System and method for increasing accuracy of approximating query results using neural networks

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:

• sending a received query to a first neural network and a second neural network;
• receiving from the first neural network a first predicted result to the received query;
• determining a confidence level for the first predicted result, wherein the received query is sent to the second neural network only when the confidence level is below a first predefined threshold;
• providing the first predicted result as a first output to a device having initiated the received query;
• receiving from the second neural network a second predicted result to the received query;
• and
• providing the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.

A system for increasing the accuracy of generated approximations of query results, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• send a received query to a first neural network and a second neural network;
• receive from the first neural network a first predicted result to the received query;
• determine a confidence level for the first predicted result, wherein the received query is sent to the second neural network only when the confidence level is below a first predefined threshold;
• provide the first predicted result as a first output to a device having initiated the received query;
• receive from the second neural network a second predicted result to the received query;
• and
• provide the second predicted result as a second output to the device having initiated the received query, wherein the first neural network requires less computational resources than the second neural network, and whereby the first output is provided before the second output.