WO2024196660A1

WO2024196660A1 - Loss prevention tracking systems and methods

Info

Publication number: WO2024196660A1
Application number: PCT/US2024/019698
Authority: WO
Inventors: Mark Gentle; Clare WATKINSON
Original assignee: Checkpoint Systems, Inc.
Priority date: 2023-03-17
Filing date: 2024-03-13
Publication date: 2024-09-26

Abstract

Provided are loss prevention tracking systems and methods to identify and alert security-related threats or events. The systems and methods can identify and track potential offenders and provide real time alerts for potential future security threats. The systems and methods can determine and provide real time alerts for an attempted theft of an item with an active tag. The captured images and information can be used to monitor the reentrance of offenders to the store.

Description

TITLE

LOSS PREVENTION TRACKING SYSTEMS AND METHODS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to and the benefit of U.S. Provisional Application No. 63/452,743 entitled “Loss Prevention Tracking Systems and Methods” filed on March 17, 2023, the disclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

[0002] The present disclosure relates to loss prevention and merchandise preservation technologies and tracking systems and, more particularly, to systems and methods that integrate facial recognition and/or artificial intelligence with electronic article surveillance systems to identify and alert to potential security-related events.

BACKGROUND

[0003] Retail stores may employ various loss prevention techniques to prevent and deter theft of merchandise, including radio frequency identification (RFID) tags and electronic article surveillance (EAS) systems. These RFID tags and EAS systems may be used to track inventory and/or alarm under specified conditions likely to be associated with an attempted theft without a point of sale. Tags may be attached to merchandise pre-sale. In some applications, the tags may be removed by a specialized tool at a point of sale by a cashier. In other applications, the tags may be deactivated at a point of sale by a cashier. If the tags are not removed or otherwise deactivated, EAS antennas may alarm at the exit of a store if it detects an associated item moving through the exit of a store. This alarm may signal attempted theft of items that have not been purchased, e.g.. items that have not had the tags removed or deactivated.

[0004] Although these systems can alarm as attempted thefts are actually occurring, the systems do not offer preventative identification and tracking of potential offenders or events that occur prior to an attempted theft.

SUMMARY

[0005] The following presents a summary of this disclosure to provide a basic understanding of some aspects. This summary is intended to neither identify key or critical elements nor define any limitations of embodiments or claims. This summary may provide a simplified overview of some aspects that may be described in greater detail in other portions of this disclosure. Furthermore, any of the described aspects may be isolated or combined with other described aspects without limitation to the same effect as if they had been described separately and in every possible combination explicitly.

[0006] Disclosed are loss prevention tracking systems and methods that integrate facial recognition and/or artificial intelligence with electronic article surveillance systems to identify and alert to potential security-related events. Conventional EAS systems do not have capabilities to identify, track, and monitor potential offenders or events after an attempted theft. The present systems and methods can identify and track potential offenders as they enter a store prior to a potential theft of merchandise and provide real time alerts for potential future security threats. The systems and methods can determine and provide real time alerts for an attempted theft of items that have not been purchased, e.g., items that have not had the tags removed or deactivated and can capture identifying images and information related to offenders as they leave a store. The captured images and information can be used to monitor the reentrance of offenders to the store. [0007] Provided, in one aspect, is a loss prevention system comprising an EAS gate configured to detect an active tag affixed to merchandise, and a capturing device associated with the EAS gate, wherein the capturing device is configured to capture information associated with an individual passing by the EAS gate, wherein the system is configured to process the information to detect a security’ threat.

[0008] In one embodiment, the system is configured to send an alert based on the detection of the security threat in real time.

[0009] In one embodiment, the system is communicatively coupled to an alert management system configured to send the alert via an email or text message notification.

[0010] In one embodiment, the system is configured to send an alert or notification based on a condition associated with the individual in real time.

[0011] In one embodiment, the sy stem utilizes a decision tree or artificial intelligence to determine whether to send the alert or the notification based on the information.

[0012] In one embodiment, the condition comprises facial recognition of the individual against a database of known individuals.

[0013] In one embodiment, the system creates a faceprint of the individual based on the information, and compares the faceprint against a database of known individuals to determine if the condition is met.

[0014] In one embodiment, the database is selected from the group consisting of a police database, a legal database, an online reporting database, and a store database.

[0015] In one embodiment, the system automatically adds the faceprint to the database if the database does not include the faceprint.

[0016] In one embodiment, the condition comprises detecting the active tag via the EAS gate. [0017] In one embodiment, the system further comprises a memory, and the system is configured to record the information in the memory if the condition is met. [0018] In one embodiment, the system is communicatively coupled to a cloud system comprising rules for sending a notification or alert.

[0019] In one embodiment, the information comprises an image or video of the individual.

[0020] In one embodiment, the information comprises entry data associated with an individual entering a retail location, and security event data associated with a detection of the active tag.

[0021] In one embodiment, the system is configured to send an alert or notification based on a first condition and a second condition.

[0022] In one embodiment, the first condition is facial recognition of the individual against a database of known individuals, and the second condition is a detection of a security event.

[0023] In one embodiment, the detection of the security event comprises detecting the active tag.

[0024] In one embodiment, the loss prevention system is communicatively coupled with a network of retail locations and configured to send the information to the network of retail locations.

[0025] In one embodiment, the capturing device is oriented toward an exterior of a retail location.

[0026] In one embodiment, the capturing device is oriented toward an interior of a retail location.

[0027] Provided in another aspect, is a loss prevention method that comprises receiving, via a capturing device, information associated with an individual passing an EAS gate, processing the information to determine if a condition is met, and sending an alert if the condition is met.

[0028] In one embodiment, the method further comprises recording the information if the condition is met.

[0029] In one embodiment, the method further comprises deleting the information if the condition is not met. [0030] In one embodiment, the condition comprises facial recognition of the individual based on a database of known individuals.

[0031] In one embodiment, the condition comprises a detection of a security event via the EAS gate.

[0032] In one embodiment, the security event is based on a detection of an active tag via the EAS gate.

[0033] In one embodiment, the method further comprises recording information preceding the security event, and sending an alert.

[0034] In one embodiment, the method further comprises sending the information to a database accessible by a network of retail locations.

[0035] These and other aspects and embodiments of the present technology are further understood and described in the Detailed Description that follows. It is noted that any combination of the foregoing is contemplated herein. The following description and the drawings disclose various illustrative aspects. Some improvements and novel aspects may be expressly identified, while others may be apparent from the description and drawings.

DESCRIPTION OF THE DRAWINGS

[0036] The present teachings may be better understood by reference to the following detailed description taken in connection with the following illustrations, wherein:

[0037] FIG. 1 is a block diagram of an embodiment of a loss prevention system in accordance with various disclosed aspects herein;

[0038] FIG. 2 is a flow chart of an embodiment of a loss prevention method in accordance with various disclosed aspects herein; and

[0039] FIG. 3 is a flow chart of another embodiment of a loss prevention system method in accordance with various disclosed aspects herein. [0040] The invention may be embodied in several forms without departing from its spirit or essential characteristics. The scope of the invention is defined in the appended claims, rather than in the specific description preceding them. All embodiments that fall within the meaning and range of equivalency of the claims are therefore intended to be embraced by the claims.

DETAILED DESCRIPTION

[0041] Reference will now be made in detail to embodiments of the present teachings, examples of which are illustrated in the accompanying drawings. It is to be understood that other embodiments may be utilized and structural and functional changes may be made without departing from the scope of the present teachings. Moreover, features of the embodiments may be combined, switched, or altered without departing from the scope of the present teachings, e.g., features of each disclosed embodiment may be combined, switched, or replaced with features of the other disclosed embodiments. As such, the following description is presented by way of illustration and does not limit the various alternatives and modifications that may be made to the illustrated embodiments and still be within the spirit and scope of the present teachings.

[0042] As used herein, the words “example” and “exemplary” mean an instance, or illustration. The w ords “example” or "exemplary" do not indicate a key or preferred aspect or embodiment. The word “or” is intended to be inclusive rather an exclusive, unless context suggests otherwise. As an example, the phrase “A employs B or C,” includes any inclusive permutation (e.g., A employs B; A employs C; or A employs both B and C). As another matter, the articles “a” and “an” are generally intended to mean “one or more” unless context suggests otherwise. [0043] A “processor”, as used herein, processes signals and performs general computing and arithmetic functions. Signals processed by the processor can include digital signals, data signals, computer instructions, processor instructions, messages, a bit, a bit stream, or other means that can be received, transmitted and/or detected. Generally, the processor can be a variety of various processors including multiple single and multicore processors and coprocessors and other multiple single and multicore processor and co-processor architectures. The processor can include various modules to execute various functions.

[0044] A ‘"memory”, as used herein can include volatile memory and/or nonvolatile memory. Non-volatile memory can include, for example, ROM (read only memory), PROM (programmable read only memory ). EPROM (erasable PROM), and EEPROM (electrically erasable PROM). Volatile memory’ can include, for example, RAM (random access memory), synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), and direct RAM bus RAM (DRRAM). The memory can also include a drive (disk). The memory can store an operating system that controls or allocates resources of a computing device. The memory can also store data for use by the processor.

[0045] A “controller”, as used herein, can include a variety' of configurations, for example a processor and memory. Controller, can also include a microcontroller having on-board processor and memory'.

[0046] A “drive”, as used herein can be, for example, a magnetic drive, a solid state drive, a floppy drive, a tape drive, a Zip drive, a flash memory' card, and/or a memory' stick. Furthermore, the drive can be a CD-ROM (compact disk ROM), a CD recordable drive (CD- R drive), a CD rewritable drive (CD-RW drive), and/or a digital video ROM drive (DVD ROM). The drive can store an operating system and/or program that controls or allocates resources of a computing device.

[0047] Some portions of the detailed description that follows are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps

(instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical non-transitory signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations or transformation of physical quantities or representations of physical quantities as modules or code devices, without loss of generality. The algorithms can also be selected from an Artificial Intelligence (A.I.) algorithm. Such algorithms can include machine learning algorithms including, but not limited to, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transduction, learning to leam algorithms, genetic algorithms, ant algorithms, tabu search algorithms, or Monte Carlo algorithms (e.g., simulated annealing) or some other suitable network.

[0048] However, all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing device (such as a specific computing machine), that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices. [0049] Certain aspects of the embodiments described herein include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the embodiments could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by a variety of operating systems. The embodiments can also be in a computer program product which can be executed on a computing system.

[0050] Provided are loss prevention tracking systems and methods that integrate facial recognition and/or artificial intelligence with electronic article surveillance systems to identify and alert to potential security-related events. The systems and methods can identify and track potential offenders as they enter a store prior to a potential theft of merchandise and provide real time alerts for potential future security threats. The systems and methods can determine and provide real time alerts for an attempted theft of items that have not been purchased, e.g., items that have not had the tags removed or deactivated and can capture identifying images and information related to offenders as they leave a store. The captured images and information can be used to monitor the reentrance of offenders to the store.

[0051] FIG. 1 depicts an embodiment of a loss prevention system 100 in accordance with aspects of the present technology⁷. FIGs. 2-3 depict exemplary methods 200, 300 that may be associated with a loss prevention system such as system 100.

[0052] In an embodiment, the systems and methods comprise at least one capturing device 103. Capturing device 103 may include an image recording device, a video recording device, a sound recording device, a device capable of capturing one or more of images, video, and sound, a combination of one or more thereof, etc. Capturing device 103 may be a camera. In an embodiment, the systems and methods comprise two capturing devices 103.

[0053] Capturing devices 103 may be associated with an electronic article surveillance (EAS) gate 110. EAS gates 1 10 may also be referred to as an EAS antenna, and the terms may be used herein interchangeably. A system may employ one or more EAS gates 110. Typically, an EAS system comprises at least two EAS antennas disposed opposite one another at an entrance or exit such that a customer must walk between two opposing EAS antennas to enter or leave a facility. The EAS gates may be positioned at each of the entrances and/or exits of a store and may provide tracking of merchandise. EAS gates 110 may be able to determine whether merchandise passing between the gates 110 has been purchased by a consumer. For example, EAS gates 110 may track inventory by reading associated tags on select merchandise. EAS gates 110 may alarm under specified conditions likely to be associated with an attempted theft without a point of sale. In an embodiment, EAS gates 110 may include antennas functioning on 8.2MHZ frequency and may be used to detect and alarm when product leaves a store without payment or a live tag leaving the store without being retired or deactivated.

[0054] In some applications, the tags may be removed from merchandise by a specialized tool at a point of sale by a cashier. EAS gates 110 would not detect the presence of a tag on the merchandise passing through gates 110 and would not alarm. In other applications, the tags may be deactivated at a point of sale by a cashier. EAS gates 110 w ould not detect the presence of an activated tag on the merchandise passing through gates 110 and would not alarm. If the tags are not removed or otherwise deactivated, EAS gates 110 may alarm at the exit of a store if it detects an associated item moving through the exit of a store. This alarm may signal attempted theft of items that have not been purchased, e.g., items that have not had the tags removed or deactivated.

[0055] System 100 and components thereof may be integrated into new EAS gates 110 or may be retrofitted or attachable to existing EAS gates 110 to utilize the existing structure. Capturing devices 103 may be associated with one or both of the EAS gates 110. Capturing devices 103 may be positioned at any position on an EAS gate as may be suitable to capture an image of a person walking past the EAS gates. The image capturing device can be positioned at a height that enables view of individuals’ faces as they approach the EAS gates 110. Capturing devices

103 may be positioned at the entrances and/or exits of a store. EAS gates 110 may provide a suitable angle, height, and location to capture identifying information relating to individuals as they approach the entrances or exits of a store. Integrated new and existing EAS antennas with system 100 may revitalize the technology and offer a value added addition to EAS antennas that stores already use and rely on for loss prevention and merchandise tracking.

[0056] EAS gates 110 may include one capturing device 103 oriented toward an exterior of the store or positioned to view and capture identifying information relating to individuals as they approach the entrances or exits of a store. EAS gates 110 may include one capturing device 103 oriented toward an interior of the store or positioned to view and capture identifying information relating to individuals as they approach the exit of the store. In an embodiment, system 100 may comprise at least one capturing device facing a first direction and at least one capturing device facing a second, opposite direction. Capturing devices 103 may be able to capture identifying information, such as images, video, and the like, related to individuals as they enter a store (e.g., in a first direction) and as they exit a store (e.g., in a second direction). In such embodiments, system 100 may include two capturing devices 103, facing in different directions, at each EAS gate 110.

[0057] It is also noted that the capturing device 103, in an example, may be a camera that is movable from a first direction to a second direction, e.g., in response to detected motion or external control and that, in another example, the capturing device 103 may be a 360° camera able to simultaneously record in more than one direction. In such examples, the system 100 may include a single capturing device 103 at an EAS gate 100 that is capable of recording in more than one or at least two directions.

[0058] In an embodiment, system 100 and capturing device 103 utilizes either or both facial recognition and artificial intelligence. In an embodiment, system 100 and capturing device 1 3 may be used as a preventative notification system prior to a potential theft that scans, identifies, and alerts the store to high risk situations or potential security threats, see method 200 for example. In an example, system 100 may analyze captured data as individuals enter a store. Alerts and recordation of the event may be based on one or more conditions or factors, such as facial recognition confirmation of a prior offender or high risk individual. In an embodiment, system 100 and capturing device 103 may be used concurrently as a potential theft is occurring or after, to detect and capture images of actual violators possessing stolen merchandise leaving a store, see method 300 for example. In an example, system 100 may analyze captured data as individuals leave a store. Alerts and recordation of the event may be based on one or more conditions or factors, such as detection of merchandise having an active tag passing through the EAS gate 110.

[0059] In an example, system 100 and capturing device 103 may record or capture identifying information, such as an image, of individuals as they enter a store, see method 200 in FIG. 2, for example. System 100 may analyze and identify potential security threats from this captured information. For example, system 100 may utilize facial recognition of a captured image and run the captured image against a database of prior offenders or high risk individuals. System 100 may be able to determine when a prior offender or high risk individual enters a store and provide an alert to notify retailers in real time that such individual has entered (or left) the store. System 100 may utilize artificial intelligence to provide an alert to notify retailers in real time that such individual has entered (or left) the store. The alert may be able to minimize direct intervention or need for confrontation by store workers and instead provide targeted or limited surveillance of certain individuals, e.g., prior offenders or high risk individuals. The alert may also provide additional time prior to an attempted theft to organize security or other surveillance. The database may include police databases, legal databases, online reporting databases, and/or store databases. [0060] System 100 may base alerts on one or more conditions or factors associated with an individual, including individuals who have commited prior offenses at this store location or at other store locations, individuals who have exhibited violence in or directed to the store, persons on watch lists, etc. If none of the factors are present as determined by system 100, then system 100 may not save or retain the captured information pertaining to the individual. If one or more of the factors are present as determined by system 100, then system 100 may record the captured data and store the information in a local memory, cloud memory, etc.

[0061] FIG. 2 shows an exemplary method 200. In an embodiment, at step 210, the method 200 may include capturing information through capturing device 103 as person enters store (e.g., through EAS gate 110). This capturing step may be referred to as an active capture or active monitoring where the data and information captured or monitored is analyzed and may be stored or retained depending on the analysis. In an embodiment, at step 220, the method 200 may include analyzing captured information to determine if one or more conditions are met. As described herein, conditions can include facial recognition against a database of persons of interest. If conditions are met, the method 200 may include recording captured information and providing alert at step 230. If conditions are not met, the method 200 may include overwriting or deleting captured information at step 240.

[0062] In an example, system 100 and capturing device 103 may record or capture identifying information, such as an image, of individuals as they exit a store, see method 300 in FIG. 3, for example. System 100 may analyze and identify⁷ actual security threats from this captured information, such as when merchandise including an active tag moves through the EAS gates 110 indicating that the item may not have been purchased. The captured data and information (e.g., video) may include data and information from the time preceding the security event. Upon detection of a security event, system 100 may record the information, such as video, captured prior to the security event, e.g., the previous 1-180 seconds of video prior to the security event. In an embodiment, if no detection of a security event were to occur, any captured information would not be retained or saved (or would be immediately deleted or overwritten). In an embodiment, if there is detection of a security event, then the captured data and information may be recorded and stored in a local memory, cloud memory, etc. In an embodiment, if there is detection of a security event, then the captured data and information may be analyzed and/or added to a database of offenders.

[0063] In an embodiment, system 100 may analyze the captured data and information and may use facial recognition to identify presence of any faces in the video. System 100 may create faceprints and compare the faceprints against a database of prior offenders or high risk individuals. This may be the same database as described in relation to preventative security threats and method 200. If the faceprints are not already in the database, the faceprints of the offenders may be automatically, or manually upon confirmation of a security' event, added to the database to track whether the person of interest enters the store again.

[0064] FIG. 3 shows an exemplary' method 300. In an embodiment, at step 310, the method 300 may include capturing information through capturing device as person exits store. This capturing step may be referred to as a passive capture or passive monitoring where the data and information captured or monitored is not stored, retained, or analyzed unless a security event is detected or other specified conditions or factors are met. In an embodiment, at step 320, the method 300 may include detecting security event through EAS antennas 110 and merchandise tag. For example, the system 100 may determine or receive an alert/signal from the EAS antennas 110 that merchandise with an active tag has been moved through the gate, which may indicate the merchandise was not purchased by the individual. Upon such detection, at step 330, the method 300 may include recording preceding captured information (e g., preceding 1- 180 seconds of video prior to the security' event) and providing an alert. If no security event was detected, no information or data may be actively captured, analyzed, or retained (or would be immediately deleted or overwritten).

[0065] In an embodiment, system 100 and method 300 may capture important data and information (including images or video, for example) associated with EAS activations and alerts. Such captured information may provide evidence and may provide avenues for aftertheft apprehension and prevention of theft by the same person on multiple occasions rather than relying on immediate apprehension of suspects by store personnel. In an embodiment, system 100 and method 300 may also build a database or contribute to a database additional offenders or persons of interest to preventatively monitor. In an embodiment, system 100 and method 300 may be able to build or contribute to the database automatically or with minimal store or manual intervention (e g., confirmation that the security event was a theft).

[0066] In an embodiment, system 100 and methods 200, 300 may be deployed in a selfcheckout area. System 100 may be placed at both entry and exit to the self-checkout area, both entry and exit to the store, and/or at the self-checkout registers individually. System 100 may detect when non-payment occurs at a self-checkout register, may automatically capture data and information of the individual (such as images and video) exiting the store without payment made, may optionally provide an alert, may automatically add the individual to a database of persons of interest, and may use EAS gates 110 at entry to the store or self-checkout area to alert the store to the entry of the persons of interest to the store in the future.

[0067] It is noted that the system 100 and methods 200, 300 may be applicable to individual EAS gates 110. It is noted that the system 100 and methods 200, 300 may also be applicable to several or more than one EAS gates 110, 111, 112, e.g., all of the EAS gates that are located and operational in a store, for example. Singular EAS gates 1 10, 111, 112 may be tethered, connected to each other, or otherwise linked together as a part of system 100 and methods 200, 300. For example, more than one EAS gate 110, 11 1 , 1 12 may transmit captured or other data to components in the system 100. such as system 120. server 121, database 125, other EAS gates, etc. For example, more than one EAS gate 110, 111, 112 may access or receive captured or other data from components in the system 100, such as system 120, server 121, database 125, other EAS gates, etc. In an example, system 100 and methods 200, 300 may comprise a network of EAS gates and/or data from more than one EAS gate.

[0068] The system 100 and methods 200, 300 may be applicable to a network of EAS gates in a single store. The system 100 and methods 200, 300 may also be applicable to a network of EAS gates 110. I l l, 112 in several or more than one stores. For example, a store having system 100 and EAS gates may transmit captured or other data through system 100 to EAS gates or other components in system 100 that are in or associated with another store. For example, a store having system 100 and EAS gates in one store may access or receive captured or other data from EAS gates or other components in system 100 that are in another store through system 100. In an example, system 100 and methods 200, 300 may comprise a network of EAS gates and/or data from more than one EAS gate from a network of stores. In an embodiment, the database, for example, may be a collective database and may include information and identified offenders from one or more stores, EAS gates, as well as one or more sources (e.g., police department offender lists).

[0069] In an embodiment, captured or analyzed information in one store or from multiple EAS gates may be shared with captured or analyzed information in a second store such that an exit security event in store A may be shared with an entrance security event in store B based on the facial data from A. That is, offenders detected in store A by method 300 for example, having exited the store A with unpaid merchandise and system 100 having thereby captured facial data of the offender, may be shared with store B (or system 100 and database generally, which may then be accessible by the EAS gates in store B) so that store B can utilize the captured information from store A in method 200 for example, and prevent or monitor for the same offender from store A at that store B.

[0070] As shown in FIG. 1, captured data from the system 100 may be transmitted to system 120, such as a video management system, and a server 121. Captured data can include entry data 116 and EAS detected security events 113. Rules including conditions for alerting and retaining information may be provided in the cloud system 125. Alerts may be provided by email, text, or another form of communication to a specified store employee or manager and may be implemented by an alert management system 130. A self-learning module may be included in the system 100 and in the cloud system 125 for example. A decision tree may be deployed for identification of known persons of interest entering the store, e.g., when entry data 116 is compared against a known persons of interest database 125 and the condition for identifying a known persons of interest is met. A decision tree may be deployed after detection of a security threat by the EAS event 113, e.g., when the EAS gate detects merchandise with an active tag moving through the gate as a person exits the store. The decision trees can include saving, retaining, and analyzing captured data and notifying of a security event or person upon confirmation of a person of interest entering the store or tagged merchandise leaving the store. A secondary validation system 140 may be used to add new persons of interest to the database based on theft occurrences 126 and may be used to store EAS event capture 123.

[0071] What has been described above includes examples of the present specification. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the present specification, but one of ordinary skill in the art may recognize that many further combinations and permutations of the present specification are possible. Each of the components described above may be combined or added together in any permutation to define embodiments disclosed herein. Accordingly, the present specification is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes'’ is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

Claims

CLAIMS What is claimed is:

1. A loss prevention system comprising: an EAS gate configured to detect an active tag affixed to merchandise; and a capturing device associated with the EAS gate, wherein the capturing device is configured to capture information associated with an individual passing by the EAS gate, wherein the system is configured to process the information to detect a security threat.

2. The loss prevention system of claim 1, wherein the system is configured to send an alert based on a detection of the security threat in real time.

3. The loss prevention system of claim 2, wherein the system is communicatively coupled to an alert management system configured to send the alert via an email or text message notification.

4. The loss prevention system of claims 2 or 3, wherein the system utilizes a decision tree or artificial intelligence to determine whether to send the alert or notification based on the information.

5. The loss prevention system of claim 1, wherein the system is configured to send an alert or notification based on a condition associated with the individual in real time.

6. The loss prevention system of claim 5, wherein the condition comprises facial recognition of the individual against a database of known individuals.

7. The loss prevention system of claims 5 or 6, wherein the system: creates a faceprint of the individual based on the information, and compares the faceprint against a database of known individuals to determine if the condition is met.

8. The loss prevention system of claim 7, wherein the database is selected from the group consisting of a police database, a legal database, an online reporting database, and a store database.

9. The loss prevention system of claims 7 or 8, wherein the system automatically adds the faceprint to the database if the database does not include the faceprint.

10. The loss prevention system of claim 5, wherein the condition comprises detecting the active tag via the EAS gate.

11. The loss prevention system of any of claims 5-10, wherein the system further comprises a memory, and wherein the system is configured to record the information in the memory if the condition is met.

12. The loss prevention system of any of claims 5-11, wherein the system is communicatively coupled to a cloud system comprising rules for sending a notification or alert.

13. The loss prevention system of any of claims 1-12, wherein the information comprises an image or video of the individual.

14. The loss prevention system of any of claims 1-12 wherein the information comprises entry data associated with an individual entering a retail location, and security event data associated wi th a detection of the active tag.

15. The loss prevention system of claim 1, wherein the system is configured to send an alert or notification based on a first condition and a second condition.

16. The loss prevention system of claim 15, wherein the first condition is facial recognition of the individual against a database of known individuals, and the second condition is a detection of a security event.

17. The loss prevention system of claim 16, wherein the detection of the security event comprises detecting the active tag.

18. The loss prevention system of claims 16 or 17, wherein the loss prevention system is communicatively coupled with a network of retail locations and configured to send the information to the network of retail locations.

19. The loss prevention system of any of claims 1-18, wherein the capturing device is oriented toward an exterior of a retail location.

20. The loss prevention system of any of claims 1-18, wherein the capturing device is oriented toward an interior of a retail location.

21. A loss prevention method comprises: receiving, via a capturing device, information associated with an individual passing an EAS gate; processing the information to determine if a condition is met; and sending an alert if the condition is met.

22. The loss prevention method of claim 21. further comprising recording the information if the condition is met.

23. The loss prevention method of claim 21, further comprising deleting the information if the condition is not met.

24. The loss prevention method of any of claims 21 - 23, wherein the condition comprises facial recognition of the individual against a database of known individuals.

25. The loss prevention method of any of claims 21 - 24, wherein the condition comprises a detection of a security event via the EAS gate.

26. The loss prevention method of claim 25, wherein the security event is based on a detection of an active tag via the EAS gate.

27. The loss prevention method of any of claims 25 and 26, further comprising: recording information preceding the security’ event; and sending an alert.

28. The loss prevention method of any of claims 21-27, further comprising: sending the information to a database accessible by a network of retail locations.