Automated Docket Event Linking for Motion-Order Analysis
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Solution Overview
Problem
Information about docket events in structured proceedings, such as court cases, is often hidden and not easily accessible, requiring manual and error-prone processing to link orders with affected motions.
Innovation Solution
A system and method that uses automated analysis to identify and link docket entries associated with motions and orders by applying customized rules and machine learning algorithms, including hybrid approaches like rule-based and machine learning, to accurately determine and connect affecting orders with affected motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing is used to link orders with affected motions, then users can interpret the data, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of sifting through docket data with an automated computer-based system that uses natural language processing and machine learning algorithms to identify and link motions to orders, eliminating time-consuming manual interpretation while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service by automatically analyzing docket entries, identifying motions and orders, and creating links between them without requiring user intervention, allowing the system to serve itself in performing the data linking task
2Ease of operation
If docket data is stored in structured format, then data can be organized systematically, but information about docket events is hidden and not readily accessible
Solution Approach 1:
The patent extracts relevant information about docket events from structured docket data by using natural language processing to identify motions and orders within the data, making previously hidden information accessible and visible to users
Solution Approach 2:
The system introduces an intermediary processing layer that translates structured docket data into meaningful, accessible information about docket events, acting as a mediator between the raw data and user comprehension
3Productivity
If automated analysis is used to identify and link docket entries, then processing time is reduced, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional system that can perform multiple tasks including identifying motions, identifying orders, extracting relevant information, and creating links between them, consolidating what could be multiple separate complex systems into one unified automated solution
Data Source
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AI summary
The present disclosure relates to systems and methods for analyzing and extracting docket data related to a structured proceeding, for identifying docket entries associated with motions and docket entries associated with orders, and for identifying motions affected by orders. Embodiments provide for receiving docket data associated with a structured proceeding, the docket data including at least one docket entry. Embodiments also include identifying, by an automated analysis, docket entries associated with motions in the structured proceeding, and docket entries associated with orders in the structured proceeding. In embodiments, identifying the docket entries associated with orders includes identifying at least one order that includes a results-affecting decision affecting at least one motion. Embodiments further include linking, by the automated analysis, the affected at least one motion to the affecting order.