Real-Time Athlete Interview System Using Dynamic Question Grouping
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Solution Overview
Problem
Athletes in traditional interview formats become disengaged due to repetitive and impersonal questions, leading to a disconnection with their fans, who are treated as mere consumers rather than active participants in content creation.
Innovation Solution
A system and method for real-time online user interaction with athletes, utilizing a processor and memory to access, generate, and transmit questions to athletes based on user input, machine learning models for question scoring and filtering, and natural language processing, allowing fans to engage in live interviews through bidding and voting processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional interview formats with repetitive questions are used, then interview structure is maintained, but athlete engagement and fan connection deteriorate
Solution Approach 1:
The system dynamically generates interview questions in real-time based on sporting event data and fan inputs, transforming the static traditional interview format into a dynamic adaptive process. The machine learning model continuously processes multiple data streams (event statistics, fan questions, athlete performance) to generate customized questions that adapt to the specific context of each interview.
Solution Approach 2:
The system enables fan participation in content creation by allowing fans to submit questions that are then processed and incorporated into the interview. This self-service mechanism empowers fans to influence the interview content directly, moving beyond passive consumption to active participation in content generation.
2Adaptability or versatility
If fan inputs are processed through machine learning models for question generation, then personalized content is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary processing of fan-submitted questions by filtering out profanity and grouping questions by topic before the interview occurs. This advance preparation reduces the computational burden during the actual interview, enabling real-time question generation while maintaining personalized content quality.
Solution Approach 2:
The patent replaces manual question selection and filtering processes with automated machine learning models and natural language processing algorithms. This substitution enables rapid processing of large volumes of fan inputs while maintaining high standards for question quality and appropriateness, achieving both personalization and real-time performance.
3Loss of information
If multiple fan questions are grouped and scored by machine learning, then content relevance is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the question processing task into distinct stages: filtering inappropriate content, grouping questions by topic, scoring questions based on relevance to the sporting event, and selecting final questions for the interview. This segmentation allows each component to be optimized independently while maintaining overall system efficiency and question quality.
Data Source
AI summary
A system for real-time online user interaction with an athlete, includes a processor and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to: access a plurality of questions; group the plurality of questions into a plurality of groups of questions that include the same topic relating to an athlete of the sporting event; sort the plurality of groups of questions based on which of the plurality of groups of questions have the largest quantity of questions; access an end time of a sporting event; determine if the sporting event has ended based on the end time; and transmit the group of questions that includes the largest quantity of questions to the athlete in response to the sporting event ending.