AI-Personalized Self-Help Content from Modular Answer Components
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Solution Overview
Problem
Traditional customer self-help systems face challenges such as redundant information, stylistic incompatibility, insufficient user input, and the impracticality of manual content revision, leading to inefficient and costly user experiences.
Innovation Solution
Employing artificial intelligence to characterize, categorize, and personalize self-help content by analyzing user queries and profiles, using algorithms like natural language processing and classifiers to generate relevant and stylistically appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user generated content is provided as answers to users' questions, then the system can assist users in finding answers, but the content becomes difficult to reuse because it is drafted for specific circumstances
Solution Approach 1:
The patent segments user generated content into reusable components or templates that can be adapted to different circumstances. Instead of treating each answer as a complete, immutable unit, the system breaks down answers into modular elements that can be recombined and customized for new situations, enabling both reusability and personalization.
Solution Approach 2:
The system allows dynamic modification of content parameters such as tone, length, style, and specific details based on user preferences and context. By enabling parameter changes in the generated content, the system maintains the core answer structure while adapting it to fit different user needs and situations, resolving the contradiction between reusability and personalization.
2Quantity of substance
If multiple user generated content responses are submitted for similar questions, then more answers are available, but it becomes impossible to manually generate content to cover all variations
Solution Approach 1:
The system implements self-service through automated content generation using artificial intelligence. Instead of relying on manual creation of content for every possible question variation, the AI system autonomously generates appropriate responses by learning from existing user generated content and applying it to new queries, enabling the system to handle large quantities of content without proportional increases in manual effort.
Solution Approach 2:
The system uses copying and adaptation of existing high-quality content patterns to generate new responses. By identifying effective answer structures and content from existing user generated responses, the system replicates and adapts these patterns to new situations, efficiently producing numerous quality responses without manual creation of each individual answer.
3Productivity
If user generated content is provided without manual review, then the system operates efficiently, but the content quality and style consistency deteriorates
Solution Approach 1:
The system implements feedback mechanisms where AI models analyze user interactions, preferences, and satisfaction signals to continuously improve content quality. By incorporating feedback from user behavior data, the system automatically adjusts content generation parameters to maintain quality standards without requiring manual review of each response, thus preserving both efficiency and quality.
Solution Approach 2:
The system dynamically adjusts content generation parameters such as tone, length, and style based on learned patterns from high-quality examples and user preferences. By automatically modifying these parameters through AI-driven processes, the system maintains consistent content quality across diverse responses without manual intervention, balancing productivity with manufacturing precision.
Data Source
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AI summary
A customer self-help system employs artificial intelligence to generate personalized self-help content that is responsive to a user query submitted to the customer self-help system, according to one embodiment. The customer self-help system includes a pre-processor that characterizes and categorizes the self-help content into self-help content components, by using one or more content processing algorithms (e.g., a natural language processing algorithm), according to one embodiment. The customer self-help system includes an intent extractor engine that determines characteristics of the user query based on the user query and user profile data, according to one embodiment. The customer self-help system aggregates portions of the self-help content components into a personalized self-help content by matching characteristics of the user query with characteristics of the self-help content, according to one embodiment.