Personalized Reminder Engine for Faster SaaS Claim Completion
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Solution Overview
Problem
Existing Software as a Service (SaaS) providers face inefficiencies in claim processing, particularly in insurance claims, due to time-consuming manual procedures and suboptimal communication with users and policy providers, leading to frustration and delays.
Innovation Solution
A computing system implementing AI and machine learning techniques to optimize information gathering and claim processing, using large language models (LLMs) for summarization, dynamic content generation, and personalized reminder strategies to streamline interactions between users, call center representatives, and policy providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual procedures are used for claim processing, then communication with users can be personalized and flexible, but processing time increases and efficiency decreases
Solution Approach 1:
The system segments the claim processing workflow into distinct phases (information gathering, claim assessment, communication, and resolution) and applies different processing modes to each segment. AI and machine learning handle data processing and pattern recognition tasks, while human representatives focus on complex decision-making and personalized user interaction, creating an efficient hybrid workflow that reduces overall processing time.
Solution Approach 2:
The patent introduces AI-powered intermediaries including chatbots for initial user engagement, machine learning models for claim assessment, and automated notification systems. These intermediaries handle routine communication tasks and data analysis, freeing human representatives to focus on complex cases and improving overall processing efficiency while maintaining personalized service quality.
2Productivity
If automated systems are used for claim processing, then efficiency increases and processing time reduces, but communication with users becomes less personalized
Solution Approach 1:
The system applies different levels of automation to different stages of claim processing and different types of users. High-volume, routine claims receive automated processing with standardized communication, while complex or high-value claims are routed to human representatives for personalized attention. This local differentiation optimizes efficiency for routine tasks while preserving communication quality for cases requiring human judgment.
Solution Approach 2:
The patent implements dynamic routing that automatically adjusts the level of human versus automated intervention based on real-time factors including claim complexity, user preferences, representative availability, and historical interaction patterns. This dynamic allocation ensures that personalized communication is provided when and where it adds most value while maintaining high overall efficiency.
3Adaptability or versatility
If traditional reminder strategies are used, then all users receive the same communication approach, but user engagement and responsiveness vary significantly
Solution Approach 1:
The system implements closed-loop feedback mechanisms where user responses to reminders are continuously monitored and fed back into the machine learning models. This feedback enables the system to learn from actual user behavior patterns and progressively refine reminder strategies for each user, improving engagement rates over time while capturing valuable responsiveness data for future optimization.
Solution Approach 2:
The patent dynamically adjusts multiple parameters of reminder communications including timing, frequency, channel selection (email, SMS, phone), and message content based on individual user preferences, historical responsiveness patterns, and contextual factors. This parameter optimization is achieved through machine learning analysis of user behavior data, enabling personalized reminder strategies that significantly improve engagement while systematically capturing responsiveness information.
Data Source
AI summary
A computing system can detect, from a user computing device, a user associated with a claim process, the claim process involving the user providing incident information corresponding to an incident to the computing system. Based on a set of response data individual to the user, the system generates an optimized reminder strategy to provide reminders to the user to complete the claim process. The system may then transmit a set of reminders to the user computing device in accordance with the optimized reminder strategy.