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 .
The Amendment filed on 10/09/25 has been received and entered. Application No. 18/351,191 of which claim 10 has been canceled. Claims 1-9 and 11-20 are pending in the application, all of which are ready for examination by the examiner.
Response to Amendment
Applicant’s amendment necessitated new grounds of rejection.
Applicant’s response, filed on 10/09/25, with respect to 112 rejections of claims 1-9 and 11-20 have been fully considered and are persuasive. The rejections are withdrawn.
Applicant’s response with respect to 101 rejections directed to an abstract idea of claims 1-9 and 11-20 have been fully considered and are persuasive. The rejections are withdrawn.
This action is made final in view of the new grounds of rejection.
Response to Arguments
Applicant's arguments with respect to 35 USC § 101 rejections of claims 1-9 and 11-20 have been fully considered and are persuasive. The rejections are withdrawn in light of Applicant’s amendments and arguments.
Applicant’s arguments with respect to 35 USC § 103 rejections of claims 1-9 and 11-20 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
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 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 of this title, 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 1-5, 11-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bestfleisch (U.S. PGPub 2018/0285416) in view Maheshwari et al. (U.S. PGPub 2021/0224275; hereinafter “Maheshwari”) and Ramish (U.S. PGPub 2015/0331850).
As per claims 1, 11 and 20, Bestfleisch discloses a computer-implemented method for runtime query processing, comprising: receiving, by a query processing engine, an incoming query statement; (See Fig. 8, paras. 5, 24, 37, wherein receiving query process in which “During runtime, the system may periodically analyze query execution plans stored in the cache and apply query hints to the query execution plans. This way, when the system receives a query and the cache has a query execution plan for the query, the system can use the query execution plan for the query, which has one or more query hints applied to the query execution plan” [0024] are disclosed; as taught by Bestfleisch.)
searching, at runtime and by a hint manager of the query processing engine, a query hint registry located in a memory store for a patterned query statement that matches the incoming query statement or a non-patterned query statement that is identical to the incoming query statement; (See Figs. 2-3, paras. 10, 29-31, wherein mapping of query statement pattern and query hint, the process of matching hash value of query string, query plan are disclosed; as taught by Bestfleisch.)
responsive to the hint manager determining a match with the patterned query statement and not with the non-patterned query statement, appending, by the hint manager at runtime, a first statement hint paired with the patterned query statement to the incoming query statement; (See Figs. 2, 4, 6, paras. 29, 34, 41, wherein process of applying query hints in which “If the hash value of the query statement of the query execution plan matches a statement hash value and/or the query statement of the query execution plan matches a statement pattern specified in a mapping in table 200, query hint manager 120 sends, at operation 525, query plan cache storage 130 a request to replace the query execution plan with a version of the query execution plan with the query hint associated with the mapping applied to it” [0034] are disclosed; as taught by Bestfleisch.)
obtaining, by a query optimizer of the query processing engine, a query execution plan for the incoming query statement appended with the first statement hint or the second statement hint; (See Fig. 2, 4B, paras. 31-34, 36, wherein process of applying query hints, returning query execution plan are disclosed; as taught by Bestfleisch.)
and executing, by the query executor of the query processing engine, the query execution plan. (See Figs. 4B, 8, paras. 24-25, 31-32, wherein returning query execution plan are disclosed; as taught by Bestfleisch.)
However, Bestfleisch fails to disclose responsive to the hint manager determining matches with both the patterned query statement and the non-patterned query statement, appending, by the hint manager at runtime, a second statement hint paired with the non-patterned query statement to the incoming query statement instead of the first statement hint.
On the other hand, Maheshwari teaches responsive to the hint manager determining matches with both the patterned query statement and the non-patterned query statement, appending, by the hint manager at runtime, a second statement hint paired with the non-patterned query statement to the incoming query statement instead of the first statement hint. (See paras. 47, 51-53, wherein ML engine functions, adjustments to database queries, rewriting SQL statements process in which “ML engine 114 may be configured to automatically learn and infer query patterns that are predictive of poor runtime performance and good runtime performance. ML engine 114 may automatically flag, modify, and/or otherwise perform responsive actions when performance for a query is predicted to be below a threshold to help optimize query performance at runtime. Additionally or alternatively, ML engine 114 may provide hints to query optimizer 108 for optimizing a query. For example, ML engine 114 may predict that rewriting one or more portions of a SQL statement will yield better query execution times. Query optimizer 108 may use the hints to select and/or rewrite poorly written database queries” [0047] and “each query may be labeled as performant or non-performant to train an ML model that classifies queries accordingly. As another example, each query may be labeled with an associated total execution time, compile time, I/O throughput, and/or other execution metrics. Based on the labels and the associated queries, training logic 120 may set and adjust weights and/or other parameters for each neuron/cell. A trained model may be used to classify and/or predict one or more performance metrics associated with executing a new query based on learned query patterns even though the new query has not previously been seen or executed within an environment” (analogous to determining matches with both the patterned query statement and the non-patterned query statement) [0051] are disclosed, also See paras. 55, 67-68, wherein providing hints to query optimizer in which “ML engine 114 may provide hints to query optimizer 108 on how to rewrite non-performant queries... Query optimizer 108 may rewrite the query and/or query execution plan, such as by adding a missing lead index column or adding join conditions, based on the input provided by ML engine” (analogous to appending second statement hint paired with the non-patterned query statement) [0055] are disclosed; as taught by Maheshwari.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Maheshwari teachings in the Bestfleisch system. Skilled artisan would have been motivated to incorporate the method of query classification and processing using neural network based machine learning taught by Maheshwari in the Bestfleisch system for effectively applying query hints to query execution plan. In addition, both of the references (Bestfleisch and Maheshwari) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as plan optimisation. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Bestfleisch and Maheshwari fails to explicitly disclose wherein a wildcard expression contained in the patterned query statement matches one or more characters of the incoming query statement; the patterned query statement and the non-patterned query statement.
On the other hand, Ramish teaches wherein a wildcard expression contained in the patterned query statement matches one or more characters of the incoming query statement; (See para. 11, wherein identifying matching patter forms in which “identifying matching pattern forms, a pattern form of the matching pattern forms being a pattern with wildcard characters in place of variables to indicate that the variables are undistinguished; selecting, from the identified pattern forms, a pattern form that binds to the most elements of the intermediate logical statement and is maximally specific to the intermediate logical statement; identifying patterns in frames that match the selected pattern form” [0011] are disclosed; as taught by Ramish.)
the patterned query statement and the non-patterned query statement. (See paras. 81-86, wherein patterns, non-identical patters (analogous to non-patterned query statement) and matching of query in which “set of patterns that express the meaning of the frame: each pattern can appear as an intermediate logical statement that can include a verb, one or more variables of the frame, and possibly other constants. All patterns in a frame should have equivalent meanings” [0081] and “modifiers must apply equally well to parent and child. Non-identical patterns can be linked with lateral (non-hierarchical) implications” [0084] and “each frame can be represented with a numeric interval [ a, b] such that the intervals for all descendants are contained within this numeric interval. This enables efficient querying for relations and events matching a particular frame (including all descendant frames)” [0086] are disclosed; as taught by Ramish.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Ramish teachings in the combination of Bestfleisch and Maheshwari system. Skilled artisan would have been motivated to incorporate the method for semantic interpretation taught by Ramish in the combination of Bestfleisch and Maheshwari system for effectively applying query hints to query execution plan. In addition, both of the references (Bestfleisch, Maheshwari, and Ramish) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as plan optimisation. This close relation between both of the references highly suggests an expectation of success.
As per claims 2 and 12, the combination of Bestfleisch, Maheshwari, and Ramish further discloses registering the patterned query statement and the first statement hint as a pair in the query hint registry. (See Fig. 6, paras. 25, 34, wherein query hints, types of joins in query execution plan, process of rewriting query are disclosed; as taught by Bestfleisch.)
As per claims 3 and 13, the combination of Bestfleisch, Maheshwari, and Ramish further discloses comprising, after registering the pair of patterned query statement and the first statement hint in the query hint registry, evicting a selected one of one or more different query execution plans stored in a query execution plan cache, wherein the one or more different query execution plans were generated after compiling one or more previously received incoming query statements that match the patterned query statement and are without the first statement hint. (See Figs. 6, 8, paras. 38-40, wherein storing query execution plan in a cache, replacing first query execution plan with second execution plan are disclosed, also See paras. 25, 34, wherein query hints, types of joins in query execution plan, process of rewriting query are disclosed; as taught by Bestfleisch.)
As per claims 4 and 14, the combination of Bestfleisch, Maheshwari, and Ramish further discloses removing the patterned query statement and the first paired statement hint from the query hint registry. (See Figs. 6, 8, paras. 38-40, wherein storing query execution plan in a cache, replacing first query execution plan with second execution plan are disclosed; as taught by Bestfleisch.)
As per claims 5 and 15, the combination of Bestfleisch, Maheshwari, and Ramish further discloses after removing the patterned query statement and the paired first statement hint from the query hint registry, evicting a selected one of one or more different query execution plans stored in a query execution plan cache, wherein the one or more different query execution plans were generated after compiling one or more previously received incoming query statements that match the patterned query statement and are appended with the first statement hint. (See Fig. 2, paras. 10, 29-31, wherein mapping of query statement pattern and query hint, the process of matching hash value of query string, query plan are disclosed, also See Fig. 4, paras. 29, 34, wherein process of applying query hints are disclosed, also See Figs. 6, 8, paras. 38-40, wherein storing query execution plan in a cache, replacing first query execution plan with second execution plan are disclosed; as taught by Bestfleisch.)
Claims 6-9 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bestfleisch (U.S. PGPub 2018/0285416) in view of Maheshwari et al. (U.S. PGPub 2021/0224275; hereinafter “Maheshwari”) and further in view of Ramish (U.S. PGPub 2015/0331850) and further in view of Bartels et al. (U.S. PGPub 2009/0070300; hereinafter “Bartels”).
As per claims 6 and 16, the combination of Bestfleisch, Maheshwari, and Ramish fails to disclose wherein the wildcard expression comprises a set of constants in a where clause of the patterned query statement, wherein at least one constant in the set is represented by the one or more characters of the incoming query statement.
On the other hand, Bartels teaches wherein the wildcard expression comprises a set of constants in a where clause of the patterned query statement, wherein at least one constant in the set is represented by the one or more characters of the incoming query statement. (See Fig. 2a, paras. 61-66, 68, 71-73, wherein SQL hints, appending SQL hints to data query process, first data query, second data query are disclosed, also See paras. 83, 93, wherein SQL statements, statement ID/NoSTMID are disclosed, also See paras. 94-96, wherein processing statements with respect to priority are disclosed; as taught by Bartels.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Bartels teachings in the combination of Bestfleisch, Maheshwari, and Ramish system. Skilled artisan would have been motivated to incorporate the method of processing data queries taught by Bartels in the combination of Bestfleisch, Maheshwari, and Ramish system for effectively applying query hints to query execution plan. In addition, both of the references (Bestfleisch, Maheshwari, Ramish, and Bartels) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as plan optimisation. This close relation between both of the references highly suggests an expectation of success.
As per claims 7 and 17, the combination of Bestfleisch, Maheshwari, and Ramish fails to disclose wherein the wildcard expression comprises a range in a where clause of the patterned query statement, wherein the range includes a constant represented by the one or more characters of the incoming query statement.
On the other hand, Bartels teaches wherein the wildcard expression comprises a range in a where clause of the patterned query statement, wherein the range includes a constant represented by the one or more characters of the incoming query statement. (See Fig. 2a, paras. 61-66, 68, 71-73, wherein SQL hints, appending SQL hints to data query process, first data query, second data query are disclosed, also See paras. 83, 93, wherein SQL statements, statement ID/NoSTMID are disclosed, also See paras. 94-96, wherein processing statements with respect to priority are disclosed; as taught by Bartels.)
See claims 1 and 11 for motivation above.
As per claims 8 and 18, the combination of Bestfleisch, Maheshwari, and Ramish fails to disclose wherein the wildcard expression is included in a from clause of the patterned query statement and matches an object name represented by the one or more characters of the incoming query statement.
On the other hand, Bartels teaches wherein the wildcard expression is included in a from clause of the patterned query statement and matches an object name represented by the one or more characters of the incoming query statement. (See Fig. 2a, paras. 61-66, 68, 71-73, wherein SQL hints, appending SQL hints to data query process, first data query, second data query are disclosed, also See paras. 83, 93, wherein SQL statements, statement ID/NoSTMID are disclosed, also See paras. 94-96, wherein processing statements with respect to priority are disclosed; as taught by Bartels.)
See claims 1 and 11 for motivation above.
As per claims 9 and 19, the combination of Bestfleisch, Maheshwari, and Ramish fails to disclose wherein the wildcard expression comprises a predefined data type.
On the other hand, Bartels teaches wherein the wildcard expression comprises a predefined data type. (See Fig. 2a, paras. 61-66, 68, 71-73, wherein SQL hints, appending SQL hints to data query process, first data query, second data query are disclosed, also See paras. 59-61, 83, 93-95, wherein SQL statements, statement ID/NoSTMID are disclosed, also See paras. 94-96, wherein processing statements with respect to priority are disclosed; as taught by Bartels.)
See claims 1 and 11 for motivation above.
Conclusion
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 LIN LIN M HTAY whose telephone number is (571)272-7293. The examiner can normally be reached on M-F, 7am-3pm, PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/ Supervisory Patent Examiner, Art Unit 2153