Dynamic UI Modification via Local Knowledge Graph
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Solution Overview
Problem
Existing knowledge graph systems fail to dynamically adapt to user knowledge and interests, leading to inefficient data retrieval and lack of personalization, as they rely on static remote databases and manual updates, and do not effectively differentiate between user interest and knowledge.
Innovation Solution
A method and system for constructing a user-specific knowledge graph within an electronic device, dynamically modifying the User Interface (UI) by collating usage information, categorizing it into knowledge clusters, and forming a knowledge graph to reflect the user's knowledge level and interests, allowing for local storage and real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge graph data is stored in a remote database and retrieved via query, then data can be accessed by multiple users, but network bandwidth usage increases and data retrieval efficiency decreases
Solution Approach 1:
The patent extracts and stores knowledge graph data locally on user devices rather than keeping all data centralized in remote databases. This extraction of frequently accessed knowledge data to local storage reduces network bandwidth consumption while maintaining data accessibility, directly resolving the contradiction between versatile access and energy loss.
2Reliability
If knowledge graph data is manually updated by administrators, then data accuracy can be maintained, but system complexity and update time increase
Solution Approach 1:
The patent implements self-service mechanisms where the knowledge graph system automatically updates itself by extracting data from multiple sources, processing it through NLP algorithms, and maintaining the knowledge graph without requiring manual administrator intervention. This automation maintains data accuracy while significantly reducing system complexity and update time.
Solution Approach 2:
The system performs preliminary data processing and validation before updating the knowledge graph, preparing data in advance through automated extraction and NLP processing. This preliminary action ensures data accuracy is maintained while reducing the complexity of actual update operations.
3Measurement precision
If traditional classification systems use supervised models with manual topic updates, then classification accuracy can be maintained, but user burden and time consumption increase
Solution Approach 1:
The patent replaces manual supervised classification with self-service automated classification systems that use NLP algorithms and machine learning to automatically categorize and classify knowledge graph data. This maintains or improves classification accuracy while eliminating the time burden on users to manually update classification models.
4Ease of operation
If conventional systems do not differentiate between user interest and knowledge, then system simplicity is maintained, but personalization and user experience quality decrease
Solution Approach 1:
The patent segments user data into distinct categories of 'interest' and 'knowledge' profiles. By dividing user information into these separate segments, the system can maintain operational simplicity while enabling sophisticated personalization capabilities that adapt to both what users are interested in and what they already know.
Data Source
AI summary
A method and a system for dynamically modifying at least one element of a User Interface (UI) of a first electronic device are provided. The method includes collating usage information of at least one data source in the first electronic device, categorizing the collated usage information into one or more knowledge clusters, forming a knowledge graph using the one or more knowledge clusters, and dynamically modifying the at least one element of the UI based on the knowledge graph.