Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This office action is in response to the REM filed 06/17/2026.
3. Claims 1-4, 7, 8, 10-14, 17, 18, and 20-26 are pending.
4. Claims 1 and 11 are in independent form.
5. Claims are being amended. Claims 21-26 are new. Claims 5-6, 9, 15-16, and 19 are being canceled without prejudice. No new matter has been added.
6. The office action is made Final.
Examiner Note
7. The Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Claim Rejections - 35 USC § 103
8. 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 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.
9. 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) A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
10. Claims 1-4, 7, 8, 10-14, 17, 18, and 20-26 are rejected under 35 U.S.C.103 as being unpatentable over Mutalik Desal (US 20200226117 A1) hereinafter as Mutalik in view of Alfaras et al (US 20240281419 A1) hereinafter as Alfaras hereinafter as Alfaras.
11. Regarding claims 1-4, 7, 8 and 10 those claims recite a system performs the method of claims 11-14, 17, 18 and 20 respectively and are rejected under the same rationale.
12. Regarding claims 21-26, those claims recite a system performs the method of claims 11-14, 17, 18 and 20 respectively and are rejected under the same rationale.
13. Regarding claim 11, Mutalik teaches A method performed by a multi-tenant system comprising one or more computers, the method comprising:
obtaining consumption records, the consumption records comprising consumption event attributes and consumption event attribute values (Fig 2, [0026-0028],, “retrieving transactional data (consumption records)” [0035-0036], ” quantitative elements of events or facts (e.g., quantities, sales amounts, unit prices, etc.) (quantity attributes)”, [0038], “transaction data 112 related to business events, such as payment information, order information, customer information, products, employee information (e.g., identifiers, salary, etc.), transaction dates or periods (temporal attributes), or any other information related to an event or a business's transactions.”, [0043], “events or transactions in a business environment.”, [0048], “retrieval of transactional data (e.g., event records)”, [0053], “Event records 322 may comprise transaction data”, [0054], “The event occurrence data (event id, timestamp, etc.) are held in a single table, while the dimension records and event record to dimension mapping are stored in one or more simple key-value type lookup tables in hypergeneralized staging database 314.”);
obtaining one or more schemas corresponding to the obtained consumption records ([0052], “the SQL query, when executed, may enable the data to be placed in a table in a particular or predetermined format (e.g., by identifying dimension attributes as metadata) as determined by the schema of hypergeneralized staging database 314 that will be used during generation of the tabular model”, [0054], [0057], “the schema of the staging database is determined based on the encoded column names in the SQL query”, [0071], “dimension mapper 310 may be configured to generate a database schema that associates each event with a dimension and dimension attribute as dimension metadata 316. In some further implementations, dimension mapper 310 may also automatically create a schema, based on the encoded information in the query, to store dimensions and related dimension attributes in hypergeneralized staging database 314.”, Fig 8A-8G, [0085-0092]);
generating a consumption dataset to compile the obtained consumption records based on the obtained one or more schemas ([0026], “Using the fact and dimension tables from the presentation database, a tabular model builder can generate a tabular model that may be stored and/or presented in a suitable platform for analyzing transactional data.”, [0052], “The SQL query, when executed, may enable the data to be placed in a table in a particular or predetermined format (e.g., by identifying dimension attributes as metadata) as determined by the schema of hypergeneralized staging database 314 that will be used during generation of the tabular model.”, [0061], “warehouse builder 318 may be configured to translate a schema of hypergeneralized staging database 314 into a domain specific database (i.e., presentation database 320) from which a tabular model may be generated.”, [0062], “automatically generate a star or snowflake schema in connection with generation of tabular model 328.”, [0085]);
In line with Applicant Pre-Grant Pub:
[0005], [0033], “compiling the data from the consumption records into a structured format, e.g., an array or a tabular format.”
Mutalik implicitly teaches determining, from the generated consumption dataset, cardinalities of each of a plurality of eligible consumption event attributes ([0052], “if new dimension is to be added, removed, or modified, the SQL query for the appropriate events may simply be updated to modify the dimension mapping”, [0081], “a developer or analyst may determine that certain measures (cardinalities of each of eligible consumption event attributes) may be desirable to analyze for a given event, and accordingly may define such measures in the SQL query associated with that event. In this manner, if measures are to be added, removed, and/or modified, the developer or analyst may revise the query for the event, rather than engaging in a complex and time-consuming process of defining measures in other manners.”);
In line with Applicant Pre-Grant Pub:
[0034-0035], determines a cardinality of each dimension, e.g., for each consumption event attribute. The cardinality corresponds to a number of unique values of each dimension in the consumption records.
generating a cubed consumption dataset from the consumption dataset based on the determined cardinalities ([0035], “Tabular model generation system 104 may be configured to automatically generate a tabular cube (a cubed consumption dataset) from transactional data 112 in one or more source system(s) 110 (or any other operational databases).”);
Further, Mutalik explicitly teaches storing the cubed consumption dataset in one or more directories of a datastore (Abstract, “the tabular model may be stored and/or presented as a tabular cube (a cubed consumption dataset) in a platform for analyzing transactional data.”, [0035], “The generated cube may be stored in memory (e.g., RAM) and/or presented in one or more business intelligence solutions described herein that may access the tabular model.”);
outputting the computed analytics results ([0006], [0034]).
Mutalik didn’t specifically teach
determining, from the generated consumption dataset, cardinalities of each of a plurality of eligible consumption event attributes, each cardinality indicative of a number of unique consumption event attribute values of each of the eligible consumption event attributes;
generating a cubed consumption dataset from the consumption dataset based on the determined cardinalities, including: selecting, from the plurality of eligible consumption event attributes, one or more to-be-removed consumption event attributes having a cardinality that exceeds a cardinality threshold, consolidating, into a bundle, matching events corresponding to consumption events having common consumption event attributes values for eligible consumption event attributes not selected as to-be-removed consumption event attributes, and generating one or more metrics associated with the bundle;
Computing analytics based on the generated metrics associated with the bundle; and outputting the computed analytics results.
However, Alfaras Explicitly teaches determining, from the generated consumption dataset, cardinalities of each of a plurality of eligible consumption event attributes, each cardinality indicative of a number of unique consumption event attribute values of each of the eligible consumption event attributes ([0273], “Data profiling logic 437 may also involve capturing statistics and summary metrics about data values, such as frequency distributions, cardinality, and uniqueness, to assess the diversity and variability of data within different fields or columns”);
In line with Applicant Pre-Grant Pub:
[0034-0035], determines a cardinality of each dimension, e.g., for each consumption event attribute. The cardinality corresponds to a number of unique values of each dimension in the consumption records.
generating a cubed consumption dataset from the consumption dataset based on the determined cardinalities ([0006], “generating a data cube from data. A candidate granularity is selected from a plurality of candidate granularities determined for a dimension of the data cube, where a data distribution obtained in the selected candidate granularity satisfies a predetermined condition. The data cube is then generated based on the selected candidate granularity for the dimension (the determined cardinalities).”, Fig 2, [0041-0042], “the data cube may be generated in the selected granularity for the dimension.”, [0058], “The generation apparatus 302 may generate the data cube in the selected granularity for the dimension.”), including:
selecting, from the plurality of eligible consumption event attributes, one or more to-be-removed consumption event attributes having a cardinality that exceeds a cardinality threshold ([0144], “duplicate removal, and data standardization to ensure data quality and consistency.”, [0274], “performing data quality checks and validations on sampled data to assess its accuracy, completeness, consistency, and timeliness. Data profiling logic 437 may incorporate predefined rules, thresholds, and criteria for detecting anomalies such as missing values, outliers, duplicates (to-be-removed), or data discrepancies”, [0275], “data cleansing to enhance the consistency and quality of data within the pipeline. This may involve identifying recurring patterns, formats, or structures within data values and applying standardization rules and transformations to ensure consistency and uniformity. Data profiling logic 437 may also involve data cleansing techniques such as data deduplication (to-be-removed), data normalization, and data enrichment to remove redundancies, inconsistencies, and inaccuracies from the data (to-be-removed), improving its overall quality and usability.”, [0305], “Loading data 452 may also involve data deduplication, where duplicate records or entries are identified and removed (to-be-removed) to ensure data consistency and eliminate redundancy in the target system.”),
consolidating, into a bundle, matching events corresponding to consumption events having common consumption event attributes values for eligible consumption event attributes not selected as to-be-removed consumption event attributes ([0041], [0150], “This integration process allows for the consolidation of diverse datasets into a unified, comprehensive dataset (a bundle) that forms the foundation for subsequent analysis and processing. By seamlessly combining data from multiple sources through joins, organizations can create a cohesive dataset (a bundle) that facilitates meaningful insights and decision-making.”, [0305], “loading data 452 may involve data integration processes to combine data from multiple sources or partitions into a unified dataset (a bundle) for storage or analysis. This can include merging data from different staging tables or files, joining data from disparate sources or systems, or consolidating data from different partitions or segments into a single dataset (a bundle).”, [0328], “aggregated or summarized data that has been processed and aggregated from raw or source data to provide a consolidated view of metrics, trends, or performance indicators. This can include summary tables, performance indicators (KPIs)”), and
generating one or more metrics associated with the bundle ([0054], “implementing data quality dashboards, reports, and metrics to provide stakeholders with visibility into the state of data quality across the organization.”, [0058], “quality metrics for different types of data”, [0068], [0218], [0227], “These metrics provide a holistic view of data quality across different dimensions, allowing organizations to prioritize data quality issues and allocate resources effectively for remediation.”, [0269], “generating reports and dashboards to track performance indicators (KPIs) and metrics”, [0273], “Data profiling logic 437 may also involve capturing statistics and summary metrics about data values, such as frequency distributions, cardinality, and uniqueness, to assess the diversity and variability of data within different fields or columns.”, [0304], “performing any necessary data conversions, aggregations, or calculations to derive derived fields or metrics.”, [0328], “aggregated or summarized data that has been processed and aggregated from raw or source data to provide a consolidated view of metrics, trends, or performance indicators.”, [0340], “Reporting data 458 represents the culmination of the data pipeline process, where raw or source data is transformed, cleansed, and aggregated to provide meaningful insights and metrics for reporting purposes.”);
Computing analytics based on the generated metrics associated with the bundle; and outputting the computed analytics results ([0054], “implementing data quality dashboards, reports, and metrics to provide stakeholders with visibility into the state of data quality across the organization.”, [0058], “quality metrics for different types of data”, [0068], [0218], [0227], “These metrics provide a holistic view of data quality across different dimensions, allowing organizations to prioritize data quality issues and allocate resources effectively for remediation.”, [0269], “generating reports and dashboards to track performance indicators (KPIs) and metrics”, [0273], “Data profiling logic 437 may also involve capturing statistics and summary metrics about data values, such as frequency distributions, cardinality, and uniqueness, to assess the diversity and variability of data within different fields or columns.”, [0304], “performing any necessary data conversions, aggregations, or calculations to derive derived fields or metrics.”, [0328], “aggregated or summarized data that has been processed and aggregated from raw or source data to provide a consolidated view of metrics, trends, or performance indicators.”, [0340], “Reporting data 458 represents the culmination of the data pipeline process, where raw or source data is transformed, cleansed, and aggregated to provide meaningful insights and metrics for reporting purposes.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of teachings suggested in Alfaras’s system into Mutalik and by incorporating Alfaras into Mutalik because both systems are related to data management platforms would provide increased data visibility and quality management.
14. Regarding claim 12, Mutalik and Alfaras teach the invention as claimed in claim 11 above and Mutalik further teaches wherein the generating of the consumption dataset comprises generating the consumption dataset in a tabular format having a tabular schema consistent with the obtained one or more schemas ([0026], “Using the fact and dimension tables from the presentation database, a tabular model builder can generate a tabular model that may be stored and/or presented in a suitable platform for analyzing transactional data.”, [0052], “The SQL query, when executed, may enable the data to be placed in a table in a particular or predetermined format (e.g., by identifying dimension attributes as metadata) as determined by the schema of hypergeneralized staging database 314 that will be used during generation of the tabular model.”, [0061], “warehouse builder 318 may be configured to translate a schema of hypergeneralized staging database 314 into a domain specific database (i.e., presentation database 320) from which a tabular model may be generated.”, [0062], “automatically generate a star or snowflake schema in connection with generation of tabular model 328.”, [0085]).
15. Regarding claim 13, Mutalik and Alfaras teach the invention as claimed in claim 11 above and Mutalik further teaches wherein at least a portion of the obtained consumption records have different schemas, and wherein obtaining the one or more schemas comprises obtaining one or more combined schemas ([0056], “hypergeneralized staging database 314 may be set up with the appropriate schemas to store dimension records, business event records, and measures.”, Fig 8A-8G).
16. Regarding claim 14, Mutalik and Alfaras teach the invention as claimed in claim 11 above and Mutalik further teaches wherein the one or more schemas comprises recognizing a changed schema, the changed schema comprising an additional consumption event attribute or a removal of a previous consumption event attribute, and the generating of the consumption dataset is based on the recognized changed schema ([0025], “updating a schema in the staging database, modifying a presentation database schema, and modifying the tabular model”, [0049], [0052], [0081], “a developer or analyst may determine that certain measures may be desirable to analyze for a given event, and accordingly may define such measures in the SQL query associated with that event. In this manner, if measures are to be added, removed, and/or modified, the developer or analyst may revise the query for the event, rather than engaging in a complex and time-consuming process of defining measures in other manners.”).
17. Regarding claim 17, Mutalik and Alfaras teach the invention as claimed in claim 11 above, Alfaras further teaches wherein the selecting of the one or more to-be-removed consumption event attributes comprises selecting for removal a given number of the eligible consumption event attributes having highest cardinalities ([0052], [0144], [0273-0275], [0305]).
18. Regarding claim 19, Mutalik and Alfaras teach the invention as claimed in claim 11 above, Alfaras further teaches wherein the generating of the cubed consumption dataset comprises: after removing the one or more to-be-removed consumption event attributes, consolidating matching events corresponding to consumption events having common consumption event attribute values for the remaining eligible consumption event attributes into a bundle, and generating metrics associated with the bundle ([0052], [0144], [0273-0275], [0305], “consolidating data from different partitions or segments into a single dataset”).
19. Regarding claim 20, Mutalik and Alfaras teach the invention as claimed in claim 15 above and Mutalik further teaches wherein the eligible consumption event attributes exclude temporal attributes indicative of a time or day of consumption and quantity attributes indicative of a quantity of consumption (Fig 2, [0026-0028],, “retrieving transactional data” [0035-0036], ” quantitative elements of events or facts (e.g., quantities, sales amounts, unit prices, etc.) (quantity attributes)”, [0038], “transaction data 112 related to business events, such as payment information, order information, customer information, products, employee information (e.g., identifiers, salary, etc.), transaction dates or periods (temporal attributes), or any other information related to an event or a business's transactions.”, [0043], “events or transactions in a business environment.”, [0048], “retrieval of transactional data (e.g., event records)”, [0053], “Event records 322 may comprise transaction data”, [0054], “The event occurrence data (event id, timestamp, etc.) are held in a single table, while the dimension records and event record to dimension mapping are stored in one or more simple key-value type lookup tables in hypergeneralized staging database 314.”), also Chen teaches the limitation at ([0002], [0021], “the data from which the data cube is generated are transaction data generated by an e-commerce company. the data are not limited to the transaction data, and may be any other types of data such as electricity consumption data of cities, power consumption amounts of devices, populations of countries in different time, or the like. Additionally, the data may be discrete data such as data related to geographical locations, or continuous data such as time series data.”, [0022], “the transaction data may include various attributes regarding related transactions, including a time attribute (temporal attributes) indicating time at which the transactions occur, a geographical location attribute indicating geographical locations where the transactions occur, a transaction amount attribute (quantity attributes) indicating amounts of the transactions, and a product category attribute indicating categories of products involved in the transactions, or the like. An attribute may correspond to a dimension,”).
Respond to Amendments and Arguments
20. In the remarks, applicant amended the independents claims to incorporate come features of claims 5 and 6, and respectfully submits that the cited portions of Mutalik and Alfaras, either alone or in combination, do not disclose or suggest these features of amended claim 1.
Applicant respectfully submits that the cited portion of Alfaras does not disclose or suggest "consolidating, into a bundle, matching events corresponding to consumption events having common consumption event attribute values for eligible consumption event attributes not selected as to-be-removed consumption event attributes," as recited by amended claim 1.
Examiner presents the following responses to Applicant’s arguments:
Applicant's arguments received on 06/17/2026 have been fully considered but they are not persuasive. Referring to the previous Office action, Examiner has cited relevant portions of the references as a means to illustrate the systems as taught by the prior art. As a means of providing further clarification as to what is taught by the references used in the first Office action, Examiner has expanded the teachings for comprehensibility while maintaining the same grounds of rejection of the claims, except as noted above in the section labeled “Status of Claims.” This information is intended to assist in illuminating the teachings of the references while providing evidence that establishes further support for the rejections of the claims.
Alfaras teaches "consolidating, into a bundle, matching events corresponding to consumption events having common consumption event attribute values for eligible consumption event attributes not selected as to-be-removed consumption event attributes,"
CONCLUSION
21. The prior art made of record and not relied upon is considered pertinent to applicant s disclosure.
PROCOPS ROY (WO 2017091410 A1) discloses STORING AND RE TRIE VING DATA OF A DATA CUBE.
THIS ACTION IS MADE FINAL. 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 HICHAM SKHOUN whose telephone number is (571)272-9466. The examiner can normally be reached Normal schedule: Mon-Fri 10am-6:30pm.
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/HICHAM SKHOUN/Primary Examiner, Art Unit 2164
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