Game Event NLP Analytics for Richer Player Experience Insights
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Solution Overview
Problem
Conventional game analytics systems are resource-intensive, have limited ability to convey event ramifications, and require complex macro events that exponentially increase development costs, leading to lost data and incomplete player experience understanding.
Innovation Solution
Implement natural language processing (NLP) techniques to analyze a text log of game events, using sentiment analysis and semantic NLP algorithms to determine player experience characteristics, and adjust analytics based on player interviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analytics systems log player actions and game events with predetermined sets of actions and events, then analytics measurement code can be added to log information, but the quality of insights is limited by the predetermined set and developers must specify every action and event to monitor
Solution Approach 1:
The patent replaces the mechanical system of predetermined analytics code instrumentation with a natural language processing system. Instead of manually adding analytics measurement code for each action and event, the system uses NLP to automatically analyze game logs and extract insights, substituting automated linguistic analysis for manual coding efforts.
Solution Approach 2:
The NLP-based analytics system enables self-service by automatically processing game logs and generating insights without requiring developers to pre-specify every action and event. The system autonomously identifies patterns and extracts meaningful information from raw log data, eliminating the need for extensive manual configuration.
2Loss of information
If developers add additional macro events to better understand player experience, then understanding of game events improves, but development cost increases exponentially
Solution Approach 1:
The patent substitutes the mechanical approach of creating complex macro events with an NLP-based analysis system. Instead of manually defining and triggering macro events under specific circumstances, the system uses natural language processing to automatically analyze player behavior patterns and generate comprehensive understanding of player experience from standard log data.
Solution Approach 2:
The NLP analytics system provides universal analysis capabilities that can handle diverse player experience insights without requiring separate macro events for each scenario. A single NLP processing pipeline can analyze various aspects of player experience including emotions, motivations, and behaviors, replacing the need for multiple specialized macro event systems.
3Loss of information
If focus groups and interviews are used to gather player feedback, then insights into player experience are obtained, but the process is time and resource intensive
Solution Approach 1:
The patent replaces the mechanical process of organizing and conducting focus groups and interviews with an automated NLP analysis system. Instead of manually recruiting beta testers, scheduling interviews, and analyzing feedback, the system automatically processes game log data using natural language processing to extract player experience insights, dramatically reducing time and resource requirements.
4Loss of information
If analytics measurement code is added to log every action and event, then comprehensive data is captured, but the system becomes complex and data that was never captured cannot be recovered
Solution Approach 1:
The patent substitutes the mechanical approach of instrumenting every game action and event with analytics code with an NLP-based post-processing system. The system accepts standard game logs as input and uses natural language processing to extract comprehensive insights, eliminating the need to modify game code extensively while still achieving complete data capture.
Data Source
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AI summary
A processor executes program code that represents a portion of a video game and adds a sequence of text strings that represent game events to a text log during execution of the program code. The processor (or another processor that has access to the text log) performs a natural language processing (NLP) analysis of the text log to determine one or more characteristics of the portion of the video game. In some cases, the NLP analysis includes a sentiment analysis that attempts to determine characteristics of a player's experience while playing the video game, summarization technology that creates a human-readable summary of an aspect of the game or a portion of the video game, a semantic NLP ML algorithm in the semantic similarity modality to answer questions regarding the player's experience during the video game, or grouping players in a multiplayer game based on in-game behavior.