Consumers suffer millions of dollars of losses annually to fraudulent online shopping websites. In some cases, a fraudulent shopping website may offer deals that are clearly too good to be true, but in other cases, a fraudulent website may offer deals that a reasonable person might consider to be within the realm of possibility. Some fraudulent shopping websites may take customers’ payments and never provide any products while others may provide products that are far inferior to what was actually advertised. In many cases, it is time-consuming, difficult, or impossible for customers to obtain refunds from fraudulent shopping websites.
While many applications exist to detect fraudulent websites, they often rely on imprecise or outdated methods. For example, a method that flags a website that does not have a secure socket layer certificate may catch some fraudulent shops but will miss the large quantity of fraudulent websites with functioning certificates. Other methods that rely on user reports may protect later users, but not the early users whose reports are relied upon to flag the website as fraudulent. Some methods may analyze the layout or formatting of a website for characteristics that indicate the website was constructed by a specific known scammer, but such methods must be constantly updated as scammers change and improve their website presentation. The present disclosure, therefore, identifies and addresses a need for systems and methods for detecting fraudulent shopping websites.
As will be described in greater detail below, the present disclosure describes various systems and methods for detecting fraudulent shopping websites based on an analysis of allegedly available payment options offered by such websites.
In one example, a computer-implemented method for detecting fraudulent shopping websites may include (i) identifying a shopping website that advertises a group of allegedly available payment options for completing transactions on the shopping website, (ii) determining, based at least in part on an analysis of the plurality of allegedly available payment options, that at least one of the allegedly available payment options is suspicious, (iii) calculating a trustworthiness score for the shopping website that is based at least in part on the determination that at least one of the allegedly available payment options is suspicious, and (iv) displaying an alert to a user based on the trustworthiness score of the shopping website.
In some examples, identifying the shopping website that advertises the allegedly available payment options may include identifying the allegedly available payment options by analyzing text of the shopping website. In some examples, analyzing the text of the shopping website may include analyzing the text via a named entity recognition classifier.
In some embodiments, identifying the shopping website that advertises the allegedly available payment options may include identifying the allegedly available payment options by analyzing images on the shopping website. In some examples, analyzing the images on the shopping website may include analyzing the images via a deep learning image classification algorithm.
In some examples, determining that at least one of the plurality of allegedly available payment options is suspicious may include detecting that at least a portion of the allegedly available payment options are non-functional. In addition, detecting that at least a portion of the plurality of allegedly available payment options are non-functional may include attempting to complete a mock transaction with each payment option within the allegedly available payment options. In one embodiment, attempting to complete the mock transaction with each payment option may include (i) detecting, via a web crawler, a product offered by the shopping website, (ii) detecting, via the web crawler, an interactable element on the shopping website that allegedly enables purchase of the product, and (iii) attempting, via the web crawler, to purchase the product via each payment option.
In some embodiments, determining that at least one of the plurality of allegedly available payment options is suspicious may include identifying a trustworthiness score for each payment option within the allegedly available payment options and determining that the trustworthiness score for at least one payment option within the plurality of allegedly available payment options fails to satisfy a predetermined threshold. In some examples, the trustworthiness score for the shopping website may be calculated based at least in part on the trustworthiness score for each payment option within the plurality of allegedly available payment options.
In some examples, displaying the alert to the user based on the trustworthiness score of the shopping website may include warning the user that the shopping website is potentially fraudulent. Additionally or alternatively, displaying the alert to the user based on the trustworthiness score of the shopping website may include preventing the user from completing a transaction on the shopping website in response to determining that the trustworthiness score for the shopping website fails to satisfy a threshold for safe shopping websites.
In one embodiment, a system for detecting fraudulent shopping websites may include at least one physical processor and physical memory that includes computer-executable instructions that, when executed by the physical processor, cause the physical processor to (i) identify a shopping website that advertises a group of allegedly available payment options for completing transactions on the shopping website, (ii) determine, based at least in part on an analysis of the plurality of allegedly available payment options, that at least one of the allegedly available payment options is suspicious, (iii) calculate a trustworthiness score for the shopping website that is based at least in part on the determination that at least one of the allegedly available payment options is suspicious, and (iv) display an alert to a user based on the trustworthiness score of the shopping website.
In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, may cause the computing device to (i) identify a shopping website that advertises a group of allegedly available payment options for completing transactions on the shopping website, (ii) determine, based at least in part on an analysis of the plurality of allegedly available payment options, that at least one of the allegedly available payment options is suspicious, (iii) calculate a trustworthiness score for the shopping website that is based at least in part on the determination that at least one of the allegedly available payment options is suspicious, and (iv) display an alert to a user based on the trustworthiness score of the shopping website.
Features from any of the embodiments described herein may be used in combination with one another in accordance with the general principles described herein. These and other embodiments, features, and advantages will be more fully understood upon reading the following detailed description in conjunction with the accompanying drawings and claims.
The accompanying drawings illustrate a number of example embodiments and are a part of the specification. Together with the following description, these drawings demonstrate and explain various principles of the present disclosure.
Throughout the drawings, identical reference characters and descriptions indicate similar, but not necessarily identical, elements. While the example embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the example embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the present disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims.
The present disclosure is generally directed to systems and methods for detecting fraudulent shopping websites. As will be explained in greater detail below, by analyzing the payment options offered by shopping websites, the systems and methods described herein may be able to detect fraudulent websites that otherwise appear legitimate and may be missed by other website classifiers. By detecting fraudulent websites in this way, the systems and methods described herein may be able to improve and/or increase the classification accuracy of trustworthiness classifiers, thereby reducing the number of resulting false positives and/or false negatives, when compared to traditional classifiers that rely on user reviews, website format, website hosting, and/or other types of data.
In addition, the systems and methods described herein may improve the functionality of a computing device by detecting potentially fraudulent shopping websites with increased accuracy, resulting in an improved overall state of security for the computing device and thus protecting end users from potential scams. These systems and methods may also improve the field of online shopping and/or fraud detection by accurately identifying fraudulent shopping websites.
The following will provide, with reference to
In certain embodiments, one or more of modules 102 in
As illustrated in
As illustrated in
Example system 100 in
Computing device 202 generally represents any type or form of computing device capable of reading computer-executable instructions. In some embodiments, computing device 202 may represent a personal computing device. Examples of computing device 202 include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), smart vehicles, smart packaging (e.g., active or intelligent packaging), gaming consoles, so-called Internet-of-Things devices (e.g., smart appliances, etc.), variations or combinations of one or more of the same, and/or any other suitable computing device.
Server 206 generally represents any type or form of computing device that is capable of hosting a shopping website. In some embodiments, server 206 may represent a dedicated e-commerce server. Additional examples of server 206 include, without limitation, security servers, application servers, web servers, storage servers, and/or database servers configured to run certain software applications and/or provide various security, web, storage, and/or database services. Although illustrated as a single entity in
Network 204 generally represents any medium or architecture capable of facilitating communication or data transfer. In one example, network 204 may facilitate communication between computing device 202 and server 206. In this example, network 204 may facilitate communication or data transfer using wireless and/or wired connections. Examples of network 204 include, without limitation, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the Internet, Power Line Communications (PLC), a cellular network (e.g., a Global System for Mobile Communications (GSM) network), portions of one or more of the same, variations or combinations of one or more of the same, and/or any other suitable network.
Shopping website 208 generally represents any type or form of website and/or web application that lists one or more products that are allegedly available for purchase by users. In some examples, shopping website 208 may list discounted and/or knock-off versions of popular products, difficult-to-find niche products, and/or other desirable products.
Payment options 210 generally represents any type or form of services and/or platforms that process payment information. In some examples, payment options 210 may include, without limitation, credit card transaction processors, bank transfer processors, digital payment processors, cryptocurrency transaction processors, and/or any other suitable type of payment processor. In some examples, one or more of payment options 210 may include instructions on how to use the payment option, such as, “use the ‘friends and family' setting when sending your payment.”
Trustworthiness score 212 generally represents any type or form of classification of whether or not a website or payment option is likely fraudulent or suspicious. In some embodiments, trustworthiness score 212 may include a numeric value (e.g., a percentage likelihood that a shopping website is fraudulent). Additionally alternatively, trustworthiness score 212 may include a textual classification, such as “likely fraudulent,” “likely trustworthy,” or “very likely trustworthy.”
Alert 214 generally represents any type or form of notification to a user. In some embodiments, alert 214 may include trustworthiness score 212. Additionally or alternatively, alert 214 may include suggested actions for a user (e.g., “do not purchase from this website”). Examples of alert 214 may include, without limitation, a dialog window, a browser overlay, an interstitial page, and/or an in-app notification.
As illustrated in
Identification module 104 may identify a shopping website in a variety of ways and/or contexts. In some embodiments, identification module 104 may monitor websites loaded by a browser and/or app with Internet browsing functionality in order to identify shopping websites. In one embodiment, identification module 104 may be part of a browser plug-in. Additionally or alternatively, identification module 104 may be part of a security application (e.g., a firewall, and anti-malware application, etc.) that monitors Internet activities on a device. In some examples, identification module 104 may identify a shopping website by scanning the website for characteristics that indicate shopping. For example, identification module 104 may scan the website for shopping cart functionality, keywords related to shopping, mentions of payment options, and/or prices.
Identification module 104 may scan and/or analyze various parts of a shopping website to identify the payment options that are offered by the website. For example, as illustrated in
Additionally or alternatively, identification module 104 may identify the allegedly available payment options by analyzing images on the shopping website. For example, shopping website 404 may have a footer 408 that includes icons 410 representing available payment options. In some examples, icons 410 may be located outside footer 408. In one embodiment, identification module 104 may identify the images on the shopping website by analyzing icons 410 via a deep learning image classification algorithm such as a residual network model. For example, identification module 104 may match icons 410 to the logos of popular payment options.
Returning to
Detection module 106 may determine that a payment option is suspicious in a variety of ways. In some embodiments, detection module 106 may determine that a payment option is suspicious if it is non-functional. For example, detection module 106 may attempt to complete a mock transaction with each payment option offered by a shopping website to determine whether all of the payment options are functional. In some examples, the systems described herein may complete enough steps of a mock transaction to verify whether a payment option is functional without completing the full transaction and being charged for the product. For example, the systems described herein may select a payment option, input payment information, and attempt to proceed to the next step, but may not activate a final “make order” button that completes the transaction.
In one embodiment, detection module 106 may use a web crawler to attempt to complete the mock transactions. For example, as illustrated in
In other examples, detection module 106 may determine that a payment option is suspicious if a trustworthiness score for the payment option fails to satisfy a predetermined threshold. For example, some payment options may be inherently less trustworthy than others, such as unusual or obscure payment options (such as cryptocurrency, travelers’ checks, gift cards, etc.), payment options that do not offer refunds, and/or payment options that are non-standard ways of using a payment service (e.g., marking a transfer as “sending to a friend” when the transfer is payment for a product). As such, in some examples calculation module 108 may calculate a trustworthiness score for each payment option 210 offered by shopping website 208. In these examples, if the trustworthiness score for a payment option fails to satisfy a predetermined threshold, then detection module 106 may determine that the payment option is suspicious.
Calculation module 108 may calculate trustworthiness scores for payment options in a variety of ways. In some examples, these trustworthiness scores may be based on the type of payment option being offered. For example, calculation model 108 may assign lower trustworthiness scores to unusual or obscure payment options (e.g., gift cards or travelers’ checks) than to more common payment options (e.g., credit cards). In another example, trustworthiness scores may be based on whether the payment option is being used in an unusual or non-standard way. For example, calculation module 108 may assign lower trustworthiness scores to payment options that are being used in unusual ways (e.g., marking a transfer as “sending to a friend” when the transfer is payment for a product). Calculation module 108 may also assign lower trustworthiness scores to payment options that do not offer refunds.
Returning to
Calculation module 108 may calculate the trustworthiness score for shopping website 208 in a variety of ways. In one example, calculation module 108 may calculate the trustworthiness score based in part on what proportion (e.g., what percentage) of allegedly available payment options are non-functional. Similarly, calculation module 108 may calculate the trustworthiness score based in part on the total number of non-functional payment options.
Additionally or alternatively, calculation module 108 may calculate the trustworthiness score for the shopping website by identifying a trustworthiness score for each payment option and calculating the trustworthiness score for the shopping website based at least in part on the trustworthiness score for each payment option. For example, calculation module 108 may calculate a low trustworthiness score for a website that offers a large number and/or portion of non-trustworthy payment options. For example, if a website offers to let users pay via cryptocurrency, travelers’ checks, a payment service meant for sending money to friends, gift cards, and/or an obscure payment service that is not known to be trustworthy, calculation module 108 may calculate a low trustworthiness score for the website. By contrast, if a website offers to let users pay via credit card and/or popular and trusted online payment services used properly, calculation module 108 may calculate a high trustworthiness score for the website.
Calculation module 108 may also calculate the trustworthiness score for the shopping website based on a combination of the approaches described herein (e.g., based on whether the payment options offered are both functional and trustworthy). For example, if a website only permits users to pay via unusual or especially suspicious payment methods (such as non-refundable methods like bank transfer, gift cards, and/or cryptocurrency), calculation module 108 may calculate a low trustworthiness score for the website even if all of the payment options are functional. By contrast, if a website offers to let users pay via credit card and/or popular and trusted online payment services, calculation module 108 may still calculate a low trustworthiness score for the website if one or more of these payment options are non-functional. In some embodiments, calculation module 108 may generate a score based on the percentage and/or amount of available payment options that are non-functional, generate a score based on the trustworthiness of the payment options available, weight each score, and combine the waited scores to arrive at an overall trustworthiness score.
At step 308, one or more of the systems described herein may display an alert to a user based on the trustworthiness score of the shopping website. For example, alert module 110 may, as part of computing device 202 in
Alert module 110 may display the alert in a variety of different ways. For example, alert module 110 may display a pop-up, dialog box, and/or overlay in a browser and/or app indicating whether the shopping website is trustworthy.
In some examples, alert module 110 may display the alert to the user based on the trustworthiness score of the shopping website by warning the user that the shopping website is potentially fraudulent. In some embodiments, alert module 110 may only display the alert if the website has a trustworthiness score that fails to satisfy a predetermined threshold and may not display the alert otherwise.
In some embodiments, alert module 110 may display the alert to the user in response to determining that the trustworthiness score for the shopping website fails to satisfy a threshold for safe shopping websites. For example, alert module 110 may redirect the web browser from the shopping website to an alert page and/or may disable interactable elements of the shopping website (e.g., payment forms, transaction completion buttons, etc.). In some embodiments, the systems described herein may enable the user to complete a transaction after clicking through a warning.
As explained above in connection with method 300 in
Computing system 610 broadly represents any single or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system 610 include, without limitation, workstations, laptops, client-side terminals, servers, distributed computing systems, handheld devices, or any other computing system or device. In its most basic configuration, computing system 610 may include at least one processor 614 and a system memory 616.
Processor 614 generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions. In certain embodiments, processor 614 may receive instructions from a software application or module. These instructions may cause processor 614 to perform the functions of one or more of the example embodiments described and/or illustrated herein.
System memory 616 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or other computer-readable instructions. Examples of system memory 616 include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory device. Although not required, in certain embodiments computing system 610 may include both a volatile memory unit (such as, for example, system memory 616) and a non-volatile storage device (such as, for example, primary storage device 632, as described in detail below). In one example, one or more of modules 102 from
In some examples, system memory 616 may store and/or load an operating system 640 for execution by processor 614. In one example, operating system 640 may include and/or represent software that manages computer hardware and software resources and/or provides common services to computer programs and/or applications on computing system 610. Examples of operating system 640 include, without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS MOBILE, MAC OS, APPLE’S IOS, UNIX, GOOGLE CHROME OS, GOOGLE’S ANDROID, SOLARIS, variations of one or more of the same, and/or any other suitable operating system.
In certain embodiments, example computing system 610 may also include one or more components or elements in addition to processor 614 and system memory 616. For example, as illustrated in
Memory controller 618 generally represents any type or form of device capable of handling memory or data or controlling communication between one or more components of computing system 610. For example, in certain embodiments memory controller 618 may control communication between processor 614, system memory 616, and l/O controller 620 via communication infrastructure 612.
l/O controller 620 generally represents any type or form of module capable of coordinating and/or controlling the input and output functions of a computing device. For example, in certain embodiments l/O controller 620 may control or facilitate transfer of data between one or more elements of computing system 610, such as processor 614, system memory 616, communication interface 622, display adapter 626, input interface 630, and storage interface 634.
As illustrated in
As illustrated in
Additionally or alternatively, example computing system 610 may include additional l/O devices. For example, example computing system 610 may include l/O device 636. In this example, l/O device 636 may include and/or represent a user interface that facilitates human interaction with computing system 610. Examples of l/O device 636 include, without limitation, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanner, a microphone, a touchscreen device, variations or combinations of one or more of the same, and/or any other l/O device.
Communication interface 622 broadly represents any type or form of communication device or adapter capable of facilitating communication between example computing system 610 and one or more additional devices. For example, in certain embodiments communication interface 622 may facilitate communication between computing system 610 and a private or public network including additional computing systems. Examples of communication interface 622 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface. In at least one embodiment, communication interface 622 may provide a direct connection to a remote server via a direct link to a network, such as the Internet. Communication interface 622 may also indirectly provide such a connection through, for example, a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.
In certain embodiments, communication interface 622 may also represent a host adapter configured to facilitate communication between computing system 610 and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communication interface 622 may also allow computing system 610 to engage in distributed or remote computing. For example, communication interface 622 may receive instructions from a remote device or send instructions to a remote device for execution.
In some examples, system memory 616 may store and/or load a network communication program 638 for execution by processor 614. In one example, network communication program 638 may include and/or represent software that enables computing system 610 to establish a network connection 642 with another computing system (not illustrated in
Although not illustrated in this way in
As illustrated in
In certain embodiments, storage devices 632 and 633 may be configured to read from and/or write to a removable storage unit configured to store computer software, data, or other computer-readable information. Examples of suitable removable storage units include, without limitation, a floppy disk, a magnetic tape, an optical disk, a flash memory device, or the like. Storage devices 632 and 633 may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system 610. For example, storage devices 632 and 633 may be configured to read and write software, data, or other computer-readable information. Storage devices 632 and 633 may also be a part of computing system 610 or may be a separate device accessed through other interface systems.
Many other devices or subsystems may be connected to computing system 610. Conversely, all of the components and devices illustrated in
The computer-readable medium containing the computer program may be loaded into computing system 610. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory 616 and/or various portions of storage devices 632 and 633. When executed by processor 614, a computer program loaded into computing system 610 may cause processor 614 to perform and/or be a means for performing the functions of one or more of the example embodiments described and/or illustrated herein. Additionally or alternatively, one or more of the example embodiments described and/or illustrated herein may be implemented in firmware and/or hardware. For example, computing system 610 may be configured as an Application Specific Integrated Circuit (ASIC) adapted to implement one or more of the example embodiments disclosed herein.
Client systems 710, 720, and 730 generally represent any type or form of computing device or system, such as example computing system 610 in
As illustrated in
Servers 740 and 745 may also be connected to a Storage Area Network (SAN) fabric 780. SAN fabric 780 generally represents any type or form of computer network or architecture capable of facilitating communication between a plurality of storage devices. SAN fabric 780 may facilitate communication between servers 740 and 745 and a plurality of storage devices 790(1)-(N) and/or an intelligent storage array 795. SAN fabric 780 may also facilitate, via network 750 and servers 740 and 745, communication between client systems 710, 720, and 730 and storage devices 790(1)-(N) and/or intelligent storage array 795 in such a manner that devices 790(1)-(N) and array 795 appear as locally attached devices to client systems 710, 720, and 730. As with storage devices 760(1)-(N) and storage devices 770(1)-(N), storage devices 790(1)-(N) and intelligent storage array 795 generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions.
In certain embodiments, and with reference to example computing system 610 of
In at least one embodiment, all or a portion of one or more of the example embodiments disclosed herein may be encoded as a computer program and loaded onto and executed by server 740, server 745, storage devices 760(1)-(N), storage devices 770(1)-(N), storage devices 790(1)-(N), intelligent storage array 795, or any combination thereof. All or a portion of one or more of the example embodiments disclosed herein may also be encoded as a computer program, stored in server 740, run by server 745, and distributed to client systems 710, 720, and 730 over network 750.
As detailed above, computing system 610 and/or one or more components of network architecture 700 may perform and/or be a means for performing, either alone or in combination with other elements, one or more steps of an example method for detecting fraudulent shopping websites.
While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered example in nature since many other architectures can be implemented to achieve the same functionality.
In some examples, all or a portion of example system 100 in
In various embodiments, all or a portion of example system 100 in
According to various embodiments, all or a portion of example system 100 in
In some examples, all or a portion of example system 100 in
In addition, all or a portion of example system 100 in
In some embodiments, all or a portion of example system 100 in
According to some examples, all or a portion of example system 100 in
The process parameters and sequence of steps described and/or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and/or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed. The various example methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein or include additional steps in addition to those disclosed.
While various embodiments have been described and/or illustrated herein in the context of fully functional computing systems, one or more of these example embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these software modules may configure a computing system to perform one or more of the example embodiments disclosed herein.
In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. For example, one or more of the modules recited herein may receive website or web app data to be transformed, transform the data to identify payment options, output a result of the transformation to a web crawler, use the result of the transformation to test payment options, and store the result of the transformation to calculate a trustworthiness score. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.
The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the example embodiments disclosed herein. This example description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the spirit and scope of the present disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the present disclosure.
Unless otherwise noted, the terms “connected to” and “coupled to” (and their derivatives), as used in the specification and claims, are to be construed as permitting both direct and indirect (i.e., via other elements or components) connection. In addition, the terms “a” or “an,” as used in the specification and claims, are to be construed as meaning “at least one of.” Finally, for ease of use, the terms “including” and “having” (and their derivatives), as used in the specification and claims, are interchangeable with and have the same meaning as the word “comprising.”
Number | Date | Country | Kind |
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21386066.1 | Nov 2021 | EP | regional |