Sensor Statistics for Player Authentication
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Solution Overview
Problem
Existing user authentication systems in online gaming are vulnerable to false authentication due to shared, stolen, or faked credentials, leading to issues like level boosting and account cheating.
Innovation Solution
Implementing a system that uses sensor statistics, such as gameplay attributes like reflex time, eye motion, and heart rate, to authenticate users by comparing real-time input data to historical or ranked player data, and identifying inauthentic gameplay patterns, potentially involving machine learning models for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user credentials are used for authentication, then authentication simplicity is maintained, but authentication reliability deteriorates due to shared, stolen, or faked credentials
Solution Approach 1:
The patent combines traditional credential-based authentication with sensor-based authentication. The system merges username/password verification with biometric sensor data (heart rate, eye motion, tremors) to create a multi-factor authentication system that improves reliability while managing complexity through integrated processing
Solution Approach 2:
The patent introduces sensor data as an intermediary layer between the user and the authentication system. Instead of directly verifying credentials, the system uses sensor measurements of physiological responses as a mediator to validate whether the person presenting credentials is the legitimate account holder
2Measurement precision
If sensor data is collected and analyzed, then authentication accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the authentication process into distinct components: credential verification, sensor data collection, physiological response analysis, and decision-making. Each component processes specific types of data independently, then integrates results to make the final authentication decision, reducing overall processing complexity
Solution Approach 2:
The patent transforms complex sensor data into simplified physiological parameters such as heart rate variability, eye motion patterns, and tremor frequencies. These standardized parameters are easier to process and compare against threshold values, reducing computational complexity while maintaining high authentication accuracy
Data Source
AI summary
Methods, systems, and devices for using sensor statistics for player authentication are described. A device or system (e.g., a video game, an electronic sports (Esports) system, a gaming console, etc.) may utilize sensors to authenticate a player (e.g., a user) to an account during gameplay (e.g., in real-time during gameplay). Users may be authenticated by matching sensed gameplay attributes (e.g., indicators, reflex time, sensed patterns, eye motion, heart rate, tremors, frequency of operation, periodicity, spiky behavior, eye concentration, etc.) to the account. For example, collected sensor data may be compared to the account's history or past sensor data, or may be compared to similar ranked players' sensor data, for authentication. The device or system may further use additional data including robot (e.g., BOT) gameplay data, sensor data collected from other players of different (e.g., higher) rank, etc. to identify when collected sensor data may be associated with inauthentic gameplay.