Semantic NLP ML Algorithm for Dynamic Video Game Player Experience
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Solution Overview
Problem
Existing video game technologies lack adaptable and engine-agnostic methods to influence player experiences based on game context, as they rely on predefined heuristics specific to individual games, limiting their applicability across different game types and requiring developers to build bespoke systems from scratch.
Innovation Solution
A computer-implemented method using a semantic natural language processing (NLP) machine learning (ML) algorithm to record game events in a text log, generate scores for labeled actions or content based on the log and target player experience curves, and serve selected content to influence player experiences, thereby providing engine-agnostic and game-agnostic techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined heuristics are used to modify game difficulty based on game state data, then the player experience can be influenced in a specific game, but the system lacks adaptability to other game types and requires bespoke implementation for each game
Solution Approach 1:
The patent creates a universal system that can influence player experience across different game types using the same core architecture. The heuristic evaluation module and content delivery mechanism are designed to work with various game engines and genres, allowing one system to serve multiple game types without requiring complete redesign for each specific game.
Solution Approach 2:
The patent introduces an intermediary layer between the game engine and the player experience management. This intermediary system uses natural language processing to interpret game state data and translate it into appropriate content delivery decisions, acting as a mediator that simplifies the connection between diverse game types and the experience influence system.
2Ease of manufacture
If game-specific code is used to implement a bespoke system for influencing player experience, then the system can be tailored to the specific game, but developers must build the system from scratch for each game
Solution Approach 1:
The patent implements preliminary action by pre-configuring the system with universal heuristics and content delivery mechanisms that can be applied across different games. The system comes pre-built with evaluation modules and decision-making logic that developers can deploy immediately, eliminating the need to build systems from scratch for each game while still allowing customization through configuration rather than code.
Data Source
AI summary
Game decisions are coordinated using a semantic natural language processing (NLP) machine learning (ML) algorithm, which is stored in a memory in some cases. In response to a game event, a processor records a text string that represents the game event in a text log that includes a sequence of text strings that represent game events that have transpired during a portion of the game. The processor also generates, using the semantic NLP ML algorithm, scores for labeled actions or content based on the text log and a curve that represents a target player experience as a function of progress through the game. The processor further serves one or more of the labeled actions or content that is selected based on the scores. The labeled actions or content are served to a display associated with the processor.