18301980. LOCAL PLANNING OPTIMIZATION USING MACHINE LEARNING AND SIGNAL STRENGTH simplified abstract (International Business Machines Corporation)
Contents
LOCAL PLANNING OPTIMIZATION USING MACHINE LEARNING AND SIGNAL STRENGTH
Organization Name
International Business Machines Corporation
Inventor(s)
Aaron K. Baughman of Cary NC (US)
Eduardo Morales of Key Biscayne FL (US)
Jeremy R. Fox of Georgetown TX (US)
LOCAL PLANNING OPTIMIZATION USING MACHINE LEARNING AND SIGNAL STRENGTH - A simplified explanation of the abstract
This abstract first appeared for US patent application 18301980 titled 'LOCAL PLANNING OPTIMIZATION USING MACHINE LEARNING AND SIGNAL STRENGTH
The computer-implemented process described in the abstract involves a queue management system that identifies users and configurable nodes, determines the proximity between a user and a specific node in real-time, calculates the probability of user engagement based on proximity and historical data, adds the user to a queue if the probability exceeds a threshold, and alters the state of the node accordingly.
- Identification of users and configurable nodes in a queue management system
- Real-time determination of user proximity to specific nodes
- Calculation of user engagement probability using machine learning and historical data
- Addition of users to a queue based on probability exceeding a threshold
- Alteration of node state in response to user addition to the queue
Potential Applications: - Optimizing customer service queues in retail stores - Streamlining appointment scheduling systems in healthcare facilities - Enhancing virtual queuing systems for online services
Problems Solved: - Efficiently managing user queues based on real-time data - Improving user engagement by predicting behavior - Enhancing overall system performance and user experience
Benefits: - Reduced wait times for users - Increased user satisfaction and engagement - Improved system efficiency and resource allocation
Commercial Applications: Title: "Enhanced Queue Management System for Improved User Engagement" This technology could be utilized in various industries such as retail, healthcare, and online services to optimize queue management systems, enhance user experience, and increase operational efficiency. The market implications include improved customer satisfaction, increased productivity, and potential cost savings for businesses.
Prior Art: Further research can be conducted in the field of queue management systems, machine learning algorithms for user behavior prediction, and real-time data analysis in similar technological applications.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for predictive analytics, real-time data processing technologies, and user engagement strategies in queue management systems.
Questions about the technology: 1. How does the system dynamically determine user proximity to configurable nodes? 2. What are the key factors considered in calculating the probability of user engagement based on historical data and proximity?
Original Abstract Submitted
A computer-implemented process using a queue management system includes the following operations. A user and a plurality of configurable nodes are identified in a scope of the queue management system. A proximity between the user and a particular one of the plurality of configurable nodes is dynamically determined in real-time. Using a machine learning engine and based upon the proximity and historical data associated with the scope of the queue management system, a probability measure that the user will engage with the particular one of the plurality of configurable nodes is determined. Based upon the probability measure exceeding a threshold, the user is added to a queue managed by the queue management system. Based upon the user being added to the queue, a state of the one of the plurality of plurality of configuration nodes is altered by the queue management system.
