Cricket Multimodal Agent Architecture for Accurate Match Insights
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Solution Overview
Problem
Existing solutions are unable to generate accurate information relating to sports events, particularly in cricket, using generative AI techniques.
Innovation Solution
A multimodal sports learning language model (LLM) processes user inputs such as text, audio, or video to determine contextual and intentional information, mapping metadata to generate sports tracking data, which is used by agents to perform actions like generating highlights, narratives, or predicting game outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing generative AI techniques are used for sports events, then basic content generation is possible, but accuracy and performance are insufficient
Solution Approach 1:
The system segments the generative AI functionality into multiple specialized agents (cricket agent, football agent, basketball agent, etc.), each trained on sport-specific data and rules. This segmentation allows each agent to specialize in one sport domain, improving accuracy for that specific sport while maintaining overall system reliability through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer between user input and content generation that includes intent detection, context extraction, and query rewriting components. This intermediary processes and refines user queries before passing them to appropriate agents, significantly improving the accuracy of information retrieval and generation while maintaining system reliability.
2Measurement precision
If sport-specific orchestrators and agents are introduced, then accuracy of sports information generation improves, but system complexity increases
Solution Approach 1:
The system implements a universal orchestrator framework that handles multiple sports through a common architecture. The superior orchestrator manages overall workflow, while sport-specific orchestrators inherit common functionalities from the universal framework, reducing redundancy. This multi-functional design allows the system to handle different sports with varying complexity levels while maintaining a unified management structure.
Solution Approach 2:
The system employs a nested hierarchical structure where sport-specific agents are nested within sport-specific orchestrators, which are in turn nested within the superior orchestrator. This nesting allows each layer to manage specific aspects while benefiting from the coordination of outer layers, improving accuracy through specialization while containing complexity within manageable nested modules.
3Measurement precision
If contextual and intentional information processing is added, then quality of generated content improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user inputs through intent detection and context extraction before passing them to the generative agents. Query rewriting and context enrichment are performed in advance, allowing the main content generation process to focus on creating accurate sports information without re-processing basic input analysis, thus improving quality while managing processing time.
Solution Approach 2:
The system maintains continuity of useful action by implementing parallel processing where multiple agents can work simultaneously on different aspects of a query. Context extraction, intent detection, and content generation occur in overlapping timeframes rather than strictly sequential steps, reducing total processing time while maintaining the quality improvements from comprehensive contextual analysis.
Data Source
AI summary
Disclosed techniques relate to using one or more of cricket match statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as match statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.