CGR Objective-Effectuator Configuration Automation
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Solution Overview
Problem
Existing devices that present computer-generated reality (CGR) environments are ineffective in presenting representations of objects associated with actions, requiring excessive user inputs for configuration parameters, leading to increased power consumption and decreased user experience.
Innovation Solution
A method and system for configuring objective-effectuators in CGR environments, where configuration parameters are determined based on the type of previously instantiated objective-effectuators, reducing the need for user inputs and automating the setup process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration of parameters is required for each objective-effectuator, then configuration precision is improved, but device complexity and user input requirements increase
Solution Approach 1:
The system performs preliminary action by automatically determining configuration parameters for new objective-effectuators based on the types of previously instantiated objective-effectuators. This pre-computation of parameters eliminates the need for manual configuration, reducing user input requirements while maintaining appropriate configuration precision through automated type-based inference.
2Manufacturing precision
If manual configuration inputs are required, then configuration accuracy is improved, but power consumption increases
Solution Approach 1:
The system implements self-service by automatically determining configuration parameters without requiring user inputs. The device serves itself by using its own stored information about previously instantiated objective-effectuators to configure new ones, thereby eliminating the power consumption associated with displaying configuration interfaces and processing user input events while maintaining configuration accuracy.
3Productivity
If automated parameter determination is implemented, then productivity is improved, but configuration precision may deteriorate
Solution Approach 1:
The system applies parameter changes by determining configuration parameters based on the types of previously instantiated objective-effectuators. This approach uses categorical parameters (types) to infer specific configuration values, enabling automated configuration that maintains precision through type-based relationships while significantly improving productivity by eliminating manual input requirements.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods for configuring objective-effectuators. A device includes a display, a non-transitory memory and one or more processors coupled with the display and the non-transitory memory. A method includes, while displaying a computer-generated reality (CGR) representation of a first objective-effectuator in a CGR environment, determining to display a CGR representation of a second objective-effectuator in association with the CGR representation of the first objective-effectuator. In some implementations, the second objective-effectuator is associated with a set of configuration parameters. In some implementations, the method includes determining a value for at least a first configuration parameter of the set of configuration parameters based on a type of the first objective-effectuator. In some implementations, the method includes displaying the CGR representation of the second objective-effectuator in the CGR environment in accordance with the value for the first configuration parameter.