Deep Learning Model for Vague Location Threat Detection

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Solution Overview

Problem

Existing NLP technologies struggle to identify potential location threats in natural language data without specific location or address mentions, failing to provide real-time situational awareness of security risks to companies or organizations, especially in complex and unpredictable environments.

Innovation Solution

An AI-based location threat monitoring system utilizing a deep learning model that recognizes vague or generic location references, trained to infer intent and point of view, capable of processing data from disparate sources, including social media and the dark web, to detect potential threats without relying on predefined rules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional NLP systems use statistical machine learning with predefined lexicons and grammar rules to process natural language data, then they can process and analyze data in structured ways, but they fail to identify potential location threats when no specific location or address is mentioned in the data

Engineering Contradiction:

Improvethreat detection accuracyVSAvoidhandling of vague location references

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent transforms the approach from rule-based parameter matching to deep learning parameter transformation, where the deep learning model automatically learns and transforms textual parameters into location threat assessments without relying on predefined lexicons or grammar rules

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the mechanical rule-based NLP system with a deep learning-based intelligent system that can automatically infer location threats from contextual patterns in natural language data, eliminating the need for manually coded lexicons and grammar rules

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If AI-based systems use deep learning models to recognize vague location references and infer intent, then they can identify potential threats without specific location mentions, but the system complexity and training requirements increase significantly

Engineering Contradiction:

Improvehandling of generic location referencesVSAvoiddeep learning model complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The deep learning model serves multiple functions simultaneously: it processes natural language data, identifies vague location references, infers intent and point of view, and detects potential threats, replacing what would otherwise require multiple separate systems or manual analysis processes

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The deep learning model is self-trained on natural language data to automatically learn patterns of location threat indicators, eliminating the need for manual programming of rules and lexicons by domain experts

Inventive Principle:

Principle #25Self-service

3Productivity

If NLP systems process each word individually with tagged parts of speech and apply statistical machine learning, then they can analyze structured language data, but they cannot understand the meaning or intent behind the data to identify threats

Engineering Contradiction:

Improvedata processing speedVSAvoidcontextual meaning and intent

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent merges multiple NLP processing stages into a unified deep learning model that simultaneously performs tokenization, part-of-speech tagging, semantic understanding, and threat detection, preserving contextual meaning while maintaining processing efficiency

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent adds a semantic understanding dimension to traditional NLP processing by using deep learning to capture contextual meanings and intents that go beyond surface-level word tagging and statistical analysis

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20240281658A1Systems and methods for location threat monitoring

Publication Date: 2024.08.22 GOLDMAN SACHS BANK USA

AI summary

Disclosed is a new location threat monitoring solution that leverages deep learning (DL) to process data from data sources on the Internet, including social media and the dark web. Data containing textual information relating to a brand is fed to a DL model having a DL neural network trained to recognize or infer whether a piece of natural language input data from a data source references an address or location of interest to the brand, regardless of whether the piece of natural language input data actually contains the address or location. A DL module can determine, based on an outcome from the neural network, whether the data is to be classified for potential location threats. If so, the data is provided to location threat classifiers for identifying a location threat with respect to the address or location referenced in the data from the data source.