Retail Watch List Generation Using Predictive Threat Scoring
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Solution Overview
Problem
Retail environments face challenges in effectively identifying and mitigating security threats due to shoplifting and other security-related incidents, as existing security systems often rely on subjective observations and fail to provide a concise and objective means to monitor and manage potential threats.
Innovation Solution
A system and method for generating a culled watch list of guests who pose specific security threats by combining data from various devices and systems, using machine learning models to predict future threats and assign threat scores, and creating summary videos for in-store employees to objectively identify and monitor high-risk individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If security officers actively and continuously monitor the camera security system to identify potential shortages and security events, then detection capability is improved, but the complexity of operation and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically analyzing customer behavior, generating case files, calculating threat scores, and creating watch lists before security officers need to make decisions. This pre-processing of security intelligence reduces the time officers spend on manual monitoring while maintaining high detection capability.
Solution Approach 2:
The system creates a simplified copy or representation of the complex security monitoring task through automated case files and watch lists. Instead of officers directly monitoring all camera feeds, they review curated lists of suspicious cases generated by the system, reducing time consumption while preserving detection effectiveness.
2Reliability
If a comprehensive list of all guests who pose security threats is generated, then completeness of security coverage is improved, but the ease of operation and memorability for employees deteriorate
Solution Approach 1:
The system segments the comprehensive security threat information into prioritized groups based on threat scores. Instead of presenting all suspicious guests equally, the system divides them into tiers with the highest threat scores appearing first on the watch list, making it easier for employees to focus on the most critical cases while maintaining comprehensive coverage.
Solution Approach 2:
Different portions of the watch list have different levels of importance and detail. The system applies local quality by providing more prominent display and potentially more detailed information for high-threat individuals, while lower-threat individuals receive less prominent placement, optimizing employee attention allocation.
3Adaptability or versatility
If subjective observations are used to identify security threats, then flexibility in judgment is improved, but objectivity and consistency of threat identification deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where threat scores are calculated based on multiple objective factors including case file data, customer behavior patterns, and historical information. This structured feedback approach maintains objectivity and consistency while allowing flexibility through weighted factors that can be adjusted based on specific retail environments.
Solution Approach 2:
The system changes the parameters of threat identification from purely subjective human judgment to a hybrid approach using objective quantifiable parameters (threat scores, case file counts, behavior metrics) combined with flexible weighting. This maintains adaptability through configurable parameters while improving objectivity through data-driven scoring.
Data Source
AI summary
Described herein are systems and methods for generating a watch list of users who pose specific security threats to a store. The method can include retrieving, by a computer system from a data store, case files that document activity that poses a security threat by a user at the store, predicting, based on applying prediction models to the case files, future activity associated with the case files, determining threat scores for the case files based on the predicted future activity, ranking the case files into a candidate list from highest to lowest threat score, generating a watch list for the store that includes a subset of the ranked case files based on which case files pose a greatest current threat to the store, generating summary videos for each case file in the watch list, and transmitting the watch list and summary videos to a user device.