AI-Driven HID Mood Sensing for Adaptive IHS Performance
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Solution Overview
Problem
Conventional Human Interface Devices (HIDs) lack advanced technologies to adapt to user behavior in real-time, limiting their effectiveness in providing a truly intelligent and responsive user experience.
Innovation Solution
Intelligent HIDs are configured to capture nuanced user interactions through sensors and AI-driven functionalities, integrating processors capable of running AI models to analyze and adapt their behavior accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional HIDs are used without AI capabilities, then device complexity is reduced and ease of manufacture is improved, but adaptability to user behavior and intelligence level deteriorate
Solution Approach 1:
The HID device performs self-analysis of user behavior patterns through integrated AI models, automatically adapting its own operation without requiring external system intervention. The device serves itself by monitoring sensor data, identifying user moods, and adjusting performance settings autonomously
Solution Approach 2:
The HID device dynamically adjusts its performance characteristics based on real-time analysis of user behavior. The device transitions from static operation to dynamic adaptation, modifying its response characteristics according to detected user moods and patterns
2Adaptability or versatility
If AI models are integrated into HID to identify user mood, then user experience intelligence is improved, but processing requirements and energy consumption increase
Solution Approach 1:
The system applies partial AI processing by focusing on specific sensor data types and mood categories rather than comprehensive analysis. The AI model processes only the necessary subset of data required for mood identification, avoiding excessive computation
Solution Approach 2:
The patent introduces an intermediate layer of behavior pattern recognition that mediates between raw sensor data and full AI processing. This intermediary analysis reduces the computational burden on the AI model by pre-processing and filtering data
3Loss of information
If sensor data from HID is processed to identify mood, then user behavior understanding is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary analysis of sensor data streams to identify patterns and trends before applying full AI mood recognition. By pre-processing data to extract key behavioral indicators, the system reduces the time required for comprehensive mood analysis
Data Source
AI summary
Systems and methods for artificial emotional intelligence for influencing Information Handling System (IHS) performance. In some embodiments, an IHS may include a processor; and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the processor to: identify a mood of a user based, at least in part, upon sensor data obtained from a Human Interface Device (HID); and modify a performance setting of an IHS based, at least in part, upon the identification.