Shopping Cart Session Linking via Distance-Action Correlation
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Solution Overview
Problem
Conventional automated checkout systems require manual user authentication through shopping carts, wasting computing resources and battery life, and conventional attribution models fail to accurately attribute user actions to recipe suggestions, leading to inefficient resource usage and ineffective recommendations.
Innovation Solution
An automated checkout system establishes sessions between users and shopping carts based on sensor data correlations and action events, attributing recipe suggestions to user actions by determining when items are added to the cart, thereby reducing resource waste and improving recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user authentication is required through shopping carts, then user identity verification is achieved, but the shopping cart interface becomes more difficult and time-consuming to utilize
Solution Approach 1:
The system automatically detects user presence and establishes sessions without requiring manual authentication. The shopping cart system self-identifies users through sensor data correlation, eliminating the need for users to manually sign in and reducing interface complexity.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring sensor data and pre-establishing session correlations before users need to interact with the cart. This allows the system to be ready to attribute actions to users automatically, eliminating wait time for authentication.
2Reliability
If manual user authentication is required, then user identification is achieved, but computing resources and battery life are wasted while the cart stands idle
Solution Approach 1:
The system continuously monitors sensor data and maintains session correlations in real-time, eliminating idle periods where computing resources would be wasted. The continuous action of detecting and correlating sensor data ensures user identification is ready immediately when needed.
Solution Approach 2:
The system performs preliminary session establishment by continuously analyzing sensor data before authentication is needed. This preliminary correlation of user actions with cart sessions ensures that when a user interacts with the cart, identification is already complete, eliminating wasted idle time.
3Adaptability or versatility
If additional recommendations are made after users decide to procure items, then recommendation coverage is increased, but attribution models fail to properly attribute user procurement actions
Solution Approach 1:
The system performs preliminary attribution by establishing session correlations and tracking user actions before recommendations are made. This ensures that when items are procured, the system has already recorded the precise timing and context, enabling accurate attribution even if recommendations are delivered later.
Solution Approach 2:
The system uses feedback from tracked user actions to refine attribution. By continuously monitoring whether users procure recommended items and comparing this with recommendation timing and session data, the system can accurately attribute procurement actions to specific recommendations, even when there is a time delay.
4Device complexity
If conventional attribution models are used, then recommendation delivery is simplified, but computing resources are wasted providing ineffective recommendations
Solution Approach 1:
The system performs preliminary filtering by analyzing session correlations and user action patterns before generating recommendations. This preliminary analysis identifies users who are most likely to act on recommendations, allowing the system to skip generating recommendations for users who would not respond, thus saving computing resources.
Solution Approach 2:
The system applies attribution only to recommendations that have measurable impact on user procurement actions. Rather than attempting to attribute all recommendations, the system focuses computational resources on tracking and attributing only those recommendations that actually influence user behavior, reducing wasted effort on ineffective recommendations.
Data Source
AI summary
An automated checkout system automatically establishes sessions between users and shopping carts by correlating action events with distances of the user’s client device to the shopping cart. The automated checkout system determines the client device’s distance from the shopping cart at timestamps when an action event occurs with respect cart. If the distances and the action events are correlated, the system establishes a session between the user and the shopping cart. Additionally, the automated checkout system attributes target actions to recipe suggestions. The automated checkout system displays a recipe suggestion to a user on a display of a shopping cart, and identifies an item added to the shopping cart. If the added item matches an item in the set of recipes, the automated checkout system applies an attribution model that determines whether to attribute a target action that relates to the item with the recipe suggestion.