AI Middleware for Video Game Interaction Summarization
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Solution Overview
Problem
Users of multi-player video games may miss interactions with other users while temporarily stepping away from the game, leading to a lack of immersion upon returning.
Innovation Solution
A system and method for generating a summary of interactions exchanged between users during a specified time window of video game play, allowing users to quickly catch up on missed content and re-immerse in the game.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users step away from the game to engage in other activities, then users can take breaks from gaming, but users miss interactions and lose immersion upon returning
Solution Approach 1:
The system proactively monitors and records user absence periods, automatically capturing interactions that occur during these intervals. By performing the information gathering action beforehand (during the absence), the system eliminates the need for users to manually check what they missed, thus preventing information loss while allowing time away from the game.
Solution Approach 2:
The system acts as an intermediary between the user and the game interactions. While the user is away, the system intermediates by monitoring, filtering, and storing interactions. Upon user return, the intermediary provides a curated summary, bridging the gap between the user's absence and the continuous stream of game interactions.
2Loss of information
If all interactions are presented to users upon return, then users get complete information, but users are overwhelmed by excessive content
Solution Approach 1:
The system extracts only the most relevant and significant interactions from the complete set of game communications. By filtering out redundant, low-importance, or duplicate information and keeping only high-value interactions, the system provides complete meaningful information while reducing overall content volume and processing complexity.
Solution Approach 2:
Different interactions are treated with different levels of detail and prominence based on their importance. High-importance interactions receive full detail and prominent display, while less critical interactions are summarized or grouped. This localized quality adjustment optimizes information delivery by matching detail level to interaction significance, reducing overall complexity while maintaining completeness of important information.
Data Source
AI summary
Methods and systems for providing a summary of interactions exchanged between users includes receiving a request for the summary for a time window of gameplay of a video game from a user and, in response, identifying a subset of the interactions generated during the time window. The subset of interactions are analyzed to identify keywords representing topics discussed within and presenting the keywords using visual representation defining a level of prominence assigned to the keywords on a user interface for rendering. Selection of a keyword results in the summary associated with the keyword to be presented to the user.