UI State Machine for Picker Task Batches
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Solution Overview
Problem
Conventional online concierge systems are limited in optimizing task allocation for pickers due to order-based batching, which restricts the solution space and does not consider optimizations from collecting items at multiple retailer locations, and lacks efficient UI management for task updates.
Innovation Solution
The system generates task units based on orders and assigns batches of these units to pickers, using a scoring function to optimize task batches and employs a UI state machine to dynamically update the task user interface based on task units, allowing for broader solution space exploration and improved computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If order-based batching is used to assign tasks to pickers, then the system structure remains simple and easy to implement, but the solution space is constrained and optimization opportunities are lost
Solution Approach 1:
The patent segments orders into task units, where each task unit represents a discrete task (e.g., collecting an item, delivering an item). This segmentation allows the system to treat tasks independently rather than being constrained by order boundaries, enabling pickers to collect items from multiple retailer locations and delivering to multiple locations within a single batch assignment.
Solution Approach 2:
The patent transitions from a one-dimensional order-based batching approach to a multi-dimensional task unit approach. By breaking down orders into constituent tasks and allowing tasks to be assigned across multiple dimensions (retailer locations, delivery locations, task types), the system expands the solution space while maintaining manageable complexity through structured task unit management.
2Adaptability or versatility
If tasks are broken down into constituent task units, then optimization opportunities increase and solution space expands, but computational complexity increases
Solution Approach 1:
By segmenting orders into task units, the system can process and optimize tasks independently, which simplifies the computational problem compared to optimizing entire orders. The task unit structure allows for more efficient algorithms that can handle larger solution spaces without exponential complexity increases.
Solution Approach 2:
The patent applies partial action by processing only the necessary task units for each batch assignment rather than considering all possible order permutations. This selective processing approach reduces computational complexity while still exploring sufficient solution space to find optimal or near-optimal assignments.
3Device complexity
If conventional order-based batching is used, then computational resources are easier to manage, but optimization of task allocation is limited
Solution Approach 1:
The patent introduces dynamic task unit management where tasks can be flexibly assigned, reassigned, and optimized based on real-time conditions. The system can dynamically adjust batch assignments and task sequences to optimize productivity while managing computational resources through structured algorithms that process task units efficiently.
Solution Approach 2:
The system changes the fundamental parameters of task assignment from order-level to task unit-level. This parameter change enables fine-grained optimization of task allocation, allowing the system to consider factors such as picker location, task proximity, and delivery timing to optimize productivity without overwhelming computational resources.
4Device complexity
If a static user interface is used for task display, then the interface is simpler and more stable, but it cannot adapt to unusual task permutations or changes
Solution Approach 1:
The patent implements a dynamic user interface that automatically adapts to different task permutations and changes. The interface uses state machine patterns to transition between different display states based on the current task batch composition, allowing pickers to receive customized interfaces for unusual task permutations without manual configuration.
Solution Approach 2:
The user interface is designed with universal components that can handle multiple task types and permutations through a single unified interface structure. By using parameterizable template patterns, the interface can display collection tasks, delivery tasks, and edge cases uniformly, reducing interface complexity while increasing adaptability.
Data Source
AI summary
The online concierge system generates task units based on orders and assigns batches of task units to pickers. The online concierge system generates task units based on received orders. The online concierge system generates permutations of these task units to generate candidate sets of task batches. The online concierge system scores each of these candidate sets, and selects a set of task batches to assign to pickers based on the scores. Additionally, to determine which task UI to display to the picker, the picker client device uses a UI state machine. The UI state machine is a state machine where each state corresponds to a task UI to display on the picker client device. The state transitions between the UI states of the UI state machine indicate which UI state to transition to from a current UI state based on the next task unit in the received task batch.