Document Management System Tracking Time-Based Conditions
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Solution Overview
Problem
Existing document management systems face challenges in automatically determining whether agreements with time-based conditions have been fully executed, due to the complexity of training machine-learned models and the need for manual data labeling, which results in inaccurate predictions and arduous processes.
Innovation Solution
A document management system that tracks events related to time-based conditions by accessing agreements, capturing event information from client devices and sensors, and using a machine-learned model trained on robust data to determine if conditions have been met, transmitting alerts to parties when conditions are breached or fulfilled.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual monitoring by external operators is used to determine agreement execution, then flexibility in handling complex time-based conditions is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically monitoring time-based conditions and determining agreement execution status without requiring external operators. The computer system autonomously tracks events, evaluates time-based conditions, and determines whether agreements have been fully executed, eliminating the need for manual monitoring and consultation.
Solution Approach 2:
The patent replaces the mechanical system of manual monitoring with an automated computer-based system. The system uses electronic event tracking, database queries, and automated logic to monitor time-based conditions, substituting human operators with computational processes that can handle complex conditions efficiently and without time loss.
2Extent of automation
If machine-learned models are used to automatically detect time-based conditions, then automation level improves, but training complexity and data labeling requirements increase
Solution Approach 1:
The system introduces an intermediary layer between raw agreement text and condition detection. A natural language processing component serves as a mediator that parses agreement documents, identifies time-based condition clauses, and structures them into a format suitable for automated evaluation. This intermediary simplifies the automation process by handling the complexity of interpreting legal language.
Solution Approach 2:
The system segments the agreement document into distinct clauses and identifies specific time-based condition elements within them. By breaking down the complex agreement text into manageable segments (events, timeframes, conditions), the system reduces the complexity of training machine-learned models, as each segment can be processed and evaluated independently with clearer labeling requirements.
3Reliability
If external operators manually engage workflows indicated in agreements, then accuracy in interpreting party intentions is improved, but productivity decreases
Solution Approach 1:
The system implements feedback loops where event data is continuously collected, time-based conditions are automatically evaluated, and execution status is determined and communicated back to relevant parties. This automated feedback mechanism maintains reliability by systematically comparing actual events against agreed-upon conditions, while significantly improving productivity through rapid, parallel processing of multiple agreements simultaneously.
Solution Approach 2:
The patent replaces manual operator engagement with automated computational processes for workflow execution. The system uses electronic event tracking, automated time calculations, and logical evaluation to determine whether conditions are met, substituting human interpretation with consistent, scalable automated decision-making that maintains accuracy while dramatically increasing processing speed and productivity.
Data Source
AI summary
A document management system accesses a document signed by one or more parties. The document may indicate one or more events that the parties contracted to occur in relation to the time-based conditions. The document management system inputs the document to a machine-learned model configured to identify one or more time-based conditions indicated in the document. The document management system receives one or more time-based conditions from the machine-learned model. For each time-based condition, the document management system identifies a respective database that catalogs event information corresponding to the time-based condition. The document management system obtains the event information related to the time-based condition and determines whether the time-based condition has been met based on the event information. For each time-based condition that has not been met, the document management system transmits an alert to one or more of the parties.