Computational Risk Scoring Model for Media Scene Analysis
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Solution Overview
Problem
Current solutions lack the capability to detect and measure the risk intensity level of a scene captured by media in real-time, essential for immediate and appropriate action, despite being able to detect objects via machine learning.
Innovation Solution
A computer-implemented method using a computational scoring model to generate risk type, cause, and evidence scores based on detected objects within a scene, calculating a risk intensity level, and performing defined actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If machine learning is used to detect objects in media, then object detection capability is improved, but the ability to measure risk intensity level in real-time deteriorates (lacks capability)
Solution Approach 1:
The patent segments the risk assessment process into distinct components: object detection, attribute extraction, risk factor identification, and risk intensity calculation. Each component processes specific aspects of the media content independently, then integrates results to provide comprehensive risk measurement. This segmentation enables real-time processing while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary computational framework that bridges object detection and risk measurement. This intermediary layer extracts attributes from detected objects, identifies relevant risk factors, and calculates risk intensity levels. The intermediary processes transform raw detection data into meaningful risk assessments, enabling both real-time operation and precise measurement.
2Loss of time
If real-time risk measurement is implemented, then response time to threats is improved, but computational complexity deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-defining risk factors, causes, and evidence types before actual risk assessment. The system pre-processes media content to identify and categorize objects and their attributes in advance. This preliminary processing reduces the computational burden during real-time risk calculation, enabling faster response times without excessive complexity.
Solution Approach 2:
The patent implements dynamic processing that adapts computational resources based on scene complexity and risk levels. The system dynamically adjusts the depth of analysis, focusing computational effort on high-risk areas while using simplified assessment for low-risk scenes. This dynamic approach maintains real-time performance while managing computational complexity efficiently.
Data Source
AI summary
Measuring risk intensity level of a scene captured within media is provided. A score is generated, using a computational scoring model, for each risk type, cause, and evidence taxon element described in a risk taxonomy corresponding to a scene class by matching one or more taxon elements with each attribute of each detected object of a set of detected objects within the scene captured by the media. The media of the scene is tagged with risk type, cause, and evidence scores of each taxon element of the risk taxonomy corresponding to the scene class. A risk intensity level of the scene is calculated based on the risk type, cause, and evidence scores tagged to the media. An action of a set of defined actions is performed based on the risk intensity level of the scene.