DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions. This action is in response to Amendment/RCE filed on May 21, 2025 and entered with an RCE.
Claims 1, 3, 8, 10, 15 and 17 have been amended. Currently, claims 1-20 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 21, 2025 has been entered.
Response to Arguments
Applicant’s remarks and arguments presented on 05/21/25 have been fully considered but they are moot in view of the new ground of rejection presented in this office action.
Objection
Claims 1-15 are objected because of the following reasons:
Claims 1 and 15 recited the limitations of “causing updating of the query configuration”. Claim 8 recited the limitations of “provide the query events”. The terms “causing” or “provide” does not necessarily update all the times and may not provide any update or query events. As such, the step will not have a patentable weight. Examiner suggest to change the term as ‘configure to’.
Dependent claims are objected for incorporating the same deficiencies of their respective base claims.
Claim Rejections – 35 USC § 101
35 USC 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture and composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, e.g., claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The judicial exception is not integrated into a practical application.
Step 1. The method of claims 1-7, system of claims 8-14 and memory device of claims 15-20 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. Prong one of the 2019 PEG:
1. In accordance with Step 2A, prong one, the limitations are directed to additional elements include database, processor and memory device.
2. The limitations are recited in claims 1, 8 and 15 are a database cluster, receiving a query workload, determining a first query configuration for the query workload from one of a plurality of query configuration candidates generated by a baseline query configuration model, wherein the first query configuration defines at least one of a number of executor instances or a size of an executor instance; allocating the one or more compute resources of the database cluster based on at least one of the number of executor instances or the size of the executor instance; executing, by the database cluster, the query workload using the allocated compute resources, a backend configuration, query event listene etc., is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer components. That is, other than reciting database, processor and memory device nothing in the claim element precludes the step from practically being performed in the mind. The steps can be done my nominally, insignificantly or can consider as a data gathering performance or managing human activities. Further, the claim does not define how it is determining. The query configuration can be evaluating configuration, which is a mental process. Allocating resources can be allocating computational thread. Receiving query workload is merely an input step. Executor instance and query configuration can be processing thread and executing thread, which are mental process. Furthermore, query configuration can interpret as a user have many tables, configurating and organizing those tables or merely processing thread. Further, the whole claim limitations can interpret as processing thread and outputting those thread. Thus, the limitations are directed to abstract mental process and can be manually performed by human. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, then it falls with mental process grouping of abstract ideas.
With respect to Step 2A, Prong two of the 2019 PEG: the judicial exception is not integrated into a practical application. In particular, the claim only recites database, processor and memory device. The database, processor and memory device steps are recited at a high-level of generality or insignificant extra solution activity such that it amounts no more than mere instructions to apply the exception using a generic computer component, Accordingly, these additional element (database, processor and memory device) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B.
Claims 1, 8 and 15 recited additional limitations, such that collecting query features corresponding to each query of the query workload; and proving the query features to enable causing updating of the query configuration model based on the collected query features, query model updater. These additional limitations merely describing executing query and providing and an output step. As such, the additional elements are broadly applied to the abstract idea at a high level of generality, they are directed to extra solution activity or they operate in a well-understood, routine, and conventional manner (MPEP § 2106.05(f); MPEP § 2106.05(d)(II)). Receiving or transmitting data over a network, e.g., using the internet to gather data (e.g., Symantec...; TLI Communications LLC v. AV Auto. LLC...; OIP Techs., Inc., v. Amazon.com, Inc...; buySAFE, Inc. v. Google, Inc...; Storing and retrieving information in memory (e.g. Versata Dev. Group, Inc. v. SAP Am., 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 amount to nothing more than generic computer function merely used to implement an abstract idea, such as an idea that could be done by human thinking. Using generic computing components (e.g., database, processor and memory device) does not amount to significantly more than the abstract and is not enough to transform an abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible. Accordingly, the claims are directed to an abstract idea.
Dependent claim 2 recited predetermined query workload and predetermined matrix configuration, which are merely measuring. A measurement is just a piece of data or evaluation, there is not actual action recited in the claim. Candidate selection can be done mentally. Dependent claim 3 recited centroid algorithm, which could a be a server or network environment. Neighborhood of a current centroid configuration could be a structure, which is conventional. Determining step is not describing how it is performing centroid learning algorithm. The limitations are mental process. Dependent claim 4 recited time, compute and measurement are mental process. Dependent claim 5 recited caching a workload configuration, generating and storing which are routine and conventional. Dependent claim 6 recited generating and caching work-load-level configuration, execution time for the query workload is minimized are not providing enough detail of how it is generating. The limitations minimized and measuring is just a data, which are mental process. Dependent claim 7 recited the limitations of selecting for subsequent execution of the query workload, allocating compute resources, updating the selected cached workload-level configuration can be processing thread and outputting those thread, which are mental process. The claim recited limitations are abstract idea and does not integrate into practical application. Dependent claims 9-14 correspond in scope to claims 2-7 and are similarly rejected. Dependent claims 16-20 correspond in scope to claims 2-7 and are similarly rejected. The claim recited limitations do not amount to significantly more than the abstract idea as indicated.
Claim Rejections- 35 USC § 103
7. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
8. 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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
9. Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Belghiti (US 2016/0188696 A1) in view of Jindal et al. (US 2019/0303475 A1), hereinafter Jindal.
As for claim 1, Belghiti teaches a computer-implemented method of configuring a database cluster comprising one or more compute resources configured to perform database queries, comprising: receiving a query workload (see [0003], e.g., the time taken to perform a query is called workload, or simply execution time, or query runtime, [0020], obtain clustering and runtime queries);
determining a first query configuration for the query workload from a plurality of query configuration candidates generated by a baseline query configuration model; wherein the first query configuration defines at least one of a number of executor instances or…..of an executor instance. allocating the one or more compute resources of the database cluster based on at least one of a number of executor instances or.…..of an executor instance (see [0003], optimizations relative to queries on the database, performing workload prediction, [0005], evaluate runtime of a query to quantify how much resource to put on its computation, [0029], [0036], e.g., execute query plan, or even time to execute a sub-query in case the target query inputted to the method is a sub-query, [0038]),
executing, by the database cluster the query workload using the allocated compute resources (see [0003], [0004], [0004], select execution plan and large number of candidates, [0013], e.g., clustering reference queries in a database for prediction of the runtime of a target query in the database based on similarity of the target query with the reference queries; [0005], [0036], [0059]);
….query….corresponding to each query of the query workload; and causing updating of the query configuration model based on the…query…..(see [0003], e.g., perform workload in query execution runtime, [0037], e.g., the target query inputted even be added to the reference queries once it has been performed and its real runtime is known, the method is continuously iterated and the clustering updated).
Belghiti teaches the claimed invention, but does not explicitly teach the limitations of “a size of an executor instance or the size of the executor instance”; “collecting query features”. In the same field of endeavor, Jindal teaches the limitations of “a size of an executor instance or the size of the executor instance”; “collecting query features” (see [0051], e.g., data sizes and correlations between different pieces of the data, [0101], e.g., data collected and improve performance of the queries).
Belghiti and Jindal both references teach features that are directed to analogous art and they are from the same field of endeavor, such as query performance, workload associated with queries, resource computation performed in computing storages or to perform queries.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jindal’s teaching to Belghiti’s system
to evaluate an impact of a feedback loop on the query plans. Thus, evaluate the improvements in performance and resource consumption. Evaluation of query plan helps to measure cost of running the queries in a job service and resource consumption (see Jindal, [0101], [0165]).
As for claim 8, Belghiti teaches a database cluster configuration system including a database cluster comprising one or more compute resources configured to perform database queries, the system comprising: a backend configuration engine including an event store, a model store and a model updater (see [0005], e.g., quantify how much resource to put on its computation such that the query will be performed before a given time limit, [0019], e.g.,
clustering from the stored indices comprises iteratively partitioning the numerical values, starting from the last indexed numerical value in the stored indices);
a client configuration engine, including a query processor and a query event listener component, the query processor coupled to the model store and configured to receive one or more query configuration models therefrom, the client configuration engine further coupled to the database cluster and wherein the client configuration engine is configured to: receive a query workload (see [0037], e.g., query associated to one of the clusters according to a predetermined query distance criterion evaluated between the target query and all queries, or one or more representative queries . The runtime of the target query then be predicted based on such information, [0038], e.g., numerical relate to a database by representing runtimes of queries (the reference queries) on/in this database);
determine, through the query processor, a first query configuration for the query workload from a plurality of query configuration candidates generated by a baseline query configuration model received from the model store, wherein the first query configuration defines at least one of a number of executor instances or……of an executor instance (see [0004], e.g., query runtime prediction is query optimization, which relies on these predictions to select a particular execution plan from an often very large number of candidates, scheduling is based on different criteria, [0005], [0036]);
allocate the one or more compute resources of the database cluster based on at least one of the number of executor instances or…..of the executor instance; and execute, by the database cluster the query workload using the allocated compute resources; wherein the query event listener is configured to….query events corresponding to each executed query of the query workload, and to provide the query events to the event store; and wherein the model updater is configured to receive the query…from the events store and to update the query configuration model based on the query…. (see [0003], [0004], [0019], [0037], e.g., the target query inputted even be added to the reference queries once it has been performed and its real runtime is known, the method is continuously iterated and the clustering updated, [0005], [0029], [0036], [0038]).
Belghiti teaches the claimed invention, but does not explicitly teach the limitations of “a size of an executor instance or the size of the executor instance; collect query events; receive the query features; the query configuration model based on the query features”. In the same field of endeavor, Jindal teaches the limitations of ““a size of an executor instance or the size of the executor instance; collect query events; receive the query features; the query configuration model based on the query features” (see [0051], e.g., data sizes and correlations between different pieces of the data, abstract, [0101], e.g., data collected and improve performance of the queries).
Belghiti and Jindal both references teach features that are directed to analogous art and they are from the same field of endeavor, such as query performance, workload associated with queries, resource computation performed in computing storages or to perform queries.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jindal’s teaching to Belghiti’s system
to evaluate an impact of a feedback loop on the query plans. Thus, evaluate the improvements in performance and resource consumption. Evaluation of query plan helps to measure cost of running the queries in a job service and resource consumption (see Jindal, [0101], [0165]).
As for claim 15,
The limitations therein have substantially the same scope as claim 1 because claim 15 is a computer-readable memory device claim for implementing those steps of claim 1. Therefore, claim 15 is rejected for at least the same reasons as claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jindal’s teaching to Belghiti’s system
to evaluate an impact of a feedback loop on the query plans. Thus, evaluate the improvements in performance and resource consumption. Evaluation of query plan helps to measure cost of running the queries in a job service and resource consumption (see Jindal, [0101], [0165]).
As to claim 2, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
wherein the baseline query configuration model is configured to select the plurality of query configuration candidates based on execution measurements of a plurality of predetermined query workloads comprising a plurality of queries, executed across a predetermined matrix of configurations (see Belghiti, [0013]).
As to claim 3, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
wherein the determining the first query configuration for the query workload from the plurality of query configuration candidates is based on a centroid learning algorithm wherein the plurality of query configuration candidates comprises candidates in a neighborhood of a current centroid configuration (see Belghiti, [0004], [0008]).
As to claim 4, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
wherein the execution measurements comprise at least one of: a query execution time, a memory requirement, or a compute resource requirement (see Belghiti, [0003], [0013]).
As to claim 5, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
further comprising: generating and caching a workload-level configuration based on the collected query features (see Belghiti, [0008]; Also, see Jindal, ([0051]).
As to claim 6, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
wherein said generating a workload-level configuration is further based on the collected query features of each query of the query workload such that execution time for the query workload is minimized (see Belghiti, [0004]; Also, see Jindal, ([0051]).
As to claim 7, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
further comprising: selecting, for subsequent executions of the query workload, the cached workload- level configuration; allocating the one or more compute resources for the database cluster based on the selected cached workload-level configuration; updating the selected cached workload-level configuration based on the collected query features (see Belghiti, [0003], [0005], [0037]; Also, see Jindal, (abstract, [0051], [0101]).
As to claim 12, this claim is rejected based on the same reason as above to reject the claim above and are similarly rejected including the following:
Belghiti and Jindal teaches:
wherein the backend configuration engine further comprises: a workload-level configuration generator and a workload-level configuration store, the workload-level configuration generator configured to generate and cache a workload-level configuration based on the collected query features of each query of the query workload, and store said workload-level configuration in the workload- level configuration store (see Belghiti, [0003], [0008]; Also, see Jindal, ([0051], [0101]).
Claims 9-11, 13 and 14 correspond in scope to claims 2-7 and are similarly rejected.
Claims 16-20 correspond in scope to claims 2-7 and are similarly rejected.
Prior Arts
10. US 2005/0192921 A1 teaches execution code path of database events enables real-time control of database performance. The database events that are selected to be enabled for monitoring is configured to branch to a rule engine during execution that evaluates the conditions in monitoring rules. The rule engine evaluates any rule that is triggered by a given database event against a condition in the rule ([0027]).
US 2016/0034530 A1 teaches SQL-driven distributed operating system. The SQL-driven distributed operating system turn off the engine of a stolen vehicle. The SQL-driven distributed operating system turn off electricity supply to flooded areas to minimize the risk of electrocution. The SQL-driven distributed operating system facilitate the development of applications to which the SQL-driven distributed operating system serves as the underlying platform ([0155]).
WO2023/070417 A1 teaches a set of query execution evet records extracted from on one or more database query execution logs. Groups of related query execution event records within the set of query event records are identified. The activity data structure encodes query execution dependencies for the activity ([0003]).
Also see, US 11500830, US 10437843, US 20220270319, US 20230306127, US 1132190, US 20080222093, US 7958113, US 20080195577, US 11182360, US 20160034530, US 20230315702, US 20200349161, US 20190303475, US 11275735, US 11934398, US 11138266, US 9710493, US 20160034530, US 20050192921, these references also read the claim recited limitation. These references are state of the art at the time of the claimed invention.
Conclusion
11. The examiner suggests, in response to this Office action, support being 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 (see 37 C.F.R. § 1.75(d)(1), 37 C.F.R. § 1.83(f)).
12. The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action (see MPEP § 7.96). 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).
13. Any inquiry concerning this communication or earlier communication from the examiner should be directed to Daniel A Kuddus whose telephone number is (571) 270-1722. The examiner can normally be reached on Monday to Thursday 8.00 a.m.-5.30 p.m. The examiner can also be reached on alternate Fridays from 8.00 a.m. to 4.30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Boris Gorney can be reached on (571) 270-5626. The fax phone number for the organization where this application or processing 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 the 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.
/DANIEL A KUDDUS/ Primary Examiner, Art Unit 2154
08/06/25