Walmart apollo, llc (20240257035). SYSTEMS AND METHODS FOR DRIVER PLATFORM ANALYSIS simplified abstract
SYSTEMS AND METHODS FOR DRIVER PLATFORM ANALYSIS
Organization Name
Inventor(s)
Minghui Liu of San Bruno CA (US)
Yuan Wang of San Francisco CA (US)
Jing Huang of San Jose CA (US)
Mingang Fu of Palo Alto CA (US)
SYSTEMS AND METHODS FOR DRIVER PLATFORM ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240257035 titled 'SYSTEMS AND METHODS FOR DRIVER PLATFORM ANALYSIS
Simplified Explanation: The patent application describes systems and methods that use historical driver search information to optimize the timing of offers for delivery services, reducing delays and ensuring deliveries are made within specified time windows.
- Machine learning model is built based on driver search information to determine metrics.
- Optimization model is used to analyze metrics and determine optimal offer publish time.
- Orders for delivery are transmitted to drivers based on the optimized timing to reduce delays and ensure on-time deliveries.
Key Features and Innovation:
- Utilization of historical driver search data for optimizing offer publish timing.
- Integration of machine learning and optimization models to improve delivery service efficiency.
- Focus on reducing driver lag time and ensuring deliveries are made within specified time windows.
Potential Applications: The technology can be applied in the logistics and transportation industry to enhance delivery operations and improve customer satisfaction by ensuring timely deliveries.
Problems Solved: The technology addresses the challenges of optimizing offer publish timing to reduce delays and ensure deliveries are made within specified time windows, improving overall delivery service efficiency.
Benefits:
- Reduced driver lag time
- Improved delivery service efficiency
- Enhanced customer satisfaction through on-time deliveries
Commercial Applications: Optimized offer publish timing technology can be utilized by delivery service providers to streamline operations, reduce delays, and enhance customer experience, leading to increased market competitiveness and profitability.
Prior Art: Further research can be conducted in the field of optimizing delivery service operations through historical data analysis and machine learning models to identify any existing technologies or methodologies related to this innovation.
Frequently Updated Research: Continued research in the optimization of delivery service operations through data analysis and machine learning can lead to further advancements in improving delivery efficiency and customer satisfaction.
Questions about Optimized Offer Publish Timing: 1. How does the technology of optimized offer publish timing impact delivery service efficiency? 2. What are the potential implications of using historical driver search data for optimizing delivery operations?
Original Abstract Submitted
systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. other embodiments are disclosed herein.