Context Modeler Activation Model Entity Segmentation
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Solution Overview
Problem
Conventional applications lack effective context modeling, leading to poor context information availability for generic algorithms, resulting in context sensitivity that needs to be hard-coded for each screen, and limited relevance of data displayed to users.
Innovation Solution
A context modeler that utilizes a database with collections of references to entities and an activation model to assign importance attributes to context entities, enhancing context representation by accounting for activation attributes in modeling the context representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If context information is kept implicitly through user interface screens, then the application structure is simple, but the context information quality is poor and unavailable for generic algorithms
Solution Approach 1:
The context is segmented into multiple independent entities (user entities, application entities, data entities) that can be individually modeled, tracked, and managed. This segmentation allows generic algorithms to process specific entity types while maintaining overall context quality without requiring a monolithic complex structure.
Solution Approach 2:
An activation model acts as an intermediary between the implicit UI context and the explicit context representation. This intermediary layer processes and transforms screen context into structured entity activations, improving context information quality while keeping the underlying implementation modular and manageable.
2Adaptability or versatility
If context sensitivity is hard-coded for each screen, then the context information is available for display, but the adaptability to different situations is reduced
Solution Approach 1:
The activation model provides a universal mechanism that works across all screens and contexts. Instead of hard-coding context sensitivity for each screen, the same activation model processes entities from any screen, assigning activation values based on entity characteristics and relationships, thereby improving adaptability while reducing implementation complexity.
Solution Approach 2:
The context sensitivity becomes dynamic rather than static. The activation model continuously updates entity activation values based on current situation, user interactions, and entity relationships, allowing the system to adapt to different contexts automatically without requiring separate hard-coded rules for each scenario.
3Loss of information
If all entities are displayed with equal importance, then the interface is comprehensive, but the relevance of data displayed to users is reduced
Solution Approach 1:
Different entities are assigned different activation values based on their local characteristics and current context relevance. This local quality differentiation allows the interface to display entities with varying prominence based on their importance, improving data relevance while maintaining a unified interface structure that manages complexity.
Solution Approach 2:
The activation value parameter is used to dynamically adjust the display priority and visibility of entities. By changing this parameter based on entity characteristics and context, the system highlights relevant data while suppressing less important information, improving relevance without requiring complex interface structures.
Data Source
AI summary
A context modeler models a context representation and a method models a context representation. The context modeler models a context representation in an application. The context is represented in a current situation by at least one context entity that is included in at least one collection of references to a plurality of entities in a database in accordance with the situation. The context modeler includes an activation model for assigning an activation attribute to the at least one context entity indicating the importance of the at least one context entity in the current situation. The context modeler takes into account the activation attribute in modeling the context representation.