Automated Matter Classification and Data Extraction
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Solution Overview
Problem
Completing matter intake forms is laborious and time-consuming for personnel due to the need to determine the appropriate form and fill out various fields with relevant information, which can vary significantly by matter type.
Innovation Solution
A method and system that automatically classify the matter type using trained models and input data, selecting the appropriate matter forms, and extracting field data to populate the forms, thereby reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual completion of matter intake forms is used, then personnel can accurately determine the appropriate form and fill out fields, but it consumes significant time and labor resources
Solution Approach 1:
The system enables self-service by using trained machine learning models to automatically classify matter types and extract relevant field data from input information. The automated matter classification model determines the appropriate matter intake form, and the automated field data extraction model populates form fields without requiring manual completion by personnel, thereby resolving the contradiction between accuracy and time consumption
Solution Approach 2:
The patent replaces the mechanical manual process of form completion with automated computational systems. Trained models perform the classification and data extraction functions that previously required human cognitive and manual input, substituting the mechanical action of manual form filling with an automated digital process that maintains accuracy while dramatically reducing time requirements
2Adaptability or versatility
If multiple matter intake forms with different fields are used for different matter types, then specific information requirements for each matter type are met, but the complexity of determining and completing the correct form increases
Solution Approach 1:
The system introduces an intermediary automated classification layer between the input matter information and the matter intake forms. The trained matter classification model acts as a mediator that automatically determines which form is appropriate based on the input data, eliminating the need for personnel to navigate the complexity of selecting among multiple forms with different field requirements
Solution Approach 2:
The system performs self-service by automatically adapting to different matter types through the trained classification model. When input information is received, the system autonomously determines the appropriate matter intake form and populates it with relevant data, eliminating the need for personnel to understand or manage the complexity of multiple forms and their specific field requirements
Data Source
AI summary
Systems and methods for automatic extraction of electronic data are disclosed. In one aspect, a method of automatically generating a matter form, the method includes receiving input data from one or more sources, the input data relating to a matter, classifying, using a trained model, a matter classification based at least in part on the input data, selecting one or more matter forms based on the matter classification, automatically extracting field data from the input data, and automatically populating fields of the one or more matter forms with the extracted field data.