Hierarchical Group Data Selection via Tree Structure Tracing
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Solution Overview
Problem
Existing data management methods, such as using folders and playlists, are cumbersome for specifying data across multiple groups and fail to consider data attributes and similarities, leading to inefficient processing and resource-intensive operations.
Innovation Solution
An information processing device manages data using a tree structure, allowing for the identification of superior groups by retracing the tree structure and specifying data within subordinate groups as processing objects without referencing meta-information or content data, enabling efficient data selection based on usage frequency and association.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is grouped using folders or playlists, then data organization is achieved, but specifying data across multiple groups becomes cumbersome and requires creating new folders or playlists
Solution Approach 1:
The patent applies nesting by implementing a hierarchical group structure where groups can contain subgroups, forming a tree structure. This allows data to be organized in nested groups without requiring flat-folder approaches, enabling seamless specification of data across multiple groups by simply selecting the top-level group while automatically including all subordinate groups.
Solution Approach 2:
The patent implements multi-functionality by enabling a single group selection operation to simultaneously specify multiple data items across different subordinate groups. The group management function serves multiple purposes: organizing data hierarchically, enabling cross-group data specification, and providing automatic inclusion of subordinate groups without requiring separate operations for each group.
2Ease of operation
If all data is set as processing objects regardless of grouping, then data selection across groups is simplified, but attributes and similarities of data are not considered
Solution Approach 1:
The patent applies local quality by enabling different selection approaches for different hierarchical levels. Users can select specific subordinate groups when wanting to consider data attributes and similarities, or select the top-level group when wanting to include all data across groups. This localized approach to group selection preserves data attribute information while providing flexibility in specification scope.
3Measurement precision
If automatic extraction of similar data is performed by referencing contents and meta-information, then data selection with consideration of attributes and similarities is achieved, but resource consumption increases and user waiting time increases
Solution Approach 1:
The patent applies preliminary action by pre-organizing data into hierarchical groups based on attributes and similarities before processing. This pre-grouping eliminates the need for real-time automatic extraction and analysis of data attributes during processing operations. The hierarchical structure is established in advance, allowing rapid data specification without time-consuming analysis of contents and meta-information at the moment of processing.
Solution Approach 2:
The patent implements copying by using the hierarchical group structure as a pre-computed representation of data relationships. Instead of analyzing and extracting similar data in real-time, the system uses the pre-established group hierarchy that already captures data attributes and similarities. This copied structure enables rapid data specification without repeating the resource-intensive analysis process.
4Productivity
If hierarchical group structure with tree structure is implemented, then data specification across groups becomes efficient, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the data management system into hierarchical levels (top-level groups and subordinate groups). This segmentation allows independent management and selection at each level, simplifying the overall complexity. Users can work with only the relevant portion of the hierarchy at any given time, whether that's a single top-level group or specific subordinate groups, rather than managing all data at once.
Data Source
AI summary
An example computer 10 that manages folders containing files by using a tree structure is caused to execute a step S104 of identifying a superior folder from predetermined folders managed by the tree structure by retracing the tree structure by at least one level, and a step S501 of specifying a file belonging to a subordinate folder that is subordinate to the superior folder in the tree structure as a processing object.