Personalized Knowledge Session Content With Feedback-Driven User Grouping
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Solution Overview
Problem
Conventional methods for educating users about new software features in SaaS services are inefficient, as they often involve generic invitations that do not consider user interests, roles, and habits, leading to low engagement and unnecessary resource consumption.
Innovation Solution
An end-to-end, fully automated process using generative machine learning models processes user profile and service description data to group users, generate personalized messages, and refine models based on feedback to improve accuracy and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation of knowledge session content by domain experts is used, then content quality and accuracy are improved, but time consumption and resource costs increase significantly
Solution Approach 1:
The system enables automated self-service content generation where the machine learning model autonomously creates personalized knowledge session content without requiring manual intervention by domain experts. The model processes user profiles and service descriptions to generate tailored invitations automatically.
Solution Approach 2:
The patent replaces the mechanical manual process of content creation by domain experts with an automated machine learning-based system. The ML model substitutes human cognitive and creative work with algorithmic processing, transforming text generation from a manual task to an automated computational process.
2Loss of energy
If generic invitations are sent to all users, then resource consumption is reduced, but user engagement and effectiveness decrease
Solution Approach 1:
The system applies local quality by customizing invitation content according to each user's specific characteristics, interests, and profile. Instead of uniform generic messages, the ML model generates personalized content that adapts to individual user needs, making each invitation uniquely suited to its recipient.
Solution Approach 2:
The invitation generation process is dynamic and adaptive, adjusting content based on user profile data, historical behavior, and service descriptions. The system continuously learns from feedback and refines its personalization strategy, making the content generation flexible and responsive to user characteristics.
3Productivity
If personalized content is generated for each user, then user engagement is improved, but computational resources and processing time increase
Solution Approach 1:
The system implements partial personalization by focusing on the most relevant user characteristics and features for each context. Rather than processing all possible user attributes equally, the ML model selectively incorporates key profile elements and behavioral indicators that have the greatest impact on engagement, reducing unnecessary computational overhead.
4Loss of information
If manual segmentation and targeting of user groups is performed, then message relevance is improved, but time and labor costs increase
Solution Approach 1:
The patent replaces manual user segmentation and targeting processes with automated machine learning-based clustering and classification. The system automatically analyzes user profiles, identifies patterns and segments, and assigns users to appropriate groups without human intervention, transforming a labor-intensive manual process into an automated computational task.
Data Source
AI summary
A method, computer system, and computer program product are provided for generating personalized knowledge session content using a generative machine learning model. User profile data of a plurality of users and service description data that is descriptive of a service are processed to assign the plurality of users to a plurality of user groups. A personalized message is generated for each user group based on the user profile data and the service description data, wherein the personalized message is generated using one or more machine learning models. Each personalized message is provided to each user group and feedback data is collected based on the providing of each personalized message. Based on analyzing of the feedback data, one or more of: the user profile data, the one or more machine learning models, and the service description data are updated.