DETAILED ACTION
This action is in response to the amendment filed on 18/391,339 on 11/12/2025.
Response to Amendment
Applicant’s amendment filed on 11/12/2025 has been entered. Claims 1, 10, 18 and 22 have been amended. Claims 6 and 15 have been canceled. No claims have been added. Claims 1 – 5, 7 – 14 and 16 – 22 are still pending in this application, with claims 1, 10, 18 and 22 being independent.
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, 3, 9, 10, 12, 18, 20 and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Price et al. (US 11, 436,239) (“Price”) and further in view of SRU (“CQL: Contextual Query Language”).
For claims 1 and 22, Jiao discloses a method for generating training examples (Abstract), the method comprising: generating, by a machine learning system (query converter comprising a machine learning model, Fig.2, 213 and 219; [0015] [0016]), one or more formal queries based on data contained in a database repository ([0017 – 0021] [0029] [0034] [0035- 0040]), wherein generating the one or more formal queries comprises generating one or more parameterized SQL queries (e.g. SELECT*FROM CUSTOMERS WHERE …, wherein CUSTOMERS is a parameter) using a parameterized grammar (SQL grammar uses parameters to identify column names, etc., 2; [0021 – 0024] [0035] [0036] [0048]); generating, by the machine learning system, a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries ([0017 – 0021] [0029] [0034] [0035- 0040]) by applying a general grammar for a language of each formal query (grammar including SQL language, [0035 – 0040]); and training, by the machine learning system, a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries generated by the machine learning system ([0041]).
Yet, Jiao fails to teach, wherein generating the one of more formal queries comprises that the parameterized grammar is a parameterized contextual grammar.
However, Price discloses a system and method for processing queries (Abstract), comprising the following: training a prediction model to accept a natural language query and transform it into a query language format, wherein the query language format is SQL or Contextual Query Language (CQL) (column 7 lines 41 -column 8 line 36).
Furthermore, SRU discloses a specification for CQL, wherein CQL is a parameterized grammar (CQL defines a set of index parameters., Indexes).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the Jiao’s invention in the same way that Price’s invention has been improved to achieve the predictable results of generating one or more SQL or contextual (CQL) queries for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify the combined teachings of Jiao and Price with SRU’s teachings so that the one or more formal queries comprises generating one or more parameterized CQL queries using CQL which is a parameterized grammar for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
For claims 3, 12 and 20, Jiao further discloses receiving, prior to the generation of the one or more formal queries, a database schema describing structure of the data in the database repository (Jiao, [0017 – 0020]).
For claim 9, Jiao further discloses processing, by a trained neural network, an input natural language query to predict a formal query for the input natural language query (Jiao, [0043 – 0048]).
For claim 10, Jiao discloses a computing system (Abstract) comprising: processing circuitry ([0057]; claim 1) in communication with storage media (memory, [0057] [0058]; claim 1), the processing circuitry configured to execute a machine learning system ([0057]; claim 1) configured to: generate one or more formal queries based on data contained in a database repository ([0017 – 0021] [0029] [0034] [0035- 0040]), wherein generating the one or more formal queries comprises generating one or more parameterized SQL queries (e.g. SELECT*FROM CUSTOMERS WHERE …, wherein CUSTOMERS is a parameter) using a parameterized grammar (SQL grammar uses parameters to identify column names, etc., 2; [0021 – 0024] [0035] [0036] [0048]); generate a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries by applying a general grammar for a language (grammar including SQL language, [0035 – 0040]) of each formal query ([0017 – 0021] [0029] [0034] [0035- 0040]); and train a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries ([0041]).
Yet, Jiao fails to teach, wherein generating the one of more formal queries comprises that the parameterized grammar is a parameterized contextual grammar.
However, Price discloses a system and method for processing queries (Abstract), comprising the following: training a prediction model to accept a natural language query and transform it into a query language format, wherein the query language format is SQL or Contextual Query Language (CQL) (column 7 lines 41 -column 8 line 36).
Furthermore, SRU discloses a specification for CQL, wherein CQL is a parameterized grammar (CQL defines a set of index parameters., Indexes).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the Jiao’s invention in the same way that Price’s invention has been improved to achieve the predictable results of generating one or more SQL or contextual (CQL) queries for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify the combined teachings of Jiao and Price with SRU’s teachings so that the one or more formal queries comprises generating one or more parameterized CQL queries using CQL which is a parameterized grammar for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
For claim 18, Jiao discloses a non-transitory computer-readable storage media having instructions encoded thereon ([0057] [0058]; claim 15), the instructions configured to cause processing circuitry ([0057] [0058]; claim 150 to: generate one or more formal queries based on data contained in a database repository ([0017 – 0021] [0029] [0034] [0035- 0040]), wherein generating the one or more formal queries comprises generating one or more parameterized SQL queries (e.g. SELECT*FROM CUSTOMERS WHERE …, wherein CUSTOMERS is a parameter) using a parameterized grammar (SQL grammar uses parameters to identify column names, etc., 2; [0021 – 0024] [0035] [0036] [0048]); generate a natural language query for each formal query of the one or more formal queries to generate pairs of formal queries and corresponding natural language queries by applying a general grammar for a language (grammar including SQL language, [0035 – 0040]) of each formal query ([0017 – 0021] [0029] [0034] [0035- 0040]); and train a neural network configured to translate natural language queries into formal queries using the pairs of the formal queries and corresponding natural language queries ([0041]).
Yet, Jiao fails to teach, wherein generating the one of more formal queries comprises that the parameterized grammar is a parameterized contextual grammar.
However, Price discloses a system and method for processing queries (Abstract), comprising the following: training a prediction model to accept a natural language query and transform it into a query language format, wherein the query language format is SQL or Contextual Query Language (CQL) (column 7 lines 41 -column 8 line 36).
Furthermore, SRU discloses a specification for CQL, wherein CQL is a parameterized grammar (CQL defines a set of index parameters., Indexes).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the Jiao’s invention in the same way that Price’s invention has been improved to achieve the predictable results of generating one or more SQL or contextual (CQL) queries for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
Additionally, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to modify the combined teachings of Jiao and Price with SRU’s teachings so that the one or more formal queries comprises generating one or more parameterized CQL queries using CQL which is a parameterized grammar for the purpose of improving the system by training the system to translate a natural language query to a variety of query language formats (Price, column 7 lines 40 – 60).
Claim(s) 2, 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Price et al. (US 11, 436,239) (“Price”), and further in view of SRU (“CQL: Contextual Query Language”) and further in view of Chow et al. (US 2016/0224594) (“Chow”).
For claims 2, 11 and 19, the combination of Jiao, Price and SRU fails to teach, wherein generating the one or more formal queries further comprises selecting a subset of the data contained in the database repository by randomly sampling values contained in the database repository and filtering the selected subset to generate one or more representative formal queries.
However, Chow discloses a system and method for accessing data in a database (Abstract), comprising the following: selecting a subset of the data contained in a database repository by randomly sampling values contained in the database repository ([0037]); and filtering the selected subset to enable access to the data ([0037]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Jiao, Price and SRU in the same way that Chow’s invention has been improved to achieve the following, predictable results for the purpose of reducing the time and resources required to train the model which translates natural language into a formal query: generating the one or more formal queries further comprises selecting a subset of the data contained in the database repository by randomly sampling values contained in the database repository; and filtering the selected subset to generate one or more representative formal queries (enabling access to data).
Claim(s) 4, 13 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Price et al. (US 11, 436,239) (“Price”), and further in view of SRU (“CQL: Contextual Query Language”) and in view of Khoussainova et al. (US 2013/0086067)(“Khossainova”).
For claims 4, 13 and 21, the combination of Jiao, Price and SRU fails to teach, wherein the database repository comprises a scientific relational database, and wherein the data contained in the database repository comprises scientific data.
However, Khossainova discloses a system and method for generating structured queries (Abstract), wherein structured (SQL) queries are used to analyze sets of scientific data which are stored in a scientific relational database ([0003 – 0007]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Jiao, Price and SRU in the same way that Khossainova’s invention has been improved to achieve the following, predictable results for the purpose of enabling scientists to make important scientific advances (Khossainova, [0005]): the database repository further comprises a scientific relational database, and wherein the data contained in the database repository comprises scientific data.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Price et al. (US 11, 436,239) (“Price”), and further in view of SRU (“CQL: Contextual Query Language”) and further in view of Fedorocko et al. (US 2020/0201866) (“Fedorocko”).
For claim claims 5 and 14, the combination of Jiao, Price and SRU fails to teach, wherein generating one or more formal queries based on data contained in the database repository comprises generating the one or more formal queries based on one or more sample questions provided by one or more domain experts.
However, Fedorocko discloses a system and method for processing queries (Abstract), wherein one or more sample questions used to generate formal queries of a database repository are received from one or more domain experts ([0030 – 0040] [0042]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Jiao, Price and SRU in the same way that Fedorocko’s invention has been improved to achieve the following, predictable results for the purpose of enabling a user to adequately mine datasets using questions using a model which has been trained by data received from domain experts (Fedorocko, [0001]): generating one or more formal queries based on data contained in the database repository further comprises generating the one or more formal queries based on one or more sample questions provided by one or more domain experts.
Claim(s) 7, 8,16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Price et al. (US 11, 436,239) (“Price”), and further in view of SRU (“CQL: Contextual Query Language”) and further in view of Song (US 2024/0078230).
For claims 7 and 16, the combination of Jiao, Price and SRU fails to teach, wherein generating the one or more formal queries comprises generating context for multi-turn SQL queries.
However, Song discloses a system and method for augmenting multi-turn text-to SQL datasets (Abstract), wherein generating a formal query comprises generating context (previous user utterances) for multi-turn SQL queries ([0067 – 0069] [0071 – 0075]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Jiao, Price and SRU in the same way that Song’s invention has been improved to achieve the following, predictable results for the purpose of providing context-dependent text-to-SQL translation to enable multi-turn user utterances to be turned into executable SQL queries: generating the one or more formal queries comprises generating context for multi-turn SQL queries.
For claims 8 and 17, Song further discloses, wherein the multi-turn query comprises at least a first SQL query and a second SQL query, and wherein the second SQL query is generated based on information returned by the first SQL query (Song, Fig.4; [0067 – 0069]).
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SONIA L GAY/Primary Examiner, Art Unit 2657