Dynamic Information Reclassification via Attribute-Reduced Concept Lattice
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Solution Overview
Problem
Current data structures for classifying digital information objects, such as tree structures and tag-oriented systems, face inefficiencies in search accuracy and user experience due to complex hierarchies and excessive tag competition, leading to lengthy search processes and overwhelming results.
Innovation Solution
A system and method utilizing Formal Concept Analysis (FCA) to dynamically reclassify information objects by providing Dynamical Reclassifying Hints (DRHs), which combine attribute classifiers to reduce redundant search steps and present more accurate, organized results by establishing an Attribute-Reduced Concept Lattice, facilitating faster and more precise searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If tree structure classification is used to organize information objects, then information can be systematically categorized, but search efficiency deteriorates due to multiple hierarchical layers requiring sequential navigation
Solution Approach 1:
The patent segments the hierarchical classification into multiple parallel dimensions (tags representing different attributes), allowing users to navigate through any dimension independently rather than sequentially traversing fixed hierarchical layers, thus reducing search time while maintaining systematic organization
Solution Approach 2:
The patent transforms the single-dimensional hierarchical tree structure into a multi-dimensional tag space where information objects can be classified along multiple independent attribute dimensions simultaneously, enabling efficient filtering and search across different classification criteria without sequential navigation
2Adaptability or versatility
If multiple tags are assigned to each information object to increase search flexibility, then search coverage improves, but tag competition intensifies leading to overwhelming options for users
Solution Approach 1:
The patent pre-organizes tags into structured dimensions and relationships, performing the classification work in advance so that users don't need to manually evaluate and select from all possible tags, reducing the perceived complexity while maintaining comprehensive search flexibility
Solution Approach 2:
The patent introduces dimension relationships and tag hierarchies as intermediaries between users and the full tag set, guiding users through organized groups of related tags rather than presenting all tags simultaneously, thus reducing selection overload while preserving search versatility
3Stability of the object's composition
If fixed parent classifications are used in elastic list principle, then classification consistency is maintained, but adaptability to new information objects deteriorates when no suitable classification exists
Solution Approach 1:
The patent makes the classification system dynamic by allowing tags to be added, removed, and reorganized across different dimensions without restructuring the entire classification framework, enabling the system to adapt to new information objects while maintaining consistent classification relationships among existing objects
4Measurement precision
If comprehensive tagging is performed on all information objects to improve search accuracy, then search precision improves, but the number of tags and search combinations increases leading to inefficient search processes
Solution Approach 1:
The patent segments the comprehensive tag set into meaningful dimensional groups and relationships, allowing users to focus on specific attribute dimensions relevant to their search needs rather than evaluating all tags, thus maintaining search accuracy while improving efficiency by reducing the cognitive load of tag selection
Data Source
AI summary
A system, method and user apparatus dynamically reclassify and retrieve target information object(s) among multiple information objects stored on a memory. Multiple attribute classifiers are corresponsive to the information objects. Displayable dynamical reclassifying hints (DRHs) are provided according to user input signal(s). When a first attribute classifier is determined by a central processing unit according to the user input signal, second attribute classifier(s) is determined and combined with one of the attribute classifiers together visibly on a display unit; wherein the second attribute classifier and the combined one of attribute classifier corresponds to same one(s) of the information objects. The DRH(s) combines the attribute classifiers with the same search results together, so as to eliminate possible repeated steps or processes that lead to the same search result(s), and also to reduce the remained selectable attribute classifiers and the following steps to retrieve the target information objects.