Patent ID: 11893057
Assignee: MOTOROLA SOLUTIONS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method of processing a query to a public safety database from a query source, comprising:
receiving the query for the public safety database, the query in a first format supported by the query source;
inputting the query into a first neural network;
outputting, by the first neural network, the query in a second format, wherein the second format is a semi-structured format supported by the public safety database;
receiving, from the public safety database, a response to the query, the response in the second format;
inputting the response to the query into a second neural network;
outputting, by the second neural network, the response to the query in the first format;
wherein the first neural network is trained by inputting a first plurality of pairs of semi-structured data, each pair of semi-structured data comprising a sample query in the first format and the sample query in the second format;
wherein the second neural network is trained by inputting a second plurality of pairs of semi-structured data, each pair of semi-structured data comprising a sample response in the second format and the sample response in the first format;
wherein the first neural network is pre-trained with a plurality of queries in the second format without providing access to the plurality of queries to the query source, wherein the pre-training occurs in a trusted execution environment, wherein the trusted execution environment is a system of computers and a network that excludes all communication between computers in the environment and any computers outside the environment, except for specifically sanctioned communications;
determining if a parser already exists for the public safety database, and on determination of the existing parser, training the first and second neural networks in a supervised manner using the existing parser, otherwise training the first and second neural networks in an unsupervised pre-training manner followed by a supervised manner on labelled training data.