Decision Tree E-Commerce Personalization

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Solution Overview

Problem

E-commerce platforms lack personalized interactions similar to in-person shopping, relying on costly and complex coding for digital human-like customer service, which limits their ability to tailor experiences to individual consumer needs.

Innovation Solution

Integration of decision trees with front-end user interface templates and natural language processing to dynamically guide consumer interactions, allowing for personalized and targeted recommendations without requiring extensive coding, enabling real-time changes and more natural user experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If digital human-like customer service is implemented to provide personalized interactions, then user experience is improved, but operational costs and system complexity increase

Engineering Contradiction:

Improvepersonalized interaction qualityVSAvoidcoding complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent uses decision trees as a simplified copy or representation of human decision-making processes in customer service. Instead of implementing complex digital human-like systems, the invention creates a structured decision tree that mirrors human consultation logic, providing personalized recommendations through predefined pathways that are easier to implement and maintain

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The customer service interaction is segmented into discrete decision nodes and pathways within the decision tree structure. Each node represents a specific customer need or preference, and each branch represents a possible response or recommendation. This segmentation breaks down complex personalization into manageable, coded segments that are easier to implement and modify

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If decision trees are used to guide consumer interactions, then adaptability to consumer inputs is improved, but system complexity increases

Engineering Contradiction:

Improveinteraction adaptabilityVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The decision tree is designed to be dynamic rather than static. The system can adapt to different consumer inputs by following different pathways through the tree, and the tree structure itself can be modified and updated without requiring complete system redesign. This dynamic nature allows the system to accommodate varying customer needs while maintaining a manageable structure

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20240112237A1Using decision trees to provide a guided e-commerce experience

Publication Date: 2024.04.04 AT&T INTELLECTUAL PROPERTY I L P

AI summary

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.