18514778. Optimize Shopping Route Using Purchase Embeddings simplified abstract (Capital One Services, LLC)
Contents
- 1 Optimize Shopping Route Using Purchase Embeddings
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Optimize Shopping Route Using Purchase Embeddings - 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 How does the technology handle privacy concerns related to tracking user purchase history and behavior for recommendations?
- 1.11 What are the computational requirements for implementing purchase embeddings in a multi-dimensional space for large-scale e-commerce platforms?
- 1.12 Original Abstract Submitted
Optimize Shopping Route Using Purchase Embeddings
Organization Name
Inventor(s)
Anh Truong of Champaign IL (US)
Jeremy Goodsitt of Champaign IL (US)
Galen Rafferty of Mahomet IL (US)
Austin Walters of Savoy IL (US)
Vincent Pham of Seattle WA (US)
Optimize Shopping Route Using Purchase Embeddings - A simplified explanation of the abstract
This abstract first appeared for US patent application 18514778 titled 'Optimize Shopping Route Using Purchase Embeddings
Simplified Explanation
The patent application describes methods, systems, and apparatuses for recommending purchases to a user based on their past purchasing history and the purchase history of others, using a new descriptor called "purchase embeddings" in a multi-dimensional space.
- Purchase embeddings are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.
Potential Applications
The technology could be applied in e-commerce platforms to enhance personalized recommendations for users based on their purchase history and the behavior of similar users.
Problems Solved
1. Lack of personalized recommendations based on individual purchase history. 2. Difficulty in identifying relevant products for users based on their preferences and behavior.
Benefits
1. Improved user experience through personalized recommendations. 2. Increased likelihood of user engagement and satisfaction. 3. Enhanced marketing strategies based on user behavior analysis.
Potential Commercial Applications
Enhancing recommendation systems in online retail platforms for improved customer engagement and increased sales.
Possible Prior Art
There may be existing recommendation systems in e-commerce platforms that utilize user purchase history and collaborative filtering techniques, but the use of "purchase embeddings" in a multi-dimensional space could be a novel approach.
Unanswered Questions
The patent application does not provide details on how user data privacy is maintained while utilizing purchase embeddings for recommendations.
What are the computational requirements for implementing purchase embeddings in a multi-dimensional space for large-scale e-commerce platforms?
The patent application does not address the scalability and computational complexity of implementing purchase embeddings in real-world applications with a large user base and extensive product catalog.
Original Abstract Submitted
Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.
