Game Session Media Generation via Contextual Segment Selection
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Solution Overview
Problem
Media generation systems, such as NLP systems, face challenges in generating situation-specific content, often producing incorrect or inappropriate summaries due to lack of consideration for unique background information in each situation.
Innovation Solution
A system that automatically generates media content for game sessions by storing session data and participant characteristics, determining relevant criteria based on these characteristics, and selecting media segments to create customized content items responsive to specific events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a generic NLP system generates text summaries based on general data, then the system is simple and easy to operate, but the generated content is not contextually relevant or appropriate for specific situations
Solution Approach 1:
The system segments the media content into multiple discrete segments, each associated with specific criteria. By dividing the content generation process into segment selection based on participant characteristics and event criteria, the system achieves contextual relevance without requiring complete redesign of the entire generation pipeline
Solution Approach 2:
The system performs preliminary actions by pre-defining multiple media segments and their associated criteria before actual content generation. Participant characteristics and event criteria are established in advance, allowing the system to quickly assemble contextually relevant content by selecting appropriate pre-defined segments rather than generating content from scratch
2Measurement precision
If the NLP system uses general training data without participant-specific information, then the system operates quickly with simple data requirements, but the generated summaries lack accuracy and appropriateness for individual situations
Solution Approach 1:
The system applies local quality by associating specific media segments with particular participant characteristics and event criteria. Each media segment is tailored to match specific local conditions (participant age, experience level, event type), ensuring content accuracy without requiring complete redesign of the entire data structure
3Adaptability or versatility
If the system generates media content without considering participant characteristics, then the processing time is short and computational resources are minimized, but the content appropriateness for different audiences deteriorates
Solution Approach 1:
The system implements dynamics by making media segment selection adaptive to participant characteristics and event criteria. The system dynamically selects appropriate media segments based on the specific combination of participant attributes and event types, allowing content adaptability while maintaining efficient processing through pre-defined selection rules
Data Source
AI summary
In some aspects, the disclosure is directed to systems and methods for automatic media generation for game sessions. A system may include one or more processors that are configured to store one or more identifications of a plurality of game sessions, data corresponding to one or more events of the plurality of game sessions, and a plurality of timestamps indicating when the plurality of game sessions occurred or are scheduled to occur; detect a future game session of the plurality of game sessions; retrieve data for one or more historical game sessions associated with the first entity or the second entity; determine a set of criteria is satisfied based at least on the retrieved data; select one or more media segments based on the satisfied set of criteria; and generate a media content item for the future game session.