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 Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-6, 9-14, 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 6, and 9-14 refer to “the processor.” This element has not yet been introduced into the claims.
Claim 5 requires “the processor to develop BI tables.” It is noted that an element of “BI tables” has already been introduced into the claim 1. It is unclear if the BI tables introduced into claim 5 are new or the same BI tables as those in the parent claim.
Claims 9-14 refer to “the memory.” This element has not yet been introduced into the claims.
Claim 18 refers to “the analytical data model.” This element has not yet been introduced into the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Claim 1 recites:
“1. A method, comprising:
detecting changes in source data stored in a transactional database, the source data being represented by a restored previous snapshot table and a restored current snapshot table derived from previous and current snapshots of the transactional database, respectively;
detecting changes in data stored in reconstructed tables of an analytical database corresponding to the source data, the reconstructed tables having been derived from merges of raw events captured from the transactional database;
developing business intelligence (BI) tables based on the raw events and data mapping rules;
detecting drift between the data stored in the reconstructed tables and the source data stored in the transactional database; and detecting drift between the BI tables based on the source data, the data mapping rules, and changes in the BI tables.”
The claim contains mental process steps of “detecting changes in source data stored in a transactional database…”, “detecting changes in data stored in reconstructed tables of an analytical database corresponding to the source data,” “developing business intelligence (BI) tables based on the raw events and data mapping rules,” and “detecting drift between the data stored in the reconstructed tables and the source data stored in the transactional database; and detecting drift between the BI tables based on the source data, the data mapping rules, and changes in the BI tables.”
This is a mental process because each of these steps are either data analysis steps (the “detecting…” steps) or a generic data generation step (“developing … tables”). A human being equipped with pen and paper or a generic computer is capable of detecting data and is capable of developing tables.
There are no additional elements in the claim beyond the mental process steps.
This judicial exception is not integrated into a practical application because there are no additional elements that appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
There are also no additional elements that are sufficient to amount to significantly more than the judicial exception, in part or in whole. No additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional.
Dependent claims 2-14 are merely directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis and extraction steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
Claims 4, 6, and 9-14 refer to “the processor.” Claims 9-14 refer to “the memory.” Assuming that these are generic hardware components, it is noted that the recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2). The recitation of generic hardware does not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
Additionally, the recitation of generic hardware does not, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Claim 15 recites:
“15. A method, comprising:
detecting drift between data stored in a plurality of reconstructed tables and source data stored in a transactional database, the reconstructed tables having been derived from raw events captured from the transactional database, and the source data being reflected in restored tables comprising first and second tables having been derived from previous and current snapshots of the transactional database; and
performing reconciliation on the data stored in the plurality of reconstructed tables to remove the detected drift by artificially generating corrective events, and applying the corrective events to the raw events captured from the transactional database.”
The claim contains mental process steps of “detecting drift between data stored in a plurality of reconstructed tables and source data stored in a transaction database…” and “performing reconciliation on the data stored in the plurality of reconstructed tables...”
This is a mental process because these steps are a data analysis step and a generic data generation step. A human being equipped with pen and paper or a generic computer is capable of detecting data and is capable of developing tables.
There are no additional elements in the claim beyond the mental process steps.
This judicial exception is not integrated into a practical application because there are no additional elements that appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
There are also no additional elements that are sufficient to amount to significantly more than the judicial exception, in part or in whole. No additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional.
Dependent claims 16-18 are merely directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis and extraction steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
Claim 19 recites:
“A system, comprising: a processor; and memory comprising instructions that when executed, cause the processor to:
detect changes in source data stored in a transactional database, the source data being represented by a restored previous snapshot table and a restored current snapshot table generated based on previous and current snapshots of the transactional database, respectively;
detect changes in data stored in reconstructed tables of an analytical database and in data stored in business intelligence (BI) tables, the data stored in the reconstructed tables being generated from an initial data load from the transactional database to which captured raw events from the transactional database have been applied, the data stored in the BI tables being generated by applying data mapping rules to the raw events;
detect drift between the data stored in the reconstructed tables and the source data stored in the transactional database by comparing the detected changes in data stored in the reconstructed database and the detected changes in the source data in the transactional database; and
detect drift in the data stored in the BI tables based on the source data, the data mapping rules, and changes in the BI tables.”
The claims contain mental process steps of “detect changes in source data stored in a transactional database…,” “detect changes in data stored in reconstructed tables of an analytical database and in data stored in business intelligence (BI) tables…,” “detect drift between the data stored in the reconstructed tables and the source data stored in the transactional database…”, and “detect drift in the data stored in the BI tables.”
This is a mental process because each of these steps are data analysis steps. A human being equipped with pen and paper or a generic computer is capable of detecting data and is capable of developing tables.
The claims contain additional elements beyond the mental process in the “processor” and “memory.”
This judicial exception is not integrated into a practical application because the claimed additional elements do not appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem.
The processor and memory are recited at a high level of generality. They appear to be generic computing hardware elements. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The recitation of generic hardware of the processor and memory is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f)(2).
None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or add a specific limitation other than what is well understood, routine, or conventional. As such, none of the additional elements appears to be, in part or in whole, significantly more than the judicial exception.
Dependent claim 20 is directed towards additional limitations that further define data types or further describe analyses that will occur. It is noted that the claimed data definitions and data analysis and extraction steps do not appear to include additional elements that incorporate the claimed subject matter into a practical application. The dependent claims also do not include additional elements that, in part or in whole, appear to be significantly more than the abstract idea.
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.
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 1-9, 12-14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Beatty et al. (US Patent 8,364,640), in view of Raspudic et al. (US Pre-Grant Publication 2024/0256399), and further in view of Daniel et al. (US Pre-Grant Publication 2019/0050441).
As to claim 1, Beatty teaches a method, comprising:
detecting changes in source data stored in a transactional database, the source data being represented by a restored previous snapshot table and a restored current snapshot table derived from derived from previous and current snapshots of the transactional database of the transactional database, respectively (see Beatty 9:60-10:5. A backup agent maintains a copy of two restored tables. These restored tables are a restored previous and a restored current snapshot table. Both are derived from backups. A mapping of GUIDs of updated data to the restored data may be maintained);
detecting changes in data stored in reconstructed tables of an analytical database corresponding to the source data … (see Beatty 4:50-5:2 for how Beatty defines a database. See 9:12-32 for the initiation of a rebuilt process. Notably, a reconstructed database may be generated from a template);
…
detecting drift between the data stored in the reconstructed tables and the source data stored in the transactional database (see Beatty 10:18-26. A comparison may be performed to detect differences between a rebuilt version and a backed up version of data. If there are detected differences, an overwrite or update operation may be performed); and
Beatty does not teach:
the reconstructed tables having been derived from merges of raw events captured from the transactional database;
developing business intelligence (BI) tables based on the raw events and data mapping rules;
detecting drift between the BI tables based on the source data, the data mapping rules, and changes in the BI tables.
Raspudic teaches:
the reconstructed tables having been derived from merges of raw events captured from the transactional database (see paragraph [0035]. Raspudic shows to rebuild a database based on recovering the database to an initial state followed by replaying records from a transaction log);
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Raspudic because both references are directed towards rebuilding databases. Raspudic provides the benefit of a recovery pipeline designed to rebuild a database to a particular state that makes use of cloud computing resources that are dynamically scalable and helps to ensure that Beatty manages resources more efficiently.
Daniel teaches
developing business intelligence (BI) tables based on the raw events and data mapping rules (see Daniel paragraphs [0028]-[0031]. Data from a source database is copied through data mapping rules to an analytics (or business intelligence) database);
detecting drift between the BI tables based on the source data, the data mapping rules, and changes in the BI tables (see Daniel paragraphs [0031] and [0040]-[0042]. Changes to the source data can be identified and tracked and mapped to the BI database).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Daniel because Daniel provides the benefit of a more efficient business intelligence systems for complex queries for analysis or reporting of source data.
As to claim 2, Beatty teaches the method of claim 1, wherein the method further comprising restoring the previous and current snapshots to the same database schema (see Beatty 9:60-10:26. Backups are restored to the same schema they were saved as, the corrected if needed).
As to claim 3, Beatty as modified by Raspudic teaches the method of claim 1, wherein the merges of the raw events comprises reconstructions of tables comprising an initial data load from the transactional database to the analytical database in accordance with changes to the data reflected in the raw events (see Raspudic paragraphs [0035] and [0015] for a definition of a transaction log, or record of “raw events”).
As to claim 4, Beatty as modified by Raspudic teaches the method of claim 1, wherein the instructions that when executed cause the processor to detect changes in data stored in the reconstructed tables, comprise further instructions that when executed cause the processor to obtain the raw events via a continuous data capture (CDC) connector in the analytical database (see Raspudic paragraph [0015]. The transaction log continuously captures raw events as they occur).
As to claim 5, Beatty as modified by Raspudic teaches the method of claim 4, wherein the CDC connector is operatively connected to a CDC module capturing the raw events that occurred on the data stored in the transactional database (see Raspudic paragraph [0015]).
As to claim 6, Beatty as modified by Daniel teaches the method of claim 5, wherein the instructions that when executed, cause the processor to develop BI tables based on the raw events and data mapping rules comprise further instructions that when executed, cause the processor to apply the data mapping rules to the raw events in the analytical database (see Daniel paragraphs [0028]-[0031] and [0040]-[0042]).
As to claim 7, Beatty as modified by Daniel teaches the method of claim 6, wherein the data mapping rules comprise at least one of metadata and data transformations that are maintained for data governance in the analytical database (see Daniel paragraphs [0028]-[0031], [0037], and [0040]-[0042])
As to claim 8, Beatty as modified by Daniel teaches the method of claim 6, wherein the application of the data mapping rules to the raw events results in the development of the BI tables from a transactional data model via an extract, transform, and load operation (see Daniel paragraphs [0028]-[0031] and [0040]-[0042]).
As to claim 9, Beatty as modified by Daniel teaches the method of claim 8, wherein the memory comprises further instructions that when executed, cause the processor to generate corrective events using the BI tables and the data mapping rules (see Daniel paragraphs [0028]-[0031] and [0040]-[0042]).
As to claim 12, Beatty as modified teaches the method of claim 9, wherein the memory comprises further instructions that when executed, cause the processor to generate the corrective events using additional data from the restored current snapshot table (see Beatty 10:18-36).
As to claim 13, Beatty as modified by Daniel teaches the method of claim 9, wherein the memory comprises further instructions that when executed, cause the processor to apply the raw events and the corrective events to the reconstructed tables and the BI tables of the analytical database to match the current snapshot of the transactional database thereby reconciling the detected drift (see Beatty 10:18-36 for applying changes to the reconstructed tables. See Daniel paragraphs [0028]-[0031] and [0040]-[0042] for applying corrective events to the BI tables).
As to claim 14, Beatty as modified teaches the method of claim 1, wherein the memory comprises further instructions that when executed, cause the processor to, when the drift is detected in only a subset of the reconstructed tables, fetch rows of tables from the current snapshot corresponding to the subset of the reconstructed tables to generate corrective events (see Beatty 10:18-36).
As to claim 17, Beatty as modified teaches the method of claim 15.
Beatty does not teach further comprising applying data mapping rules to the raw events to construct business intelligence (BI)tables,
detect drift in the BI tables using the source data, the data mapping rules, and changes in the BI tables, and
performing reconciliation on the data stored in the BI tables to remove the detected drift by further applying the data mapping rules to the corrective events.
Daniel teaches further comprising applying data mapping rules to the raw events to construct business intelligence (BI)tables (see Daniel paragraphs [0028]-[0031]),
detect drift in the BI tables using the source data, the data mapping rules, and changes in the BI tables (see Daniel paragraphs [0031] and [0040]-[0042].), and
performing reconciliation on the data stored in the BI tables to remove the detected drift by further applying the data mapping rules to the corrective events (see Daniel paragraphs [0031] and [0040]-[0042]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Daniel because Daniel provides the benefit of a more efficient business intelligence systems for complex queries for analysis or reporting of source data.
As to claim 18, Beatty as modified by Daniel teaches the method of claim 17, further comprising using the analytical data model in conjunction with the data mapping rules to artificially generate the corrective events (see Daniel paragraphs [0028]-[0031] and [0040]-[0042]).
As to claim 19, Beatty teaches a system, comprising: a processor; and memory comprising instructions that when executed, cause the processor to:
detect changes in source data stored in a transactional database, the source data being represented by a restored previous snapshot table and a restored current snapshot table generated based on previous and current snapshots of the transactional database, respectively (see Beatty 9:60-10:5. A backup agent maintains a copy of two restored tables. These restored tables are a restored previous and a restored current snapshot table. Both are derived from backups. A mapping of GUIDs of updated data to the restored data may be maintained);
detect changes in data stored in reconstructed tables of an analytical database (see Beatty 4:50-5:2 and 9:12-32 and the rejection of claim 1)
…
the data stored in the reconstructed tables being generated from an initial data load from the transactional database (see Beatty 10:18-26)…
detect drift between the data stored in the reconstructed tables and the source data stored in the transactional database by comparing the detected changes in data stored in the reconstructed database and the detected changes in the source data in the transactional database (see Beatty 10:18-26); and
Beatty does not teach:
and in data stored in business intelligence (BI) tables,
the data stored in the reconstructed tables being generated from an initial data load from the transactional database to which captured raw events from the transactional database have been applied, the data stored in the BI tables being generated by applying data mapping rules to the raw events;
detect drift in the data stored in the BI tables based on the source data, the data mapping rules, and changes in the BI tables.
Raspudic teaches
the data stored in the reconstructed tables being generated from an initial data load from the transactional database to which captured raw events from the transactional database have been applied (see paragraph [0035]. Raspudic shows to rebuild a database based on recovering the database to an initial state followed by replaying records from a transaction log);
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Raspudic because both references are directed towards rebuilding databases. Raspudic provides the benefit of a recovery pipeline designed to rebuild a database to a particular state that makes use of cloud computing resources that are dynamically scalable and helps to ensure that Beatty manages resources more efficiently.
Daniel teaches:
detect changes in data stored in data stored in business intelligence (BI) tables (see Daniel paragraphs [0031] and [0040]-[0042] and the rejection of claim 1),
the data stored in the BI tables being generated by applying data mapping rules to the raw events (see Daniel paragraphs [0028]-[0031] and the rejection of claim 1);
detect drift in the data stored in the BI tables based on the source data, the data mapping rules, and changes in the BI tables (see Daniel paragraphs [0031] and [0040]-[0042] and the rejection of claim 1).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Daniel because Daniel provides the benefit of a more efficient business intelligence systems for complex queries for analysis or reporting of source data.
As to claim 20, Beatty as modified teaches the system of claim 19, wherein memory comprises further instructions that when executed cause the processor to apply the raw events and generated corrective events to the reconstructed tables and the BI tables of the analytical database to match the current snapshot of the transactional database thereby reconciling the detected drifts (see Raspudic paragraph [0035] for applying raw events to rebuild a database. See Beatty 9:60-10:5 for reconciling drifts with of reconstructed tables with snapshots. And see Daniel paragraphs [0028]-[0031] and [0040]-[0042] for applying corrective events to BI tables).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Beatty et al. (US Patent 8,364,640), in view of Raspudic et al. (US Pre-Grant Publication 2024/0256399).
As to claim 15, Beatty teaches a method, comprising:
detecting drift between data stored in a plurality of reconstructed tables and source data stored in a transactional database (see Beatty 4:50-5:2 for how Beatty defines a database. See 9:12-32 for the initiation of a rebuilt process. Notably, a reconstructed database may be generated from a template)
…
and the source data being reflected in restored tables comprising first and second tables having been derived from previous and current snapshots of the transactional database (see Beatty 9:60-10:5. A backup agent maintains a copy of two restored tables. These restored tables are a restored previous and a restored current snapshot table. Both are derived from backups. A mapping of GUIDs of updated data to the restored data may be maintained)); and
performing reconciliation on the data stored in the plurality of reconstructed tables to remove the detected drift by artificially generating corrective events (see Beatty 10:18-26. A comparison may be performed to detect differences between a rebuilt version and a backed version of data. If there are detected differences, an overwrite or update operation may be performed), and
applying the corrective events to the raw events captured from the transactional database (see Beatty 10:18-26).
Beatty does not clearly teach:
the reconstructed tables having been derived from raw events captured from the transactional database,
Raspudic teaches:
the reconstructed tables having been derived from raw events captured from the transactional database (see paragraph [0035]. Raspudic shows to rebuild a database based on recovering the database to an initial state followed by replaying records from a transaction log);
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Raspudic because both references are directed towards rebuilding databases. Raspudic provides the benefit of a recovery pipeline designed to rebuild a database to a particular state that makes use of cloud computing resources that are dynamically scalable and helps to ensure that Beatty manages resources more efficiently.
As to claim 16, Beatty teaches the method of claim 15, further comprising restoring the previous and current snapshots to the same database schema (see Beatty 9:60-10:26. Backups are restored to the same schema they were saved as, the corrected if needed).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Beatty et al. (US Patent 8,364,640), in view of Raspudic et al. (US Pre-Grant Publication 2024/0256399), in view of Daniel et al. (US Pre-Grant Publication 2019/0050441), and further in view of Abushwashi (US Patent 10,635,645).
As to claim 10, Beatty as modified teaches the method of claim 8, wherein the memory comprises further instructions that when executed, cause the processor to detect an incorrect aggregation value (see Beatty 10:6-26. Beatty may detect an incorrect value for a grouping of data values).
Beatty does not clearly teach
when the extract, transform, and load operation involves periodic aggregation.
Abushwashi teaches:
when the extract, transform, and load operation involves periodic aggregation (see 3:46-58. Abushwashi teaches to periodically regenerate an aggregate table based on additional data).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Beatty by the teachings of Abushwashi because both references are directed towards extracting data from source tables and ensuring that the data is correct. The teachings of Abushwashi will assist Beatty in ensuring that aggregate tables are updated and available for queries, which will increase efficiency.
As to claim 11, Beatty as modified by Abushwashi teaches the method of claim 10, wherein the memory comprises further instructions that when executed, cause the processor to obtain all data records from the current snapshot within a relevant aggregation window to re-perform the periodic aggregation (see Beatty 10:6-26. Beatty may resolve a discrepancy by obtaining records from a backed up version. ZZZ teaches a period aggregation. Abushwashi 3:46-58 teaches to periodically regenerate an aggregate table based on additional data).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2152
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