Fulfillment Attribute Generation from Item Imagery for Faster Checkout
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Solution Overview
Problem
Customers in brick-and-mortar stores often abandon purchases due to the inconvenience of waiting for a store clerk to provide a shipping quote, leading to potential sales loss for merchants.
Innovation Solution
A computing device using computer vision and machine learning to automatically generate fulfillment attributes, such as shipping quotes, by analyzing item imagery, reducing the need for manual calculations and network lookups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a store clerk manually provides a shipping quote, then the customer receives personalized service, but the customer has to wait a long time and may abandon the purchase
Solution Approach 1:
The system enables self-service by allowing the customer to scan the item's barcode themselves using a mobile device, and the shipping quote is automatically generated and displayed without requiring store clerk intervention. This eliminates the waiting time while maintaining service quality.
Solution Approach 2:
The manual process of a store clerk calculating and providing shipping quotes is replaced by an automated computer vision system that captures item imagery, extracts characteristics, and generates shipping quotes automatically. This substitution of mechanical human labor with an automated system resolves the time delay issue.
2Adaptability or versatility
If the merchant uses manual methods to determine shipping costs, then flexibility is maintained, but productivity decreases and sales are lost
Solution Approach 1:
The system performs preliminary actions by pre-extracting item characteristics from images and pre-calculating shipping quotes before the customer completes the purchase. This allows shipping information to be ready immediately when needed, maintaining flexibility while dramatically improving transaction speed.
Solution Approach 2:
An automated intermediary system acts as a bridge between the customer's item selection and the shipping cost calculation. This intermediary automatically processes item imagery, extracts characteristics, and retrieves shipping rates, maintaining the flexibility of customized quotes while eliminating manual processing delays.
3Productivity
If computer vision and machine learning are used to automatically generate fulfillment attributes, then productivity and speed are improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional approach where a single integrated platform performs multiple tasks: capturing item imagery, extracting characteristics using computer vision, determining fulfillment attributes, and generating shipping quotes. This universal system handles diverse item types and shipping scenarios without requiring separate specialized systems for each function.
Data Source
AI summary
Generating a fulfillment attribute(s) associated with an item(s) based on inputs associated with the item(s) is described. One or more inputs (e.g., images) of an item (packaged or unpackaged) can be analyzed, and characteristics associated with the item can be determined based on the analysis of the inputs. Upon receiving a user-specified delivery location, fulfillment attribute(s) can be determined based on the delivery location and the characteristics associated with the item, and the fulfillment attribute(s) can be displayed to a user. For example, a point-of-sale (POS) device can be used during a checkout process at a merchant location to capture an image(s) of an item, and to display a shipping quote for selection by a user based on the captured item imagery.