Control Code Segmentation Into Validity Clusters for Compliance
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Solution Overview
Problem
Large entities face challenges in ensuring compliance with statutes, regulations, and contract terms across diverse jurisdictions and operations, particularly with the rise of digital document processing and AI-based ingestion, leading to potential oversight of relevant legal requirements.
Innovation Solution
A data processing system partitions control code blocks into clusters using AI, sorting them into valid segments with specific validity types and generating cluster-specific directive records for execution by agent devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI-based processing is used to ingest and process documents, then processing efficiency and speed are improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the control code into multiple segments and categorizes them by validity status (valid/invalid) and validity types (actionable, conditional, recommended, informational). This segmentation allows the AI system to process different types of content differently, improving efficiency while managing complexity through structured organization.
Solution Approach 2:
The patent introduces an intermediary classification layer between the raw control code and the final execution decisions. By assigning validity types and categories to code segments, the system creates a manageable intermediate representation that simplifies the overall processing architecture and enables more efficient AI-based analysis.
2Reliability
If comprehensive review of all control code is performed, then compliance accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts and separates valid control code segments from invalid ones, and further extracts actionable items from the valid segments. This extraction allows the system to focus AI processing time on the most critical elements (actionable items) while quickly dismissing irrelevant content, thereby maintaining high compliance accuracy without excessive processing time.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the control code in detail (valid segments with actionable items) while applying simpler processing to other valid segments (conditional, recommended, informational types). This selective processing approach maintains compliance accuracy for critical items while reducing overall processing time.
3Ease of operation
If control code is partitioned into multiple segments and categories, then ease of operation and compliance checking is improved, but device complexity increases
Solution Approach 1:
The patent segments control code into manageable pieces with clear categorizations (valid/invalid, actionable/conditional/recommended/informational). This segmentation makes compliance checking easier by breaking down complex code into discrete, understandable units with explicit characteristics, though it does increase organizational complexity that is managed through automated processing.
Data Source
AI summary
A system automatically sorts a received block of control code into multiple control code segments and assigns a respective logic of valid or invalid. A validity type is assigned based on contents. Some control code segments are assigned a validity type of actionable. Other control code segments are indicated as of one of conditional, recommended, and informational. The system automatically associates at least each control code segment indicated as valid with one of multiple clusters by which at least the control code segments indicated as valid are partitioned; automatically generates, for each cluster with which at least one control code segment indicated as valid is associated, a cluster-specific directive record; and transmits the cluster-specific directive record to an agent device to be at least one of executed and installed by a cluster-specific agent.