Sentiment Analysis Engine for Threat Detection
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Solution Overview
Problem
Current systems lack effective methods to analyze internet communications for identifying the probability of anomalous events, such as physical or cyber-attacks, and to take remedial measures to mitigate these threats.
Innovation Solution
A system that utilizes a centralized server with a data analysis engine to search for negative sentiment on the internet, perform keyword searches to identify potential threats, and activate smart sensors near identified locations to capture non-personally identifiable biometric and facial sentiment data, which is then analyzed to confirm the likelihood of an anomalous event and trigger security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If internet communications are monitored and analyzed for negative sentiment to detect potential threats, then security detection capability is improved, but privacy concerns and data protection compliance become more difficult to maintain
Solution Approach 1:
The system extracts only sentiment information and thematic content from internet communications, separating the useful security intelligence from personally identifiable information. The patent specifically monitors negative sentiment, keywords, and patterns while deliberately excluding personal identity data to maintain privacy compliance.
Solution Approach 2:
The system uses automated sentiment analysis algorithms and natural language processing as intermediaries between raw internet communications and security decision-making. These intermediaries process and interpret communications without requiring direct human access to personal data, maintaining a protective barrier.
2Reliability
If smart sensors are activated to capture biometric and facial sentiment data at identified locations, then real-time threat verification is improved, but device complexity and operational cost increase
Solution Approach 1:
The system performs preliminary sentiment analysis on internet communications to identify potential threat locations and contexts before activating physical sensors. This preliminary digital analysis filters and prioritizes which physical locations require sensor activation, avoiding unnecessary sensor deployment.
Solution Approach 2:
The system activates smart sensors only at specific identified locations where threats are detected, rather than deploying sensors universally. This partial activation approach maintains reliability at critical points while reducing overall system complexity and costs.
3Measurement precision
If comprehensive keyword searches are performed on webpages to identify threats, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the keyword search process into multiple stages: initial broad keyword filtering, followed by targeted sentiment analysis, and finally focused verification at specific locations. This segmentation allows comprehensive analysis to be performed efficiently in discrete steps rather than as a single time-consuming operation.
Solution Approach 2:
The system implements periodic monitoring of internet communications at multiple locations, analyzing data in scheduled intervals rather than continuously processing all data at once. This periodic approach maintains detection precision while managing computational load and processing time.
Data Source
AI summary
A method for analyzing communications on the internet to identify a probability of an occurrence of an anomalous event relating to a pre-determined entity is provided. The method may include searching on the internet, to identify communications comprising negative sentiment associated with the pre-determined entity. When identified, the method may include further searching for communications including data regarding a physical location and an intended action, both being associated with the pre-determined entity. When one or more instances of these communications are identified, the method may include activating smart sensors embedded within proximity to the identified physical location to capture non-identifiable data and transmit the captured data to a data analysis engine to identify and confirm a probability of the occurrence of the anomalous event.