User Engagement Detection in Videogame Environments
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Solution Overview
Problem
Videogame developers face challenges in managing the trade-off between richness of environment, user engagement, and developer resources, particularly in complex multivariate games with diverse character classes, quests, and in-game capabilities, making it difficult to provide an engaging experience.
Innovation Solution
A method and apparatus that determine user engagement by receiving data from remote entertainment devices, aggregating user actions and game feature states, and evaluating their correspondence to identify levels of engagement, allowing for the generation of heat maps to rebalance game content and features based on user interaction patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content is spaced apart to provide sense of scale and coherent progression, then environment richness is improved, but user engagement deteriorates
Solution Approach 1:
The system implements feedback by collecting user action data from entertainment devices, processing this data through a server, and generating engagement level indicators that feed back to developers. This closed-loop feedback mechanism allows developers to measure and respond to actual user engagement patterns, resolving the difficulty of detecting and measuring engagement in spaced-apart open world environments.
Solution Approach 2:
The server acts as an intermediary between user entertainment devices and developers. It receives raw user action data, processes it through aggregation and analysis, and transforms it into meaningful engagement metrics. This intermediary layer simplifies the complex task of measuring engagement across distributed game environments.
2Ease of manufacture
If content is spaced apart to meet development resource constraints, then ease of manufacture is improved, but user engagement deteriorates
Solution Approach 1:
The system provides developers with actionable feedback about where users disengage, allowing targeted resource allocation. Instead of uniformly increasing content density throughout the game world, developers can use engagement data to strategically place additional content or adjust spacing in specific areas, optimizing the balance between resource constraints and engagement.
Solution Approach 2:
The system enables dynamic adjustment of game content parameters based on engagement metrics. Developers can modify content density, spacing, and distribution parameters in response to measured engagement patterns, allowing the game to adapt to actual user behavior rather than relying on static design assumptions.
3Adaptability or versatility
If the game environment is made more complex with diverse character classes and quests, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of measuring engagement across diverse game elements by breaking it down into discrete user actions. Each user action is independently tracked and categorized, then aggregated at the server level. This segmentation approach makes the data processing manageable despite the complexity of the game environment.
Solution Approach 2:
The server implements a universal data processing framework that handles multiple types of user actions and game features through a single cohesive system. Rather than creating separate tracking mechanisms for each character class or quest type, the system uses a unified approach that processes all user interactions through common aggregation and analysis routines.
Data Source
AI summary
A method of determining user engagement in a game includes: receiving data from a plurality of remote entertainment devices at a server, the data from a respective entertainment device associating at least a first feature state of the game with an action by a user of that respective entertainment device indicative of a predetermined degree of engagement by the user with the game, aggregating the data received from the plurality of entertainment devices, and determining a level of correspondence between one or more feature states and user actions indicative of the predetermined degree of engagement.