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 .
DETAILED ACTION
This action is issued in response to Application filed September 10, 2025.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 11, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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) 1, 2, 4-8, 13-15, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haynes (U.S. Patent Application No. 2023/0350892) in view of Vogelsgesang (U.S. Patent No. 10,901,990).
Regarding Claim 8, Haynes discloses a computerized method for semantic equivalence detection, the method comprising:
receiving a workload comprising a subexpression pair that includes a first database query subexpression and a second database query subexpression (par [0030], [0045], Haynes – each query may comprise a plurality of subexpressions (or subqueries), wherein a database server application may generate a query plan for each subexpression and store the generated query plans for queries (and its subqueries) in a query workload repository… workload analyzer may be configured to then determine subexpression pairs from among the subset that have a particular relationship. For example, workload analyzer may be configured to determine subexpression pairs that have at least one of a semantically equivalent relationship);
generating, using an equivalence model filter, a confidence score indicating a likelihood
that the first and second database query subexpressions are semantically equivalent (par [0071-0072], Haynes - machine learning model comprises one or more probabilities (e.g., each having a value between 0.0 and 1.0) that a given subexpression of the query has a semantically equivalent relationship with another query subexpression for which a materialized view has already been generated… If any of the probabilities exceeds the threshold, classification verifier may verify classification utilizing a deterministic semantically equivalent determination algorithm. For instance, suppose the threshold is set to 0.90 and the classification verifier outputs a semantic equivalent probability of 0.95 indicating that a particular subexpression is very likely semantically equivalent to another query subexpression);
based on the confidence score being below a confidence threshold (par [0072], Haynes - classification verifier may compare each of the probabilities of classification to a threshold): applying an automated verifier to determine with perfect precision whether the first
database query subexpression and the second database query subexpressions are
semantically equivalent (par [0072-0073], Haynes - classification verifier may compare each of the probabilities of classification to a threshold. If any of the probabilities exceeds the threshold, classification verifier may verify classification utilizing a deterministic semantically equivalent determination algorithm. For instance, suppose the threshold is set to 0.90 and the classification verifier outputs a semantic equivalent probability of 0.95 indicating that a particular subexpression is very likely semantically equivalent to another query subexpression… In the event that classification determines that classification is correct, classification verifier provides a notification to query rewriter),
labeling the subexpression pair as equivalent or non-equivalent based on a result of the
automated verifier (par [0046], Haynes - after determining query subexpression pairs having a semantically equivalent relationship, workflow analyzer may be configured to label such pairs accordingly. For instance, a query subexpression pair having a semantically equivalent relationship may be labeled as having a semantically equivalent relationship, and a query subexpression pair having neither a containment relationship nor a semantically equivalent may be labeled as having no such relationships), and
using the labeled subexpression pair, fine-tuning the equivalence model filter (par [0048-0050], Haynes).
While Haynes teaches a determination that the first database query subexpression and the second database query subexpression are semantically equivalent (see par [0046], Haynes); and Haynes teaches at least a portion of the first materialized view is returned as a query result to the query. For example, with reference to FIG. 5, database server application retrieves at least a portion of materialized view (that was generated for the other query expression) from view repository and returns it as a query result to query (see par [0074], [0082]). However, Haynes is not as explicitly detailed with respect to performing the first database query subexpression and excluding performance of the second database query subexpression.
On the other hand, Vogelsgesang discloses performing the first database query subexpression and excluding performance of the second database query subexpression (col.11, lines 19-25, Vogelsgesang – common subexpression elimination allows the database engine to de-duplicate some expression subtrees… col.3, lines 4-10, Vogelsgesang - enhancing real-time data exploration through Common Subexpression Elimination (CSE) and Common Subexpression Hoisting (CSH) techniques. In CSE, an expression appears two or more times in a query, and the database engine optimizes the execution by calculating a value for the expression only once (per tuple). The result of the calculation is saved and then reused as needed).
Haynes and Vogelsgesang are analogous art because they are from the same field of endeavor of subexpressions in database 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 Vogelsgesang’s teachings into Haynes’ system. A skilled artisan would have been motivated to combine in order to improve query execution performance by optimizing certain types of complex queries.
Regarding Claim 13, the combination of Haynes in view of Vogelsgesang, disclose the computerized method of claim 8, further comprising:
monitoring confidence scores generated by the equivalence model filter for a plurality of pairs of database query subexpressions in a workload; computing an aggregate confidence level based on the monitored confidence scores (par [0071-0072], Haynes); and
in response to determining that the aggregate confidence level falls below a predefined threshold, fine-tuning the equivalence model filter using a newly generated set of labeled subexpression pairs obtained from the workload (par [0048-0052], [0039], Haynes).
Regarding Claim 14, the combination of Haynes in view of Vogelsgesang, disclose the computerized method of claim 13, wherein the newly generated set of labeled subexpression pairs comprises an approximately class-balanced sample including: pseudo-equivalent subexpression pairs identified by applying a schema filter and a vector matching filter to the workload and randomly sampled non-equivalent subexpression pairs (par [0070], Haynes).
Claim 15 contains similar subject matter as claim 8 above; and is rejected under the same rationale.
Claims 19 and 20 contain similar subject matter as claims 13 and 14 above; and are rejected under the same rationale.
Claim 1 contains similar subject matter as claim 8; and is rejected under the same rationale with the addition of an equivalence optimizer engine (par [0023], [0034], Haynes – materialized views based on semantically equivalent relationships with other expressions. In addition, machine learning-based techniques that require a relatively small seed training set are utilized to identify such relationships which requires no changes to a query optimizer implemented by a database application… a materialized view engine is configured to identify query subexpressions that have a semantically equivalent relationship with each other) and a trained machine learning model (par [0023], [0051], Haynes – training a machine learning algorithm).
Regarding Claim 2, the combination of Haynes in view of Vogelsgesang, disclose the computer system of claim 1, further comprising using an upstream filter to determine that the subexpression pair is likely equivalent (par [0071-0072], Haynes - machine learning model comprises one or more probabilities (e.g., each having a value between 0.0 and 1.0) that a given subexpression of the query has a semantically equivalent relationship with another query subexpression for which a materialized view has already been generated… If any of the probabilities exceeds the threshold, classification verifier may verify classification utilizing a deterministic semantically equivalent determination algorithm. For instance, suppose the threshold is set to 0.90 and the classification verifier outputs a semantic equivalent probability of 0.95 indicating that a particular subexpression is very likely semantically equivalent to another query subexpression), and performing the generating of the confidence score based on the determination that the subexpression pair is likely equivalent, wherein the upstream filter comprises a vector matching filter (par [0070], Haynes - Machine learning model is configured to output an indication (e.g., a classification) as to whether query comprises a semantically equivalent relationship with another query subexpression for which a materialized view has already been generated, a containment relationship with another query subexpression for which a materialized view has already been generated, or has no such relationships with other query subexpressions for which materialized views have been generated. For instance, machine learning model may compare each of feature vector(s) to feature vectors of subexpressions for which materialized views have been generated to determine whether any of such relationships exist).
Regarding Claim 4, the combination of Haynes in view of Vogelsgesang, disclose the computer system of claim 1, wherein general equivalence optimizer engine executes in conjunction with a query optimizer and configured to detect semantic equivalences missed by the query optimizer (par [0022-0023], Haynes).
Regarding Claim 5, the combination of Haynes in view of Vogelsgesang, disclose the computer system of claim 1, wherein the equivalence model filter comprises a multi-layer perceptron trained to classify subexpression pairs as semantically equivalent or non-equivalent (par [0046], Haynes - after determining query subexpression pairs having a semantically equivalent relationship, workflow analyzer may be configured to label such pairs accordingly. For instance, a query subexpression pair having a semantically equivalent relationship may be labeled as having a semantically equivalent relationship, and a query subexpression pair having neither a containment relationship nor a semantically equivalent may be labeled as having no such relationships).
Regarding Claim 6, the combination of Haynes in view of Vogelsgesang, disclose the computer system of claim 1, wherein the confidence threshold is a predefined value used to determine when fallback to the automated verifier is triggered (par [0072-0073], Haynes - classification verifier may compare each of the probabilities of classification to a threshold. If any of the probabilities exceeds the threshold, classification verifier may verify classification utilizing a deterministic semantically equivalent determination algorithm. For instance, suppose the threshold is set to 0.90 and the classification verifier outputs a semantic equivalent probability of 0.95 indicating that a particular subexpression is very likely semantically equivalent to another query subexpression… In the event that classification determines that classification is correct, classification verifier provides a notification to query rewriter).
Claim 7 contains similar subject matter as claim 13 above; and is rejected under the same rationale.
Claim(s) 3, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haynes in view of Vogelsgesang, further in view of “Automated Verification of Query Equivalence Using Satisfiability Modulo Theories”; By: Qi Zhou, Published 2019; referred to hereinafter as ‘Zhou’.
Regarding Claim 12, the combination of Haynes in view of Vogelsgesang, discloses the computerized method of claim 8, wherein the labeled subexpression pair is featurized prior to training (par [0048-0050], Haynes - featurizer may be configured to extract one or more features from labeled subexpressions and remaining subexpressions. Featurizer may also be configured to generate a feature vector for each of labeled subexpressions and subexpressions based on the features described above that are extracted therefor).
While Haynes and Vogelsgesang teaches the above referenced features. However, the combination of references are not as detailed with respect to replacing references to database schema with symbolic correspondences, whereby the equivalence model filter is schema-agnostic.
On the other hand, Zhou discloses replacing references to database schema with symbolic correspondences, whereby the equivalence model filter is schema-agnostic (Pg.1277, 1st paragraph, Zhou - We derive the symbolic representation (SR)2 of SQL queries and use satisfiability modulo theories (SMT) to determine their equivalence. This approach can model the semantics of widely-used SQLfeatures, such as complex query predicates, arithmetic operations, and three-valued logic… pg.1280, Section 3.3, Zhou).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Zhou’s teachings into the Haynes’ and Vogelsgesang system. A skilled artisan would have been motivated to combine in order to effectively transform a wide range of SQL queries into first order logic formulae and then use satisfiability modulo theories to efficiently verify their equivalence; thus making a better case for determining query equivalence based on symbolic representation.
Claim 3 contains similar subject matter as claim 12 above; and is rejected under the same rationale.
Claim 18 contains similar subject matter as claim 12 above; and is rejected under the same rationale.
Allowable Subject Matter
Claims 9-11 and 16-17 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter: the equivalence model filter is arranged in a semi-supervised feedback loop (SSFL) pipeline including an upstream filter and the equivalence model filter, the equivalence model filter receiving the subexpression pair only when the upstream filter determines that the subexpression pair are likely to be equivalent.
Points of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHELCIE L DAYE whose telephone number is (571) 272-3891. The examiner can normally be reached on Monday-Friday 7:30-4:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Chelcie Daye
Patent Examiner
Technology Center 2100
September 3, 2026
/CHELCIE L DAYE/Primary Examiner, Art Unit 2161
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