In-Game Item Generation Using Player Desirability Scores
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Solution Overview
Problem
Existing video game systems often provide in-game items randomly, leading to disappointing experiences for players who desire specific items, which can negatively impact user engagement and interaction between players.
Innovation Solution
A method that involves receiving an instruction to generate an in-game item for a first player, identifying a second player based on a likelihood of engagement parameter, generating a user-specific item desirability score for the second player, and providing the in-game item to the first player, thereby encouraging interaction and improving user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random item generation is used, then item generation simplicity is maintained, but player engagement and satisfaction deteriorate
Solution Approach 1:
The patent changes the parameter of item selection from random probability-based to data-driven desirability-based. The system generates a user-specific item desirability score for the target player and selects items according to this score, transforming the item generation approach from purely random to personalized, thereby improving player engagement while maintaining implementation feasibility through existing data collection mechanisms
Solution Approach 2:
The system leverages the game engine's existing data collection and processing capabilities to automatically generate desirability scores and select items. The game engine serves itself by utilizing already-gathered player data, play history, and item interaction records to autonomously determine item selections, eliminating the need for complex external systems while enhancing player satisfaction
2Reliability
If player-specific item selection is implemented, then user engagement is improved, but system complexity increases
Solution Approach 1:
The patent makes the game engine universal by enabling it to perform multiple functions: existing item generation, player data collection, desirability score calculation, and item selection. By integrating these functions into the single game engine, the system avoids adding separate complex subsystems while still achieving personalized item selection, thus improving engagement without proportionally increasing system complexity
Solution Approach 2:
The system performs preliminary actions by continuously collecting and storing player data, play history, and item interaction records before item generation is needed. This pre-processing of information allows the desirability score calculation to be efficient and data-driven, reducing the computational complexity during actual item selection while maintaining high personalization accuracy
3Manufacturing precision
If item desirability scoring is implemented, then item relevance to player is improved, but data processing requirements increase
Solution Approach 1:
The patent applies local quality by calculating item desirability scores specifically for each player based on their individual preferences, play history, and interaction patterns. Rather than using uniform criteria for all players, the system tailors the scoring to each player's unique characteristics, improving item relevance while managing data processing requirements by focusing computation only on relevant player-specific data
Data Source
AI summary
Systems and methods are described for providing an in-game item to a player. An instruction is received to generate an in-game item for a first player. A second player is identified based on a first metric. A second metric is generated based on at least data associated with the second player. The in-game item is generated based on the second metric and the in-game item is provided to the first player.