Adaptive Human Computer Interface (AAHCI)

• US 20120310652A1
• Filed: 06/01/2009
• Published: 12/06/2012
• Est. Priority Date: 06/01/2009
• Status: Abandoned Application

• Current Assignee: Gyst, Inc.
• Original Assignee: Gyst, Inc.
• Inventors: O'Sullivan, Daniel
• Application Number:
  US12/475,681
• Publication Number:
  US 20120310652A1
• Time in Patent Office: Days
• US Class Current: 704/270.1
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/225 Feedback of the input speech
• CPC Class Codes: G10L 2015/227 of the speaker; Human-factor methodology
• CPC Class Codes: G10L 25/63 for estimating an emotional state

Abstract

An Adaptive Human-Computer Interface (AAHCI) allows an electronic system to automatically monitor and learn from normal in-use behavior exhibited by a human user via responses generated by the supported input devices and to adjust output to the supported output devices accordingly. This Auto-Learning process is different than computer-directed training sessions and takes place as the user begins to use the device for the first time and with repeated use over time. The purpose of AHCI is to provide a user experience that is tailored to the skills, preferences, deficiencies and other personal attributes of the user automatically via machine-learned processes. This in turn provides an improved user experience that is more productive and cost efficient and that can automatically optimize itself over time with repeated use.

Claims

1. An Adaptive Human-Computer Interface that allows an electronic system to monitor behavior exhibited by a human user via responses generated by the supported input devices and to adjust output to the supported output devices accordingly.

2. An Adaptive Human-Computer Interface as recited in claim 1 wherein said interface provides a user experience that is tailored to the skills, preferences, deficiencies and other personal attributes of the user automatically via machine-learned processes.