CTNF 19/228,634 CTNF 79991 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 action is response to the application filed on 06/04/2025 . Claims 1-20 are pending in this Office Action. Claims 1, 11 and 20 are independent claims. Priority Acknowledged is that this Application claims the benefit of priority from parent Application 18473752, filed 09/25/2023 now the U.S. Patent 12353412, issued 07/08/2025. Information Disclosure Statement The information disclosure statements filed 06/04/2025 and 11/03/2025 are in compliance with 37 CFR 1.97(c) and therein have been considered. Its corresponding PTO-1449 have been electronically signed as attached. Double Patenting Rejections 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent 12353412 (issued 07/08/2025 to the parent application 18473752, filed 09/25/2023). Although the conflicting are not patentably distinct from each other because since the claims of the U.S. Patent 12353412 contain elements of the claims of the instant application, and as such, anticipate the claims of the instant application. The conflicting claims between the instant application and the granted U.S. Patent 12353412 are listed in parallel in below table. Patent 12353412 claims 1-20 Instant Application claims 1-20 1. A computer-implemented method comprising: executing a first query plan for a query; obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein the keys comprise respective signatures for operations represented by the one or more internal nodes, wherein a signature comprises an operator and one or more operands defined by a corresponding operation. 2. The computer-implemented method of claim 1, wherein obtaining statistics for an internal node of the first query tree comprises determining a cardinality of a table resulted from an operation represented by the internal node after executing the first query plan. 3. The computer-implemented method of claim 1, further comprising registering the keys in a dictionary. 4. The computer-implemented method of claim 3, wherein searching for the matching internal node comprises: generating a target key for the selected internal node of the second query tree; and searching the dictionary for a key that matches the target key. 5. The computer-implemented method of claim 3, wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree; generating a target key for a root of the alternative subtree; and searching the dictionary for a key that matches the target key. 6. The computer-implemented method of claim 3, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; identifying an unmatched internal node of the second query tree that has no matching internal node of the first query tree; generating a new key uniquely identifying the unmatched internal node; registering the new key in the dictionary; and mapping the new key to the statistics for the unmatched internal node of the second query tree. 7. The computer-implemented method of claim 3, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; finding a matching key in the dictionary identifying the matching internal node of the first query tree corresponding to the selected internal node of the second query tree; and mapping the matching key to the statistics for the selected internal node of the second query tree. 8. The computer-implemented method of claim 1, wherein the keys further identify respective child nodes of the one or more internal nodes. 9. The computer-implemented method of claim 1, wherein at least a portion of the signature is represented by a hash value. 10. The computer-implemented method of claim 1, further comprising: identifying a first internal node and a second internal node of the second query tree, wherein the first internal node represents a group-by operation having a known selectivity, wherein the second internal node represents a pre-aggregation of the group-by operation represented by the first internal node; and applying the known selectivity of the first internal node to the second internal node. 11. A computing system, comprising: memory; one or more hardware processors coupled to the memory; and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising: executing a first query plan for a query; obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein the keys comprise respective signatures for operations represented by the one or more internal nodes, wherein a signature comprises an operator and one or more operands defined by a corresponding operation. 12. The computing system of claim 11, wherein the statistics for an internal node of the first query tree comprises a cardinality of a table resulted from an operation represented by the internal node after executing the first query plan. 13. The computing system of claim 11, wherein the operations further registering the keys in a dictionary. 14. The computing system of claim 13, wherein searching for the matching internal node comprises: generating a target key for the selected internal node of the second query tree; and searching the dictionary for a key that matches the target key. 5. The computing system of claim 13, wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree; generating a target key for a root of the alternative subtree; and searching the dictionary for a key that matches the target key. 16. The computing system of claim 13, wherein the operations further comprise: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; identifying an unmatched internal node of the second query tree that has no matching internal node of the first query tree; generating a new key uniquely identifying the unmatched internal node; registering the new key in the dictionary; and mapping the new key to the statistics for the unmatched internal node of the second query tree. 17. The computing system of claim 13, wherein the operations further comprise: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; finding a matching key in the dictionary identifying the matching internal node of the first query tree corresponding to the selected internal node of the second query tree; and mapping the matching key to the statistics for the selected internal node of the second query tree. 18. The computing system of claim 11, wherein the keys further identify respective child nodes of the one or more internal nodes. 19. The computing system of claim 11, wherein at least a portion of the signature is represented by a hash value. 20. One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method comprising: executing a first query plan for a query; obtaining cardinalities for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying a cardinality for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein the keys comprise respective signatures for operations represented by the one or more internal nodes, wherein a signature comprises an operator and one or more operands defined by a corresponding operation. 1. A computer-implemented method comprising: executing a first query plan for a query; obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree; generating a target key for a root of the alternative subtree; and searching a dictionary for a key that matches the target key. 2. The computer-implemented method of claim 1, wherein obtaining statistics for an internal node of the first query tree comprises determining a cardinality of a table resulted from an operation represented by the internal node after executing the first query plan. 3. The computer-implemented method of claim 1, further comprising registering the keys in the dictionary. 4. The computer-implemented method of claim 1, wherein the keys further identify respective child nodes of the one or more internal nodes. 5. The computer-implemented method of claim 1, wherein searching for the matching internal node comprises: generating a target key for the selected internal node of the second query tree; and searching the dictionary for a key that matches the target key. 6. The computer-implemented method of claim 1, further comprising generating a key for an internal node of the first query tree, wherein the key comprises a signature representing an operation of the internal node. 7. The computer-implemented method of claim 6, wherein generating the signature comprises normalizing a predicate order of operands having a conjunctive or disjunctive relationship such that logically equivalent operations are represented by a same signature. 8. The computer-implemented method of claim 1, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; identifying an unmatched internal node of the second query tree that has no matching internal node of the first query tree; generating a new key uniquely identifying the unmatched internal node; registering the new key in the dictionary; and mapping the new key to the statistics for the unmatched internal node of the second query tree. 9. The computer-implemented method of claim 1, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; finding a matching key in the dictionary identifying the matching internal node of the first query tree corresponding to the selected internal node of the second query tree; and mapping the matching key to the statistics for the selected internal node of the second query tree. 10. The computer-implemented method of claim 1, further comprising: identifying a first internal node and a second internal node of the second query tree, wherein the first internal node represents a group-by operation having a known selectivity, wherein the second internal node represents a pre-aggregation of the group-by operation represented by the first internal node; and applying the known selectivity of the first internal node to the second internal node. 11. A computing system, comprising: memory; one or more hardware processors coupled to the memory; and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising: executing a first query plan for a query; obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree; generating a target key for a root of the alternative subtree; and searching a dictionary for a key that matches the target key. 12. The computing system of claim 11, wherein the statistics for an internal node of the first query tree comprises a cardinality of a table resulted from an operation represented by the internal node after executing the first query plan. 13. The computing system of claim 11, wherein the operations further comprise registering the keys in the dictionary. 14. The computing system of claim 11, wherein the keys further identify respective child nodes of the one or more internal nodes. 15. The computing system of claim 11, wherein searching for the matching internal node comprises: generating a target key for the selected internal node of the second query tree; and searching the dictionary for a key that matches the target key. 16. The computing system of claim 11, wherein the operations further comprise generating a key for an internal node of the first query tree, wherein the key comprises a signature representing an operation of the internal node. 17. The computing system of claim 16, wherein generating the signature comprises normalizing a predicate order of operands having a conjunctive or disjunctive relationship such that logically equivalent operations are represented by a same signature. 18. The computing system of claim 11, wherein the operations further comprise: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; identifying an unmatched internal node of the second query tree that has no matching internal node of the first query tree; generating a new key uniquely identifying the unmatched internal node; registering the new key in the dictionary; and mapping the new key to the statistics for the unmatched internal node of the second query tree. 19. The computing system of claim 11, wherein the operations further comprise: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree; finding a matching key in the dictionary identifying the matching internal node of the first query tree corresponding to the selected internal node of the second query tree; and mapping the matching key to the statistics for the selected internal node of the second query tree. 20. One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method comprising: executing a first query plan for a query; obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan; mapping keys which uniquely identify the one or more internal nodes of the first query tree to respective statistics; receiving a second query tree representing a second query plan for the query after execution of the first query plan has completed the query; for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree; and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of executing the second query plan to complete the query, wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree; generating a target key for a root of the alternative subtree; and searching a dictionary for a key that matches the target key. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or non-obviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37CPR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Burger et al.: "ACCURATE AND TIMELY ENFORCEMENT OF SYSTEM RESOURCE ALLOCATION RULES", (U.S. Patent Application Publication US 20100145929 A1, DATE PUBLISHED 2010-06-10 and DATE FILED 2008-12-08, hereafter "Burger"), in view of Chaudhuri et al.: " INCREMENTAL REPAIR OF QUERY PLANS ", (U.S. Patent Application Publication US 20080177694 A1, DATE PUBLISHED 2008-07-24 and DATE FILED 2007-01-19, hereafter "Chaudhuri"), and further in view of Bender et al.: "HIGH-PERFORMANCE STREAMING DICTIONARY", (U.S. Patent Application Publication 20110246503 A1, DATE PUBLISHED 2011-10-06 and DATE FILED 2010-04-06, hereafter "Bender"). As per claim 1 , Burger teaches a computer-implemented method comprising: executing a first query plan for a query (See [0238], a plan processor selects an optimal query execution plan from among the available query execution plans for each predicate and then executes the optimal query execution plan. The selected plan from the available query execution plans reads on a first plan). Burger does not explicitly teach obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan. However, Chaudhuri obtaining statistics for one or more internal nodes of a first query tree representing the first query plan during execution of the first query plan (See [0041], through implementation of the leaf -node evaluator component 214, the current query plan can continue to use the better available access method at each leaf node in the presence of updates to statistics. (based on Fig. 2, component 214 is a leaf-node, not a lead-node). Here the statistic updates of leaf-node teaches query plan execution). 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 combine Chaudhuri’s teaching with Burger because Burger is dedicated to application program interfaces for generating solutions to discrete optimization problems and complex search problems and Chaudhuri is dedicated to Incremental repair of query plans, and a combined teaching would have allowed Burger to improve system efficiency by reusing parts of the current plan rather than discarding the plan entirely . Burger in view of Chaudhuri further teaches the following: mapping keys which [uniquely] identify the one or more internal nodes of the first query tree to respective statistics (See Chaudhuri: [0041], Leaf-node evaluator component 214 can evaluate the leaf nodes of the query that has undergone a statistics change, and the current query plan can continue to use the better available access method at each leaf node in the presence of updates to statistics. Here at each leaf-node reads on uniquely identified node). As cited, Chaudhuri teaches identifying the internal nodes of the first query tree to respective statistics for each leaf-node. However, Chaudhuri does not explicitly teach uniquely identifying the leaf nodes (See [0438], counting the distinct and uniquely identified nodes in sub-trees). 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 combine Bender’s teaching with Burger because Burger is dedicated to an application program interfaces for generating solutions to discrete optimization problems and complex search problems, Chaudhuri is dedicated to Incremental repair of query plans and Bender is dedicated to a high-performance dictionary data structure and its storage, and a combined teaching would have allowed Burger in view of Chaudhuri to efficiently maintain sorted data . Burger in view of Chaudhuri and further in view of Bender further teaches the following: receiving a second query tree representing a second query plan for the query after execution of the first query plan (See Chaudhuri: [0065], recompilations could be initiated immediately after a corresponding statistics update (or a physical database change) or it could be deferred until the next time the query is re-executed. Here the execution of re-compiled query teaches receiving and executing a second query plan after execution of the first query plan; and Burger: [0237] and [0239], the plan preparation 1405 then computes a total cost for each of the query execution plans using estimated cost information (optionally) adjusted by actual cost information (if any) stored in a query capture database (QCD) 1410, and the adjustment may be invoked for all query execution plans. Here the plural plans teaches a second plan is included); for a selected internal node of the second query tree, searching for a matching internal node out of the one or more internal nodes of the first query tree (See Bender: Page 67, claim 132, deleting from the leaf node any key-value pair matching the key of the message; Chaudhuri: [0041], through implementation of the lead-node evaluator component 214, the current query plan can continue to use the better available access method at each leaf node in the presence of updates to statistics and the physical database design. Here using the better available access method at each leaf node teaches the matches internal node; and Burger: [0247], during the execution of the plan, performance information may be monitored and the cost of processing identified operators and predicates using identified access paths. Here the combined teaching of matching leaf node to key-value pair and using identified access path to process identified operators and predicates of the plan and reads on mapping the path to the query and the identified path to the identified plan, tree); and responsive to finding the matching internal node of the first query tree, applying the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of the second query plan during query optimization of the query after execution of the first query plan (See Chaudhuri: [0065], recompilations could be initiated immediately after a corresponding statistics update (or a physical database change) or it could be deferred until the next time the query is re-executed. Here the execution of re-compiled query teaches receiving and executing a second query plan after execution of the first query plan; and Burger: Fig. 14 and [0238]-[0239], selecting an optimal query execution plan for each predicate from among the available query execution plans for each predicate, based on predetermined criteria such as the computed total cost. The plan processor 1415 then executes the optimal query execution plan and collects the actual cost information as the optimal query execution plan is executed. The actual cost information is then analyzed (block 1425) and stored in the QCD 1410. Thereafter, the actual cost information is available for use by the plan preparation 1405 in determining the cost of performing the identified plans. Block 1420 represents the storing of the actual cost information in the database query logs 505. The actual cost information is then analyzed (block 1425) and stored in the QCD 1410. Thereafter, the actual cost information is available for use by the plan preparation 1405 in determining the cost of performing the identified plans. Thus, as shown by the arrowed circle in FIG. 14, the processing of query requests forms a feedback loop. After the first query plan executed with statistics data stored in database and available for plan preparation to processing the identified plans teaches the second plans), wherein searching for the matching internal node comprises: selecting an alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree (See Chaudhuri: [0030], [0041] and [0065], determining a sub-tree of an existing plan and re-optimize only that sub-tree and the current query plan continues to use the better available access method at each leaf node in the presence of updates to statistics and the execution of re-compiled query. Here using the better available access method at each leaf node in the presence of updates to statistics and the execution of re-compiled query or sub-tree teaches the alternative subtree that is logically equivalent to a subtree of the selected internal node of the second query tree); generating a target key for a root of the alternative subtree (See Bender: [0098], a tree-structured data structure is organized as a search tree when non-leaf nodes of the tree comprise pivot keys (which may be keys or key-value pairs or they may be substrings of keys or key-value pairs); searching a dictionary for a key that matches the target key (See Bender: Page 66, Claim 129, searching a leaf node for a key comprising returning a key-value pair that match the key). As per claim 2 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, wherein obtaining statistics for an internal node of the first query tree comprises determining a cardinality of a table resulted from an operation represented by the internal node after executing the first query plan (See Burger: [0278], such rules include a maximum row count or a maximum processing time for the execution of a step within the query execution plan. Here counting the row teaches determining the cardinality). As per claim 3 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, further comprising registering the keys in the dictionary (See Bender: [0015], Many databases or file systems employ a dictionary mapping keys to values. A dictionary is a collection of keys, and sometimes includes values). As per claim 4 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, wherein the keys further identify respective child nodes of the one or more internal nodes (See Bender: [0083]-[0084], the left child (303) comprises two pointers (one to leaf node (LeafA (306)), and one to leaf node (LeafB (307))) and a pivot key (305), and the right child (304) comprises two pointers and a pivot key and the pivot key stored therein is greater than the employee's, so the system examines the left leaf node (308) of the right child (304) where it can find the complete record of the employee.). As per claim 5 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, wherein searching for the matching internal node comprises: generating a target key for the selected internal node of the second query tree (See Bender: [0100], [0163], a search operation can determine if a key is stored in a dictionary, and return the key's associated value if there is one; the OMT tree is a search tree, meaning that all the pairs in the left subtree of a node are less than the pair of the node, and the value of the node is less than all the pairs in the right subtree and all the pairs in the left subtree of a node are less than the pair of the node, and the value of the node is less than all the pairs in the right subtree.); and searching the dictionary for a key that matches the target key (See Bender: [0098] A tree-structured data structure is organized as a search tree when non-leaf nodes of the tree comprise pivot keys). As per claim 6 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, further comprising generating a key for an internal node of the first query tree (See Bender: [0163], the OMT tree is a search tree, meaning that all the pairs in the left subtree of a node are less than the pair of the node, and the value of the node is less than all the pairs in the right subtree and all the pairs in the left subtree of a node are less than the pair of the node, and the value of the node is less than all the pairs in the right subtree), wherein the key comprises a signature representing an operation of the internal node (See Burger: [0232] and [0243], the Operating Environment Event is an access method or a join method performed by the query execution plan, such as Product Join, Merge Join, Local Nested Join and Hash Join methods and using an index analysis 1504 to optimize access path selection, and possibly using logging directives 1506, such as INSERT EXPLAIN directives and/or BEGIN/END directives, in order to log information about the plan and its run-time performance. Here the join reads on a signature representing an operations of the internal node). As per claim 7 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 6, wherein generating the signature comprises normalizing a predicate order of operands having a conjunctive or disjunctive relationship such that logically equivalent operations are represented by a same signature (See Burger: [0232] and [0243], the Operating Environment Event is an access method or a join method performed by the query execution plan, such as Product Join, Merge Join, Local Nested Join and Hash Join methods and using an index analysis 1504 to optimize access path selection, and possibly using logging directives 1506, such as INSERT EXPLAIN directives and/or BEGIN/END directives, in order to log information about the plan and its run-time performance. Here the Product Join, Merge Join, Local Nested Join and Hash Join reads on the conjunctive or disjunctive relationship such that logically equivalent operations). As per claim 8 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree (See Burger: Fig. 14 and [0237] and [0244], generates query execution plans for each of the predicates and their associated access paths, computing a total cost for each of the query execution plans using estimated cost information (optionally) adjusted by actual cost information and an optimized query execution plan is represented by tree structure. The access paths reads on the internal nodes. Here the query execution plans teaches a second plan); identifying an unmatched internal node of the second query tree that has no matching internal node of the first query tree (See Burger: [0237], generates one or more query execution plans for each of the predicates and their associated access paths. The plan preparation 1405 then computes a total cost for each of the query execution plans using estimated cost information (optionally) adjusted by actual cost information (if any) stored in a query capture database (QCD) 1410, as described in more detail below. Note that this adjustment may be invoked for all query execution plans or it may be invoked more selectively for only some of the query execution plans. Here the different plans resulted in different costs teaches plans of unmatched access paths or predicates); generating a new key uniquely identifying the unmatched internal node (See Burger: Abstract, The estimated cost information may be re-calculated using the actual cost information when confidence in the estimated cost information is low, but the estimated cost information may not be re-calculated when confidence in the estimated cost information is high); registering the new key in the dictionary (See Bender: [0015], employing a dictionary mapping keys to values. A dictionary is a collection of keys, and sometimes includes values.); and mapping the new key to the statistics for the unmatched internal node of the second query tree (See Burger: [0247], during the execution of the plan, performance information may be monitored and the cost of processing identified operators and predicates using identified access paths. Using identified access path to process identified operators and predicates of the plan reads on mapping the path to the query and the identified path to the identified plan, tree, suggested mapping keys to uniquely identify). As per claim 9 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, further comprising: responsive to executing the second query plan as a result of query optimization of the query, obtaining statistics for one or more internal nodes of the second query tree (See Burger: [0292], determining made whether the estimated cost information associated with the step should be re-calculated using the actual cost information generated in Block 1720, namely whether a confidence rating for the estimated cost information is low or high); finding a matching key in the dictionary identifying the matching internal node of the first query tree corresponding to the selected internal node of the second query tree (See Burger: Abstract, The estimated cost information may be re-calculated using the actual cost information when confidence in the estimated cost information is low, but the estimated cost information may not be re-calculated when confidence in the estimated cost information is high); and mapping the matching key to the statistics for the selected internal node of the second query tree (See Burger: [0029], The estimated cost information may be re-calculated using the actual cost information when confidence in the estimated cost information is low, but the estimated cost information may not be re-calculated when confidence in the estimated cost information is high). As per claim 10 , Burger in view of Chaudhuri and further in view of Bender teaches the computer-implemented method of claim 1, further comprising: identifying a first internal node and a second internal node of the second query tree (See Burger: [0237], generates one or more query execution plans for each of the predicates and their associated access paths. The plan preparation 1405 then computes a total cost for each of the query execution plans using estimated cost information (optionally) adjusted by actual cost information (if any) stored in a query capture database (QCD) 1410, as described in more detail below. Note that this adjustment may be invoked for all query execution plans or it may be invoked more selectively for only some of the query execution plans), wherein the first internal node represents a group-by operation having a known selectivity (Bender: [0437], the system can maintain a count of the number of unique keys nkeys (3303) in a leaf node, along with correct values for minkey (3305) and maxkey (3306). A count on keys teaches a grouping by operation of the keys with unque keys selected), wherein the second internal node represents a pre-aggregation of the group-by operation represented by the first internal node (See ender: [0437], the system can maintain a count of the number of unique keys nkeys (3303) in a leaf node, along with correct values for minkey (3305) and maxkey (3306). A count on keys teaches a grouping by operation of the keys with unque keys selected); and applying the known selectivity of the first internal node to the second internal node (See ender: [0437], the system can maintain a count of the number of unique keys nkeys (3303) in a leaf node, along with correct values for minkey (3305) and maxkey (3306). A count on keys teaches a grouping by operation of the keys with unque keys selected). As per claims 11-19 , the claims recite a computing system, comprising: Memory, one or more hardware processors coupled to the memory (See Burger: [0259], a balanced usage of system resources, including CPU, disk I/O, network, and memory); and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors (See Burger: Page 20, claim 19, storage devices tangibly embodying instructions that, when executed by a computer system, result in the computer system) to perform operations comprising the steps as recited in the claims 1, 9 and rejected, respectively, as unpatentable under 35 U.S.C. § 103 as being unpatentable over Burger in view of Chaudhuri and further in view of Bender. Therefore, claims 11-19 are rejected along the same rationale that rejected claims 1-9, respectively. As per claim 20, the claim recites one or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors (See Burger: [0259] and Page 20, claim 19, a balanced usage of system resources, including CPU, memory, and storage devices tangibly embodying instructions that, when executed by a computer system, result in the computer system) to perform operations comprising the steps as recited in the claim 1, as unpatentable under 35 U.S.C. § 103 as being unpatentable over Burger in view of Chaudhuri and further in view of Bender. Therefore, claim 20 is rejected along the same rationale that rejected claim 1. Related Prior Arts 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited . Conclusion Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in 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. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123. In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. KUEN S LU /Kuen S Lu/ Art Unit 2165 Primary Patent Examiner May 10, 2026 Application/Control Number: 19/228,634 Page 2 Art Unit: 2165 Application/Control Number: 19/228,634 Page 3 Art Unit: 2165 Application/Control Number: 19/228,634 Page 4 Art Unit: 2165 Application/Control Number: 19/228,634 Page 5 Art Unit: 2165 Application/Control Number: 19/228,634 Page 6 Art Unit: 2165 Application/Control Number: 19/228,634 Page 7 Art Unit: 2165 Application/Control Number: 19/228,634 Page 8 Art Unit: 2165 Application/Control Number: 19/228,634 Page 9 Art Unit: 2165 Application/Control Number: 19/228,634 Page 10 Art Unit: 2165 Application/Control Number: 19/228,634 Page 11 Art Unit: 2165 Application/Control Number: 19/228,634 Page 12 Art Unit: 2165 Application/Control Number: 19/228,634 Page 13 Art Unit: 2165 Application/Control Number: 19/228,634 Page 14 Art Unit: 2165 Application/Control Number: 19/228,634 Page 15 Art Unit: 2165 Application/Control Number: 19/228,634 Page 16 Art Unit: 2165 Application/Control Number: 19/228,634 Page 17 Art Unit: 2165 Application/Control Number: 19/228,634 Page 18 Art Unit: 2165 Application/Control Number: 19/228,634 Page 19 Art Unit: 2165 Application/Control Number: 19/228,634 Page 20 Art Unit: 2165 Application/Control Number: 19/228,634 Page 21 Art Unit: 2165 Application/Control Number: 19/228,634 Page 22 Art Unit: 2165 Application/Control Number: 19/228,634 Page 23 Art Unit: 2165 Application/Control Number: 19/228,634 Page 24 Art Unit: 2165 Application/Control Number: 19/228,634 Page 25 Art Unit: 2165 Application/Control Number: 19/228,634 Page 26 Art Unit: 2165 Application/Control Number: 19/228,634 Page 27 Art Unit: 2165 Application/Control Number: 19/228,634 Page 28 Art Unit: 2165 Application/Control Number: 19/228,634 Page 29 Art Unit: 2165 Application/Control Number: 19/228,634 Page 30 Art Unit: 2165 Application/Control Number: 19/228,634 Page 31 Art Unit: 2165 Application/Control Number: 19/228,634 Page 32 Art Unit: 2165 Application/Control Number: 19/228,634 Page 33 Art Unit: 2165 Application/Control Number: 19/228,634 Page 34 Art Unit: 2165 Application/Control Number: 19/228,634 Page 35 Art Unit: 2165 Application/Control Number: 19/228,634 Page 36 Art Unit: 2165 Application/Control Number: 19/228,634 Page 37 Art Unit: 2165 Application/Control Number: 19/228,634 Page 38 Art Unit: 2165 Application/Control Number: 19/228,634 Page 39 Art Unit: 2165
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