System and method for generating training sets for neural networks

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process being to develop a trained computer neural network capable of providing responses to queries to a table-based database and comprising:

• receiving a plurality of query pairs, wherein each of the plurality of query pairs includes a query for the table-based database and a real result previously determined for the query that is a response to the query;
• determining, for each query in the received plurality of query pairs, a variable and a first subset of potential values for the variable;
• determining at least one second subset of potential values for the variable of each query in the plurality of received query pairs, wherein each of the at least one second subset of potential values for the variable is different from the first subset of potential values for the variable;
• and
• generating a training set based on the determined variable for each query, the at least one first subset of potential values for each variable, the at least one second subset of potential values for each variable, and the previously determined real result for each query, wherein generating the training set includes generating at least one training query, wherein each training query is generated for the variable of one of the queries in the received plurality of query pairs based on the at least one second subset of potential values determined for the variable;
• and
• causing a recurrent neural network to become a trained recurrent neural network through training of the recurrent neural network using the generated training set.

A system for developing a trained computer neural network capable of providing responses to queries to a table-based database, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• receive a plurality of query pairs, wherein each of the plurality of query pairs includes a query for the table-based database and a real result previously determined for the query that is a response to the query;
• determine, for each query in the received plurality of query pairs, a variable and a first subset of potential values for the variable;
• determine at least one second subset of potential values for the variable of each query in the plurality of received query pairs, wherein each of the at least one second subset of potential values for the variable is different from the first subset of potential values for the variable;
• and
• generate a training set based on the determined variable for each query, the at least one first subset of potential values for each variable, the at least one second subset of potential values for each variable, and the previously determined real result for each query, wherein generating the training set includes generating at least one training query, wherein each training query is generated for the variable of one of the queries in the received plurality of query pairs based on the at least one second subset of potential values determined for the variable;
• and
• cause a recurrent neural network to become a trained recurrent neural network through training of the recurrent neural network using the generated training set.