DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/8/2026 has been entered.
Status of Claims
Claims 1, 3-6, 8-11, 13-16, 18-20 are pending of which claims 1, 11 and 20 are in independent form.
Claims 1, 3-6, 8-11, 13-16, 18-20 are rejected under 35 U.S.C. 101.
Claims 1, 3-6, 8-11, 13-16, 18-20 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the 35 USC 101 (Abstract Idea), remarks made by the applicant.
Examiner specifies that, the newly added amendments do not overcome the 35 USC 101 rejection.
With respect to step 2A, Prong One:
The claims recite:
Generating a current execution plan for a query,
Serializing the current execution plan into an object,
Converting the object into string representation (tree-shape string)
Comparing the string with stored strings,
Storing the object if no match exists,
Reusing the object to mitigate regression.
In plain language, this is: representation, comparison, and storage of information describing a query plan.
These steps fall into recognized abstract idea:
Mental Process/Mathematical Concepts: evaluating and representing a structure (query plan), converting/comparing representation formats, determining matches, storing if no match.
Nothing in the claim requires a new ML, new hardware structure, improved communication technique, or any specific technological improvement.
The claims recite an abstract idea: analyzing, classifying, and processing information using mathematical/ML models.
With respect to step 2A, Prong Two:
The claims are generic computer components preforming their routine functions. The claims include:
Query processing engine,
Query Optimizer,
Query Plan Stability Manager,
serializer,
comparator,
plan repository.
The recited components perform their generic/ordinary, expected functions, which is considered insufficient. These claims are merely functional software components performing generic task. Therefore, the claims do not integrate the abstract idea into a practical application.
The additional elements are simply used to perform: generic database components, and performing the abstract idea on a computer. The claims do not: improve computer functionality; improve query execution mechanics, or provide a new data structure or algorithm for execution. Furthermore, no element: changes how hardware works; modifies indexing, caching, or memory structure. The “mitigate performance regression” language is only a result/goal and not a technical solution, it does not explain how performance is technically improved.
Therefore, the claims do not integrate the abstract idea into a practical application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8-11, 13-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) generating and implementing query execution plans.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claim 1 is directed to a method, which is a process.
Independent Claim 11 is directed to a system, including “a processor; and a memory”, which is a machine, and directed to one of the 4 categories of patent eligible subject matter.
Independent Claim 20 is directed to One or more non-transitory computer-readable media, which is a machine, and directed to one of the 4 categories of patent eligible subject matter.
All other claims depend on claims 1, 11 and 20. As such, claims 1-20 are directed to a statutory category.
Regarding claims 1, 11, and 20,
With respect to step 2A, Prong One, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claims recite:
“serializing,…, the current query execution plan into a current query plan object” the limitation as drafted recites a mental process involving organizing and representing information in a different format,
“converting the current query plan object into a current tree-shape string” the limitation as drafted recites a mental process involving reformatting or translating information from one representation into another,
“the current tree-shape string comprises a plurality of operator names … for respective nodes of the query tree” the limitation as drafted recites a mental process involving identifying and labeling elements of a structure,
“comparing, … , the current tree- shape string with one or more tree-shape strings … from stored query plan objects” the limitation as drafted recites a mental process involving evaluation and comparison of information,
“responsive to finding that no stored query plan object matches …, storing, …, the current query plan object in the plan repository” the limitation as drafted recites a mental process involving decision making based on comparison results and storing information accordingly.
These limitations correspond to concept that can be performed in the human mind (Mental Process) and therefore fall within the mental process category of abstract idea (see MPEP 2016.04(a)(2)).
In plain language, this is: collecting information (query plans, attributes), analyzing information (comparing objects), storing information (QP repository), using results (reuse to mitigate regression).
These steps involve data processing/manipulation and/or information management, which are fundamental concepts in information processing. The core of the claim is directed to collecting, analyzing, and storing data, which is a form of mental process, all of which are recognized categories of abstract idea.
Nothing in the claim requires a new QP analysis, new hardware structure, improved storage technique, or any specific technological improvement.
With respect to step 2A, Prong Two, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims are generic computer components preforming their routine functions. The claims include:
“a query processing engine…a query optimizer…a query executor” as drafted recites generic computing components, performing their ordinary functions of processing and executing queries (see MPEP 2106.05(f)(2)).
“query plan stability manager…a serializer…a comparator” as drafted recites generic functional modules, that merely implement the abstract idea, without specifying any particular technological implementation or improvement.
“serializing…converting…comparing…storing….in a plan repository” as drafted recites data manipulation and data processing operations, including: reformatting data (sterilization, conversion), analyzing data (comparison), and storing results, which constitutes insignificant extra-solution activity (see MPEP 2106.05(g)).
“plan repository” as drafted recites generic data storage component used to store information, which amounts to no more than applying the abstract idea using conventional storage mechanism (see MPEP 2106.05(f)).
The additional elements mentioned above fail to integrate the abstract idea into a practical application because the additional elements, individually and in combination, amount to no more that:
Generic computer components performing generic computer functions; and
Insignificant extra-solution activity, including data collection, analysis, scoring and presentation of results.
The claims do not:
Improve the functioning of a computer or database system;
Improve query optimization or execution mechanism;
Provide a specific technical solution for managing query plan stability;
Implement any particular algorithm or technical mechanism for generating “tree-shape string” beyond a result-oriented conversion;
Improve concurrency or process execution in a technological way.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract mathematical encoding, data analysis, and decision making, with the result merely being applied in a generic manner.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the computer components are used as tools to perform the abstract idea of collecting, organizing, and associating information about nodes and their relationships.
The recited components perform their generic/ordinary, expected functions, which is considered insufficient. These claims are merely functional software components performing generic task. Therefore, the claims do not integrate the abstract idea into a practical application.
The additional elements are simply used to perform: generating plans; serializing data; compare records, store results and reuse, are well known, routine, conventional database operations. There are no improvements to: database architecture; query execution mechanics, network or processor performance, cache coherence, reduces I/O in a specific way. Furthermore, no element: changes how hardware works; modifies indexing, caching, or memory structure. The “mitigate performance regression” language is only a result/goal and not a technical solution, it does not explain how performance is technically improved.
Therefore, the claims do not integrate the abstract idea into a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are directed to a computer readable storage medium, computer, memory, and processor, at a very high level of generality and without imposing meaningful limitations on the scope of the claim. There are no improvements to: database architecture; query execution mechanics, network or processor performance, cache coherence, reduces I/O in a specific way. Furthermore, no element: changes how hardware works; modifies indexing, caching, or memory structure. The “mitigate performance regression” language is only a result/goal and not a technical solution, it does not explain how performance is technically improved. In addition, the published instant specification describe generic off‐the‐shelf computer‐based elements for implementing the claimed invention, which does not amount to significantly more than the abstract idea and is not enough to transform an abstract idea into eligible subject matter. Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Regarding claims 2, and 12, (canceled).
Regarding claims 3, and 13,
The claim recites:
Parsing the query plan object to identify operator names.
Parsing data to extract identifiers is data analysis and classification. This merely mental process performed on a computer which uses generic parsing techniques. This is simply analyzing and aggregating data. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 4, and 14,
The claim recites:
Traversing the query tree in an order, and concatenating operator names.
Tree traversing and concatenation are routine data processing steps. This merely organizes existing information. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 5, and 15,
The claim recites:
Grouping operator names in parentheses based on parent operator.
Grouping symbols based on hierarchy is presentation and formatting. This purely a conceptual organization similar to outputting and diagramming information. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 6, and 16,
The claim recites:
Identifying and appending a hint string to an operator name.
Attaching metadata, annotations or descriptors is information labeling. This merely augmenting existing data. There is a no technical effect on execution. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 7, and 17, (Canceled).
Regarding claims 8, and 18,
The claim recites:
Generating hash values from tree-shape string and storing in a hash map.
Hashing and storing values is conventional data indexing. This merely indexing, applied generally. There is a no specialized algorithm disclosed. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 9, and 19,
The claim recites:
Comparing hash values instead of full QP object.
Hashing comparison is another form of data matching. This merely optimization of comparison technique. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claim 10,
The claim recites:
Limiting repository size and removing older plans.
Managing storage capacity is routine memory management. This merely cache eviction, performed using generic rules. There is no improvement to computer functionality, data structures, or processing architecture.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
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, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over BANDARCHUK; Aliaksandr Sergeyevich (US 20250124041 A1) [Bandarchuk] in view of Chang; Won Jun et al. (US 20210303571 A1) [Chang] in view of Park; Hoyong et al. (US 20160098449 A1) [Park] in view of Wang; Donglin et al. (US 20130031085 A1) [Wang].
Regarding claims 1, 11 and 20, Bandarchuk discloses, a computer-implemented method for improved [query plan stability management] through distinct query plan capture, the method comprising: generating, by a query optimizer of a query processing engine of a database management system, a current query execution plan for a query (uses the supergraph schema to generate a query plan for the query 202. The query 202 is stored in the query store 208 as a historical query, and the query plan generated by the gateway 204 is stored in the query store 208 as a historical query plan in association with the historical query ¶ [0029]-[0030]. Also see ¶ [0003]-[0004], [0025]-[0026], [0052]);
comparing, by a comparator of the [query plan stability manager], the current [tree- shape] string with one or more [tree-shape strings which were respectively converted from stored query plan objects contained in a plan repository], wherein the stored query plan objects [were serialized from previous query execution plans] generated for the query; responsive to finding that no stored query plan object matches the current query plan object, storing, [by the query plan stability manager], the current query plan object in the plan repository, (see Fig. 4. If the query received at block 402 is determined to match a historical query, query metadata for the historical query is updated in the query store, as shown at block 406. For instance, the query metadata could include a timestamp of when the query was received, an update of a count of times the query has been received, as well as additional information regarding the query. In some instances, a new query plan is generated by the gateway that differs from the historical query plan associated with the historical query in the query store. In such instances, the new query plan is also stored in the query store ¶ [0035]-[0044]).
However, Bandarchuk does not explicitly facilitate serializing, by a serializer of a query plan stability manager of the query processing engine in communication with the query optimizer, the current query execution plan into a current query plan object, wherein the current query plan object includes a plurality of attribute- value pairs extracted from a query tree which defines a plurality of query operators of the current query execution plan; serialized from previous query execution plans.
Park discloses, serializing, by a serializer of [a query plan stability manager of the query processing engine in communication with the query optimizer], the current query execution plan into a current query plan object (converting the physical execution plan logical tree into an XML representation ¶ [0084], serializing the XML representation of the execution plan ¶ [0086]-[0087], query processing components generate and process execution plans ¶ [0062]), [wherein the current query plan object] includes a plurality of attribute-value pairs [extracted from a query tree which defines a plurality of query operators of the current query execution plan] (XML representation of the execution plan ¶ [0084]; examiner specifies that execution plan is inherently structured as attribute-value pairs);
serialized from previous query execution plans (serializing the XML representation of the execution plan ¶ [0086], [0087]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Park's system would have allowed Bandarchuk to facilitates serializing, by a serializer of a query plan stability manager of the query processing engine in communication with the query optimizer, the current query execution plan into a current query plan object, wherein the current query plan object includes a plurality of attribute- value pairs extracted from a query tree which defines a plurality of query operators of the current query execution plan; serialized from previous query execution plans. The motivation to combine is apparent in the Bandarchuk’s reference, because there a need to improve integrating new data sources into the federated database.
However, Bandarchuk nor Park explicitly facilitates wherein the current query plan object [includes a plurality of attribute-value pairs] extracted from a query tree which defines a plurality of query operators of the current query execution plan; converting the current query plan object into a current tree-shape string, wherein the current tree-shape string comprises a plurality of operator names defined in the current query execution plan for respective nodes of the query tree, the respective nodes representing query operators executable by a query executor of the query processing engine according to the current query execution plan; comparing, …, the current tree-shape string with one or more tree-shape strings which were respectively converted from stored query plan objects ….
Wang discloses, wherein the current query plan object [includes a plurality of attribute-value pairs] extracted from a query tree which defines a plurality of query operators of the current query execution plan (execution plan is a tree structure of nodes representing operations/operators ¶ [0069], [0102], [0117]);
converting the current query plan object into a current tree-shape string (converting execution plan into string representation ¶ [0117]. Also see ¶ [0065]-[0066], [0152], [0157]), wherein the current tree-shape string comprises a plurality of operator names defined in the current query execution plan for respective nodes of the query tree, the respective nodes representing query operators executable by a query executor of the query processing engine according to the current query execution plan (string encoding derived from nodes of execution plan tree ¶ [0117]; nodes corresponds to operations/operator ¶ [0069], [0102]. Also see ¶ [0067], [0102], [0105], [0107], [0110]);
comparing, …, the current tree-shape string with one or more tree-shape strings which were respectively converted from stored query plan objects … (using string representations for identification/comparison ¶ [0076], string encoding derived from nodes of execution plan tree ¶ [0117]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Wang's system would have allowed Bandarchuk and Park to facilitates wherein the current query plan object [includes a plurality of attribute-value pairs] extracted from a query tree which defines a plurality of query operators of the current query execution plan; converting the current query plan object into a current tree-shape string, wherein the current tree-shape string comprises a plurality of operator names defined in the current query execution plan for respective nodes of the query tree, the respective nodes representing query operators executable by a query executor of the query processing engine according to the current query execution plan; comparing, …, the current tree-shape string with one or more tree-shape strings which were respectively converted from stored query plan objects …. The motivation to combine is apparent in the Bandarchuk and Park’s reference, because there a need to improve creating a well extendable, scalable and maintainable docbase management system.
However, neither one of Bandarchuk, Park or Wang explicitly facilitates a query plan stability manager of the query processing engine in communication with the query optimizer; by the query plan stability manager/management; wherein the current query plan object stored in the plan repository is configured to be reused to mitigate performance regression of the current query execution plan for the query.
Chang discloses, a query plan stability manager of the query processing engine in communication with the query optimizer (In the example of FIG. 2, the query plan optimizer 222 includes a rewriter module 230, an enumerator module 232, an execution plan generator module 234, and a query plan stability module 236. In some examples, the rewriter module 230 receives the initial query plan and rewrites the initial query plan to provide a rewritten query plan. In some examples, rewriting of the initial query plan can include changing an order of operations within the initial query plan to provide the rewritten query plan. The rewritten query plan is provided to the enumerator module 232, which includes one or more enumerators. Example enumerators include a logical enumerator and a cost-based enumerator. The enumerator module 232 process the rewritten query plan to provide an optimized query plan. The optimized query plan is provided to the execution plan generator 234, which provides the query plan (e.g., a query execution plan (QEP)) for execution by the executor 212 ¶ [0046]-[0047]);
by the query plan stability manager/management (In some implementations, and as noted above, the server system 104 can host an in-memory database system (e.g., SAP HANA). In addition, and as described in further detail herein, the server system 104 can host a source database system, a target database system, and a query plan stability (QPS) system in accordance with implementations of the present disclosure. As described in further detail herein, the QPS system preserves the performance of queries by capturing query plans in the source database system and reusing the query plans in the target database system. In accordance with implementations of the present disclosure, the QPS system uses abstract query plan filters to selectively capture (record) query plans in the source database system ¶ [0021], [0025], [0026]);
wherein the current query plan object stored in the plan repository is configured to be reused to mitigate performance regression of the current query execution plan for the query (In some implementations, and as noted above, the server system 104 can host an in-memory database system (e.g., SAP HANA). In addition, and as described in further detail herein, the server system 104 can host a source database system, a target database system, and a query plan stability (QPS) system in accordance with implementations of the present disclosure. As described in further detail herein, the QPS system preserves the performance of queries by capturing query plans in the source database system and reusing the query plans in the target database system. In accordance with implementations of the present disclosure, the QPS system uses abstract query plan filters to selectively capture (record) query plans in the source database system ¶ [0021]-[0027]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Chang's system would have allowed Bandarchuk, Park, and Wang to facilitates a query plan stability manager of the query processing engine in communication with the query optimizer; by the query plan stability manager/management; wherein the current query plan object stored in the plan repository is configured to be reused to mitigate performance regression of the current query execution plan for the query. The motivation to combine is apparent in the Bandarchuk, Park, and Wang’s reference, because there a need to improve query optimizer which optimizes query plan to enhance performance of the query plan when executed.
Regarding claim 2 and 12, (Canceled).
Regarding claims 7 and 17, (Canceled).
Claim(s) 3-4, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Bandarchuk in view of Park in view of Wang in view of Chang in view of Arnold; Jason (US 20210191942 A1) [Arnold].
Regarding claims 3 and 13, the combination of Bandarchuk, Park, Wang, and Chang teaches all the limitations of claim 1.
However, neither one of Bandarchuk, Park, Wang, or Chang explicitly facilitates wherein converting the current query plan object comprises parsing the current query plan object, wherein parsing the current query plan object identifies the plurality of operator names in corresponding attributes of the plurality of query operators.
Arnold discloses, wherein converting the current query plan object comprises parsing the current query plan object, wherein parsing the current query plan object identifies the plurality of operator names in corresponding attributes of the plurality of query operators (Arnold: FIG. 25A illustrates an embodiment where the query expression processing module 2512 is implemented by node 37, for example, where some or all nodes 37 discussed herein are operable to implement the query expression processing module 2512. In such embodiments, all query expression processing modules 2512 implemented by all nodes at a same level of the query execution plan 2405 can be operable to deterministically generate query operator execution flows 2433 in the same fashion, for example, where each node at this same level of the query execution plan 2405 independently determines the same query operator execution flows 2433 for its execution of a same given query ¶ [0135]-[0136]. see Fig. 25B, tree generation, query selection and conversion).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Arnold's system would have allowed Bandarchuk, Park, Wang, and Chang to facilitates wherein converting the current query plan object comprises parsing the current query plan object, wherein parsing the current query plan object identifies the plurality of operator names in corresponding attributes of the plurality of query operators. The motivation to combine is apparent in the Bandarchuk, Park, Wang, and Chang’s reference, because there a need to improve the response time for completion of the service, application, and/or function.
Regarding claims 4 and 14, the combination of Bandarchuk, Park, Wang, Chang and Arnold discloses, wherein converting the current query plan object further comprises traversing the query tree in an ordered sequence, and concatenating the plurality of operator names based on the ordered sequence (Arnold: The CNF cost function 2572 and DNF cost function 2574 can be performed via a recursive function performed upon of the operator tree 2552 and/or otherwise via a depth-first traversal of the operator tree 2552. In particular, performing the CNF cost function 2572 can include assigning each node a CNF cost as a function of the CNF costs assigned to its child nodes. Similarly performing the DNF cost function 2574 can include assigning each node a DNF cost as a function of the DNF costs assigned to its child nodes. As both cost functions require a same depth-first traversal of the operator tree, a single recursive function and/or single depth-first traversal of the operator tree 2552 can be performed to generate both the CNF cost data and the DNF cost data in tandem. Thus, performing the CNF cost function 2572 and DNF cost function 2574 can include performing a single recursive cost function that generates both CNF cost data and DNF cost data at each node of the operator tree as a function of the CNF cost data and DNF cost data, respectively, generated for its child nodes ¶ [0153]).
Claim(s) 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bandarchuk in view of Rark in view of Wang in view of Arnold in view of Chang in view of Fanberg; Victor V. (US 11295062 B1) [Fanberg].
Regarding claim 5, the combination of Bandarchuk, Park, Wang, Chang and Arnold teach all the limitation of claim 4.
However, neither one of Bandarchuk, Park, Wang, Chang or Arnold explicitly facilitates, wherein converting the current query plan object further comprises grouping at least two of the operator names within a pair of parentheses if the two operator names correspond to two query operators that share a parent query operator in the query tree.
Fanberg discloses, wherein converting the current query plan object further comprises grouping at least two of the operator names within a pair of parentheses if the two operator names correspond to two query operators that share a parent query operator in the query tree (FIG. 27D is a flow diagram depicting the process of adding a new clause to a SQL string where each new clause is to be OR′ed together. Process 2889 starts in step 2891 where parenthesis may be placed around the new clause. If it is known that no future clause may contain a logical operator other than “OR”, “IN” and “NOT IN” then no parenthesis are necessary around the new clause. However, if some SQL string might include the other logical operators (e.g., “AND”), a parenthesis should be placed around the new clause. Step 2893 checks if the SQL string is empty. If the SQL string is empty, step 2895 copies the new clause to the SQL string. If the SQL string is not empty, step 2897 appends the word “OR” to the SQL string, followed by the new clause. Step 2899 returns the final SQL string [col. 56, ll. 37-50]. Also see Fig. 28A-28D).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Fanberg's system would have allowed Bandarchuk, Park, Wang, Chang and Arnold to facilitates wherein converting the current query plan object further comprises grouping at least two of the operator names within a pair of parentheses if the two operator names correspond to two query operators that share a parent query operator in the query tree. The motivation to combine is apparent in the Bandarchuk, Park, Wang, Chang and Arnold’s reference, because there a need to improve displaying a plurality of documents, especially documents.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bandarchuk in view of Park in view of Wang in view of Arnold in view of Chang in view of Doshi; Lyric Pankaj et al. (US 20230153303 A1) [Doshi].
Regarding claims 6 and 16, the combination of Bandarchuk, Park, Wang, Chang and Arnold teaches all the limitation of claims 3 and 13 respectively.
However, neither one of Bandarchuk, Park, Wang, Chang or Arnold explicitly facilitates, wherein parsing the current query plan object further identifies a hint string associated with a selected query operator, wherein converting the current query plan object further comprises appending the hint string to an operator name of the selected query operator.
Doshi discloses, wherein parsing the current query plan object further identifies a hint string associated with a selected query operator, wherein converting the current query plan object further comprises appending the hint string to an operator name of the selected query operator (In some implementations, selecting the query plan is based on a predicted resource usage of each query plan of the set of query plans. Each query plan of the set of query plans may include a hint string and selecting the query plan may be based on the hint string of each query plan of the set of query plans. In some examples, the operations further include, prior to generating the set of query plans, selecting a query template from a set of query templates. Generating the set of query plans includes using the selected query template. Selecting the query plan may be based on a state of the data processing hardware ¶ [0006], [0009] and [0032]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Doshi's system would have allowed Bandarchuk, Park, Wang, Chang and Arnold to facilitates wherein parsing the current query plan object further identifies a hint string associated with a selected query operator, wherein converting the current query plan object further comprises appending the hint string to an operator name of the selected query operator. The motivation to combine is apparent in the Bandarchuk, Park, Wang, Chang and Arnold’s reference, because there a need to improve optimizing database queries via parameter-sensitive plan selection.
Claim(s) 8, 9, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bandarchuk in view of Park in view of Wang in view of Arnold in view of Chang in view of Waas; Florian Michael et al. (US 10031944 B1) [Waas].
Regarding claim 8 and 18, the combination of Bandarchuk, Park, Wang, Chang and Arnold teaches all the limitation of claim 3.
However, neither one of Bandarchuk, Park, Wang, Chang or Arnold explicitly facilitates, further comprising generating a current hash value based on the current tree-shape string, generating one or more hash values based on the one or more tree-shape strings and storing the one or more hash values in a hash map.
Waas discloses, further comprising generating a current hash value based on the current tree-shape string, generating one or more hash values based on the one or more tree-shape strings and storing the one or more hash values in a hash map (In some embodiments, it may be preferable to hash the query plan. The hash may be generated through a variety of methods, and may be compared to a hash of a previous query. Identical queries can reuse work files, and an efficient way to determine if two queries are identical is by comparing the hashes of each query. In some embodiments, a query plan may be reduced to a string of text, and the resulting string of text may be hashed. The resulting hash may be used as the fingerprint for the query. This allows for a quick comparison since hashes are relatively small in size. Thus, the hash may be stored in a table (or hash table) and associated with the query's temporary work files. The table and the associated temporary work files may be stored in a non-volatile storage device, or the table may be stored in memory while the temporary work files are stored in a non-volatile device [col. 4, ll. 33-48]. Also see [col. 5, ll. 11-26]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Waas' system would have allowed Bandarchuk, Park, Wang, Chang and Arnold to facilitates further comprising generating a current hash value based on the current tree-shape string, generating one or more hash values based on the one or more tree-shape strings and storing the one or more hash values in a hash map. The motivation to combine is apparent in the Bandarchuk, Park, Wang, Chang and Arnold’s reference, because there a need for an improved method, article of manufacture, and apparatus for processing information.
Regarding claims 9 and 19, the combination of Bandarchuk, Park, Wang, Chang, Arnold, and Waas discloses, wherein comparing the current query plan object with a stored query plan object comprises comparing the current hash value with a hash value stored in the hash map that corresponds to the stored query plan object (Waas: In some embodiments, it may be preferable to hash the query plan. The hash may be generated through a variety of methods, and may be compared to a hash of a previous query. Identical queries can reuse work files, and an efficient way to determine if two queries are identical is by comparing the hashes of each query. In some embodiments, a query plan may be reduced to a string of text, and the resulting string of text may be hashed. The resulting hash may be used as the fingerprint for the query. This allows for a quick comparison since hashes are relatively small in size. Thus, the hash may be stored in a table (or hash table) and associated with the query's temporary work files. The table and the associated temporary work files may be stored in a non-volatile storage device, or the table may be stored in memory while the temporary work files are stored in a non-volatile device [col. 4, ll. 33-48]. Also see [col. 5, ll. 11-26]).
Claim(s) 10 are rejected under 35 U.S.C. 103 as being unpatentable over Bandarchuk in view of Park in view of Wang in view of Chang in view of Bhatia; Sandeep et al. (US 11093496 B1) [Bhatia].
Regarding claim 10, the combination of Bandarchuk, Park, Wang, and Chang teach all the limitations of claim 1.
However, neither one of Bandarchuk, Park, Wang, or Chang explicitly facilitates, wherein the plan repository has a predefined size, wherein responsive to detecting that a count of the stored query plan objects reaches the predefined size, removing one of the stored query plan objects from the plan repository before storing the current query plan object in the plan repository.
Bhatia discloses, wherein the plan repository has a predefined size, wherein responsive to detecting that a count of the stored query plan objects reaches the predefined size (The query execution platform may issue storage requests to perform the query according to the plan (which may be received and interpreted by storage engine 234 in order to access the data the underlying storage) [col. 7, ll. 13-17]. In some embodiments, the size of storage (e.g., memory) or number of slots (e.g., an allocated portion of memory for a plan) may be determined based on performance metrics for queries [col. 10, ll. 56-59]), removing one of the stored query plan objects from the plan repository before storing the current query plan object in the plan repository (wherein at least one of the one or more management operations is an operation to remove at least one of the cached database query plans [claim 11]).
It would have been obvious to one ordinary skilled in the art at the time of the present invention to combine the teachings of the cited references because Bhatia's system would have allowed Bandarchuk, Park, Wang, and Chang to facilitates wherein the plan repository has a predefined size, wherein responsive to detecting that a count of the stored query plan objects reaches the predefined size, removing one of the stored query plan objects from the plan repository before storing the current query plan object in the plan repository. The motivation to combine is apparent in the Bandarchuk, Park, Wang, and Chang’s reference, because there a need to reduce the burden of query planning without sacrificing the benefits of query planning are thus highly desirable.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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4/29/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
