Natural Language Video Analytics for Real-Time Command Validation
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Solution Overview
Problem
Conventional video analytics systems require technical expertise and are inefficient, leading to operational delays and increased error likelihood due to reliance on graphical user interfaces and manual interaction, limiting scalability and responsiveness, especially in real-time applications.
Innovation Solution
A natural language video analytics system utilizing a trained neural network to process commands, determine their validity, generate machine-readable instructions, and update displays, enabling real-time decision-making and efficient video analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional graphical user interfaces and command-line controls are used to operate video analytics systems, then the system can process video data, but the operation becomes complex and inefficient requiring extensive training
Solution Approach 1:
The patent replaces the mechanical interaction of graphical user interfaces and command-line controls with a voice-based natural language processing system. Users can issue commands through spoken language which is converted to text and processed by machine learning models, eliminating the need for complex manual navigation and technical training while maintaining system functionality.
2Productivity
If manual interaction methods are used to operate video analytics systems, then the system can function, but operational delays increase and error likelihood increases
Solution Approach 1:
The system incorporates self-correcting mechanisms where the machine learning models automatically validate and correct user commands. The system can identify ambiguous voice inputs, request clarifications, and automatically correct common errors without requiring manual intervention, thereby reducing operational delays and error rates while maintaining high productivity.
3Adaptability or versatility
If conventional video analytics systems are used, then video data can be analyzed, but scalability and responsiveness are restricted especially in real-time applications
Solution Approach 1:
The patent implements dynamic resource allocation and adaptive processing where the system can adjust its computational resources and processing depth based on real-time requirements. The machine learning models can operate at different levels of detail and speed, allowing the system to scale efficiently while maintaining responsive real-time performance when needed.
Data Source
AI summary
An aspect of the present disclosure provides a natural language video analytics system. The system includes at least one processor and at least one memory including computer program code. The at least one processor, at least one memory and the computer program code are configured to allow the system to receive one or more natural language video analytics commands associated with video data, determine a validity of the one or more natural language commands using a trained neural network, in response to a positive determination of the validity of the one or more natural language commands, generate machine-readable video analytics instructions based on the one or more natural language commands using the trained neural network, and transmit the machine-readable video analytics instructions to a display module, the display module configured to update an output display based on the machine-readable video analytics instructions.