Self-Learning Predictive Search System for Dynamic Dashboards
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Solution Overview
Problem
Traditional dashboards are static and unable to anticipate user requests, failing to provide dynamic and context-specific information, which limits their ability to assist users in achieving their desired outcomes.
Innovation Solution
A self-learning natural language predictive searching system that combines natural language processing with machine learning to anticipate user queries and guide users through a dynamic dashboard, using context, user behavior, and previous searches to provide relevant and associated results, allowing for personalized and multi-dimensional searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional dashboards use fixed or pre-determined visual representations, then the dashboard structure is simple and easy to manufacture, but the dashboard cannot anticipate user requests and fails to provide dynamic context-specific information
Solution Approach 1:
The patent implements dynamic dashboards that automatically adjust their structure and content based on user behavior patterns, context analysis, and machine learning predictions. The dashboard transitions from static pre-determined layouts to dynamic configurations that adapt in real-time to user needs, resolving the contradiction between adaptability and structural simplicity
Solution Approach 2:
The system employs machine learning algorithms that enable the dashboard to self-learn from user interactions and automatically optimize its own structure and content presentation. The dashboard serves itself by anticipating user requests and reconfiguring without manual intervention, achieving adaptability while maintaining ease of operation
2Productivity
If dashboards are designed to be static with pre-determined layouts, then the ease of operation is high, but the productivity in terms of information delivery and user outcome achievement is limited
Solution Approach 1:
The system performs preliminary actions by using machine learning to predict and prepare relevant information and dashboard configurations before users actually request them. The dashboard anticipates user needs and pre-loads or pre-displays relevant data, improving information delivery efficiency while maintaining ease of operation through automatic preparation
Solution Approach 2:
The system implements feedback loops where user interactions with the dashboard are continuously analyzed to refine predictions and improve future information delivery. This feedback mechanism enables the dashboard to learn from user behavior patterns and progressively enhance productivity while adapting to individual user preferences for ease of operation
Data Source
AI summary
Systems and methods are provided for self-learning natural language predictive searching including receiving a first input, the first input being related to the desired outcome; retrieving a first information related to the first input; determining a first output based on at least the first input and the first information; outputting the first output; receiving a second input based on the outputted first output in response to the first output being different from the desired outcome, the second input being related to the desired outcome; retrieving, by the processor, a second information related to the second input; determining a second output based on at least the second input, the second information, the first input and the first information; and outputting the second output.