Public Event Detection Platform Using Density Thresholds
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Solution Overview
Problem
Current communication methods for emergency and public incidents are slow, manual, and unreliable, overwhelming even trained personnel with fragmented information, making it difficult to execute timely actions during critical events.
Innovation Solution
A system and method for gathering, verifying, and disseminating information from heterogeneous data feeds, including mobile devices, beacons, and social media, by generating event data objects, parsing keywords, assigning tags and sentiments, filtering, clustering, and determining the validity of incidents based on density thresholds to provide accurate and timely notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual information relay methods are used, then communication between individuals and emergency services is possible, but the process becomes slow and unreliable
Solution Approach 1:
The patent replaces manual mechanical communication processes with an automated electronic system that collects data from multiple sources (social media, news feeds, sensor networks), processes it through algorithms, and automatically generates incident alerts. This substitution of manual reporting with automated data aggregation and analysis eliminates the delays and unreliability of human-to-human information relay.
2Loss of information
If comprehensive data from multiple sources is collected, then situational awareness improves, but information becomes overwhelming and difficult to process
Solution Approach 1:
The patent segments the overwhelming stream of raw data into distinct, structured components by collecting information from separate sources (social media platforms, news feeds, sensor networks) and processing each through dedicated modules. The system divides data into structured fields such as incident type, location, severity, and source, making the information manageable and analyzable rather than overwhelming.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw data sources and end users. This intermediary system aggregates data from multiple heterogeneous sources, standardizes formats, applies filtering and validation rules, and presents processed information in a unified, comprehensible format, thereby reducing the complexity burden on both the system and users.
3Measurement precision
If individual reports are analyzed manually, then each report can be reviewed, but emergency services personnel cannot execute time critical actions efficiently
Solution Approach 1:
The patent enables the system to perform self-service by automatically collecting, analyzing, and processing incident data without requiring manual review of each individual report. The automated system validates data quality, identifies incident patterns, determines severity levels, and generates alerts independently, freeing emergency services personnel from tedious analysis tasks while maintaining high accuracy through algorithmic consistency.
Solution Approach 2:
The patent transforms the analysis process by changing key parameters from manual evaluation to automated computational assessment. The system applies predefined thresholds, weighting factors, and algorithmic rules to evaluate data quality and incident severity, replacing subjective human judgment with objective, reproducible parameter-based analysis that maintains precision while dramatically increasing processing speed and productivity.
Data Source
AI summary
Methods and systems for determining public events are described. Data is received from a plurality of heterogeneous data feeds each having a native format. Event data objects are generated based on the received data, the event data objects each including at least a human readable message, an event time, and an event location. The human readable message of each of the event data objects is parsed into keywords, and the event data objects are grouped into one or more public events based on the keywords, time, location, and a sentiment based on the keywords. The public events are filtered based on a filter score, and the event data objects are clustered based on sentiment, location, and time. A density of the clustered event data objects is determined, and the validity of the public events is determined by a comparison of the density to a predetermined density threshold.