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 .
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 23 January 2025 and 9 July 2025 are n compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7, 9-15, 17, 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Siddiqui et al (US 20200349161 A1) provided by the applicant, also of record in parent application 18055502 now U.S. Patent 12235840.
. Regarding claim 1, Siddiqui substantially discloses teaches or suggests a computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising:
receiving a database query (see at least 0005);
generating based on the database query, a set of query plans each configured to execute the database query (see at least Fig.2);
for each respective query plan in the set of query plans, determining, using a trained model (see at least 0005), a corresponding query plan score (see at least Fig.2);
selecting, based on the corresponding query plan score determined for each respective query plan, a respective one of the query plans from the set of query plans (see at least Fig.2); and
executing the database query using the respective one of the query plans (see at least 0005).
Regarding claim 2, Siddiqui further teaches the method of claim 1, wherein the database query comprises a Structured Query Language (SQL) query (see at least 0031).
Regarding claim 3, Siddiqui further teaches the method of claim 1, wherein generating the set of query plans comprises generating the set of query plans using a database query planner (see at least Figs 1-2).
Regarding claim 4, Siddiqui further teaches the method of claim 1, wherein selecting the respective one of the query plans is based on an amount of memory available or an amount of cache available (see at least 0049).
Regarding claim 5, Siddiqui further teaches the method of claim 1, wherein selecting the respective one of the query plans is based on a predicted latency of each query plan of the set of query plans (see at least 0024).
Regarding claim 7, Siddiqui further teaches the method of claim 1, wherein selecting the respective one of the query plans is based on a predicted resource usage of each query plan of the set of query plans (see at least 0025).
Regarding claim 9, Siddiqui further teaches the method of claim 1, wherein the operations further comprise, prior to generating the set of query plans, selecting a query template from a set of query templates (see at least 0135).
Regarding claim 10, Siddiqui further teaches the method of claim 9, wherein generating the set of query plans comprises generating the set of query plans using the selected query template (see at least 0134).
Claims 11-15, 17, 19-20 essentially recite limitations similar to claims 1-5, 7, 9-10 in form of system thus are rejected for the same reasons discussed in claims 1-5, 7, 9-10 above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Siddiqui et al (US 20200349161 A1), further in view of He, Henry: "Storage: How 'tail latency' impacts customer-facing applications", 13 August 2019 (2019-08-13), XP093021814, Retrieved from the Internet: URL:https://www.computer weekly.com/opinion/Storage-How-tail-latency-impacts-customer-facing- applications [retrieved on 2023-02-07], 6 pages, provided by the applicant.
Regarding claim 6, Siddiqui does not specifically show the method of claim 5, wherein the predicted latency comprises a tail latency. However tail latency as shown by He slows down responses to I/O requests thus should be taken into consideration when selecting a query plan. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of Sidiqui in order to obtain the most effective query plans for customer-facing applications as taught by He.
Claim 16 essentially recites the limitations of claim 6 in form of a system thus is rejected for the same reasons discussed in claim 6 above.
Claim(s) 8, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Siddiqui et al (US 20200349161 A1), further in view of Singh R, Sharma S, Singh S, Singh B. Reducing Run-time Execution in Query Optimization. International Journal of Computer Applications. 2014 Jan 1;96(6) of record in parent application 18055502 now U.S. Patent 12235840.
Regarding claim 8, Siddiqui does not specifically show the method of claim 1, wherein: each query plan of the set of query plans comprises a hint string; and selecting the respective one of the query plans is based on the hint string of each query plan of the set of query plans.
However it is customary in the art for query plans to contain query hints for performance optimization of queries as shown by Singh S et al (see at least 2.2 Plan Guide).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of Siddiqui in order to optimize performance of queries based on contexts as shown by Singh.
Claim 18 essentially recites the limitations of claim 8 in form of a system thus is rejected for the same reasons discussed in claim 8 above.
Double Patenting
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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of parent U.S. Patent No. 12235840. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are merely broader than claims 1-18 of the U.S. Patent thus anticipated by the claims of the U.S. Patent as shown in the method claims 1-10 mapping below.
Claims of instant application
Claims of U.S. Pat 12235840
c1. A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising:
receiving a database query;
generating, based on the database query, a set of query plans each configured to execute the database query;
for each respective query plan in the set of query plans, determining, using a trained model, a corresponding query plan score;
selecting, based on the corresponding query plan score determined for each respective query plan, a respective one of the query plans from the set of query plans; and
executing the database query using the respective one of the query plans.
c9. The method of claim 1, wherein the operations further comprise, prior to generating the set of query plans, selecting a query template from a set of query templates.
c10. The method of claim 9, wherein generating the set of query plans comprises generating
the set of query plans using the selected query template.
…this limitation is copied from c1 above for claim mapping: generating, based on the database query, a set of query plans each configured to execute the database query; for each respective query plan in the set of query plans, determining, using a trained model, a corresponding query plan score;
selecting, based on the corresponding query plan score determined for each respective query plan, a respective one of the query plans from the set of query plans; and
executing the database query using the respective one of the query plans.
c1. A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising: receiving a database query requesting a database to conditionally return one or more data blocks stored at the database, the database stored on memory hardware in communication with the data processing hardware and the database query comprising a plurality of respective parameters characterizing the database query;
NOTE see the mapping of the corresponding limitations at the end of this claim
selecting, based on the received database query, a query template from a set of query templates, the selected query template comprising a plurality of operations for performing the database query;
generating, based on the selected query template, a set of query plans, each query plan in the set of query plans configured to execute the database query using the plurality of operations of the selected query template in a corresponding different order; training a model using historical database queries;
generating,
using the trained model, a query plan score for each query plan in the set of query plans based on the plurality of respective parameters; selecting,
using the query plan score generated for each query plan in the set of query plans, a query plan from the set of query plans; and
executing the database query using the selected query plan.
c2. The method of claim 1, wherein the database query comprises a Structured Query Language (SQL) query.
c2. The method of claim 1, wherein the database query comprises a Structured Query Language (SQL) query.
c3. The method of claim 1, wherein generating the set of query plans comprises generating the set of query plans using a database query planner.
c3. The method of claim 1, wherein generating the set of query plans comprises generating the set of query plans using a database query planner
c4. The method of claim 1, wherein selecting the respective one of the query plans is based on an amount of memory available or an amount of cache available.
c4. The method of claim 1, wherein selecting the query plan is based on an amount of memory available or an amount of cache available.
c5. The method of claim 1, wherein selecting the respective one of the query plans is based on a predicted latency of each query plan of the set of query plans.
c5. The method of claim 1, wherein selecting
the query plan is based on a predicted latency of each query plan of the set of query plans.
c6. The method of claim 5, wherein the predicted latency comprises a tail latency.
c6. The method of claim 5, wherein the predicted latency comprises a tail latency.
c7. The method of claim 1, wherein selecting the respective one of the query plans is based on a predicted resource usage of each query plan of the set of query plans.
c7. The method of claim 1, wherein selecting
the query plan is based on a predicted resource usage of each query plan of the set of query plans.
c8. The method of claim 1, wherein: each query plan of the set of query plans comprises a hint string; and selecting the respective one of the query plans is based on the hint string of each query plan of the set of query plans.
c8. The method of claim 1, wherein: each query plan of the set of query plans comprises a hint string; and selecting the query plan
is based on the hint string of each query plan of the set of query plans.
Claim 1 of the instant application is a mere broader version of claim 1 of the U.S. Patent thus anticipated by the U.S. Patent. It is obvious to remove limitations to broaden a claim. Claim 1 of the instant application as shown in the claim mapping above removes limitations present in claim 1 of the U.S. Patent for example:
“requesting a database to conditionally return one or more data blocks stored at the database, the database stored on memory hardware in communication with the data processing hardware and the database query comprising a plurality of respective parameters characterizing the database query”
Note also the language and the order of the recited limitations of claim 1 are slightly different than the language and the order recited in claim 1 of the U.S. Patent, for example “selecting the respective one of the query plans” vs. “selecting the query plan”. However the meanings are similar.
Note also the limitations of claims 9, 10 that depend from claim 1 of the instant application also map to limitations of claim 1 of the U.S. Patent as shown in the mapping above.
Claim 11 of the instant application similarly maps to system claim 10 of the U.S. Patent.
Claims 2-8, 12-18 of the instant application are mere duplicates of claims 2-8, 12-18 of the U.S. Patent.
Claims 9-10, 18-20 of the instant application are mere obvious variations of claims 1, 10 of the U.S. Patent.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nawrocke et al (US 20200356873 A1) teach a unified access layer (UAL) and scalable query engine receive queries from various interfaces and executes the queries with respect to non-heterogeneous data management and analytic computing platforms that are sources of record for data they store. Query performance is monitored and used to generate a query performance model. The query performance model may be used to generate alternatives for queries of users or groups of users or to generate policies for achieving a target performance. Performance may be improved by monitoring queries and retrieving catalog data for databases referenced and generating a recommendation model according to them. Duplicative or overlapping sources may be identified based on the monitoring and transformations to improve accuracy and security may be suggested. A recommendation model may be generated based on analysis of queries received through the UAL. Transformations may be performed according to the recommendation model in order to improve performance.
Freedman et al (US 20200142990 A1) teach a database system includes a query optimizer which applies transformations to a logical representation of an input query in a declarative query language to generate expressions for a query execution plan. The query optimizer stores information, for each rule application, indicating a transformation applied and bindings for the transformation, the bindings indicating expressions input to the transformation. When a new query execution plan is to be generated for the input query, the query optimizer uses this stored information, for expressions in an original query execution plan, to guide the query optimizer to produce a current query execution plan while avoiding transformations not used to generate expressions in the original query execution plan.
Zait (US 20080010240 A1) teaches under automated alternate plan analysis, a query optimizer generates candidate execution plans. The candidate execution plans are selected as alternate execution plans for the query and execution. Output describing characteristics of each alternate execution plan and/or its execution is generated and/or compared. From this information, it may be determined, for example, whether results returned by any of the alternate execution plans are the same and whether the least cost execution plan is actually the most efficiently executed.
Bossman et al (US 7139749 B2) teach a method, system, and program for tuning a database query. Provided are a base access plan to implement the database query and statistics including information on a layout of the database against which the query will be executed. The statistics are processed to determine performance problems with the base access plan as part of a first analysis of the base access plan and the determined performance problems are processed as part of a second analysis to provide an analysis of the determined performance problems and processing cost of the base access plan. Recommendations are generated to tune the base access plan to improve performance.
Bueche et al (US 11899714 B1) teach voice data from a current conversation between a user and a voice-controlled user device can be used to determine a search constraint for searching a database. Other search constraints can be determined based at least in part on the current conversation, a previous conversation, and/or a previous action. Properties can be associated with the search constraints. Once the search constraints have been determined, a plurality of search query plans is determined and a first search query plan is executed to query the database.
Narayanaswamy et al (US 11636124 B1) teach a database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model. in some embodiments. A linear classifier may be applied to score the features of the query plan according to a weighted sum of the features (e.g., by applying coefficients to the features determined from training the classifier according to logistic regression). In some embodiments, other features in addition to the query plan may be considered, such as the source of the query (e.g., what user submitted the query), time of day, what table(s) are identified in the query, among others.
Wang Hao (WO 2020198925 A1) teaches using machine learned models to predict properties for database query planning. One of the methods includes receiving a query to be executed over one or more relations of a database. A query planner generates a candidate query plan comprising a plurality of operators to be executed to generate query results for the query. A predicted property of the portion of the query plan when executed on the database is computed for each of one or more portions of the query plan, including providing a respective representation of each portion of the query plan as input to a trained model configured to generate predicted properties of tuples generated by portions of query plans when executed on the database. A score for the candidate query plan is computed using the predicted property generated by the trained model.
Bei et al (US 20200285642 A1) teach current physical resources utilization of a computing system as a whole is monitored. The number of queries concurrently being executed against a database by a database management system (DBMS) running on a computing system is monitored. A query plan for a received query to be executed against the database is generated. The query plan includes operators; the generation of the query plan includes generation of query-based statistics for the received query on a per-operator basis without consideration of the queries concurrently being executed. An estimated execution time of the received query is dynamically predicted using a machine-learning model based on the query-based statistics generated for the received query on the per-operator basis, the current physical resources utilization of the computing system, and the number of queries concurrently being executed. The received query is executed against the database based on the dynamically predicted estimated execution time for the received query.
Gu et al (US 10140336 B1) teaches a method for computing weight of a particular query plan, where the weight represents comparison of actual cost of a highest-ranked plan to actual cost for the particular plan. Distance between a set of incorrectly ranked plans is weighted according to first weight for first plan and second weight for second plan. The weighted distance for the set of plans is added to rank correlation score for a query optimizer. The set of query optimizers is ranked according to the respective rank correlation score computed for each of the set of query optimizers. Performance of a database system is enhanced by selecting a highest-ranked query optimizer according to the ranking of the set of query optimizers as a preferred query optimizer for generating query plans for the database system by a testing system.
Gu et al (US 9262477 B1) teach determining first rank correlation score that reflects accuracy of first query optimizer in estimating costs associated with executing query plans based on first ranking of query plans and second ranking of the query plans. Second rank correlation score associated with second query optimizer that is different from the first query optimizer, is determined. One of the first query optimizer or the second query optimizer is selected as a more accurate query optimizer based on comparison of the first rank correlation score and the second rank correlation score.
Liu et al (US 20150149434 A1) teach a method involves enumerating plans for a current query using a processor and building a dominance graph for the current query. The regret value and a score for the plan are determined based on the regret value and cost. The query plans are selected in an online fashion for query processing in big data analytics platforms, where intermediate results are materialized and reused later.
Asaad et al (US 20150046430 A1) teach a method involves receiving a database query from an application (202) by a processing device. Analysis is performed on the query. A set of available accelerators (206) is identified. Cost information for templates available on the set of available accelerators is retrieved. A query execution plan is determined based on the information and the analysis on the query. Query operations to one of the set of accelerators are offloaded based on the query execution plan, where information defines throughput and latency of the templates available on the set of available accelerators.
Paul D, Cao J, Li F, Srikumar V. Database workload characterization with query plan encoders. arXiv preprint arXiv:2105.12287. 2021 May 26.
Abstract- Smart databases are adopting artificial intelligence (AI) technologies to achieve instance optimality, and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database runs on different workloads, demands specific resources, and settings to achieve optimal performance. It prompts the necessity to understand workloads running in the system along with their features comprehensively, which we dub as work load characterization. To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans. Our pretrained encoders captures the structural and the computational performance of queries independently. We show that our pre trained encoders are adaptable to workloads that expedites the transfer learning process. We performed independent assessments of structural encoder and performance encoders with multiple downstream tasks. For the overall evaluation of our query plan encoders, we architect two downstream tasks (i) query latency prediction and (ii) query classification. These tasks show the importance of feature-based workload characterization. We also performed extensive experiments on individual encoders to verify the effectiveness of representation learning, and domain adaptability.
Karri N. AI-Powered Query Optimization. International Journal of Artificial Intelligence, Data Science, and Machine Learning. 2021 Mar 30;2(1):63-71.
Abstract - The framework for AI-powered query optimization that augments, rather than replaces, a classical cost-based optimizer. The design incorporates three components of learned: (i) a learned cardinality estimator that learns the correlation between joins and predicates; (ii) a neural residual cost corrector that learns cost error at the operator level; and (iii) a reinforcement-learning (RL) planner that focuses on high-leverage transformations of the plan under the constraints of latency and resource cost. The system works in two steps, first, offline training of the system based on past workloads and schema-sensitive synthetic queries, and lastly online adaptation that is done cautiously by using execution feedback (observed row counts, operator run times, spill events). Uncertainty gating is used to enforce safety, time-out sandboxed trials, and immediate fallback to baseline heuristics. We detail an integration path that keeps optimizer modularity intact (Volcano/Cascades memorization, rule rewrites) while exposing pluggable inference hooks. Compared to a robust cost-based baseline, TPC-H/DS and JOB evaluation indicate a consistent decrease in p95/p99 latency, plan stability and decreased re-optimization, as well as a decrease in the CPU and memory consumption during peak loads. Failure modes out-of-distribution predicates, opaque UDFs and drift and demonstrate how risk can be mitigated using drift detection and canaried fine-tuning. The findings show that AI help produces empirical, trustworthy returns in combination with strong guardrails and observability.
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/UYEN T LE/Primary Examiner, Art Unit 2156 2 April 2026