AI Query Processing for Cloud File Stores
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Solution Overview
Problem
Current systems for processing queries in cloud-based network file stores require significant configuration and human intervention, leading to inefficient use of resources and potential for human error, as they struggle to automatically interpret and respond to queries without extensive formatting and review.
Innovation Solution
Implementing a system where cloud-based network file store servers use artificial intelligence engines to automatically or semi-automatically process queries by analyzing communications from client devices, identifying relevant files, and generating recommendations, which can be reviewed or sent automatically, with the AI engine updating based on user modifications to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional query processing systems are used in cloud-based network file stores, then human intervention and configuration are required to process queries, but this leads to inefficient resource utilization, increased operational complexity, and potential human error
Solution Approach 1:
The system enables self-service through AI-powered automatic query processing. The natural language processing engine autonomously interprets user queries, identifies relevant files and documents, and generates appropriate responses without requiring manual configuration or human intervention. The system learns from user interactions and continuously improves its performance, making the complex tasks of query interpretation and document retrieval automatic and adaptive.
2Reliability
If manual review and modification of query responses is required, then response accuracy can be ensured, but this increases processing time and reduces system productivity
Solution Approach 1:
The system implements feedback mechanisms where user interactions with generated responses are continuously monitored and used to refine the AI models. When users modify or correct system-generated responses, this feedback is fed back into the learning system to improve future accuracy. This creates a continuous improvement loop that enhances response reliability over time while maintaining high processing speeds, as the system learns from rather than requiring manual review of every query.
3Adaptability or versatility
If extensive configuration of client devices and applications is performed to enable query processing, then query functionality can be achieved, but this increases implementation complexity and training requirements
Solution Approach 1:
The system achieves universality by implementing a centralized AI-powered query processing engine that can handle diverse query types across multiple file stores and user contexts without requiring specific configuration of individual client devices or applications. The natural language processing capability is designed to adapt to different domains and file types, providing versatile query processing functionality through a single unified system that works across various platforms and applications without requiring device-specific setup or user training.
Data Source
AI summary
A system for intelligently processing queries for cloud-based network file stores includes a file store that stores groups of documents that are associated with files, a non-transitory storage medium that stores instructions, and a processor. The processor may execute the instructions to receive a ticket based on a communication associated with a query to the file store, pull data for the ticket, check a sender of the communication, identify a file using the data, and/or analyze the communication using an artificial intelligence engine to generate a recommendation. The processor may monitor changes to the recommendation made before being sent in order to update the artificial intelligence engine for the changes.