Trained Model Identifies Wrong Delivery Location
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Solution Overview
Problem
Current online concierge systems face challenges in automatically identifying and correcting wrong delivery locations at a large scale, leading to manual and inefficient processes.
Innovation Solution
Utilizing a trained computer model to predict the likelihood of a delivery location being correct based on features of the order, such as content and user history, and generating a confidence score to prompt users to verify the accuracy of the delivery location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process is used to correct wrong delivery location, then accuracy of delivery location can be ensured, but productivity and scalability are reduced
Solution Approach 1:
The system performs self-service by automatically detecting potential wrong delivery locations using a trained computer model that analyzes order features and user history, generating confidence scores without requiring manual intervention for each case
Solution Approach 2:
The manual mechanical process of verifying delivery locations is replaced with an automated computer model that uses machine learning to predict likelihood of wrong delivery location based on order features and user behavior patterns
2Reliability
If manual update of delivery location is required every time user changes location, then accuracy can be maintained, but ease of operation and user convenience are reduced
Solution Approach 1:
The system performs preliminary action by proactively identifying and flagging potential wrong delivery locations before the user completes the order, using the computer model to predict errors based on order features and user history
Solution Approach 2:
The system implements feedback by providing confidence scores to the user about the likelihood of wrong delivery location, allowing the user to make informed decisions without manually updating every location change
3Productivity
If automated identification of wrong delivery location is implemented, then productivity and scalability are improved, but device complexity increases
Solution Approach 1:
The computer model acts as an intermediary between the order data and the delivery location verification process, analyzing order features and user history to generate confidence scores that guide the verification workflow
4Productivity
If computer model is used to predict likelihood of correct delivery location, then productivity is improved, but measurement precision requirements increase
Solution Approach 1:
The system changes parameters by using multiple order features and user history data as inputs to the computer model, transforming these parameters into a confidence score that reflects the likelihood of wrong delivery location
Data Source
AI summary
A trained computer model for automatic identification of a wrong delivery location for an order placed at an online system. The online system receives, via a user interface, a user input that includes a delivery location for the order. The online system compares the received delivery location with a stored delivery location for the user. Responsive to identifying that the received and stored delivery locations are different, the online system accesses and applies a computer model to predict, based on features of the order, a likelihood of the received delivery location being correct. The online system generates, based on the predicted likelihood, a confidence score of the received delivery location being correct. Responsive to the confidence score being below a threshold score, the online system causes a device of the user to display a user interface with a message prompting the user to verify accuracy of the received delivery location.