Geo-temporal Incident Navigation with Dynamic Credibility Assessment
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Solution Overview
Problem
Law enforcement and public safety personnel face difficulties in navigating and credibly assessing large amounts of data and relationships during incident response and investigation, leading to inefficient resource allocation and increased computational burdens.
Innovation Solution
A geo-temporal incident navigation system that processes data to create knowledge graphs, generates credibility scores using Bayesian inference networks, and provides a graphical user interface for interactive visualization and navigation, allowing users to track incidents in time and space with dynamic credibility assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If personnel manually navigate large document collections during incident response, then they can access detailed information, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system pre-processes and structures incident data into knowledge graphs before personnel need to access it. Outcome nodes, credibility scores, and temporal-spatial relationships are prepared in advance, allowing personnel to immediately view organized information without manual searching or filtering during critical response time
Solution Approach 2:
The patent introduces an intermediary processing layer between raw document collections and personnel. This layer automatically extracts entities, relationships, and outcomes from unstructured data, transforms them into structured knowledge graphs, and presents them through a unified interface, eliminating the need for personnel to manually navigate through raw documents
2Reliability
If the system processes and presents all incident data without credibility assessment, then complete information is provided, but computational burden and resource allocation increase
Solution Approach 1:
The system applies different levels of processing and credibility assessment to different parts of the data structure. Outcome nodes receive comprehensive credibility scoring based on multiple factors (source reliability, corroboration, temporal consistency), while supporting nodes receive proportional assessment. This localized quality approach ensures critical information is thoroughly validated without unnecessarily processing all data at maximum depth
Solution Approach 2:
The patent dynamically adjusts credibility assessment parameters based on incident context, data type, and user needs. The system modifies weighting factors for different credibility indicators (e.g., emphasizing temporal consistency for time-sensitive incidents, source reliability for evidence-based investigations) to optimize computational resources while maintaining appropriate reliability standards for each specific scenario
3Measurement precision
If the system performs repeated database searches to navigate incident data, then accurate information retrieval is achieved, but processor load and network traffic increase
Solution Approach 1:
The system pre-computes and stores structured relationships, credibility scores, and temporal-spatial indices in the knowledge graph before queries are executed. When personnel search for incident information, the system retrieves pre-organized data from the knowledge graph structure rather than performing repeated full-database searches, dramatically reducing processor load and network traffic while maintaining accurate information retrieval
Solution Approach 2:
The patent creates a copied and simplified representation of the incident data in the knowledge graph format, which mirrors the structure and relationships of the original database but is optimized for rapid querying. This copied structure allows the system to answer complex queries about incident relationships, timelines, and credibility without accessing and processing the entire original database repeatedly
Data Source
AI summary
Systems and methods for geo-temporal incident navigation. In one method, a graphical user interface (GUI) is generated. The GUI includes a primary window and a secondary window. The primary window includes a peripheral edge, a map, and an incident location indicator overlaid on the map. The secondary window includes an identifier for the incident, a plurality of outcome nodes, corresponding to potential outcomes for the incident, based on a plurality of nodes related to the incident, and a plurality of outcome credibility scores corresponding to the outcome nodes. A graphical control positioned at the peripheral edge of the primary window includes an incident indicator positioned along a timeline. The plurality of outcome nodes is selected and the plurality of credibility scores is computed based on a position of the incident indicator on the timeline. An electronic processor controls a display to present the graphical user interface.