Automated Haptic Setting Generation via User Preference Matching
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Solution Overview
Problem
Existing haptic devices often use default settings for haptic effects that do not cater to individual user preferences, leading to an inconsistent user experience across different applications.
Innovation Solution
A method to generate personalized haptic settings by maintaining a database of haptic applications and user preferences, allowing users to receive suggested settings based on similar user preferences, which can be automatically applied or adjusted periodically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default haptic settings are used for all users, then device complexity is reduced and ease of operation is improved, but user satisfaction and haptic experience quality deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting haptic preference data from users during initial device setup or during first use of haptic applications. This advance data collection enables the system to pre-generate personalized haptic settings before the user actually needs them, eliminating the need for manual user configuration while delivering customized experiences from the start
Solution Approach 2:
The system implements self-service by automatically generating and applying personalized haptic settings without requiring user intervention. The server autonomously analyzes collected preference data, determines optimal haptic parameters, and applies them to the device, allowing the system to serve itself in the configuration process while adapting to individual user preferences
2Adaptability or versatility
If personalized haptic settings are generated for each user, then user satisfaction and haptic experience quality are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary between the haptic device and the user preference data. The server handles the complex tasks of collecting data from multiple sources, analyzing user preferences, generating personalized haptic settings, and transmitting them to the device. This intermediary approach offloads computational complexity from the device itself, allowing the device to remain relatively simple while still delivering personalized experiences
Solution Approach 2:
The system transitions the complexity from the device dimension to the network/service dimension. By moving the data collection, analysis, and settings generation processes to a remote server (another dimension), the device complexity is reduced while maintaining the capability to provide personalized haptic experiences through cloud-based processing
3Measurement precision
If haptic settings are manually configured by users, then user preference accuracy is improved, but time consumption and user effort increase
Solution Approach 1:
The system implements feedback mechanisms by monitoring user interactions with haptic applications and using this information to automatically adjust and refine haptic settings. The server collects data about user behavior patterns, preference changes, and application usage, then uses this feedback to continuously optimize personalized haptic configurations without requiring explicit user input or manual reconfiguration
Solution Approach 2:
The system performs preliminary data collection and analysis to generate accurate personalized settings before the user would need to manually configure them. By proactively gathering preference information through automated means during normal device usage, the system eliminates the time users would otherwise spend on manual configuration while maintaining high preference accuracy
Data Source
AI summary
A system and method of generating a set of preferred haptic settings for a user based on haptic settings chosen by other similar users is presented. The method includes maintaining a database of haptic software applications with associated haptic configuration settings. When a user loads a haptic software application such as a computer game or video, a server receives a communication identifying the particular computer game or video and the identity of the user. The server searches the database for other users of the computer game or video where the other users have similar setting preferences as the user. The server calculates a preferred set of configuration settings and sends those settings to the user where a haptic output device produces haptic effects based on the preferred set of configuration settings. A system that generates haptic setting based on similar users is also presented.