Smart Shopping Cart Weight Prediction from Partial Load Curves
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Solution Overview
Problem
Existing automated checkout systems using load sensors for item weight detection suffer from significant latency due to the time required for load curves to converge, leading to user inconvenience and delayed shopping experiences.
Innovation Solution
A smart shopping cart equipped with load sensors and cameras uses a detection system that predicts item weight based on the shape of the load curve and applies machine learning models to reduce latency by identifying items without waiting for the load curve to fully converge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If load sensors are used to detect item weight, then measurement precision is improved, but duration of action increases due to convergence time
Solution Approach 1:
The system performs preliminary actions by capturing load sensor data immediately when an item is detected in the shopping cart, before the load curve fully converges. The machine learning model processes this preliminary data to predict item weight and identity, eliminating the need to wait for convergence while maintaining measurement accuracy.
Solution Approach 2:
The patent replaces the mechanical/conventional approach of waiting for load sensor convergence with a computational approach using machine learning models. The ML model substitutes for the traditional convergence-waiting mechanism, enabling immediate item identification based on preliminary load data and shape information.
2Measurement precision
If load curves are waited for to converge, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system captures and processes load data in the preliminary phase when items are first detected, before convergence is achieved. This preliminary action enables the system to make immediate predictions about item weight and identity, significantly reducing detection delay while maintaining precision through the machine learning model's ability to extract meaningful patterns from incomplete data.
Solution Approach 2:
The machine learning model acts as an intermediary between the load sensor data and the final item identification. It processes the preliminary load curve data, infers item characteristics, and provides predictions without requiring the load curve to fully converge, thus mediating between raw sensor data and actionable information while reducing time loss.
3Measurement precision
If users wait for load sensors to converge, then measurement precision is improved, but ease of operation deteriorates due to required pauses
Solution Approach 1:
The system performs self-service by automatically detecting items, capturing load data, and generating predictions without requiring user intervention or pauses. The machine learning model autonomously processes the load curve data and provides item identification, making the system self-sufficient and eliminating the need for users to wait or pause during the shopping process.
Solution Approach 2:
The patent replaces the conventional mechanical approach of waiting for sensor convergence with an intelligent system that uses machine learning to immediately identify items. This substitution eliminates the need for user pauses and significantly improves ease of operation while maintaining measurement precision through sophisticated data processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly reduces the time required to predict item weight and identify items, enhancing user experience by minimizing delays and improving the efficiency of automated checkout processes.
Implementation Method 1
The smart shopping cart may include one or more load sensors to measure the total weight of the storage area and the weight of items added or removed from the cart's storage area
Implementation Method 2
The smart shopping cart may also include cameras that capture image data of the storage area
Implementation Method 3
A detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by one or more load sensors when an item is added
Implementation Method 4
The detection model applies the weight prediction model to the first set of load measurements to generate a first predicted weight of the added item and a confidence score corresponding to the first predicted weight
Data Source
AI summary
A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.