18072194. Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers simplified abstract (ZEBRA TECHNOLOGIES CORPORATION)
Contents
- 1 Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers
Organization Name
ZEBRA TECHNOLOGIES CORPORATION
Inventor(s)
Karthikeyan Nagarajan of Chennai (IN)
Anil Ozdemir of Sheffield (GB)
Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers - A simplified explanation of the abstract
This abstract first appeared for US patent application 18072194 titled 'Systems and Methods for Predicting a Required Number of Opened Point of Sale (POS) Stations to Accommodate a Number of Customers
Simplified Explanation
The abstract describes a method for predicting the required number of point of sale (POS) stations to accommodate a number of customers by analyzing image data and utilizing a machine learning algorithm.
- The method involves analyzing image data to determine customer data and cart occupancy values.
- A machine learning algorithm is used to generate a value based on the customer data and cart occupancy values.
- The algorithm is trained using training data including customer data and cart occupancy values.
- An alert is generated if the generated value exceeds a certain threshold.
Potential Applications
This technology could be applied in retail stores, supermarkets, and other businesses to optimize the number of POS stations based on customer traffic.
Problems Solved
This technology helps businesses accurately predict the number of POS stations needed to efficiently serve customers, reducing wait times and improving customer satisfaction.
Benefits
The benefits of this technology include improved customer service, optimized staff allocation, and increased operational efficiency for businesses.
Potential Commercial Applications
The potential commercial applications of this technology include retail chains, grocery stores, fast-food restaurants, and any business with customer checkout processes.
Possible Prior Art
One possible prior art could be systems that use image data and machine learning algorithms for customer behavior analysis in retail environments.
What are the limitations of this technology in real-world applications?
This technology may face challenges in accurately predicting customer traffic during peak hours or special events where there are fluctuations in customer behavior.
How does this technology compare to traditional methods of determining the number of POS stations needed?
This technology offers a more data-driven and automated approach compared to traditional methods that rely on manual observation or historical data analysis.
Original Abstract Submitted
Systems and methods for method for predicting a required number of point of sale (POS) stations to accommodate a number of customers are disclosed herein. An example method includes analyzing image data to determine (i) a set of customer data and (ii) a cart occupancy value associated with the customers, and generating, utilizing a machine learning (ML) algorithm, a first value based on the set of customer data and the cart occupancy value associated with the customers. The ML algorithm may be trained using a plurality of training data including a plurality of training customer data and a plurality of training cart occupancy values. The example method further includes determining whether the first value exceeds a second value, and responsive to determining that the first value exceeds the second value, generating an alert for transmission to a device indicating that the first value exceeds the second value.
