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 .
DETAILED ACTION
This Office Action corresponds to application 19/065,874 which was filed on 02/27/2025.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/27/2026 has been entered.
Response to Amendment
In the reply filed 7/27/2026, claims 1, 4, 7, 11, 14, 17 and 20 have been amended. Claim 6 and 16 were cancelled. Accordingly claims 1-5, 7-15, and 17-20 stand pending.
Response to Arguments
Applicant's arguments filed 7/27/2026 have been fully considered but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 7-8, 11-15, 17-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elias et al. (US2015/0169686, previously presented in ‘892), hereinafter Elias, in view of Beitchman et al. (US11,055,352, previously presented in ‘892), hereinafter Beitchman, Shivarathri (US11,657,047, previously presented in ‘892), Venugopal et al. (US2024/0370434), hereinafter Venugopal, and Gjerdrum et al. (US2025/0363110), hereinafter Gjerdrum.
Regarding Claim 1:
Elias teaches:
A method of comprising: receiving a request to analyze a query associated with a dataset (Elias, figure 3, [0006, 0032], note receiving a query to analyze, which is interpreted as receiving a request to analyze a query);
analyzing the query to generate a representation indicating a plurality of commands within the query (Elias, figure 3 and 5, [0042-0045], note generating query plans for the received query; note the query plans indicate a plurality of commands within the query);
generating, by a processing device based on the representation, a plurality of profile points configured to analyze a performance of the plurality of commands (Elias, figure 3 and 5-6, [0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree);
generating, based on the representation, a transformed query comprising the plurality of commands and the plurality of profile points (Elias, figure 3 and 5-6, [0049, 0062, 0064], note selecting the data source with the lowest cost query plan to execute the query, which is interpreted as generating a transformed query since each query plan is different); and
performing, during an execution of the transformed query, a plurality of measurements on the plurality of commands based on the plurality of profile points (Elias, figures 3 and 5, [0050], note the costs of each command is determined based on statistics gathered for performing that operation previously, which is interpreted to mean the statistics of the currently executing query plan is measured and stored),
during execution, performs event-level measurements at each profile point that correspond to individual commands of the transformed query (Elias, figures 3 and 5, [0050], note the costs of each command is determined based on statistics gathered for performing that operation previously, which is interpreted to mean event-level measurements at each profile point are performed).
While Elias teaches evaluation queries and commands, Elias doesn’t specifically teach wherein the plurality of measurements comprises measurements of execution of the plurality of commands of the transformed query during the execution of the transformed query, wherein performing the plurality of measurements comprises calculating, based on a first profile point of the plurality of profile points, a size of a first output generated by a first command of the plurality of commands and calculating, based on a second profile point of the plurality of profile points, a size of a second output generated by a second command of the plurality of commands, generating performance reports and wherein the execution of the transformed query comprises executing the transformed query in a profiling mode that suppresses query result data that would otherwise be returned to a client device in response to execution of the transformed query. However, Beitchman is in the same field of endeavor, information retrieval, and Beitchman teaches:
receiving a request to analyze a query associated with a dataset (Beitchman, abstract, column 3 line 59 – column 2 line 23, note request to generate optimized query plan)
analyzing the query to generate a representation indicating a plurality of commands within the query (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan);
generating, by a processing device based on the representation, a plurality of profile points configured to analyze a performance of the plurality of commands (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interpreted to mean points are in the query plan to analyze the performance of the individual operations when executed);
generating, based on the representation, a transformed query comprising the plurality of commands and the plurality of profile points (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations when executed)
performing, during an execution of the transformed query, a plurality of measurements on the plurality of commands based on the plurality of profile points to generate a performance report comprising the plurality of measurements (Beitchman, figures 7 and 14, column 4 lines 3-23, column 8 lines 14-33, column 22 line 58 – column 23 line 13, note collecting, storing, and reporting performance metrics of the operations in a query plan), wherein the plurality of measurements comprises measurements of execution of the plurality of commands of the transformed query during the execution of the transformed query, wherein performing the plurality of measurements comprises calculating, based on a first profile point of the plurality of profile points, a size of a first output generated by a first command of the plurality of commands and calculating, based on a second profile point of the plurality of profile points, a size of a second output generated by a second command of the plurality of commands (Beitchman, figures 7, 10, 12, and 14, column 18 lines 29-59, column 21 lines 4-23, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, note the performance metrics include resource utilization costs and bandwidth which may be interpreted as a size of a first and second output; note the query may be parse and the parsed results of the query may be evaluated or analyzed),
during execution, performs event-level measurements at each profile point that correspond to individual commands of the transformed query (Beitchman, figures 7 and 14, column 4 lines 3-23, column 8 lines 14-33, column 22 line 58 – column 23 line 13, note collecting, storing, and reporting performance metrics of the operations in a query plan, e.g., event-level measurements at each profile point).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
While Elias as modified teaches evaluation queries and commands to determine performance metrics, Elias as modified doesn’t specifically teach wherein the execution of the transformed query comprises executing the transformed query in a profiling mode that suppresses query result data that would otherwise be returned to a client device in response to execution of the transformed query. However, Shivarathri is in the same field of endeavor, information retrieval, and Shivarathri teaches:
wherein the execution of the transformed query comprises executing the transformed query in a profiling mode that suppresses query result data that would otherwise be returned to a client device in response to execution of the transformed query and, during execution, performs event-level measurements at each profile point that correspond to individual commands of the transformed query (Shivarathri, figures 1, 4 and 16B, claims 1, 3-4, and 6, column 3 lines 8-17, column 7 line 50 - column 8 line 35, note the query elements are extracted and tested in a test environment to determine performance metrics of the query; note that the elements may be the operations/commands; note that the results of the tests are the performance analysis to fix the underlying issues, not the normal query output, which is interpreted as a profiling mode that suppresses query result data that would otherwise return to a client device. Since each query element is added individually to the query to test, it is interpreted as performing event-level measurements at each profile point that correspond to the individual commands of the query, e.g., the iterations of adding individual elements/commands).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Shivarathri because all references are directed towards data analysis and information retrieval and because Shivarathri would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by analysis each query element to fix underlying issues (Shivarathri, column 1 lines 44-56).
While Elias as modified teaches evaluation queries and measuring query metrics at profile points, to further support this interpretation, Venugopal is in the same field of endeavor, information retrieval, and Venugopal teaches:
performing, during an execution of the transformed query, a plurality of measurements on the plurality of commands based on the plurality of profile points to generate a performance report comprising the plurality of measurements, wherein the plurality of measurements comprises measurements of execution of the plurality of commands of the transformed query during the execution of the transformed query, wherein performing the plurality of measurements comprises calculating, based on a first profile point of the plurality of profile points, a size of a first output generated by a first command of the plurality of commands and calculating, based on a second profile point of the plurality of profile points, a size of a second output generated by a second command of the plurality of commands (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Venugopal because all references are directed towards data analysis and information retrieval and because Venugopal would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by optimizing based on query statistics (Venugopal, [0001-0004]).
While Elias as modified teaches evaluation queries and measuring query metrics at profile points, Elias as modified doesn’t specifically state the performance measurements include output sizes. However, Gjerdrum is in the same field of endeavor, information retrieval, and Gjerdrum teaches:
wherein the plurality of measurements comprises measurements of execution of the plurality of commands of the transformed query during the execution of the transformed query, wherein performing the plurality of measurements comprises calculating, based on a first profile point of the plurality of profile points, a size of a first output generated by a first command of the plurality of commands and calculating, based on a second profile point of the plurality of profile points, a size of a second output generated by a second command of the plurality of commands (Gjerdrum, [0089, 0119], note predicting the size of the result set for subqueries or subplans based on historical query statistics, which means during execution of previous subqueries, e.g., query command at a profile point, the output size was measured. When combined with the other references this would be for the query statistics measures as taught by Elias, Beitchman, and Venugopal).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Gjerdrum because all references are directed towards data analysis and information retrieval and because Gjerdrum would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by optimizing based on query statistics (Gjerdrum, [0001-0004]).
Regarding Claim 2:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
wherein analyzing the query occurs while the query is not executing (Elias, figures 3 and 5, [0042-0045], note forming query plans before executing the query).
Regarding Claim 3:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
wherein generating the plurality of profile points occurs while the query is not executing (Elias, figures 3 and 5-6, [0042-0045, 0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree, before executing the query) (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations when executed).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
Regarding Claim 4:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
wherein generating, based on the representation, the transformed query further comprises: configuring the first profile point of the plurality of profile points to include a first set of instructions dedicated to monitoring the first command of the plurality of commands (Elias, figures 3 and 5-6, [0042-0045, 0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree) (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries); and
configuring the second profile point of the plurality of profile points to include a second set of instructions dedicated to monitoring the second command of the plurality of commands (Elias, figures 3 and 5-6, [0042-0045, 0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree; note this is for each operation) (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations; note this is for each operation) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Venugopal because all references are directed towards data analysis and information retrieval and because Venugopal would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by optimizing based on query statistics (Venugopal, [0001-0004]).
Regarding Claim 5:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
inserting the first profile point of the plurality of profile points between the first command and the second command (Elias, figures 3 and 5-6, [0042-0045, 0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree; note this is for each operation which means an evaluation point is between the first and second command) (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations; note this is for each operation which means an evaluation point is between the first and second command) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries); and
inserting the second profile point of the plurality of profile points after the first command and the second command and before a third command of the plurality of commands (Elias, figures 3 and 5-6, [0042-0045, 0049-0055], note evaluating the cost of each node in the query plan tree, which is interpreted as having a point configured to analyze the performance of each command in the query plan tree; note this is for each operation which means an evaluation point is also after the first and second command and before a third) (Beitchman, figures 7, 10, and 14, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note generating a query plan and collecting, storing, and reporting performance metrics of the operations in a query plan, which is interepted to mean points are in the query plan to analyze the performance of the individual operations; note this is for each operation which means an evaluation point is also after the first and second command and before a third) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Venugopal because all references are directed towards data analysis and information retrieval and because Venugopal would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by optimizing based on query statistics (Venugopal, [0001-0004]).
Regarding Claim 7:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
wherein performing, during the execution of the transformed query, the plurality of measurements further comprises: calculating, based on the first profile point, a time for the first command to generate the first output (Elias, figure 3 and 5-6, [0028, 0050], note evaluating each node and the metrics include execution time; note the costs of each command is determined based on statistics gathered for performing that operation previously, which is interpreted to mean the statistics of the currently executing query plan is measured and stored; note this is for each operation) (Beitchman, figures 7 and 14, column 2 line 57 – column 3 line 12, column 10 lines 17-43, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note calculating execution time for operations in the query plan; note this is for each operation) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries; note metrics include response time of the subqueries); and
calculating, based on the second profile point, a time for the second command to generate the second output (Elias, figure 3 and 5-6, [0028, 0050], note evaluating each node and the metrics include execution time; note the costs of each command is determined based on statistics gathered for performing that operation previously, which is interpreted to mean the statistics of the currently executing query plan is measured and stored; note this is for each operation) (Beitchman, figures 7 and 14, column 2 line 57 – column 3 line 12, column 10 lines 17-43, column 18 lines 29-59, column 22 line 58 – column 23 line 13, note calculating execution time for operations in the query plan; note this is for each operation) (Venugopal, figures 2-4, [0029-0030, 0036, 0045-0046], note query execution monitoring to continuously record metrics of subqueries; note metrics include response time of the subqueries).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Venugopal because all references are directed towards data analysis and information retrieval and because Venugopal would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by optimizing based on query statistics (Venugopal, [0001-0004]).
Regarding Claim 8:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
wherein performing, during the execution of the transformed query, the plurality of measurements further comprises: sending the transformed query to a distributed database to cause the distributed database to execute the transformed query (Elias, figures 3 and 5, [0064-0065], note executing the selected query plan) (Beitchman, figures 7 and 14, column 13 line 58 – column 14 line 10, column 22 line 58 – column 23 line 13, note executing the query plan and collecting, storing, and reporting performance metrics of the operations in a query plan).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
Claim 11 discloses substantially the same limitations as claim 1 respectively, except claim 11 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 1 is directed to a method. Therefore claim 11 is rejected under the same rationale set forth for claim 1.
Claim 12 discloses substantially the same limitations as claim 2 respectively, except claim 12 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 2 is directed to a method. Therefore claim 12 is rejected under the same rationale set forth for claim 2.
Claim 13 discloses substantially the same limitations as claim 3 respectively, except claim 13 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 3 is directed to a method. Therefore claim 13 is rejected under the same rationale set forth for claim 3.
Claim 14 discloses substantially the same limitations as claim 4 respectively, except claim 14 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 4 is directed to a method. Therefore claim 14 is rejected under the same rationale set forth for claim 4.
Claim 15 discloses substantially the same limitations as claim 5 respectively, except claim 15 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 5 is directed to a method. Therefore claim 15 is rejected under the same rationale set forth for claim 5.
Claim 17 discloses substantially the same limitations as claim 7 respectively, except claim 17 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 7 is directed to a method. Therefore claim 17 is rejected under the same rationale set forth for claim 7.
Claim 18 discloses substantially the same limitations as claim 8 respectively, except claim 18 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 8 is directed to a method. Therefore claim 18 is rejected under the same rationale set forth for claim 8.
Claim 20 discloses substantially the same limitations as claim 1 respectively, except claim 20 is directed to a non-transitory computer-readable medium comprising a processing device (Elias, figure 1, note processor and memory) while claim 1 is directed to a method. Therefore claim 20 is rejected under the same rationale set forth for claim 1.
Claim Rejections - 35 USC § 103
Claim(s) 9-10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elias in view of Beitchman, Shivarathri, Venugopal, Gjerdrum, and Kalki (US9,244,971, previously presented in ‘892).
Regarding Claim 9:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
sending the performance report to the client device to cause the performance report to display on a screen of the client device (Beitchman, figures 7 and 14, column 10 lines 8-61, column 22 line 58 – column 23 line 13, note performance metrics may be reported to system components, data stores, or services; note requested performance metrics upon completion of a query; note query execution costs may be reported to users of the managed query service; note the managed query service comprises a graphical user interface).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
While Elias as modified teaches evaluation queries and commands, Elias as modified doesn’t specifically teach wherein the performance report indicates one or more performance bottlenecks within the query. However, Kalki is in the same field of endeavor, information retrieval, and Kalki teaches:
sending the performance report to a client device to cause the performance report to display on a screen of the client device, wherein the performance report indicates one or more performance bottlenecks within the query (Kalki, figure 10, column 18 lines 17-43, note identifying a performance bottleneck, e.g., using a higher latency data store, and notifying the user that a lower latency datastore is available to remedy this bottleneck. When combined with the previous references this would be for the query plans and analysis as taught by Elias and Beitchman).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Kalki because all references are directed towards information retrieval and because Kalki would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by identifying efficiency improvements in the query plan (Kalki, column 18 lines 44-47).
Regarding Claim 10:
Elias as modified shows the method as disclosed above;
Elias as modified further teaches:
receiving, responsive to sending the performance report to the client device, an optimized version of the query from the client device (Beitchman, figures 7 and 14, Beitchman, column 2 line 57 – column 3 line 12, column 10 lines 8-61, column 22 line 58 – column 23 line 13, note performance metrics may be reported to system components, data stores, or services; note requested performance metrics upon completion of a query; note query execution costs may be reported to users of the managed query service; note the managed query service comprises a graphical user interface; note selected lowest cost query plan) (Kalki, figure 10, column 18 lines 17-43, note identifying a performance bottleneck, e.g., using a higher latency data store, and notifying the user that a lower latency datastore is available and the user providing a remedy this bottleneck. When combined with the previous references this would be for the query plans and analysis as taught by Elias and Beitchman).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Beitchman because all references are directed towards information retrieval and because Beitchman would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by using cost based optimization for query plans (Beitchman, column 2 line 57 – column 3 line 12).
It would have been obvious to one of ordinary skill in the art before the effective date of filing to modify the cited references to incorporate the teachings of Kalki because all references are directed towards information retrieval and because Kalki would expand upon the teachings of the previously cited references in query optimization which would improve the systems performance by identifying efficiency improvements in the query plan (Kalki, column 18 lines 44-47).
Claim 19 discloses substantially the same limitations as claim 9 respectively, except claim 19 is directed to a system comprising a memory and processing device (Elias, figure 1, note processor and memory) while claim 9 is directed to a method. Therefore claim 19 is rejected under the same rationale set forth for claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ngan et al. (US2025/0335438) teaches monitoring subqueries.
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/JOHN J MORRIS/Examiner, Art Unit 2151 9/5/2026
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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