Contextual Virtual Workspace Semantic Data Suggestions
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Solution Overview
Problem
Business users face challenges in accessing relevant data within a virtual workspace due to the lack of semantic knowledge, making it difficult to identify relationships between modules and external data sources, and often encounter disjointed and unmanageable information.
Innovation Solution
A contextual virtual workspace that generates suggestions based on user interactions, providing semantically related data by analyzing metadata and user behavior, and dynamically updating modules to prioritize relevant content in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the workspace provides comprehensive data from multiple content providers, then the quantity of available information increases, but the information becomes disjointed and unmanageable
Solution Approach 1:
The patent introduces a semantic context layer as an intermediary between raw data from multiple content providers and the user interface. This semantic layer processes and structures the data, creating meaningful relationships and hierarchies that make the information manageable while preserving the comprehensive quantity of data from all sources.
Solution Approach 2:
The system dynamically changes the organizational parameters of data presentation based on user interactions and context. By transforming raw data into semantically structured information with varying levels of aggregation and relationship types, the system maintains data quantity while improving manageability through adaptive parameter changes.
2Device complexity
If the workspace has no semantic knowledge of data, then the system complexity is reduced, but the workspace cannot provide services that rely on semantic context
Solution Approach 1:
The patent segments the system into distinct layers: a simple data collection layer that gathers information from content providers, and a semantic processing layer that adds contextual knowledge. This segmentation allows the base system to remain relatively simple while the semantic layer provides advanced services, resolving the contradiction between complexity and capability.
Solution Approach 2:
The semantic context layer is nested within the workspace system, containing sophisticated semantic processing capabilities while being encapsulated as a manageable subsystem. This nesting allows complex semantic services to be provided without overwhelming the overall system architecture, maintaining clarity while enabling versatility.
3Quantity of substance
If relevant content is collected from various content providers, then the completeness of information increases, but the data requires culling and prioritization according to different criteria
Solution Approach 1:
The system implements feedback mechanisms where user interactions with data (such as selections, views, and modifications) are monitored and used to refine the prioritization and culling of content. This feedback loop automatically adjusts which content is emphasized without requiring complex manual processing, maintaining information completeness while managing processing complexity through adaptive learning.
Data Source
AI summary
Techniques for managing a virtual workspace include: generating a virtual workspace viewable by a user on a graphical user interface, the virtual workspace comprising a plurality of workspace modules comprising data contained in a plurality of data objects; identifying an interaction by the user with at least some of the data contained in a particular data object of the plurality of data objects; and providing, through the virtual workspace, at least one suggestion comprising a description of data contained in the plurality of data objects that is semantically related to the data interacted with by the user.