Neural Network Query Translation for Public Safety Databases
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Solution Overview
Problem
Law enforcement database queries across different jurisdictions face challenges due to incompatible request and response formats, requiring time-consuming human intervention and risking sensitive information disclosure.
Innovation Solution
A system utilizing neural networks to translate queries and responses between different formats, pre-trained on pairs of semi-structured data to automate the process, ensuring secure and efficient data processing without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used to translate queries and responses between different database formats, then translation accuracy can be maintained, but time consumption increases and security risks arise from sensitive information disclosure
Solution Approach 1:
The patent introduces a neural network model as an intermediary between the query source and the target database. This intermediary automatically translates queries from the source format to the target database format and translates responses back, eliminating the need for human intervention while maintaining translation accuracy through the model's learning from training data.
Solution Approach 2:
The patent replaces the mechanical system of manual translation with an automated neural network-based translation system. The neural network model learns translation patterns from training data and automatically performs format conversion, substituting human manual translation operations with an automated computational process that reduces time consumption and eliminates security risks associated with human handling of sensitive information.
2Adaptability or versatility
If manual parsing is used to handle queries across different jurisdictions, then flexibility in handling format changes can be maintained, but the process becomes tedious and requires continuous manual updates
Solution Approach 1:
The patent applies preliminary action by training the neural network model in advance on diverse training data that includes various query formats and their corresponding target database formats. This pre-training enables the model to learn translation patterns and adapt to different formats beforehand, so when actual queries need to be translated, the model can handle format changes automatically without requiring manual updates, thus maintaining flexibility while improving productivity.
3Productivity
If automated translation systems are implemented, then processing speed increases, but the system complexity increases due to the need for training data and model maintenance
Solution Approach 1:
The patent implements a universal neural network translation system that can handle multiple query formats and target database formats through a single model. The model is trained on diverse training data that encompasses various formats, enabling it to perform multiple translation tasks universally. This multi-functionality reduces system complexity compared to having separate translation systems for each format pair, while maintaining high processing speed through automated translation.
Data Source
AI summary
A method of processing a query to a database from a query source is provided, comprising: receiving the query, 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 format supported by the database; receiving, from the 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 each 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 or response in the first format and the sample query or response in the second format.