Unstructured Data Modeling for AI-Driven Protection Parameter Updates
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Solution Overview
Problem
Conventional data analysis techniques face challenges in effectively managing and analyzing unstructured data items such as claim notes, multimedia files, and customer communications, leading to inefficiencies and ineffectiveness in insurance risk evaluation and policy management.
Innovation Solution
A modeling system that processes and analyzes unstructured data items using generative AI models to identify patterns and generate insights, enabling updates to protection parameters like deductibles, coverage, and processing protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis techniques are used to process unstructured data items, then the analysis process is simple and familiar, but the accuracy of risk assessments and insights generation is insufficient
Solution Approach 1:
The patent introduces an intermediary layer (prompt generation module and AI model interface) between the unstructured data and the analysis system. The system generates structured prompts from unstructured data items and feeds them to AI models, which then produce structured outputs. This intermediary approach enables accurate risk assessment using conventional system architectures while incorporating advanced AI capabilities.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with AI-based processing. Instead of using conventional algorithms to directly analyze unstructured data, the system uses generative AI models to interpret and extract insights from unstructured data items such as claim notes, customer communications, and multimedia files, significantly improving measurement precision.
2Productivity
If unstructured data items are processed using traditional methods, then the processing approach is straightforward, but the productivity and efficiency of analysis are reduced
Solution Approach 1:
The system enables self-service processing of unstructured data by automatically generating prompts and extracting insights without requiring manual intervention. The AI models autonomously analyze unstructured data items, generate structured outputs, and update protection parameters, significantly improving productivity while managing the complexity of unstructured data analysis.
Solution Approach 2:
The patent segments the complex task of unstructured data analysis into distinct components: data ingestion, prompt generation, AI model processing, and output interpretation. This segmentation allows each component to be optimized independently, improving overall productivity while making the analysis process more manageable and measurable.
3Loss of information
If comprehensive unstructured data is collected for analysis, then the completeness of information is improved, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the essential information from unstructured data items by generating targeted prompts that focus on specific aspects relevant to risk assessment. The AI models process the complete unstructured data but extract and output only the critical insights needed for updating protection parameters, maintaining information completeness while managing data volume efficiently.
Solution Approach 2:
The patent transforms unstructured data from its original high-dimensional, unorganized form into structured, lower-dimensional representations through prompt generation and AI processing. This dimensional transformation preserves the essential information while reducing the complexity and volume of data that needs to be managed and processed.
Data Source
AI summary
In various examples, systems and methods are disclosed for modeling unstructured data items. The system may receive multiple unstructured data items associated with various protection records. The system may then generate a prompt based upon these data items by extracting associations. The system may use the unstructured data items and the prompt as input to one or more AI models to generate an output related to either predicting occurrences or identifying patterns. The system may determine an action to update at least one protection parameter of a protection product, with the action responding to the unstructured data items and the prompt.