Game Session Media Generation Using Participant-Based Segment Selection
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Solution Overview
Problem
Conventional media generation systems, such as NLP systems, fail to generate situation-specific content for game sessions, often producing incorrect or inappropriate summaries due to a lack of consideration for unique background information related to the participants in the game session.
Innovation Solution
A system and method for automatic media generation that stores game session data and participant characteristics, determines criteria based on these characteristics, and generates media content items using selected media segments tailored to the specific context of the game session, including thresholds and rules for media segment selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional NLP systems generate text summaries based on general data, then the media generation process is simple and fast, but the generated content is incorrect or inappropriate for individual situations due to lack of background information
Solution Approach 1:
The patent segments the media generation process into distinct components: (1) retrieving background information about participants, (2) analyzing game events and characteristics, (3) selecting appropriate media templates based on participant characteristics, and (4) generating customized summaries. This segmentation allows the system to incorporate complex background information processing while maintaining modular architecture that manages system complexity.
Solution Approach 2:
The system performs preliminary actions by retrieving and storing background information about participants (age, skill level, experience) before the actual media generation occurs. This pre-processing of participant data enables the system to make informed decisions about which media templates to use, improving accuracy without adding complexity during the critical generation phase.
2Loss of information
If the system incorporates participant characteristics and background information, then context-specific content accuracy improves, but the processing time and system complexity increase
Solution Approach 1:
The system retrieves participant background information and game session data in advance, before the actual media generation process. By preparing this contextual information beforehand, the system minimizes processing time during the critical generation phase while ensuring all necessary context is available for accurate content creation.
Solution Approach 2:
The patent applies different processing approaches to different types of information based on their importance and characteristics. For example, participant characteristics like age and skill level trigger specific template selections, while game events are processed according to their significance. This localized quality approach ensures comprehensive context capture without uniformly processing all data at maximum depth, thus reducing overall processing time.
3Adaptability or versatility
If generic media templates are used for all game sessions, then the system is easy to operate and maintain, but the generated summaries lack relevance to specific participants and situations
Solution Approach 1:
The system dynamically selects media templates based on participant characteristics and game session data. Instead of using static generic templates, the system adapts template selection in real-time based on factors like participant age, skill level, and game events. This dynamic approach provides high customization while maintaining ease of operation through automated template selection algorithms that require no manual intervention.
Solution Approach 2:
The patent changes key parameters such as participant age, skill level, and experience level to determine which media templates to apply. By systematically varying these parameters and corresponding template selections, the system achieves high adaptability and customization. The automated parameter-based selection process maintains operational simplicity despite the complexity of customization options.
Data Source
AI summary
In some aspects, the disclosure is directed to systems and methods for automatic text generation for game sessions. A system may include one or more processors that are configured to store an identification of a game session and data corresponding to one or more events of the game session, the game session corresponding to one or more characteristics of individuals participating in the game session; determine, based on the one or more characteristics of the individuals participating in the game session, a set of criteria for selecting a set of media segments, wherein different criteria of the set of criteria correspond to different media segments; determine which of the set of criteria is satisfied; select one or more media segments based on the satisfied criteria; and generate a media content item for at least a portion of the game session based on the selected one or more media segments.