AI Engine for Dynamic Real-Time Resource Value Generation
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Solution Overview
Problem
Conventional systems lack the capability to provide tailored, real-time resource values associated with resources, failing to dynamically update resource tags for users based on their real-time activity.
Innovation Solution
A system that uses an artificial intelligence engine to monitor user activity, generate real-time resource values, and transmit them to resource tags for display, incorporating triggers such as time spent or resource access, while also communicating with third-party devices to offer resources and receive notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are used to provide resource values, then system simplicity is maintained, but the capability to provide tailored user-specific real-time resource values is lost
Solution Approach 1:
The system is divided into distinct functional modules: a determination module that identifies user location, a monitoring module that tracks real-time activity, a trigger identification module that detects predefined conditions, and an artificial intelligence engine that generates resource values. This segmentation allows each component to perform its specific function independently, enabling tailored real-time resource value provision while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces an artificial intelligence engine as an intermediary component that processes user activity data and generates tailored resource values. This intermediary layer translates raw monitoring data into meaningful, personalized resource recommendations, bridging the gap between simple monitoring and complex personalized resource management without requiring the entire system to become uniformly complex.
2Loss of information
If real-time monitoring of user activity is implemented, then user-specific resource values can be generated, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs predefined triggers that are established in advance before real-time monitoring begins. These triggers represent predetermined conditions (such as specific user actions or time thresholds) that automatically initiate resource value generation. By preparing these triggers beforehand, the system avoids the complexity of analyzing every possible user action in real-time, reducing processing requirements while still capturing meaningful user-specific information.
Solution Approach 2:
The system continuously monitors user activity and uses this feedback to dynamically adjust and regenerate resource values. The monitoring module provides real-time feedback about user behavior, which the artificial intelligence engine processes to update resource recommendations. This feedback loop enables the system to maintain current, relevant information without requiring complex predictive modeling, as the system adapts responsively to actual user actions.
3Measurement precision
If artificial intelligence engine is used to generate real-time resource values, then tailored user-specific values are provided, but computational requirements and processing time increase
Solution Approach 1:
The artificial intelligence engine generates resource values based on partial information - specifically, only when predefined triggers are activated by user activity. Rather than continuously analyzing all possible user behaviors or generating resource values for all resources at all times, the system performs computational analysis selectively and partially, only when relevant user actions occur. This approach maintains high precision in tailoring resource values while significantly reducing overall computational power requirements.
Solution Approach 2:
The system changes the operational parameters of the artificial intelligence engine by using predefined triggers as input conditions. Instead of requiring the AI engine to learn and adapt to all possible user behaviors from scratch, the triggers provide structured, discrete parameters that the engine can process efficiently. This parameterization approach maintains the precision of tailored resource value generation while reducing the computational burden by constraining the input space to predefined, meaningful conditions.
Data Source
AI summary
Embodiments of the present invention provide a system for dynamically providing tailored user specific real-time resource values. The system is configured for determining that a user is at a third party location, in response to determining that the user is at the third party location, continuously monitoring real-time activity of the user, determining that the real-time activity meets one or more triggers, and generating, via an artificial intelligence engine, real-time resource values for one or more resources associated with the real-time activity of the user and the one or more triggers.