Attribute-Based Data Clustering for HMD Search
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Solution Overview
Problem
Existing systems for displaying information to users, such as head-mounted displays (HMDs), face challenges in efficiently and intuitively searching and navigating through vast amounts of stored visual and audio data associated with real-world experiences, making it difficult for users to access relevant information.
Innovation Solution
A data item-attribute database system that allows users to select attributes and group data items into clusters based on shared attributes, providing visual indications of these clusters for easy navigation and retrieval, utilizing a processor and non-transitory computer-readable medium to execute program instructions for maintaining and searching the database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search through vast amounts of stored visual and audio data, then complete information can be found, but the time required to access relevant information increases significantly
Solution Approach 1:
The patent segments the vast database into hierarchical clusters based on shared attributes. Data items are grouped into parent clusters and child clusters, allowing users to navigate from broad categories to specific items systematically, reducing search time while ensuring complete information access.
Solution Approach 2:
The patent introduces attribute-based clustering as an intermediary layer between the user and the raw data. This intermediary organization system mediates the search process by pre-grouping data items according to their attributes, eliminating the need for users to manually sift through all stored data.
2Ease of operation
If data is organized into detailed clusters by multiple attributes, then navigation efficiency improves, but the system complexity increases
Solution Approach 1:
The patent divides the data organization system into manageable segments: attributes, data items, parent clusters, and child clusters. This segmentation allows the complex task of organizing vast data to be broken down into simpler, hierarchical groupings that are easier to navigate and manage.
Solution Approach 2:
The patent adds a hierarchical dimension to data organization by creating parent-child cluster relationships. This dimensional transformation allows users to navigate data through multiple levels of abstraction, improving ease of operation while managing complexity through structured hierarchy rather than flat, overwhelming data presentation.
3Quantity of substance
If the system stores abundant visual and audio information from real-world experiences, then information completeness increases, but the difficulty of searching and navigating through the data increases
Solution Approach 1:
The patent introduces attribute-based clustering as an intermediary indexing system between the stored data and search operations. This intermediary structure automatically organizes the abundant visual and audio information by their attributes, transforming the search task from examining raw data to navigating organized clusters, thereby reducing search difficulty while preserving information completeness.
Solution Approach 2:
The patent performs preliminary organization of data into attribute-based clusters before the user needs to search. By pre-grouping data items according to their attributes and creating hierarchical cluster structures in advance, the system eliminates the need for users to perform difficult search operations on unorganized data, making navigation significantly easier.
Data Source
AI summary
Methods and systems for searching and navigating through data items in a database are provided. In one example, each data item in the database is associated with attributes and attribute values. The data items in the database may be grouped according to a first selection of attributes and attribute values, forming a first data item cluster of data items associated with the first selection of attributes and attribute values. From among the attributes and attribute values associated with data items in the first data item cluster, a second selection of attributes and attribute values may be made. A second data item cluster may be formed including data items associated with the second selection of attributes and attribute values. After a desired data item is found, a computing action associated with the desired data item may then be executed.