Contextual User Models for Cross-App Semantic Awareness
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Solution Overview
Problem
Traditional computing systems lack a collective, semantic model of user interactions across multiple applications, failing to understand the meaningful activities users perform, which limits communication and collaboration among users, especially in group settings.
Innovation Solution
A common interest discovery module that accesses contextual user models, incorporating gaze-tracking data and semantic descriptions of user interface elements to identify collective interaction contexts, forming a collective contextual user model that aggregates user-specific interaction data and applies cognitive parameters to infer common interests and present relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional computing systems maintain separate semantic models within individual applications, then each application can maintain its own semantic understanding, but the system cannot achieve collective semantic awareness across multiple applications and users
Solution Approach 1:
The patent introduces a contextual user model as an intermediary layer between applications and users. This model aggregates semantic information from multiple applications and users, serving as a mediator that enables collective awareness without requiring fundamental changes to individual application architectures. The contextual user model sits at the intersection of applications and users, harmonizing their separate semantic models into a unified understanding.
Solution Approach 2:
The contextual user model serves multiple functions simultaneously: it maintains individual user profiles, aggregates cross-application semantic information, tracks collective user interactions, and provides a unified interface for both applications and users. This multi-functionality allows the system to achieve collective semantic awareness without proportionally increasing system complexity.
2Productivity
If the system aggregates user-specific interaction data from multiple applications to form collective contextual models, then collaboration and communication among users improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the data processing task by maintaining separate contextual user models for different users and applications, then aggregating only the relevant semantic information at the collective level. Rather than processing all user data uniformly, the system segments interactions by user, application, and context, selectively aggregating only the semantic meanings that contribute to collective understanding, thereby reducing unnecessary computational overhead.
Solution Approach 2:
The system performs preliminary processing of user interactions within individual applications before aggregation to the collective level. By pre-processing and filtering interaction data to extract only semantically meaningful actions, the system reduces the volume of data that requires aggregation and processing at the collective level, thereby lowering overall computational requirements while maintaining collaboration efficiency.
3Ease of operation
If the system processes and aggregates real-time sensor inputs and interaction data across multiple applications, then the system can provide intelligent assistance and adaptive presentation, but energy consumption increases
Solution Approach 1:
The contextual user model performs self-service processing by maintaining its own updated representation of user interactions and semantic meanings without requiring continuous external input. The model autonomously processes incoming sensor data and interaction information, filtering and aggregating only the necessary semantic elements, thereby reducing the energy required for continuous intelligent assistance while maintaining operational effectiveness.
Solution Approach 2:
The system dynamically adjusts processing parameters based on contextual relevance and user interaction patterns. Rather than processing all sensor inputs uniformly, the system changes its processing intensity and data aggregation scope based on the current context, user preferences, and interaction history, thereby maintaining intelligent assistance capability while optimizing energy consumption by processing only when and where necessary.
Data Source
AI summary
A method, apparatus, and system for modeling interactions of a group of users with a computing system includes monitoring passive and active interaction data of multiple users and discovering common interests among the users based on the interaction data. Collective contextual user models may be dynamically formed based on the common interests. Such models have many useful applications that can benefit the group or the individual members of the group.