Image-Sensing Peripheral Control for Display-Aware Target Devices
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Solution Overview
Problem
Existing peripheral devices lack the ability to efficiently and intuitively control target devices such as computers or TVs without direct user interaction, and there is a need for advanced capabilities enabled by machine learning technologies to enhance user-device interaction.
Innovation Solution
A peripheral device equipped with an image sensor and processor, utilizing machine learning models, receives user inputs to generate instructions for target devices, analyzes displayed content, and elicits further user reactions to refine control commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If peripheral devices are equipped with camera functionalities to control target devices, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an image sensor as an intermediary component that captures displayed content from the target device screen. This mediator enables the peripheral device to understand and respond to visual information without requiring direct interaction with the target device's operating system or interface, thereby improving ease of operation while managing complexity through a dedicated intermediary component
Solution Approach 2:
The patent replaces traditional mechanical or manual control methods with vision-based control. Instead of requiring physical interaction with the target device (buttons, keyboard, mouse), the system uses an image sensor to capture screen content and processes it to generate control commands, substituting mechanical interaction with optical sensing and computational processing
2Adaptability or versatility
If machine learning models are integrated into peripheral devices to enhance interaction capabilities, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent incorporates machine learning models that are pre-trained and deployed within the peripheral device to enable immediate interpretation of captured content and generation of control commands. This preliminary preparation of intelligence within the device allows for adaptive responses without requiring complex real-time training processes, balancing adaptability with manageable device complexity
Solution Approach 2:
The patent employs machine learning models that provide universal interpretation capabilities across different types of displayed content (text, images, interfaces). This multi-functional approach allows a single ML model infrastructure to handle various interaction scenarios, improving adaptability while avoiding the need for separate specialized systems for each function
3Productivity
If the peripheral device captures and analyzes displayed content to generate control commands, then the productivity is improved, but the loss of information increases
Solution Approach 1:
The patent uses the image sensor to create a visual copy of the displayed content from the target device screen. This optical copy captures the essential information needed for control decisions without requiring direct access to the target device's internal data structures or memory, enabling productivity improvement while minimizing information loss through non-intrusive visual replication
Data Source
AI summary
Provided is a method of using at least one peripheral device including an image sensor and at least one processor for operating a target device, including receiving a first input from a user associated with the target device indicative of a task to be performed by the target device; determining a first instruction for the target device to perform based on the first input; determining whether the first instruction includes displaying content associated with the task on the target device; in response to determining that the first instruction includes displaying content associated with the task on the target device, obtaining displayed content associated with the task on the target device; and determining a second instruction for the target device to perform based on a second input received from the user indicative of a reaction to the displayed content associated with the task on the target device.