Machine-Learning Sports Data Routing for Multi-Modal Responses
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Solution Overview
Problem
Traditional systems struggle to provide customized sports content in a preferred format and lack access to the necessary data to answer user-specific queries about sporting events.
Innovation Solution
A system that integrates sports data and machine learning techniques to generate multi-modal responses to user queries by associating queries with specific functions, accessing relevant data sources, and generating responses in various formats such as text, graphics, and video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional systems are used to provide sports content, then system simplicity is maintained, but the ability to provide customized content in preferred formats is limited
Solution Approach 1:
The system is divided into distinct functional modules: a machine learning model for query classification, a routing module for selecting appropriate functions, and multiple specialized functions (text generation, graphic generation, video generation, odds calculation). This segmentation allows each component to handle specific tasks efficiently, enabling customized content delivery without requiring complete system redesign.
Solution Approach 2:
The machine learning model serves multiple purposes: it classifies user queries, determines appropriate output formats, and routes requests to suitable functions. This multi-functionality allows a single component to handle diverse query types (text, graphic, video, odds) and deliver customized sports content across different formats, improving adaptability while managing complexity.
2Adaptability or versatility
If machine learning techniques are used to generate sports content, then content customization is improved, but access to proper sports data is limited
Solution Approach 1:
The system introduces an intermediary routing layer between the machine learning model and data sources. The routing module translates user queries into appropriate function calls, which then access specialized data sources (sports databases, video archives, odds systems). This intermediary structure enables the ML model to generate customized content while ensuring proper access to relevant sports data through standardized interfaces.
3Ease of operation
If multiple output formats are supported, then user preference satisfaction is improved, but response generation complexity increases
Solution Approach 1:
The system dynamically selects output formats based on query classification and user preferences. The machine learning model determines the appropriate function type (text, graphic, video, odds) for each query, and the routing module adapts the response generation process accordingly. This dynamic approach allows the system to support multiple formats without requiring all format-generation capabilities to be active simultaneously, managing complexity while fulfilling user preferences.
Data Source
AI summary
A method for generating multi-modal response to a query using a generative machine learning model, the method including: receiving, from a client device, a query data object related to a sporting event; providing the query data object and a first prompt to a machine learning system; receiving, from the machine learning system, a function, from a set of functions, associated with the query data object; receiving, from the machine learning system, an output format; providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system, receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and outputting the response to one or more users.