Optical Deep Learning Architecture for Adverse Media Screening
Solution Overview
Problem
Conventional adverse media screening technologies face challenges in managing vast volumes of reports across multiple channels and formats, and struggle to assess credibility due to lack of consideration for political and social context, leading to inefficiencies and inaccuracies in determining the legitimacy of news claims.
Innovation Solution
A deep learning architecture that utilizes optical computing and graph neural networks to analyze text, video, and audio-based media reports, generating coherence graphs and credibility scores by incorporating fact-checking sources and adjusting weights based on similarity metrics and report sources, enabling real-time streaming analysis across various media channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text analysis is used for media screening, then the system is simple to implement, but it cannot factor in political and social context, making it difficult to determine credibility
Solution Approach 1:
The patent introduces coherence graphs as an intermediary structure that connects media reports with fact-checking sources and contextual information. These graphs serve as mediators that enable the system to incorporate political and social context without requiring direct complex analysis of all contextual factors, thus improving credibility determination while managing system complexity
Solution Approach 2:
The patent replaces conventional text analysis methods with graph neural networks that can process and understand contextual relationships. This substitution enables the system to factor in political and social context by leveraging the pattern recognition capabilities of deep learning models, thereby improving credibility assessment accuracy
2Productivity
If vast numbers of reports across many different channels are managed using conventional approaches, then all channels can be monitored, but large backlogs accumulate leading to processing delays
Solution Approach 1:
The patent applies preliminary action by pre-processing media reports to extract key features and construct coherence graphs before full analysis. This preliminary structuring of data enables faster processing during the analysis phase, allowing the system to handle vast numbers of reports across multiple channels without accumulating large backlogs
Solution Approach 2:
The patent segments the analysis process into distinct components: report ingestion, coherence graph construction, graph neural network analysis, and credibility scoring. This segmentation allows parallel processing of different report types and channels, significantly improving throughput and reducing processing delays
3Speed
If deep learning powered by optical computing is used for fast analysis, then processing speed is improved, but the system complexity increases
Solution Approach 1:
The patent replaces conventional electronic computing with optical computing for the deep learning inference process. This substitution leverages the parallel processing capabilities of optical systems to achieve fast analysis of coherence graphs, dramatically improving processing speed while the modular architecture helps manage the inherent complexity of optical computing integration
Data Source
AI summary
Systems, methods, and apparatus are provided for deep learning architecture for adverse media screening. A dynamic data stream may be generated from news reports acquired from a variety of media channels in a variety of formats. Reports may be separated by format. A graphing module for each type of media may use geometric clustering algorithms to group reports into a coherence graph. Fact-checking sources may be incorporated into the graph. A graph neural network may determine coherence between news claims and fact-checking sources, between news claims and supporting report details, between similar reports, and/or between video and audio within the report itself. The network may output a legitimacy score for a report. A credible report may be flagged and retrieved in the course of a customer screening. Materiality may be determined for a credible report and may trigger a customer account closure protocol.