Dynamic Dashboard Administration via Gesture Ontology
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Solution Overview
Problem
Traditional CRM systems are not user-configured, leading to inefficiencies and less effective results due to their generic dashboard design, which does not adapt to individual user needs.
Innovation Solution
A dynamic dashboard system that uses speech-enabled devices and a semantic graph database to capture user interactions, parse them into gesture triples, and update the dashboard based on insights generated from these interactions, allowing for customization and improvement over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a generic dashboard design is used in CRM systems, then the system is easier to implement and maintain, but the ease of operation and user effectiveness deteriorates
Solution Approach 1:
The dashboard automatically configures itself by observing user interactions and behavior patterns. The system captures user actions, analyzes them through a semantic graph database, and autonomously adapts the dashboard layout and content without requiring manual user configuration or administrator intervention, allowing the dashboard to serve itself
Solution Approach 2:
The dashboard transitions from a static generic design to a dynamic adaptive interface. The system continuously monitors user interactions, processes gesture triples through the semantic graph database, and dynamically reconfigures dashboard elements based on identified user needs and behavior patterns, making the dashboard flexible and responsive to individual users
2Adaptability or versatility
If a fixed dashboard configuration is used, then the device complexity is reduced, but the adaptability to individual user needs deteriorates
Solution Approach 1:
The system implements a continuous feedback loop where user interactions with the dashboard are captured, analyzed through the semantic graph database, and used to generate insights that drive subsequent dashboard adaptations. This closed-loop feedback mechanism enables the dashboard to learn from user behavior and continuously improve its configuration for each individual user
Solution Approach 2:
The dashboard autonomously adapts to user needs without requiring manual configuration. The system captures user interactions, processes them through the semantic graph database to identify patterns and preferences, and automatically reconfigures itself to better serve each user's specific requirements
3Productivity
If manual dashboard configuration is required for each user, then the adaptability to user needs is improved, but the productivity and time efficiency deteriorates
Solution Approach 1:
The dashboard automatically configures itself by observing user interactions and behavior patterns. The system captures user actions, analyzes them through a semantic graph database, and autonomously adapts the dashboard layout and content without requiring manual user configuration or administrator intervention, saving significant time and improving productivity
Solution Approach 2:
The system performs preliminary analysis of user interactions and behavior patterns to proactively configure the dashboard before users explicitly request changes. By anticipating user needs through pattern recognition in the semantic graph database, the system prepares optimal dashboard configurations in advance
Data Source
AI summary
Methods and systems for dynamic dashboard administration are presented. Embodiments include displaying a dashboard through a graphical user interface (‘GUI’); receiving user instructions to manipulate one or more elements of the dynamic dashboard; capturing the manipulation of the one or more GUI elements; parsing, according to a gesture taxonomy and gesture ontology, the captured manipulation into one or more gesture triples; storing the gesture triples in an enterprise knowledge graph of a semantic graph database; identifying, in dependence upon the gesture triples, dashboard insights; updating the dashboard in accordance with the dashboard insights.