Game Tag Generation via Chat Transcript Analysis
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Solution Overview
Problem
Online gaming platforms face challenges in accurately labeling and discoverability of games due to limited information about user-generated content and varying gameplay data, leading to incorrect or irrelevant tags that can result in unsatisfactory player experiences.
Innovation Solution
A system that generates text tags for games by analyzing chat transcripts from gameplay sessions using machine learning models, determining characteristics such as social metrics and collaboration/competition levels, and automatically assigning tags to improve game discoverability and player engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual tagging is used for games, then implementation is simple, but tag accuracy and relevance deteriorate leading to incorrect labels
Solution Approach 1:
The system enables games to self-tag by automatically analyzing their own gameplay data, chat transcripts, and metadata to generate accurate tags without requiring manual intervention from developers or operators
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated machine learning system that uses natural language processing and classification algorithms to generate tags, substituting human labor with computational processes
2Productivity
If automated tagging is implemented, then productivity improves, but tag accuracy may worsen without proper training data
Solution Approach 1:
The system performs preliminary actions by collecting and storing gameplay data, chat transcripts, and game metadata in advance, which are then used to train machine learning models before actual tag generation occurs
Solution Approach 2:
The system incorporates feedback mechanisms where tag performance is continuously evaluated and used to retrain and improve the machine learning models, creating a closed-loop system that enhances accuracy over time
3Measurement precision
If comprehensive game analysis is performed, then tag relevance improves, but system complexity increases
Solution Approach 1:
The system segments the complex tagging task into distinct components: data collection from multiple sources, preprocessing of chat transcripts and gameplay data, feature extraction, machine learning classification, and tag generation, allowing each component to be optimized independently
Data Source
AI summary
Some implementations relate to methods, systems, and computer-readable media to generate text tags for games. In some implementations, a computer-implemented method to generate one or more text tags includes obtaining a plurality of chat transcripts, each chat transcript associated with a respective gameplay session of a respective game of a plurality of games. Each chat transcript includes content provided by participants in the gameplay session. The method further includes programmatically analyzing the plurality of chat transcripts to determine one or more characteristics for each game of the plurality of games, and generating a text tag for at least one game of the plurality of games based on the one or more characteristics of the at least one game.