherein the fetching the storage records for the bulk data and the migrating the bulk data from the first data platform to the second data platform are performed in parallel [Patidar, page 6, Event-Driven Integration].
Claims 11-20 recite limitations similar to those recited in claims 1-10, respectively, and are rejected for the same reasons discussed above.
Conclusion
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/Scott A. Waldron/Primary Examiner, Art Unit 2156
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