Authorized Query Response Generation With Readability Validation
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently authorizing query information retrieval for secure and private data while maintaining real-time response times, without imposing performance restraints on processors, and ensuring data integrity.
Innovation Solution
A system and method using machine learning to identify user identity, parse queries, authorize access to restricted databases, and generate comprehensible responses by filling response templates with restricted data, utilizing trained classifiers and comprehension models to ensure readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If natural language processing is used to parse and interpret user queries in real-time, then user interaction responsiveness is improved, but system processing time and computational load increase
Solution Approach 1:
The system segments the query processing into distinct stages: natural language parsing, entity identification, authorization checking, database querying, and response generation. Each stage handles specific tasks independently, allowing parallel processing and reducing overall computational load while maintaining real-time responsiveness.
Solution Approach 2:
The system performs preliminary actions by pre-compiling authorization rules, pre-processing database indexes, and pre-loading frequently accessed data structures. This preparation work is done beforehand so that during actual query processing, the system can quickly evaluate authorization and retrieve data without heavy computational overhead.
2Reliability
If authorization checking is performed for each query to restricted data, then data security is improved, but query processing speed decreases
Solution Approach 1:
Authorization rules and user permissions are pre-compiled and stored in optimized data structures before query processing. When a query arrives, the system performs a quick lookup against these pre-prepared authorization matrices rather than evaluating complex security policies in real-time, thus maintaining both security and speed.
Solution Approach 2:
The system introduces an intermediary authorization layer that sits between the query processor and the restricted database. This intermediary component handles all security checks using pre-computed authorization data, isolating the security validation logic from the main query processing path and preventing it from becoming a performance bottleneck.
3Speed
If private or secure data is stored in intermediate databases for efficient access, then data retrieval speed is improved, but data integrity and security are compromised
Solution Approach 1:
The system creates controlled copies of authorized data portions in intermediate storage structures that are optimized for fast retrieval. These copies are generated only for data that the user is authorized to access, and the copying process itself is governed by authorization rules. The original secure data remains protected in its primary storage location.
Solution Approach 2:
Different portions of the database are stored with different optimization characteristics based on their access patterns and security requirements. Frequently accessed, authorized data can be stored in faster intermediate structures, while sensitive or rarely accessed data remains in more secure but slower storage, creating a hierarchy that balances speed and security locally for different data types.
4Measurement precision
If machine learning classifiers are trained to improve query understanding accuracy, then query parsing precision is improved, but training time and computational resources increase
Solution Approach 1:
The system uses a two-tiered machine learning approach where a smaller, faster model handles the majority of common query patterns with high accuracy, while a larger, more comprehensive model is only invoked when the first model is uncertain or when dealing with complex, novel queries. This partial application of heavy ML processing reduces overall training and inference time while maintaining high accuracy for most cases.
Data Source
AI summary
Systems, methods, and computer-readable storage media for responding to a query using a neural network and natural language processing. If necessary, the system can request disambiguation, then parse the query using a trained machine-learning classifier, resulting in at least one of an identified subject or an identified domain of the text query. The system can determine if the user is authorized to retrieve answers to the query and, if so, retrieve factual data associated with the query. The system can then retrieve a response template, and fill in the template with the retrieved facts. The system can then determine, by executing a machine comprehension model on the filled response template, a probable readability token, portion of text, of at least a portion of the filled response template and, upon identifying that the probable readability is above a threshold, reply to the text query with the at least a portion of the filled response template.