DETAILED ACTION
This action is in response to the RCE filed on 05/26/2026.
The specification discloses that the contextual part of the parameterized contextual grammar represents the state of a multi-turn query (paragraph 82 of the originally filed specification). As recited, the parameterized contextual grammar is interpreted broadly to refer to any context information. To further prosecution, the contextual grammar could be further defined in the claim language according to the specification.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/26/2026 has been entered.
Response to Amendment
Applicant’s amendment filed on 05/26/2026 has been entered. Claims 1, 10, 18 and 22 have been amended. No claims 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. 103 as being unpatentable over Jiao et al. (US 2021/0049158) (“Jiao”) in view of Arthur et al. (US 2023/0186161) (“Arthur”), and further 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 the following: generating the one of more formal queries comprises sampling actual values from the database repository to drive a query generation process and generating the one or more parameterized queries using the sampled actual values and the parameterized grammar which is a parameterized contextual grammar.
However, Arthur discloses a method for synthesizing training data to facilitate training a natural language to logical form model (Abstract), comprising the following: sampling actual values from the database repository to drive a query generation process (“In some instances, in order to generate the lexicalized training data 4114, at template analysis stage 4208, the constrained sampling stage 4212, and lexicalization stage 4220, the templates 4110 are analyzed to identify one or more constraints in each template, a database 4216 is analyzed to identify its components, database components are sampled based on the identified one or more constraints in each template, and the non-terminal symbols in each delexicalized utterance and its corresponding delexicalized SQL query are replaced with the sampled components” … “In some instances, at constrained sampling stage 4212, components are sampled from the database 4216 based on a database analysis 4218 of the database 4216 and the analyzed templates 4210. In some instances, the database 4216 is one or more relational databases with each database having components (e.g., tables, columns, and values)” … “0150] In some instances, components 4214 are sampled from the database 4216 based on the analysis of the non-terminal symbols in the analyzed templates 4210 at the template analysis stage 4208. In some instances, prior to sampling components, each database in the one or more databases 4216 can be analyzed at database analysis stage 4218 to identify the components (e.g., tables and their names, columns and their names, and the values in column) of the respective database. In some instances, database analysis stage 4218 is performed for a database based on schema information for the database. In some instances, in order to sample components for a respective analyzed template of the templates 4210, a database of the one or more databases 4216 can be randomly selected and its components can be sampled and used to lexicalize the respective analyzed template” … “ and a value is sampled from the sampled column for each VALUE non-terminal symbol in the respective analyzed template 4210” … “In some instances, values are sampled by determining whether the VALUE non-terminal symbol is associated with an operator (e.g., <, <=, >, >=, LIKE, etc.) and sampling values from the corresponding column is performed based on the associated operator. For less than and greater than type operators, values are randomly sampled between the minimum values and maximum of the corresponding column. For the LIKE and other operator, values are randomly sampled and the characters at the start, end, and/or a combination of both the start and end of the sampled values are replaced with a “%” character. In some instances, determining whether to replace the starting characters, the ending characters, and/or the both the starting and ending characters with a “%” character can be determined randomly and the number of characters to replace can be random..”, [0148 – 0151]); and generating parameterized queries using the sampled actual values and a parameterized grammar (Only the non-terminal symbol for a parameterized/delexicalized logical form query is replaced with the sampled value, [0142 – 0145] [0152]).
Additionally, 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 Jiao’s invention in the same way that Arthur’s invention has been improved to achieve the following, predictable results for the purpose of synthesizing training data to facilitate training a natural language to logical form model (Arthur, [0002 – 0004]): further generating the one of more formal queries by sampling actual values (Instead of using random values, such as 500 for an account balance value constraint, to generate the one or more parameterized queries as disclosed by Jiao, [0038 - 0040) from a database , e.g. the database repository, to drive a query generation process; and further generating the one or more parameterized queries using the sampled actual values.
Additionally, 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 and Arthur 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).
Furthermore, 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, Arthur 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 the following: generating the one of more formal queries comprises sampling actual values from the database repository to drive a query generation process and generating the one or more parameterized queries using the sampled actual values and the parameterized grammar which is a parameterized contextual grammar.
However, Arthur discloses a method for synthesizing training data to facilitate training a natural language to logical form model (Abstract), comprising the following: sampling actual values from the database repository to drive a query generation process (“In some instances, in order to generate the lexicalized training data 4114, at template analysis stage 4208, the constrained sampling stage 4212, and lexicalization stage 4220, the templates 4110 are analyzed to identify one or more constraints in each template, a database 4216 is analyzed to identify its components, database components are sampled based on the identified one or more constraints in each template, and the non-terminal symbols in each delexicalized utterance and its corresponding delexicalized SQL query are replaced with the sampled components” … “In some instances, at constrained sampling stage 4212, components are sampled from the database 4216 based on a database analysis 4218 of the database 4216 and the analyzed templates 4210. In some instances, the database 4216 is one or more relational databases with each database having components (e.g., tables, columns, and values)” … “0150] In some instances, components 4214 are sampled from the database 4216 based on the analysis of the non-terminal symbols in the analyzed templates 4210 at the template analysis stage 4208. In some instances, prior to sampling components, each database in the one or more databases 4216 can be analyzed at database analysis stage 4218 to identify the components (e.g., tables and their names, columns and their names, and the values in column) of the respective database. In some instances, database analysis stage 4218 is performed for a database based on schema information for the database. In some instances, in order to sample components for a respective analyzed template of the templates 4210, a database of the one or more databases 4216 can be randomly selected and its components can be sampled and used to lexicalize the respective analyzed template” … “ and a value is sampled from the sampled column for each VALUE non-terminal symbol in the respective analyzed template 4210” … “In some instances, values are sampled by determining whether the VALUE non-terminal symbol is associated with an operator (e.g., <, <=, >, >=, LIKE, etc.) and sampling values from the corresponding column is performed based on the associated operator. For less than and greater than type operators, values are randomly sampled between the minimum values and maximum of the corresponding column. For the LIKE and other operator, values are randomly sampled and the characters at the start, end, and/or a combination of both the start and end of the sampled values are replaced with a “%” character. In some instances, determining whether to replace the starting characters, the ending characters, and/or the both the starting and ending characters with a “%” character can be determined randomly and the number of characters to replace can be random..”, [0148 – 0151]); and generating parameterized queries using the sampled actual values and a parameterized grammar (Only the non-terminal symbol for a parameterized/delexicalized logical form query is replaced with the sampled value, [0142 – 0145] [0152]).
Additionally, 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 Jiao’s invention in the same way that Arthur’s invention has been improved to achieve the following, predictable results for the purpose of synthesizing training data to facilitate training a natural language to logical form model (Arthur, [0002 – 0004]): further generating the one of more formal queries by sampling actual values (Instead of using random values, such as 500 for an account balance value constraint, to generate the one or more parameterized queries as disclosed by Jiao, [0038 – 0040]) from a database , e.g. the database repository, to drive a query generation process; and further generating the one or more parameterized queries using the sampled actual values.
Additionally, 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 and Arthur 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).
Furthermore, 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, Arthur 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 the following: generating the one of more formal queries comprises sampling actual values from the database repository to drive a query generation process and generating the one or more parameterized queries using the sampled actual values and the parameterized grammar which is a parameterized contextual grammar.
However, Arthur discloses a method for synthesizing training data to facilitate training a natural language to logical form model (Abstract), comprising the following: sampling actual values from the database repository to drive a query generation process (“In some instances, in order to generate the lexicalized training data 4114, at template analysis stage 4208, the constrained sampling stage 4212, and lexicalization stage 4220, the templates 4110 are analyzed to identify one or more constraints in each template, a database 4216 is analyzed to identify its components, database components are sampled based on the identified one or more constraints in each template, and the non-terminal symbols in each delexicalized utterance and its corresponding delexicalized SQL query are replaced with the sampled components” … “In some instances, at constrained sampling stage 4212, components are sampled from the database 4216 based on a database analysis 4218 of the database 4216 and the analyzed templates 4210. In some instances, the database 4216 is one or more relational databases with each database having components (e.g., tables, columns, and values)” … “0150] In some instances, components 4214 are sampled from the database 4216 based on the analysis of the non-terminal symbols in the analyzed templates 4210 at the template analysis stage 4208. In some instances, prior to sampling components, each database in the one or more databases 4216 can be analyzed at database analysis stage 4218 to identify the components (e.g., tables and their names, columns and their names, and the values in column) of the respective database. In some instances, database analysis stage 4218 is performed for a database based on schema information for the database. In some instances, in order to sample components for a respective analyzed template of the templates 4210, a database of the one or more databases 4216 can be randomly selected and its components can be sampled and used to lexicalize the respective analyzed template” … “ and a value is sampled from the sampled column for each VALUE non-terminal symbol in the respective analyzed template 4210” … “In some instances, values are sampled by determining whether the VALUE non-terminal symbol is associated with an operator (e.g., <, <=, >, >=, LIKE, etc.) and sampling values from the corresponding column is performed based on the associated operator. For less than and greater than type operators, values are randomly sampled between the minimum values and maximum of the corresponding column. For the LIKE and other operator, values are randomly sampled and the characters at the start, end, and/or a combination of both the start and end of the sampled values are replaced with a “%” character. In some instances, determining whether to replace the starting characters, the ending characters, and/or the both the starting and ending characters with a “%” character can be determined randomly and the number of characters to replace can be random..”, [0148 – 0151]); and generating parameterized queries using the sampled actual values and a parameterized grammar (Only the non-terminal symbol for a parameterized/delexicalized logical form query is replaced with the sampled value, [0142 – 0145] [0152]).
Additionally, 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 Jiao’s invention in the same way that Arthur’s invention has been improved to achieve the following, predictable results for the purpose of synthesizing training data to facilitate training a natural language to logical form model (Arthur, [0002 – 0004]): further generating the one of more formal queries by sampling actual values (Instead of using random values, such as 500 for an account balance value constraint, to generate the one or more parameterized queries as disclosed by Jiao, [0038 - 0040) from a database , e.g. the database repository, to drive a query generation process; and further generating the one or more parameterized queries using the sampled actual values.
Additionally, 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 and Arthur 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).
Furthermore, 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, Arthur 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 Arthur et al. (US 2023/0186161) (“Arthur”), and further 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, Arthur, 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 J Jiao, Arthur, 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 Arthur et al. (US 2023/0186161) (“Arthur”), and further 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, Arthur, 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, Arthur, 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 Arthur et al. (US 2023/0186161) (“Arthur”), and further 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, Arthur, 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, Arthur, 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 Arthur et al. (US 2023/0186161) (“Arthur”), and further 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, Arthur, 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, Arthur, 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 claims 1 – 5, 7 – 14 and 16 – 22 have been considered but are moot in view of the new ground(s) of rejection. The new ground(s) of rejection were necessitated by amendment based on a change in scope of the independent claims. Previously, independent claims 1, 10, 18 and 22 recited, “wherein generating the one or more formal queries comprises generating one or more parameterized queries using a parameterized contextual grammar.” Now, independent claims 1, 10, 18 and 22 recite, “wherein generating the one or more formal queries comprises sampling actual values from the database repository to drive a query generation process and generating one or more parameterized queries using the sampled actual values and a parameterized contextual grammar.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Weir et al. (“DBPal: A Fully Pluggable NL2SQL Training Pipeline”) (discloses the inventive concept of synthesizing training data to train a model which translates natural language to SQL).
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/SONIA L GAY/Primary Examiner, Art Unit 2657
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