Security Event Case Files from Correlated Unstructured Store Data
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Solution Overview
Problem
Retail environments face challenges in accurately identifying and responding to security events such as shoplifting, as existing systems rely heavily on subjective human observations, which can be biased and time-consuming, and lack efficient methods to correlate various data sources for objective analysis.
Innovation Solution
A system that correlates unstructured data from multiple sources, including cameras, RFID readers, and POS systems, using machine learning models to generate case files that objectively identify security events and automatically generate reports for security personnel, leveraging objective identifiers to minimize human bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security officers manually monitor camera security systems and investigate security events, then they can identify potential shortages and security events, but the process is time-consuming and prone to human error and bias
Solution Approach 1:
The patent replaces manual human monitoring and investigation processes with an automated computer vision system. The system uses cameras to capture images, processes them through machine learning models to detect security events, and generates case files automatically, eliminating the need for security officers to manually review footage and reducing human error and bias in the process.
Solution Approach 2:
The system performs self-service by automatically detecting security events, correlating data from multiple sources, generating case files, and preparing reports without requiring human intervention. The machine learning models independently analyze video feeds, RFID data, and POS information to identify security incidents and create structured case files.
2Loss of information
If security officers manually gather information for law enforcement reports, then they can provide detailed information about security events, but the process is time-consuming and subjective
Solution Approach 1:
The patent merges and correlates data from multiple sources including camera security systems, RFID readers, and POS systems into unified case files. The system combines unstructured data (video images) with structured data (transaction records, RFID logs) to create comprehensive security event profiles that include all relevant information needed for law enforcement reports.
Solution Approach 2:
The system performs preliminary actions by automatically detecting security events, correlating related data from multiple sources, and pre-generating case files before human review is needed. This preliminary processing includes automatically identifying security event categories, assigning objective identifiers, and structuring information according to law enforcement requirements.
3Loss of information
If the system correlates data from multiple disparate sources, then it can provide comprehensive analysis of security events, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer consisting of machine learning models and data correlation algorithms that bridge disparate data sources. This intermediary layer standardizes data formats, correlates information across camera feeds, RFID logs, and POS systems, and transforms unstructured data into structured case files, managing system complexity through automated mediation.
Solution Approach 2:
The system achieves universality by designing a multi-functional platform that handles various security event types (shoplifting, sweethearting, barcode swapping), processes multiple data source formats, and generates different output types (case files, law enforcement reports, internal notifications) through a single integrated architecture.
4Reliability
If the system uses objective identifiers to identify shoppers, then it can reduce human bias in security event identification, but the system requires additional data processing
Solution Approach 1:
The patent replaces subjective human judgment with objective automated identification using machine learning models. The system uses camera images to automatically identify shoppers, correlate them with RFID and POS data, and generate case files without human intervention, ensuring consistent and unbiased identification while processing data rapidly through automated algorithms.
Data Source
AI summary
Described herein are systems and methods for generating security event case files with unstructured data. For example, the method can include receiving, by a computing system, unstructured data and system-based inferences from devices positioned throughout a store, and adding structure to the unstructured data and system-based inferences based on applying one or more structuring models. Adding structure can include labeling the data and system-based inferences, classifying them into security event categories, and identifying objective identifiers to identify users in the data and system-based inferences. The method also can include generating case files for each of the objective identifiers, where the case files include the associated data. The method can include determining whether the case files satisfy alerting rules. The case files can then be reported out and acted upon (e.g., based on satisfying the alerting rules) and/or stored for subsequent analysis and use.