Hierarchical Datastore for CGR Agents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing devices that present computer-generated reality (CGR) environments are ineffective in representing objects associated with actions, particularly in resource utilization and storage efficiency, as they use flat memory structures that treat all information equally without indicating relationships between different data types.
Innovation Solution
A hierarchical datastore system is implemented, which includes multiple storage hierarchies associated with specific data types, allowing sensory input data to be stored based on its type, reducing the need for computing resources to determine relationships and optimizing storage by purging data after a set time, thereby improving resource utilization and access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a flat memory structure is used to store all information equally, then the system is simple to implement, but the resource utilization efficiency deteriorates due to inability to indicate relationships between different data types
Solution Approach 1:
The patent segments the flat memory structure into multiple storage hierarchies, each dedicated to specific data types (e.g., sensory data, action data, object data). This segmentation allows the system to efficiently manage and access different types of information without treating all data equally, thereby improving resource utilization while maintaining implementation feasibility through structured organization.
Solution Approach 2:
The patent transitions from a one-dimensional flat memory structure to a multi-dimensional hierarchical structure with multiple levels and categories. This dimensional change enables the system to organize data by type, importance, and access frequency, creating a more efficient storage architecture that improves resource utilization without significantly complicating implementation.
2Loss of information
If all sensory input data is stored indefinitely, then complete information is preserved, but storage efficiency deteriorates due to unnecessary retention of outdated data
Solution Approach 1:
The patent implements a data retention policy where sensory input data is automatically discarded after serving its purpose in the CGR environment. The hierarchical structure allows the system to identify and remove outdated or redundant data from lower priority storage hierarchies, freeing up storage resources while maintaining access to essential information through the hierarchical organization.
Solution Approach 2:
The patent changes the parameter of data retention time based on data type and importance. Different storage hierarchies have different retention policies, with critical data retained longer and less critical sensory data discarded sooner. This parameter change optimizes storage efficiency by preventing indefinite retention of all data while maintaining information completeness for essential elements.
3Measurement precision
If computational resources are allocated to determine relationships between all data, then accurate data relationships are established, but processing efficiency deteriorates due to excessive computational overhead
Solution Approach 1:
The patent segments data into distinct hierarchical categories with predefined relationship rules. By organizing data into specific types (sensory, action, object) with established hierarchical relationships, the system eliminates the need for computational analysis of relationships between all data points. The hierarchical structure itself encodes the relationships, allowing accurate data relationship establishment without excessive computational overhead.
Data Source
AI summary
A method includes maintaining a hierarchical datastore for an agent instantiated in a computer-generated reality (CGR) environment. The hierarchical datastore includes a first storage hierarchy associated with a first data type and a second storage hierarchy associated with a second data type. The method includes detecting a sensory input that includes sensory input data. The method includes determining a type of the sensory input data. The method includes, in response to the sensory input data being of the first data type, storing the sensory input data in the first storage hierarchy for a first amount of time associated with the first storage hierarchy. The method includes, in response to the sensory input data being of the second data type, storing the sensory input data in the second storage hierarchy for a second amount of time that is different from the first amount of time.