Dynamic Effort-Based Delivery Value Predictions
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Solution Overview
Problem
Conventional delivery logistics platforms face inefficiencies due to undervaluation or overvaluation of deliveries, leading to reduced acceptance rates among couriers and inconsistent compensation, which affects the alignment of incentives between couriers, customers, and merchants.
Innovation Solution
A system that generates dynamic effort-based delivery value predictions using a server configured to receive events and timestamps, incorporating weighted factors like time, weather, and historical courier performance to determine a service value for real-time deliveries, adjusting active time values based on location and acceptance rates, and continuously training predictive models to refine delivery duration estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mechanisms for valuation of deliveries are used, then the system is simple to operate, but deliveries are undervalued or overvalued causing courier decline
Solution Approach 1:
The patent implements dynamic valuation by continuously updating delivery values based on real-time courier acceptance rates, historical performance data, and contextual factors (weather, time, location). The system transitions from static conventional valuation to a dynamic model that adapts to changing conditions, using machine learning algorithms to process multiple data streams and generate updated valuations that reflect current system state and courier behavior patterns.
Solution Approach 2:
The system incorporates feedback loops where courier acceptance decisions and performance data are fed back into the valuation model. The processor continuously monitors courier acceptance rates and uses this feedback to adjust delivery valuations, creating a closed-loop system that learns from actual courier behavior and refines valuation accuracy over time through iterative model training and updates.
2Productivity
If conventional delivery valuation is used, then compensation is consistent, but courier acceptance rates decrease
Solution Approach 1:
The system dynamically changes compensation parameters based on multiple factors including delivery distance, time of day, weather conditions, location characteristics, and courier performance metrics. Instead of fixed compensation rates, the system adjusts valuation parameters in real-time to reflect the actual effort and difficulty of each delivery, ensuring compensation aligns with the specific operational context and courier skill level.
Solution Approach 2:
The valuation system transitions from static compensation structures to dynamic adjustment mechanisms that respond to real-time conditions. The processor continuously recalculates delivery values based on changing parameters such as courier acceptance rates, historical performance data, and environmental factors, creating a flexible compensation system that adapts to both system-wide trends and individual delivery characteristics.
3Reliability
If dynamic effort-based valuation is implemented, then courier incentives are aligned, but computational requirements increase
Solution Approach 1:
The system performs preliminary computations by pre-processing and storing historical courier performance data, location information, and contextual factors in databases. Machine learning models are trained in advance on historical data to establish baseline valuation patterns. This preliminary action reduces the computational burden during real-time operations, as the system can leverage pre-computed models and stored data rather than performing full calculations for each delivery valuation.
Solution Approach 2:
The patent replaces complex real-time computational mechanics with pre-trained machine learning models that have already learned valuation patterns from historical data. Instead of performing exhaustive calculations for each delivery, the system uses these trained models to generate valuations more efficiently, substituting heavy computational mechanics with optimized algorithmic approaches that require less processing power while maintaining accuracy.
Data Source
AI summary
Described are systems and processes for generating dynamic effort-based delivery value predictions for real-time delivery of perishable goods. In one aspect, a system is configured for generating dynamic delivery value predictions for delivery opportunities provided to couriers. For each order, delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a predicted delivery duration with trained predictive models that use weighted factors such as order data and historical restaurant data. A service value for the delivery of the order is determined based the predicted delivery duration and a predetermined active time value. The service value is then transmitted along with the corresponding delivery opportunity to a user device of a courier. The determined service values may be adjusted based on courier acceptance rates of delivery opportunities and other factors such as customer experience.