Automated Video Content Tagging Using Neural Networks
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Solution Overview
Problem
Current methods for providing partial information about event scenarios for predictions and wagers are slow and inefficient, requiring manual processes that are time-consuming and prone to human error, especially in adding video content to libraries for gaming systems.
Innovation Solution
The implementation of automated techniques using artificial neural networks (ANNs) and convolutional neural networks (CNNs) to generate and tag video clips based on parameters, allowing for quick and accurate addition of content to libraries, and dynamic interfaces for user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to add video content to libraries, then content can be added to the library, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical processes with automated computer vision systems. Neural networks analyze video frames to detect events, identify objects, recognize scenes, and extract relevant information automatically, eliminating the need for manual review and tagging of video content while improving both speed and accuracy.
Solution Approach 2:
The system performs self-service by automatically processing, tagging, and organizing video content without human intervention. The neural network models independently analyze video data, extract features, and create metadata, allowing the system to maintain and update its own library without requiring manual operations.
2Productivity
If manual processes are used to create video clips with associated information, then content can be created, but the process requires substantial effort and increases time requirements
Solution Approach 1:
The patent replaces complex manual analysis processes with automated neural network systems that simultaneously perform multiple tasks including event detection, object identification, scene recognition, and information extraction, dramatically increasing content creation productivity while managing complexity through integrated AI processing.
Solution Approach 2:
The neural network system performs multiple functions simultaneously - detecting events, identifying objects, recognizing scenes, extracting information, and generating metadata all within a single automated processing pipeline, eliminating the need for separate manual operations for each task.
3Speed
If conventional manual techniques are used for video processing, then content can be processed, but the process is slow and inefficient
Solution Approach 1:
The patent replaces slow manual video processing with high-speed automated neural network analysis that processes thousands of video frames per second, automatically extracting events and information at rates impossible for human operators while maintaining high accuracy through sophisticated AI algorithms.
Data Source
AI summary
Example embodiments relate to predicting aspects of an unknown event. A system may involve an electronic device communicatively coupled to a server. The server may provide a set of possible parameters relating to an unknown event to the electronic device. The electronic device may enable a user to select predicted parameters via a user interface and may further transmit the predicted parameters to the server. The server may generate a combination from the set of possible parameters to use to retrieve a pre-recorded event and transmit the event to the electronic device for display to the user. The server may also compare the predicted parameters to the generated parameters to determine a result and provide the result to the electronic device for display to the user.