Personalized Navigation Menu Generation via Machine Learning
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Solution Overview
Problem
Conventional navigation menu systems are inefficient and inflexible, requiring users to navigate through multiple irrelevant prompts to achieve a desired outcome, wasting time and resources, and lacking in customization and anticipation of user needs.
Innovation Solution
The implementation of a system that uses machine learning and dynamic generation of navigation menus based on user account data and request analysis, allowing for efficient and customizable routing of user interactions, such as in IVR systems, to anticipate and directly provide relevant prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional navigation menus provide a set of default options to users, then the system structure is simple and easy to implement, but users must navigate through multiple irrelevant prompts which increases navigation time and reduces efficiency
Solution Approach 1:
The system performs preliminary actions by analyzing user account data, request patterns, and historical behavior before the user even initiates a request. This pre-analysis enables the system to predict user intent and pre-configure personalized navigation menus that anticipate user needs, eliminating the need for users to navigate through irrelevant intermediate prompts and directly routing them to relevant options.
2Adaptability or versatility
If conventional systems use rule-based programming for navigation menus, then the system logic is clear and manageable, but the system lacks flexibility and cannot adapt to individual user needs
Solution Approach 1:
The system replaces the mechanical rule-based programming approach with machine learning models that automatically learn from user data. Instead of manually configuring rules, the ML models analyze user account data, request patterns, and behavior to dynamically generate personalized navigation menus, providing adaptability without requiring complex manual rule management.
Solution Approach 2:
The system enables self-service by allowing users to benefit from automated personalization without active participation. The machine learning system automatically analyzes user data and generates customized navigation menus based on inferred user preferences and needs, eliminating the need for users to manually configure their own menu preferences while still providing tailored experiences.
3Ease of operation
If conventional IVR systems allow users to reach particular prompts without navigating through all intermediate prompts, then user control is improved, but the system requires user input and rule-based programming which reduces efficiency
Solution Approach 1:
The system performs preliminary analysis of user account data and request patterns before the user makes any selections. This pre-processing enables the system to predict user intent and pre-configure the navigation path, allowing users to be directed to relevant prompts without needing to navigate through intermediate options, thereby maintaining ease of operation while improving system efficiency.
Data Source
AI summary
Systems and methods for generating personalized navigation menus are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving account data associated with a plurality of accounts and receiving navigation menu data associated with the accounts. The operations may include training a model based on the account data and the navigation menu data. The operations may include receiving a request associated with a user and receiving user account data associated with the user. The operations may include generating, using the model, a navigation menu based on the request and the user account data. The operations may include providing the navigation menu.