Media Guidance Application Recalibration for Input Detection
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Solution Overview
Problem
Conventional systems face challenges in accurately detecting user inputs, particularly when users switch between different input types, leading to erroneous detection or failure, which affects the user experience and device functionality.
Innovation Solution
A media guidance application that monitors user inputs across various types, such as voice controls, motion controls, and brain activity, and recalibrates the device based on subsequent corrective inputs from a secondary input type to ensure accurate detection of commands intended with a primary input type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the media guidance application supports primary input types (voice controls, motion controls, biometric controls) that are hands-free and less restrictive, then ease of operation is improved, but measurement precision deteriorates leading to erroneous detection or failure to detect user inputs
Solution Approach 1:
The system implements feedback by monitoring whether primary input type commands are successfully executed. When a command issued via primary input type (voice, motion, or biometric controls) is not successfully executed, the system receives feedback about this failure and automatically recalibrates the primary input type by using secondary input type commands (keyboard, touchscreen) to adjust and refine the calibration parameters, thereby improving detection precision for future primary input type commands
2Measurement precision
If the media guidance application uses secondary input types (keyboard, on-screen icons) that require hand-operation, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service calibration by automatically using secondary input type commands to recalibrate primary input type detection parameters. When primary input type commands fail to execute successfully, the system self-corrects by interpreting subsequent secondary input type commands as calibration instructions and automatically adjusting the primary input type sensitivity parameters without requiring manual user configuration
3Reliability
If the media guidance application recalibrates the user device based on subsequent corrective inputs, then reliability is improved, but device complexity increases
Solution Approach 1:
The calibration process is implemented as a self-service mechanism where the media guidance application automatically monitors the execution status of commands and self-calibrates without external intervention. The system internally tracks whether primary input type commands are successfully executed and automatically adjusts calibration parameters based on failure patterns, eliminating the need for complex external calibration procedures or manual user setup
Data Source
AI summary
Methods and systems are described herein for a media guidance application that improves the customization and calibration of user devices to a particular user. For example, in response to erroneously detecting (or failing to detect) a user input of a first type, the media guidance application may re-calibrate the user device based on subsequent corrective inputs issued using a second input type such that future user inputs of the first type will not be erroneously detected (or fail to be detected).