Route-Value Offer Generation for Delivery Incentive Balancing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Incentivizing independent contractors to fulfill tasks is challenging, as conventional systems often lead to delayed and overpriced task completion by increasing incentive amounts.

Innovation Solution

Utilizing machine-learning models trained through reinforcement learning to generate offers for delivery drivers based on predicted delivery routes, driver behavior, and historical data, balancing task completion and cost effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If incentive amounts are increased to increase task completion, then task completion rate is improved, but cost of task completion increases

Engineering Contradiction:

Improvetask completion rateVSAvoidcost of task completion

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system dynamically adjusts offer parameters (incentive amounts, pricing) based on real-time analysis of driver behavior patterns, historical data, and route characteristics. Instead of uniformly increasing incentives, the machine learning model optimizes parameter combinations to achieve task completion at minimal cost by predicting which parameter settings will most effectively influence driver acceptance decisions.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements continuous feedback loops where driver responses to offers are analyzed and fed back into the machine learning model. This feedback mechanism allows the system to learn from actual driver behavior, refine its predictions, and progressively optimize offer generation strategies to improve task completion rates while controlling costs through data-driven adjustments.

Inventive Principle:

Principle #23Feedback

2Productivity

If incentive amounts are increased to increase task completion, then task completion rate is improved, but task completion time increases

Engineering Contradiction:

Improvetask completion rateVSAvoidtask completion time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of driver behavior patterns, route characteristics, and historical acceptance data before generating offers. By pre-processing and analyzing relevant factors in advance, the system can predict driver responses and optimize offer parameters to achieve faster acceptance rates without requiring inflated incentive amounts, thereby reducing overall task completion time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model dynamically adjusts offer parameters based on predicted driver responses and route characteristics. By optimizing the right combination of parameters (pricing, incentives, timing) rather than simply increasing incentive amounts, the system achieves higher task completion rates while minimizing delays associated with prolonged offer negotiation or driver hesitation.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If machine-learning models are used to generate offers, then offer acceptance rate is improved, but system complexity increases

Engineering Contradiction:

Improveoffer acceptance rateVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model operates autonomously to generate and optimize offers without requiring manual intervention. The system self-adjusts parameters based on learned patterns from historical data and real-time feedback, automatically improving acceptance rates while managing its own complexity through autonomous decision-making algorithms that adapt to changing conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses historical driver behavior data and past offer outcomes as training copies to learn and predict future driver responses. By analyzing copied patterns from historical data rather than starting from scratch, the machine learning model achieves high acceptance rates more efficiently, reducing the complexity burden by leveraging established behavioral patterns as reference points for offer generation.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250371445A1Reinforcement learning for generation of route-value vectors

Publication Date: 2025.12.04 VEHO TECH INC

AI summary

A method may include generating, by a reinforcement agent, an offer including a route and an offer amount, evaluating, using a simulated delivery driver within a simulated environment, the generated offer to accept or reject the generated offer, responsive to the simulated delivery driver accepting or rejecting the generated offer, updating a state of the simulated environment, and providing a reward to the reinforcement agent based on the state of the simulated environment.