Deep Learning Risk Detection in Enterprise Communications
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Solution Overview
Problem
Current software technologies lack the capability to identify and alert enterprises to potential litigation risks within their internal communications before they escalate into legal issues, resulting in significant costs and reputational damage.
Innovation Solution
A computer-enabled software system utilizing deep learning algorithms to analyze internal electronic communications, such as emails and other text documents, to generate a scored output that alerts legal personnel to potential litigation risks in real-time or near real-time, enabling proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software technologies are used to monitor internal communications, then system complexity remains low, but the capability to identify potential litigation risks is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/software-based monitoring systems with an AI-powered system using natural language processing and machine learning algorithms. This substitution enables the system to automatically analyze communication patterns, detect potential litigation risks, and provide early warnings without requiring complex manual configuration or rule-based systems.
Solution Approach 2:
The patent introduces an AI intermediary layer that sits between internal communications and legal personnel. This intermediary automatically processes communications, identifies risk patterns, and translates them into actionable alerts, thereby bridging the gap between raw data and legal expertise without requiring direct human analysis of all communications.
2Measurement precision
If deep learning algorithms are implemented to analyze all internal communications, then risk detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary filtering and preprocessing of communications before applying deep learning algorithms. By pre-processing data to extract relevant features and filter out low-risk communications, the system prepares data in advance for more efficient analysis, reducing the computational burden and processing time when full risk assessment is needed.
Solution Approach 2:
The patent applies deep learning algorithms selectively rather than uniformly to all communications. The system focuses computational resources on communications that exhibit risk indicators or fall into high-priority categories, performing partial analysis on low-risk items and excessive/detailed analysis only where necessary, thereby optimizing the balance between accuracy and processing time.
Data Source
AI summary
Deep learning is used to identify specific, potential risks to an enterprise while such risks are still internal electronic communications. The combination of Deep Learning and blockchain technologies is a system for overcoming the problem of “small training sets” for highly adverse situations. Each enterprise's data is secure; is not revealed to any other enterprise and yet is being aggregated using blockchain technology into a training set that is provably viable for building a Deep Learning model which is specific to a given adverse situation. When deployed, the Deep Learning model may provide an early warning alert to an enterprise's corporate counsel (or leaders) of a potential adverse situation the enterprise would like to know about in time to conduct an internal investigation in order to prevent or avoid the risk.