Contextual Virtual Workspace Semantic 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 workspace modules and external data sources, and prioritizing critical information for business events or processes.
Innovation Solution
A contextual virtual workspace that generates suggestions based on user interactions, ranking them by user role and relationships within the business enterprise, and dynamically updating modules to provide real-time access to relevant data from various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the workspace provides access to multiple data sources and content providers, then the quantity of available information increases, but the complexity of searching and identifying relevant content increases
Solution Approach 1:
The patent introduces a semantic context layer as an intermediary between users and multiple data sources. This layer includes a ontology model that standardizes data from different content providers, enabling unified searching and retrieval without users needing to navigate each source individually. The semantic context acts as a mediator that translates user queries into meaningful searches across heterogeneous data sources.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with workspace modules and data are tracked and used to refine future content recommendations. The semantic context learns from user behavior patterns, adjusting the relevance scoring and prioritization of suggested content based on actual user preferences and interaction history, thereby reducing search complexity over time.
2Device complexity
If the workspace lacks semantic knowledge of data content, then the simplicity of the workspace structure is maintained, but the ability to provide contextualized services and identify relationships between modules is reduced
Solution Approach 1:
The patent segments the workspace system into distinct layers: a simple user-facing workspace interface and a backend semantic context layer. The semantic context is divided into separate components including an ontology model, a context graph, and recommendation engines. This segmentation allows the workspace structure to remain simple while delegating complex semantic processing to specialized backend components.
Solution Approach 2:
A semantic context layer is introduced as an intermediary between the simple workspace interface and the underlying data sources. This layer includes an ontology model that adds semantic meaning to data without exposing complexity to users. The semantic context mediates between user actions and data retrieval, providing contextualized services while keeping the workspace structure straightforward.
3Loss of information
If relevant content from colleagues is made available to users, then the value of internal knowledge is increased, but the manageability and prioritization of information becomes more difficult
Solution Approach 1:
The system uses feedback from user interactions with colleague-generated content to refine prioritization algorithms. When users interact with (or ignore) suggested content from colleagues, the system learns from these patterns and adjusts the prioritization of future recommendations. This feedback loop enables automatic prioritization based on actual user needs rather than manual curation.
Solution Approach 2:
The patent changes the parameters used for prioritizing content from static metadata to dynamic factors including user role, contextual relevance, and interaction history. The recommendation system adjusts weighting parameters based on the specific business context and user profile, automatically prioritizing the most relevant colleague-generated content without manual intervention.
4Loss of information
If the workspace provides detailed data from multiple sources, then the completeness of information is improved, but the time required to process and analyze the data increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring data from multiple sources using the ontology model before users need it. Data is pre-tagged with semantic metadata, relationships are pre-established in the context graph, and potential recommendations are pre-computed. This preliminary processing reduces the time required for users to access and analyze complete information.
Solution Approach 2:
The patent applies local quality by providing different levels of data detail to different users based on their specific needs and roles. Rather than presenting all available data uniformly, the system tailors the completeness and detail of information to each user's contextual requirements, reducing processing time while maintaining necessary completeness for each individual user's tasks.
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 one or more workspace modules comprising data contained in a plurality of data objects associated with a business enterprise; 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; generating a plurality of suggestions comprising data contained in the plurality of data objects that is semantically related to the data interacted with by the user; ranking the plurality of suggestions based on a role of the user in the business enterprise; and presenting at least a portion of the ranked plurality of suggestions to the user.