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
1. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/13/2026 has been entered.
Claims amended: 1, 13 and 19
Claims canceled: 2 and 14
Claims newly added: none
Claims pending: 1, 3-13 and 15-24
Response to Arguments
2. Applicant’s arguments with respect to claim(s) 1 and 13 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.
Applicant argues “Neither individual or in combination do Pathak and Grosset yield a plurality output database queries each “configured to perform the first database operation as recited by amended claim 1.
Please see new reference regarding to amended claim.
3. Claim(s) 1, 3-4, 6-9, 13, 15-16 and 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over PATHAK et al. (Pub. No. US 2025/0139136 A1) in view of Achanta (Patent No. US 8,065,264 B1) and further in view of Grosset et al. (Pub. No. 2008/0201293 A1).
As to claim 1, PATHAK discloses a computer-implemented method for performing a query search to rewrite queries, the computer-implemented method comprising:
receiving, by one or more processors, an input database query to perform a first database operation, wherein the first database operation would occur within a first period of time (original query) (paragraph 0022);
analyzing, by the one or more processors, the input database query to determine a set of one or more query segments of the input database query representative of the first database operation (parsing) (paragraph 0066);
Pathak does not explicitly disclose generating, by the one or more processors, a plurality of candidate output database queries configured to perform a first database operation based at least on the one or more query segments; and the input database query includes one or more operation indicators or one or more variables associated with the first database and determining, by the one or more processors, at least one confirmed output database query of the plurality of candidate output database queries, wherein the at least one confirmed output database query, when implemented by a computing device, causes the computing device to perform a second database operation equivalent to the first database operation within a second period of time shorter than or equal to the first period of time.
Achanta discloses generating, by the one or more processors, a plurality of candidate output database queries configured to perform a first database operation based at least on the one or more query segments (generate a consolidate query based on the plurality of queries, the consolidate query comprising instructions that when executed segment the general population in the same plurality of segments as the plurality of queries using one read of the virtual attribute data records…) (claim 1).
However, Grosset discloses the input database query includes one or more operation indicators or one or more variables associated with the first database operation (the user 12A, for example, may interact with enterprise applications 25,26 to formulate a report and defined a query specification.. ) (paragraph 0040) (query specification includes one or more operation indicators or one or more variables associated with the first database operation) and determining, by the one or more processors, at least one confirmed output database query of the plurality of candidate output database queries, wherein the at least one confirmed output database query, when implemented by a computing device, causes the computing device to perform a second database operation equivalent to the first database operation within a second period of time shorter than or equal to the first period of time (the data access service may rewrite a complex DMX query as a much simpler query that returns the same data but that will take less processing time and/or consume less memory during execution) (paragraph 0035).
Therefore, it would have been obvious to one ordinary skill int the art before the effective filing date of the instant application to modify teaching of Pathak to include generating, by the one or more processors, a plurality of candidate output database queries configured to perform a first database operation based at least on the one or more query segments; and the input database query includes one or more operation indicators or one or more variables associated with the first database and determining, by the one or more processors, at least one confirmed output database query of the plurality of candidate output database queries, wherein the at least one confirmed output database query, when implemented by a computing device, causes the computing device to perform a second database operation equivalent to the first database operation within a second period of time shorter than or equal to the first period of time as disclosed by Achanta and Grosset in order to provide query rewritable for better execution.
As to claim 3, Pathak discloses the computer-implemented. method of claim 1, wherein determining the at least one confirmed output database query includes: analyzing, by the one or more processors, at least some of the plurality of candidate output database queries to calculate a score for each analyzed candidate output database query (ranking) (paragraph 0043); generating, by the one or more processors, an ordered list of the plurality of candidate output database queries based on the score for each analyzed candidate output database query (ranking) (paragraph 0043); and analyzing, by the one or more processors and in accordance with the ordering, one or more of the plurality of candidate output database queries to determine the at least one confirmed output database query (ranking) (paragraph 0043).
As to claim 4, Pathak discloses the computer-implemented method of claim 3, wherein the score for each analyzed candidate output database query is based at least on a number of shared semantic properties between the input database query and the corresponding candidate output database query (ranking the importance of the candidate instance-specific part) (Paragraph 0043).
As to claim 6, Pathak discloses the computer-implemented. method of claim 3, wherein the analyzing the one or more of the plurality of candidate output database queries occurs within a user-defined time period (the computing system 102 processes the component queries in parallel using plurality processor resources of the language model, as claimed within period of time) (paragraph 0022).
As to claim 7, Pathak discloses the computer-implemented method of claim 1, further comprising: testing, by the one or more processors, each of the at least one confirmed output database query using one or more test cases to determine whether the at least one confirmed output database query and the input database query return identical outputs for the one or more test cases (such as the BERT model) to transform a combination of the original query 106 and a candidate component response to an output score that reflect an extended which the candidate component-query response answers the original query 106. In either case, the post-processing component 502 applies rules that specifies that all component-query responses having confidence scores above a prescribed threshold levels should be include in the final responses 152, or just the most relevant N component-query responses (in which N is determined parameter selected for use in a particular environment)) (paragraph 0067).
As to claim 8, Pathak discloses the computer-implemented method of claim 7, wherein testing a first set of the at least one confirmed output database query occurs in parallel with determining a second set of the at least one confirmed output database query (parallel) (paragraph 0031).
As to claim 9, Pathak discloses the computer-implemented method of claim 8, further comprising: determining, by the one or more processors, a third set of the at least one confirmed output database query using results of testing the first set of the at least one confirmed output database query (such as the BERT model) to transform a combination of the original query 106 and a candidate component response to an output score that reflect an extended which the candidate component-query response answers the original query 106. In either case, the post-processing component 502 applies rules that specifies that all component query responses having confidence scores above a prescribed threshold levels should be
include in the final responses 152, or just the most relevant N component-query responses (in which N is determined parameter selected for use in a particular environment)) (paragraph 0067).
Claim 13 is rejected under the same reason as to claim 1, Pathak discloses a system (computing system) (paragraph 0121) for performing a query search to rewrite queries, the system comprising: one or more processors (one or more processor) (paragraph 0118) and memory (memory unit) (paragraph 0121) associated with the one or more processors (one or more processor) (paragraph 0118) and storing instructions (instructions) (paragraph 0119) that, when executed, cause the one or more processor (one or more processor) (paragraph 0118) to.
Claim 15 is rejected under the same reason as to claim 1.
Claim 16 is rejected under the same reason as to claim 4.
Claim 18 is rejected under the same reason as to claim 6.
Claim 19 is rejected under the same reason as to claim 7.
Claim 20 is rejected under the same reason as to claim 8.
Claim 21 is rejected under the same reason as to claim 9.
4. Claim(s) 5, 10-12, 17 and 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pathak et al. (Pub. No. US 2025/0139136 A1) in view of Achanta (Patent No. US 8,065,264 B1) and further in view of Grosset et al. (Pub. No. 2008/0201293A1) and further in view of Dettinger et al. (Pub. No. US 2009/0006352 A1).
As to claim 5, Pathak, Achanta and Grosset disclose the computer-implemented method of claim 3 excepting for wherein the ordered list is in order of computational cost in analyzing the at least some of the plurality of candidates. However, Dettinger discloses wherein the ordered list is in order of computational cost in analyzing the at least some of the plurality of candidates (multiple rewrites may be considered and applied to the current version of the query to generate a candidate rewritten query. An execution cost estimate of the candidate rewritten query, which reflects multiple rewrites, is then compared to the current version of the query) (paragraph 0105). This suggests wherein the ordered list is in order of computational cost in analyzing the at least some of the plurality of candidates. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Pathak to include wherein the ordered list is in order of computational cost in analyzing the at least some of the plurality of candidates as disclosed by Dettinger to compare the query cost.
Claim 17 is rejected under the same reason as to claim 5.
As to claim 10, Pathak discloses he computer-implemented. method of claim 7 excepting for mutating, by the one or more processors and using a predefined set of mutation rules, the input database query to generate at least one mutant database query; and generating, by the one or more processors, the one or more test cases including at least one test input for each mutant database query of the at least one mutant database query such that a mutant output for the corresponding mutant database query differs from an output of the input database query. However, Dettinger discloses mutating, by the one or more processors and using a predefined set of mutation rules, the input database query to generate at least one mutant database query; and generating, by the one or more processors, the one or more test cases including at least one test input for each mutant database query of the at least one mutant database query such that a mutant output for the corresponding mutant database query differs from an output of the input database query (non-permitted field may be removed form
conditions in the case that doing so does not without materially affect the query results (i.e., if the modified query returns the same result as the original query), in the case that the results of the modified query are determined to be useful despite being different from the result of the original query, and the like) (Paragraph 0052). The modified query including modified rule which applied to the query and the output is different from the original query. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Pathak, Achanta and Grosset to include mutating, by the one or more processors and using a predefined set of mutation rules, the input database query to generate at least one mutant database query; and generating, by the one or more processors, the one or more test cases including at least one test input for each mutant database query of the at least one mutant database query such that a mutant output for the corresponding mutant database query differs from an output of the input database query as disclosed by Dettinger in order to test and learn from the modified query.
Claim 22 is rejected under the same reason as to claim 10.
As to claim 11, Pathak, Achanta and Grosset disclose the computer-implemented method of claim 1 excepting for verifying, by the one or more processors and using an equivalence checking algorithm, that each of the at least one confirmed output database query and the input database query return identical outputs. However, Dettinger discloses verifying, by the one or more processors and using an equivalence checking algorithm, that each of the at least one confirmed output database query and the input database query return identical outputs (non-permitted field may be removed form query conditions in the case that doing so does not without materially affect the query results (i.e., if the modified query returns the same result as the original query), in the case that the results of the modified query are determined to be useful despite being different from the result of the original query, and the like) (Paragraph 0052). This suggests checking algorithm that each of the at least one confirmed output database query and the input database query return identical outputs. Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify Pathak, Achanta and Grosset to include checking algorithm that each of the at least one confirmed output database query and the input database query return identical outputs as disclosed by Dettinger in order to verify the candidate query perform as intended.
Claim 23 is rejected under the same reason as to claim 11.
As to claims 12, Pathak disclose the computer-implemented method of claim 11, wherein the verifying includes: encoding, by the one or more processors, semantics associated with one or more operation indicators in the input database query for each of the at least one confirmed output database query and the input database query; and wherein the verifying is based at least on the encoded semantics for each of the at least one confirmed output database query and the input database query (an encoder-decoder receives encode output information produced...) (paragraph 0103).
Claim 24 is rejected under the same reason as to claim 12.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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