0DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to amendment filed on 1/29/2026, in which claims 1, 8, and 14 was amended, and claims 1 – 4, 6 – 11, 13 – 17, and 19 - 20 was presented for further examination.
3. Claims 1 – 4, 6 – 11, 13 – 17, and 19 - 20 are now pending in the application.
Continued Examination Under 37 CFR 1.114
4. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/29/2026 has been entered.
Response to Arguments
5. Applicant’s arguments with respect to claims 1 – 4, 6 – 11, 13 – 17, and 19 - 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
6. Claims 1 – 3, 6 – 10, 13 – 16, and 19 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kapchits et al (US 2022/0147514 A1), in view of Boyangu, in view of Leung et al (US 2021/0019318 A1), and further in view of Witkowski et al (US 6,345,272 B1).
As per claim 1, Kapchits et al (US 2022/0147514 A1) discloses,
A database-based data processing method for implementing real-time Structured Query Language (SQL) data queries by a computing device (para.[0056]; “perform real-time query processing as the service receives queries from client devices”).
the materialized views configured to store pre-calculated query results (para.[0050]; “materialized view tables are utilized to store preliminary query results for popular queries that have a frequency of occurrence above a threshold” and para.[0052]; “materialized view table management component 504 may generate
various tasks, options, and configurations that are to be used, such as by a database manager, to aggregate and prepare data”).
configuring a field in each of the materialized views (para.[0051]; “ identified as candidate field combinations for which materialized view tables could be created for reducing latency of query processing by the service” and para.[0052]; “evaluate the combinations of fields within the list 508 of combinations of fields in order to select one or more combinations of fields for which materialized view tables
will be generated by the materialized view”).
automatically rewriting the SQL query statement into a plurality of sub-query statements based on determining that table does not comprise the target materialized view (para.[0059]; “query 540 may be re-written by the middleware component 523 utilizing the configuration information 525 so that the second query 540 may be executed upon the materialized view table to obtain query results to provide back to the client device”).
wherein the plurality of sub-query statements matches time granularities of at least a subset of the materialized views (para.[0059]; “some data within the materialized view table may need to be re-calculated and/or reaggregated based upon the second query 540, such as where the second query 540 specifies filters and/or spans a time interval different than how data was aggregated into the materialized view table ( e.g., data spanning two weeks instead of daily or monthly, and thus data within multiple rows may need to be combined to achieve a 2 week view of data)” and para.[0060]; “re-written to target a particular materialized view table that has a coarsest level of granularity specified by the second query”).
and executing the SQL query statement based at least on the pre-calculated query results stored in the at least one subset of the materialized views to return a data query result in real time while ensuring accuracy of the data query result (para.[0056]; “perform real-time query processing as the service receives queries from client devices, para.[0060]; “If the second query 540 specifies a monthly granularity, then the materialized view table with the monthly granularity is utilized because it has the coarser granularity of aggregated data compared to the daily granularity, and thus the second query 540 can be more quickly executed upon a single row as opposed to multiple rows (e.g., 30 rows) in the materialized view table with the daily granularity”).
Kapchits does not specifically disclose receiving a user instruction to construct materialized views at different time granularities in a table of a database, wherein the user instruction indicates a first type of the table, determining that a second type of the table is an optimal type of table for storing the materialized views at the different time granularities, creating the table of the second type and constructing the materialized views at the different time granularities in the table of the second type, wherein the field indicates a time granularity of the corresponding materialized view, receiving a Structured Query Language (SQL) query statement input via an interface, wherein the SQL query statement comprises a data query time, determining whether the table comprises a target materialized view for executing the SQL query statement by determining whether the data query time of the SQL query statement matches a time granularity indicated by the field of the target materialized view.
However, Boyangu et al (US 20200192959 A1) in an analogous art discloses,
the method comprising: receiving a user instruction to construct materialized views at different time granularities in a table of a database (para.[0041]; “creating the data model includes extracting data from one or more data sources (e.g., the data sources 110, FIG. 1) and loading the extracted data into a data structure” and para.[0044]; “the temporal granularities may be determined based on user selections”).
wherein the user instruction indicates a first type of the table (para.[0042]; “multiple distinct temporal granularities to be used for generating datasets having distinct temporal granularities are determined”).
determining that a second type of the table is an optimal type of table for storing the materialized views at the different time granularities (para.[0044]; “one or
more threshold amounts of data may be utilized such that, if the amount of data included in datasets generated based on a temporal granularity is either above or below one of the thresholds, the temporal granularity is not used”).
displaying prompt information indicating that the first type of the table is not the optimal type of the table in response to determining that the second type of the table is the optimal type of table for storing the materialized views at the different time granularities (NOTE: para.[0046]; “some of the datasets may be generated via aggregation of smaller temporal granularity datasets …….. datasets having a
first hourly temporal granularity may be generated hourly, and datasets having a second daily temporal granularity may be generated periodically by aggregating 24 of the hourly
datasets”)
creating the table of the second type and constructing the materialized views at the different time granularities in the table of the second type (para.[0044]; “a threshold for daily sales may not be met such that a daily temporal granularity is not used, while a threshold for hourly sales is met such that an hourly temporal granularity is used”).
wherein the field indicates a time granularity of the corresponding materialized view (para.[0045]; “one or more datasets is generated for each of the determined temporal granularities. Because the datasets include datasets generated based on temporal granularities having different time units, the datasets include data
representing different periods of time”).
receiving a Structured Query Language (SQL) query statement input via an interface (para.[0051]; “receiving input from the user through I/O interfaces of the client device”, and para.[0052]; “a request for data is received. The request may include an indication of desired temporal resolutions, which may be defined with respect to a particular temporal granularity”).
wherein the SQL query statement comprises a data query time (para.[0046]; “Queries which are day-specific (i.e., related to trends throughout a day) may be sent to the appropriate dataset having the first hourly temporal granularity” and para.[0052]; “a request for data is received. The request may include an indication of desired temporal resolutions, which may be defined with respect to a particular temporal granularity”).
determining whether the table comprises a target materialized view for executing the SQL query statement by determining whether the data query time of the SQL query statement matches a time granularity indicated by the field of the target materialized view (para.[0052]; “a request for data is received. The request may include an indication of desired temporal resolutions, which may be defined with respect to a particular temporal granularity’ and para.[0053]; “one or more target datasets to be queried is determined ……….. the target datasets may be determined based on the desired temporal resolutions”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate data displaying using temporal granularities of the system of Boyangu into data materialized view of the system of Kapchits to reduce processing and query response times using temporal granularities, thereby reducing latency in data retrieval.
Neither Kapchits nor Boyangu specifically disclose wherein the user instruction indicates a first type of the table, and wherein the first type of the table comprises one of an aggregate table type, a duplicate table type, or a unique table type, determining that a second type of the table is an optimal type of table for storing the materialized views at the different time granularities, wherein the second type of the table is different from the first type of the table.
However, Leung et al (US 2021/0019318 A1) in an analogous art discloses,
wherein the user instruction indicates a first type of the table (para.[0003]; “applications often submit complex structured query language (SQL) queries” and para.[0030]; “materialized view is created by the one or more processors
using the query definition”).
and wherein the first type of the table comprises one of an aggregate table type, a duplicate table type, or a unique table type (para.[0079]; “aggregate
result table 506 in response to Query3 (508) includes an amount (600) in row”).
determining that a second type of the table is an optimal type of table for storing the materialized views at the different time granularities (para.[0081]; “recommending/advising the creation of materialized views involving harmonized tables”).
wherein the second type of the table is different from the first type of the table (para.[0081]; “harmonized tables and recommending/advising the creation of materialized views involving harmonized tables”, where harmonized table is interpreted as ”second type of table” as claimed).
Therefore, it would have been obvious to one of ordinary skill in the art before the invention was filed to incorporate harmonized table of the system of Leung into data displaying using temporal granularities of the system of Boyangu to implement materialized view creation and exploitation for query optimization in database applications in the system of Kapchits.
Boyangu discloses aggregating an hourly granularity dataset into daily granularity data if daily granularity will be more efficient to store the data. To explicitly explained the process of generating prompt information indicating that the first type of the table is not the optimal type of the table in response to determining that the second type of the table is the optimal type of table for storing the materialized views at the different time granularities.
However, Witkowski et al (US 6,345,272 B1) in an analogous art discloses,
displaying prompt information indicating that the first type of the table is not the optimal type of the table in response to determining that the second type of the table is the optimal type of table for storing the materialized views at the different time granularities (col.11 lines 10 -12; “the degree that more than one materialized view may be used, the materialized view that groups data at the coarsest level
of granularity is selected” and col.11 lines 19 – 20; “MVBG 421 was selected because it grouped along the needed dimension at a courser level of granularity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialized view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu and harmonized table of the system of Leung to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity in the system of Kapchits.
As per claim 2, the rejection of claim 1 is incorporated and further Witkowski et al (US 6,345,272 B1) discloses,
wherein determining that the second type of the table is the optimal type of table for storing the materialized views at the different time granularities (col.11 lines 10 -12; “the degree that more than one materialized view may be used, the materialized view that groups data at the coarsest level of granularity is selected”)
comprises: automatically determining the optimal type of table corresponding to the materialized views based on the user instruction (col.11 lines 19 – 20; “MVBG 421 was selected because it grouped along the needed dimension at a courser level of granularity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialized view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity.
As per claim 3, the rejection of claim 1 is incorporated and further Witkowski et al (US 6,345,272 B1) discloses,
wherein determining that the second type of the table is the optimal type of table for storing the materialized views at the different time granularities (col.11 lines 10 -12; “the degree that more than one materialized view may be used, the materialized view that groups data at the coarsest level of granularity is selected”)
comprises: determining whether the user instruction contains an aggregation function (col.10 lines 19 – 20; “an aggregate query that specifies a restriction
along an ordered dimension”).
in response to determining that the user instruction contains an aggregation function, determining a type of a table model according to a type of a query data table in the user instruction and a type of the aggregation function (col.11 lines 9 – 10; “More than one materialized view may satisfy eligibility conditions relative to an aggregate query”).
and in response to determining that the user instruction does not contain an aggregation function and the type of the query data table in the user instruction is a detail type, determining that the type of the table model is a the duplicate table type (col.11 lines 22 – 24; “Extracting data from the materialized view that groups data at the courser level of granularity requires less work than extracting from one that groups at a finer level of granularity”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialized view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity.
As per claim 4, the rejection of claim 3 is incorporated and further Witkowski et al (US 6,345,272 B1) discloses,
wherein the determining the type of the table model according to the type of the query data table in the user instruction and the type of the aggregation function (col.11 lines 10 -12; “the degree that more than one materialized view may be used, the materialized view that groups data at the coarsest level of granularity is selected”)
comprises: in response to determining that the type of the query data table is the aggregate type or the duplicate table type and the type of the aggregation function is a splitable type, determining that the type of the table model is the aggregate type (col.9 lines 40 – 43; “a materialized view used to further illustrate a rewrite of a query that specifies a restriction with coinciding bounds. QBGQ 501 is an aggregate query that requests summary data from sales table”)
and in response to determining that the type of the query data table is the aggregate type or the duplicate type and the type of the aggregation function is an un-splitable type, determining that the type of the table model is the duplicate table type (col. lines 23 – 25; “function to_data extracts from a date field the components of a date e.g. day, month, or year. The function to_date is similar to the ANSI SQL function extract”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialized view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity.
As per claim 6, the rejection of claim 1 is incorporated and further Witkowski et al (US 6,345,272 B1) discloses,
wherein a select statement and a group by statement in the user instruction comprises a time granularity corresponding to each of the materialized views (col.3 lines 9 – 16; “materialized view that groups data along the same hierarchical dimension. A dimension is an attribute of a set of data, such as a column of a table ……common example of a hierarchical dimension is time. Possible values for time include a particular day, month, quarter, or year”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialized view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity.
As per claim 7, the rejection of claim 1 is incorporated and further Witkowski et al (US 6,345,272 B1) discloses,
wherein the automatically rewriting the SQL query statement into the plurality of sub-query statements (col.3 lines 2 – 10; “Q can be transparently rewritten to access pre-computed data stored in the summary table …….query may be rewritten to use a materialized view that groups data along the same hierarchical dimension” and col.10 lines 29 – 31; “QUE 601 may be rewritten as union QU 643 (FIG. 6B), 30 which is a union of queries 660, 670, and 680. Query 670 references MVBG 421”).
comprises: sequentially splitting the data query time of the SQL query statement according to a sequence of time durations from large to small of the time granularities of the at least the subset of the materialized views to obtain a split time window corresponding to the target materialized view at each time granularity (col.4 lines 7 – 15; “aggregate query 210 requests summary information from sales table 250. Sales table 250 contains columns date 260, region 262, product 264, and $Amt 266. Date 260 contains values that represent days” and col.7 lines 58 – 65; “rewrite aggregate queries to access a materialized view when (1) the queries place a restriction on an ordered dimension, (2) the materialized view aggregates the information referenced in the query and groups by the same dimension, and (3) the materialized view groups the information at a coarser level of granularity than the granularity associated with the restriction contained in the aggregate queries”).
rewriting the SQL query statement based on the split time window corresponding to the target materialized view at each time granularity, and taking an obtained sub-query which is queried based on the target materialized view as the target sub-query (col.7 lines 58 – 65; “rewrite aggregate queries to access a materialized view when (1) the queries place a restriction on an ordered dimension, (2) the materialized view aggregates the information referenced in the query and groups by the same dimension, and (3) the materialized view groups the information at a coarser level of granularity than the granularity associated with the restriction contained in the aggregate queries”).
and with respect to a residual time window which does not correspond to the time granularity of the target materialized view in the SQL query statement, rewriting the SQL query statement based on the residual time window, and taking the rewritten sub-query as the target sub-query (col.7 lines 65 – 67; “rewrite queries to access a materialized view that groups information at a coarser level of granularity than the granularity associated with restriction contained in the queries” and col.8 lines 58 – 63; “query may be rewritten to access a summary table that groups the data
based on the same column, but at a coarser level of granularity that corresponds to years, because bounds of a year correspond to the bounds of the restriction specified by the aggregate query restriction”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate rewriting of aggregation query to access materialize view of the system of Witkowski into data displaying using temporal granularities of the system of Boyangu to access materialized views, thereby enabling search result to be presented to user at a requested level of granularity.
Claims 8 – 11 and 13 are non-transitory computer-readable medium claim corresponding to method claims 1 - 4 and 7 respectively, and rejected under the same reason set forth in connection to the rejection of claims 1 - 4 and 7 respectively above.
Claims 14 – 17 and 19 - 20 are apparatus claim corresponding to method claims 1 – 4 and 6 - 7 respectively, and rejected under the same reason set forth in connection to the rejection of claims 1 – 4 and 6 - 7 respectively above.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
TITLE: Data table processing method, device, equipment and storage medium,
CN 116049191 A authors: Xiaoxing et al.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
3/21/2026