17936835. USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE simplified abstract (AT&T Intellectual Property I, L.P.)
Contents
- 1 USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE
Organization Name
AT&T Intellectual Property I, L.P.
Inventor(s)
Kapil Gupta of Flower Mound TX (US)
Karthik Viswanathan of Irving TX (US)
Sundara Subramanian Athinarayanan Sundaram Mohan Narayanan of McKinney TX (US)
Mary Narisetti of Johns Creek GA (US)
USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17936835 titled 'USING DECISION TREES TO PROVIDE A GUIDED E-COMMERCE EXPERIENCE
Simplified Explanation
The method described in the abstract involves using a decision tree to collect information from a user who wishes to purchase a product. The system presents queries to the user based on the decision tree and user inputs, ultimately recommending a product based on the collected information.
- The method involves receiving a signal from a user indicating interest in purchasing a product.
- A decision tree is initialized to gather information from the user.
- The system presents queries to the user based on the decision tree and user inputs.
- User inputs, such as feature preferences or budget constraints, are collected through the presented queries.
- The system uses the collected information to recommend a product to the user.
Potential Applications
This technology could be applied in e-commerce platforms to provide personalized product recommendations based on user preferences and constraints.
Problems Solved
This technology helps users find products that align with their preferences and budget, streamlining the shopping experience and increasing the likelihood of a successful purchase.
Benefits
The method enhances user satisfaction by offering tailored product recommendations, potentially leading to increased sales and customer loyalty.
Potential Commercial Applications
"Personalized Product Recommendation System for E-commerce Platforms"
Possible Prior Art
One possible prior art could be the use of decision trees in recommendation systems in various industries, such as e-commerce and marketing.
Unanswered Questions
How does the system handle conflicting user inputs?
The abstract does not specify how the system resolves conflicting user inputs, such as when a user's feature preference contradicts their budget constraint.
What measures are in place to protect user privacy and data security?
The abstract does not address the privacy and security measures implemented to safeguard user information collected during the recommendation process.
Original Abstract Submitted
A method includes receiving a signal that a user wishes to purchase a product, initializing a decision tree to collect information from the user, presenting a first query to the user, where the first query is selected for presentation based on the decision tree, receiving, in response to the first query, a first user input comprising at least one of: a feature preference or a budget constraint related to the product, presenting a subsequent query to the user, where the subsequent query is selected for presentation based on the first user input and the decision tree, receiving, in response to the subsequent query, a subsequent user input comprising at least one of: a feature preference or a budget constraint related to the product, and presenting information about a recommended product that is identified by using the first user input and the subsequent user input to traverse the decision tree.
