Dell products l.p. (20240104158). INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES simplified abstract
Contents
- 1 INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES - 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
INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES
Organization Name
Inventor(s)
Ravi Chandra Chigurupalli of Leander TX (US)
Irfan Gilani of Cedar Park TX (US)
INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104158 titled 'INTELLIGENT PRODUCT SEQUENCING FOR CATEGORY TREES
Simplified Explanation
The abstract describes a methodology for predicting a sequence number for a product within a category tree based on relevant features determined from information about the product.
- Receiving information about a product from another computing device
- Determining relevant features from the product information
- Generating a prediction of a sequence number using a machine learning model
- Sending the prediction to the other computing device
Potential Applications
This technology could be applied in e-commerce platforms to predict the sequence of products within categories, helping with inventory management and recommendation systems.
Problems Solved
This technology helps in predicting the sequence of products within a category tree, which can assist in optimizing product placement, inventory management, and enhancing user experience in online shopping platforms.
Benefits
- Improved inventory management - Enhanced product recommendation systems - Optimized product placement within categories
Potential Commercial Applications
Optimizing inventory management systems in e-commerce platforms for better product organization and recommendation algorithms.
Possible Prior Art
One possible prior art could be existing machine learning models used for product recommendation systems in e-commerce platforms.
Unanswered Questions
How does this methodology handle changes in product information over time?
The methodology does not specify how it adapts to changes in product information that may occur over time. This could be a potential area for further research and development to ensure the accuracy and relevance of the predictions.
What is the computational complexity of the machine learning model used in this methodology?
The abstract does not provide information on the computational complexity of the machine learning model employed. Understanding the computational requirements of the model is crucial for assessing its scalability and efficiency in real-world applications.
Original Abstract Submitted
in one aspect, an example methodology includes, by a computing device, receiving information about a product from another computing device and determining one or more relevant features from the information about the product, the one or more relevant features influencing prediction of a sequence number. the method also includes, by the computing device, generating, using a machine learning (ml) model, a prediction of a sequence number for the product based on the determined one or more relevant features, the sequence number being indicative of a sequence within a product category tree. the method may also include, by the computing device, sending the prediction to the another computing device.
