Spatiotemporal Data Processing for Augmented Video Insights
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Solution Overview
Problem
Current systems fail to effectively analyze and present meaningful insights from vast volumes of sporting event data, particularly in live events, due to difficulties in handling and transforming X, Y, Z motion data into actionable sports terminology, identifying meaningful insights, and visualizing results.
Innovation Solution
A computer-implemented method and system for generating augmented video content by processing spatiotemporal data from video feeds to identify semantic elements and contexts, allowing for user interactions and actions such as linking to websites or displaying custom user interfaces with player statistics, enhancing decision-making and entertainment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional scouting methods are used to evaluate sporting events, then human expertise and intuition can be applied, but the vast volumes of sporting information generated daily cannot be fully evaluated and stored
Solution Approach 1:
The patent replaces traditional mechanical human scouting methods with automated computer vision systems and machine learning algorithms. The system uses optical sensors (cameras) and computational algorithms to automatically capture, process, and analyze sporting event data, transforming qualitative human evaluation into quantitative automated analysis that can handle vast volumes of information simultaneously.
Solution Approach 2:
The patent introduces an intermediary layer of computational processing between raw sporting data and actionable insights. The system uses intermediate data structures, feature extraction layers, and analytical models to bridge the gap between capturing vast amounts of sporting information and deriving meaningful evaluations, enabling both high-volume processing and intelligent analysis.
2Measurement precision
If X, Y, Z motion data is captured by imaging cameras, then quantitative data is obtained, but difficulty exists in transforming this data into meaningful sports terminology and identifying insights
Solution Approach 1:
The patent introduces intermediary computational layers that translate raw X, Y, Z motion data into meaningful sports terminology. The system uses intermediate feature representations, domain-specific ontologies, and analytical models as mediators between precise quantitative measurements and actionable sports insights, making the transformation process systematic and interpretable.
Solution Approach 2:
The patent segments the complex data transformation process into distinct analytical stages: raw data capture, feature extraction, pattern recognition, and insight generation. Each stage processes specific aspects of the motion data with specialized algorithms, making the overall transformation from quantitative measurements to meaningful sports terminology more manageable and effective.
3Loss of information
If video content is augmented with semantic elements and contexts, then viewer engagement and insight presentation are improved, but system complexity increases
Solution Approach 1:
The patent creates a universal processing framework that handles multiple functions within a single integrated system. The same computational infrastructure performs semantic element detection, context analysis, data transformation, and insight generation across different sporting events and data types, reducing overall system complexity through consolidation while maintaining comprehensive analytical capabilities.
Data Source
AI summary
Data processing systems and methods are disclosed for augmenting video content with one or more augmentations to produce augmented video. Elements within video content may be identified by spatiotemporal indices and may have associated values. An advertiser can pay to have an augmentation added to an element that, for example, advertises the advertiser's goods and/or includes a link that, when activated, takes a user to the advertiser's web site. Elements may have associated contexts that can be used to determine augmentations and element value, such as a position and/or current use of the element.