SYSTEMS AND METHODS FOR USING LOCATION-RELATED DATA TO GENERATE VIRTUAL CERTIFICATION NUMBER DATA FOR AN INTERACTION

A method for generating virtual certification number data may comprise: receiving real-time image data from a camera of a user device; parsing location features data from the real-time image data; determining, based on the location features data, a location of the user device; upon determining the location of the user device, detecting, based on the real-time image data, a terminal at the location; upon detecting the terminal at the location, generating virtual certification number data for the user specific to the location; causing the user device to store the virtual certification number data for the user on a memory device associated with the user device; and transmitting the virtual certification number data to the terminal at the location.

TECHNICAL FIELD

Various embodiments of this disclosure relate generally to using location-related data, and, more particularly, to systems and methods for using location-related data to generate virtual certification number data for an interaction.

BACKGROUND

Virtual certification numbers may be used to increase interaction (e.g., transaction) security when they can be associated with a merchant. For example, a virtual certification number may be associated with a merchant when a customer is using a browser extension, and the extension can recognize the merchant associated with the website for which the customer generated the virtual certification number. However, conventional techniques for generating virtual certification numbers for in-person shopping is more difficult, if not impossible. As a result, generated virtual certification numbers may not be bound to specific merchants, which may leave virtual certification numbers susceptible to fraud.

This disclosure is directed to addressing above-referenced challenges. The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods and systems are disclosed for using location-related data to generate virtual certification number-related data for an interaction.

In one aspect, a computer-implemented method for generating virtual certification number data may include: receiving real-time image data from a camera of a user device; parsing location features data from the real-time image data; determining, based on the location features data, a location of the user device; upon determining the location of the user device, detecting, based on the real-time image data, a terminal at the location; upon detecting the terminal at the location, generating virtual certification number data for the user specific to the location; causing the user device to store the virtual certification number data for the user on a memory device associated with the user device; and transmitting the virtual certification number data to the terminal at the location.

In another aspect, a computer-implemented method for generating virtual certification number data may include: receiving real-time image data from one or more cameras of a wearable user device, wherein the real-time image data includes picture or video images of an environment surrounding the wearable user device; parsing location features data from the real-time image data; determining, based on the location features data, a location of the wearable user device; upon determining the location of the wearable user device, detecting, based on the real-time image data, a terminal at the location; upon detecting the terminal at the location, generating virtual certification number data for the user specific to the location; causing the user device to store the virtual certification number data for the user on a memory device associated with the wearable user device; transmitting the virtual certification number data to the terminal at the location; after transmitting the virtual certification number data to the terminal at the location, receiving an approval indication associated with the virtual certification number data; and causing the user device to present, via a display of the wearable user device, a graphical representation of the approval indication.

In another aspect, computer-implemented system for generating virtual certification number data, the system may include: a memory having processor-readable instructions therein; and at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions, including functions for: receiving real-time image data from a camera of a user device; parsing location features data from the real-time image data; determining, based on the location features data, a location of the user device; upon determining the location of the user device, detecting, based on the real-time image data, a terminal at the location; upon detecting the terminal at the location, generating virtual certification number data for the user specific to the location; causing the user device to store the virtual certification number data for the user on a memory device associated with the user device; and transmitting the virtual certification number data to the terminal at the location.

DETAILED DESCRIPTION OF EMBODIMENTS

According to certain aspects of the disclosure, methods and systems are disclosed for using location-related data for generating virtual certification number data, e.g., for use in an interaction at a location. However, conventional techniques may not be suitable. For example, conventional techniques may not facilitate use of merchant-specific virtual certification numbers for in-person interactions. Accordingly, improvements in technology relating to generating virtual certification numbers are needed.

As will be discussed in more detail below, in various embodiments, systems and methods are described for using location-related data to generate virtual certification number data for an interaction, e.g., for an in-person interaction (e.g., transaction). By training a machine learning model, e.g., via supervised or semi-supervised learning, to learn associations between image data and merchant location data, the trained machine learning model may be usable to use location-related data to generate virtual certification number data.

Reference to any particular activity is provided in this disclosure only for convenience and not intended to limit the disclosure. A person of ordinary skill in the art would recognize that the concepts underlying the disclosed devices and methods may be utilized in any suitable activity. The disclosure may be understood with reference to the following description and the appended drawings, wherein like elements are referred to with the same reference numerals.

In this disclosure, the term “based on” means “based at least in part on.” The singular forms “a,” “an,” and “the” include plural referents unless the context dictates otherwise. The term “exemplary” is used in the sense of “example” rather than “ideal.” The terms “comprises,” “comprising,” “includes,” “including,” or other variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, or product that comprises a list of elements does not necessarily include only those elements, but may include other elements not expressly listed or inherent to such a process, method, article, or apparatus. The term “or” is used disjunctively, such that “at least one of A or B” includes, (A), (B), (A and A), (A and B), etc. Relative terms, such as, “substantially” and “generally,” are used to indicate a possible variation of ±10% of a stated or understood value.

Terms like “provider,” “merchant,” “vendor,” or the like generally encompass an entity or person involved in providing, selling, and/or renting items to persons such as a seller, dealer, renter, merchant, vendor, or the like, as well as an agent or intermediary of such an entity or person. An “item” generally encompasses a good, service, or the like having ownership or other rights that may be transferred. As used herein, terms like “user” or “customer” generally encompasses any person or entity that may desire information, resolution of an issue, purchase of a product, or engage in any other type of interaction with a provider. The term “browser extension” may be used interchangeably with other terms like “program,” “electronic application,” or the like, and generally encompasses software that is configured to interact with, modify, override, supplement, or operate in conjunction with other software.

As used herein, a “machine learning model” generally encompasses instructions, data, and/or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output. The output may include, for example, a classification of the input, an analysis based on the input, a design, process, prediction, or recommendation associated with the input, or any other suitable type of output. A machine learning model is generally trained using training data, e.g., experiential data and/or samples of input data, which are fed into the model in order to establish, tune, or modify one or more aspects of the model, e.g., the weights, biases, criteria for forming classifications or clusters, or the like. Aspects of a machine learning model may operate on an input linearly, in parallel, via a network (e.g., a neural network), or via any suitable configuration.

The execution of the machine learning model may include deployment of one or more machine learning techniques, such as linear regression, logistical regression, random forest, gradient boosted machine (GBM), deep learning, and/or a deep neural network. Supervised and/or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth. Unsupervised approaches may include clustering, classification or the like. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. Any suitable type of training may be used, e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batch-based, etc.

In an exemplary use case, certain embodiments may use a machine learning model to process image data gathered from a user device to identify a location of a user. Certain embodiments may then detect that the user is at a merchant, such as at an interaction terminal of the merchant (terminal). Based on this, certain embodiments may generate merchant-specific virtual certification number data for use during an interaction at the merchant.

While several of the examples herein involve image data, it should be understood that techniques according to this disclosure may be adapted to any suitable type of location-related data, such as global positioning system (GPS) data, and combinations of image data and GPS data. It should also be understood that the examples herein are illustrative only. The techniques and technologies of this disclosure may be adapted to any suitable activity.

Presented below are various aspects of machine learning techniques that may be adapted to use location-related data for generating virtual certification number data. As will be discussed in more detail below, machine learning techniques adapted to generate virtual certification number data may include one or more aspects according to this disclosure, e.g., a particular selection of training data, a particular training process for the machine learning model, operation of a particular device suitable for use with the trained machine learning model, operation of the machine learning model in conjunction with particular data, modification of such particular data by the machine learning model, etc., and/or other aspects that may be apparent to one of ordinary skill in the art based on this disclosure.

FIG.1depicts an exemplary environment100for using location-related data for generating virtual certification number data for an interaction, according to one or more embodiments. The environment100may include one or more user devices102, one or more server devices104, and a network106. AlthoughFIG.1depicts a single user device102, server device104, and network106, the embodiments described herein are applicable to environments100that include two or more user devices102, server devices104, and/or networks106in any suitable arrangement. For example, in some arrangements, environment100may include two user devices102(e.g., a smartphone and a smart wearable such as smart eyeglasses), each associated with a same user. Furthermore, environment100may include one or more user devices102for each of a plurality of associated users.

The user device102may include a display108A, a processor110A, a memory112A, and/or a network interface114A. The user device102may be a mobile device, such as a smartphone, a cell phone, a tablet, a laptop computer, etc., a desktop computer, and/or the like. The user device102may be wearable, e.g., a smart wearable such as smart eyeglasses, as discussed in more detail below. The user device102may execute, by the processor110A, one or more instructions stored in the memory112A to, e.g., generate virtual certification number data, or train and/or use one or more machine learning models to generate virtual certification number data, as described elsewhere herein. One or more components of the user device102may generate, or may cause to be generated, one or more graphic user interfaces (GUIs) based on instructions/information stored in the memory112A, instructions/information received from the server device104, and/or the like, and may cause the GUIs to be displayed via the display108A. The GUIs may be, e.g., mobile application interfaces, virtual/augmented reality interfaces, or browser user interfaces and may include text, input text boxes, selection controls, and/or the like. The display108A may include a touch screen or a display with other input systems (e.g., a mouse, keyboard, etc.) for an operator of the user device102to control the functions of the user device102. The network interface114A may be a transmission control protocol/Internet protocol (TCP/IP) network interface, or another type of wired or wireless communication interface, for Ethernet or wireless communications with the server device104via the network106.

The server device104may include a display/user interface (UI)108B, a processor1106, a memory112B, and/or a network interface114B. The server device(s)104may be a computer, system of computers (e.g., rack server(s)), or a cloud service computer system (e.g., in a data center). The server device104may execute, by the processor1106, one or more instructions stored in the memory112B to, e.g., generate virtual certification number data, or train and/or use one or more machine learning models to generate the virtual certification number data, as described elsewhere herein. One or more components of the server device104may generate, or may cause to be generated, one or more GUIs based on instructions/information stored in the memory112B, instructions/information received from the user device102, and/or the like and may cause the GUIs to be displayed via the display108B.

The network106may include one or more wired and/or wireless networks, such as the Internet, an intranet, a wide area network (WAN), a local area network (LAN), a personal area network (PAN), a cellular network (e.g., a 3G network, a 4G network, a 5G network, etc.) or the like. The Internet is a worldwide system of computer networks—a network of networks in which a party at one computer or other device connected to the network can obtain information from any other computer and communicate with parties of other computers or devices. The most widely used part of the Internet is the World Wide Web (often-abbreviated “WWW” or called “the Web”). A “webpage” generally encompasses a location, data store, or the like that is, e.g., hosted and/or operated by a computer system so as to be accessible online, and that may include data configured to cause a program such as a web browser to perform operations such as send, receive, or process data, generate a visual display and/or an interactive interface, or the like. The user device102and the server device104may be connected via the network106, using one or more standard communication protocols. The user device102and the server device104may transmit and receive messages from each other across the network106, as discussed in more detail below.

As discussed in further detail below, the one or more components of exemplary environment100may process data from one or more user devices102and/or one or more server devices104. Additionally, or alternatively, and as discussed in further detail below, the one or more components of exemplary environment100may generate, store, train and/or use a machine learning model for generating virtual certification number data. The exemplary environment100or one of its components may include or be in operable communication with a machine learning model and/or instructions associated with the machine learning model, e.g., instructions for generating a machine learning model, training the machine learning model, using the machine learning model, etc. The exemplary environment100or one of its components may include instructions for retrieving data, adjusting data, e.g., based on the output of the machine learning model, and/or operating a display to output data, e.g., as adjusted based on the machine learning model. The exemplary environment100or one of its components may include, provide, obtain, and/or generate training data.

In some embodiments, a system or device other than the components shown in the exemplary environment100may be used to generate and/or train the machine learning model. For example, such a system may include instructions for generating and/or obtaining the machine learning model, the training data and ground truth, and/or instructions for training the machine learning model. A resulting trained machine learning model may then be provided to the exemplary environment100or one of its components and, for example, stored in the memory112A and/or112B.

Generally, a machine learning model includes a set of variables, e.g., nodes, neurons, filters, etc., that are tuned, e.g., weighted or biased, to different values via the application of training data. In supervised learning, e.g., where a ground truth is known for the training data provided, training may proceed by feeding a sample of training data into a model with variables set at initialized values, e.g., at random, based on Gaussian noise, a pre-trained model, or the like. The output may be compared with the ground truth to determine an error, which may then be back-propagated through the model to adjust the values of the variable. Certain embodiments may utilize, for training a machine learning model, unsupervised learning where, e.g., the sample of training data may not include pre-assigned labels or scores to aid the learning process, or may utilize semi-supervised learning, where a combination of training data with pre-assigned labels or scores and training data without pre-assigned labels or scores is used to train a machine learning model.

Training may be conducted in any suitable manner, e.g., in batches, and may include any suitable training methodology, e.g., stochastic or non-stochastic gradient descent, gradient boosting, random forest, etc. In some embodiments, a portion of the training data may be withheld during training and/or used to validate the trained machine learning model, e.g., may be used to compare the output of the trained model with the ground truth for that portion of the training data to evaluate an accuracy of the trained model. The training of the machine learning model may be configured to cause the machine learning model to learn associations between training data (e.g., computing system data) and ground truth data, such that the trained machine learning model is configured to determine an output in response to the input data based on the learned associations.

In various embodiments, the variables of a machine learning model may be interrelated in any suitable arrangement in order to generate the output. For example, in some embodiments, the machine learning model may include an architecture that is configured to determine a location of a user based on image data gathered from a user device102and may generate virtual certification number data for the user to use at the location. For example, the machine learning model may include one or more neural networks configured to identify features in the data, and may include further architecture, e.g., a connected layer, neural network, etc., configured to determine a relationship between the identified features in order to determine a location in the data. In certain embodiments, the machine learning model may include a single node for classification, as described elsewhere herein.

Although depicted as separate components inFIG.1, it should be understood that a component or portion of a component in the exemplary environment100may, in some embodiments, be integrated with or incorporated into one or more other components. For example, the server device104may be integrated in a data storage system. In some embodiments, operations or aspects of one or more of the components discussed above may be distributed amongst one or more other components. Any suitable arrangement and/or integration of the various systems and devices of the exemplary environment100may be used.

Further aspects of generating virtual certification number data or of the machine learning model and/or how it may be trained or used to generate the virtual certification number data are discussed in further detail below. In the following disclosure, various acts may be described as performed or executed by a component fromFIG.1, such as the user device102, the server device104, or components thereof. However, it should be understood that in various embodiments, various components of the exemplary environment100discussed above may execute instructions or perform acts including the acts discussed below. An act performed by a device may be considered to be performed by a processor, actuator, or the like associated with that device. Further, it should be understood that in various embodiments, various steps may be added, omitted, and/or rearranged in any suitable manner.

The example environment100described above is provided merely as an example, and may include additional, fewer, different, or differently arranged aspects than depicted inFIG.1.

FIG.2depicts a flowchart of an exemplary method200of using location-related data for generating virtual certification number data for an interaction, according to one or more embodiments. In some embodiments, the method200may be performed by a user device102. In other embodiments, the method200(or portions thereof) may be performed by a server device104.

At step202, the method200may include receiving real-time image data from a camera of a user device. For example, the user device102may receive real-time image data from a camera of the user device102. The user device102may receive the real-time image data as a stream of images, as the camera periodically captures the image data, as a response to a user input to the user device102to cause the camera to capture the image data, as a response to user device102detecting that the user device102is proximate to a location (e.g., a merchant), and/or based on the like.

The image data may include one or more images, pictures, videos, and/or the like of an environment within a field of view of a camera of the user device102. The image data may be real-time (or approximately real-time) image data. Real-time (or approximately real-time) image data may include image data gathered and/or processed as the user device102is in use, is worn, is moved about, and/or the like.

In some embodiments, the user device102may include a pair of smart eyeglasses or other such smart wearable (e.g., a wearable electronic device equipped with one or more sensors/components for collecting data and transmitting the like). In this case, the user may wear the user device102as the user goes about daily activities, and the camera of the user device102may capture the image data in real-time (or approximately real-time). The smart eyeglasses may be virtual reality (VR) or augmented reality (AR) eyeglasses. For example, in some arrangements, as a user goes about daily activities, AR eyeglasses may generate a composite view by superimposing a computer-generated image on a user's view of the real world.

The method200may include, at204, parsing location features data from the real-time image data. For example, the user device102may parse location features data from the real-time image data. The user device102may parse the location features data based on receiving the image data, based on the user inputting a command to the user device102to parse the image data, based on the user device102detecting that the user device102is proximate to a location (e.g., a merchant), and/or the like.

The user device102may use one or more machine learning models to parse the image data. For example, the user device102may input the image data into the one or more machine learning models and the one or more machine learning models may process the image data to extract the location features data and provide the same as output. Continuing with this example, the one or more machine learning models may process the image data to extract features, such as names on buildings, shapes or designs of buildings, and/or the like shown in the image data. In this way, location features data may include text, objects, and/or the like shown in the image data that may indicate a location of the user. For example, the one or more machine learning models may extract the text “Grocery Store” from a sign in the image data as the user walks toward a building entrance and may determine that this text is location features data. As specific examples in a merchant context, the location features data may include a logo or image associated with the location, a brand or trade name associated with the location, an item type associated with the location, a sign, document, or menu, associated with the location, a floorplan layout associated with the location, and/or the like.

In some embodiments, the user device102may be configured to determine whether the user device is inside a building, within a structure, at an outdoor vendor location, and/or the like. In this case, the location features data may include a back side of a door, a ceiling, one or more inside windows, artificial lighting, building structure features (e.g., single-store structure features or multi-store structure features), and/or the like. In some embodiments, the user device102may be configured to determine whether the user device is outside a building or structure. For example, the location features data may include determining that the user device is outside, e.g., by extracting features related to the environment (e.g., clouds, sunlight, etc.), cars, parking lot features (e.g., pavement, painted lines, traffic signs, etc.), building exteriors (e.g., bricks, siding, etc.), vendors (e.g., food trucks, parking lot kiosks, and/or related signage, etc.), etc.

As step206, the method200may include determining a location of the user device. For example, the user device102may determine the location of the user device. The user device102may determine the location based on the location features data, based on receiving a command from a user of the user device102to determine the location, based on time (e.g., periodically), and/or the like. Additionally, or alternatively, the user device102may determine the location based on other data indicating that the user device102may be located at, or proximate to, a certain type of location. For example, the user device102may use GPS data to determine that the user device102is at or proximate to a shopping center, a shopping mall, a store, and/or the like, and may determine the location, e.g., address or coordinates, based on the GPS data. In such examples, the user device102may include a GPS receiver for determining a location of the user device102.

In certain embodiments, the user device102may process the location features data to determine the location. For example, the user device102may process text extracted from the image data, building shapes or designs extracted from the image data, environmental data (e.g., the sky, a forest, pavement, etc.), and/or the like to determine a location associated with the text, shapes, designs, etc. The user device102may compare the location features data to a database of location features data and may determine the location based on a match. Additionally, or alternatively, the user device102may use one or more machine learning models to determine the location. For example, the user device102may input the location features data into a machine learning model and the machine learning model may determine a similarity of the text (or a stylization of the text) to other text on which the machine learning model has been trained, may determine likely missing letters from text that is partially obscured in the image data, may determine a similarity of a design or shape of a building to other building designs or shapes on which the machine learning model has been trained, and/or the like.

The machine learning model may output various locations and corresponding scores that indicate a likelihood that the user device102is at the location. The user device102may then use this output to determine the location. For example, the user device102may select the location with the highest output corresponding location score. In some embodiments, the user device102may generate and output a request for user selection regarding the determined location. For example, user device102may, after determining a likelihood that the user device102is located at one or more locations, generate a GUI to request user confirmation of the location. Accordingly, in locations with a plurality of merchants (e.g., a shopping mall), the user device102may determine that location A and location B are the most likely locations of the user device102based on the parsed location features data. In this example, location A and location B may be two stores next door to each other in a shopping mall. Once so determined, the user device102may output a GUI requesting confirmation from the user as to which of location A and location B is most accurate. In response to a user input selecting location A, the user device102may conclude that the user input102is at location A and not at location B.

In examples in which the user device102include AR eyeglasses, the user device102may output an augmented image including a GUI identifying one or more locations from which the user may select an accurate location. Selection may be completed based on interacting with the GUI via any appropriate input device or method (e.g., manual selection controller, retinal control, and/or audio control). For example, the AR glasses may provide a list of possible locations and may request the user select a location for confirmation. The AR glasses may receive confirmation via pupil tracking, gesture interaction, etc. In some examples, the AR glasses may provide informational alerts to the user. For example, the AR glasses may alert the user that the location may have been recognized as a grocery store.

The one or more machine learning models may have been trained to learn relationships between (i) training real-time image data that includes prior location features data and (ii) prior locations data associated with the prior location features data. For example, the training set of data for the one or more machine learning models may include location features data and locations data that identifies locations corresponding to the location features data. In this way, the one or more machine learning models may have been trained such that the one or more trained machine learning models are configured to use the learned relationships to determine the location of the user device102based on input of the real-time image data.

Additionally, or alternatively, the user device102may correlate a determined location with other data, such as GPS data, to determine the location. For example, the user device102may use the GPS data to confirm that the location with the highest score from the machine learning model matches the GPS data. In another example, the user device102may select a different location based on the GPS data, indicating that the user device102is at a different location than the location with the highest score from the machine learning model. This may improve location determination in scenarios where multiple locations might appear simultaneously in the image data or where various locations are densely packed, such as in a shopping mall.

In some embodiments, when determining the location, the user device102may determine whether the user device is inside a building of the location. User device102may be configured to make this determination by detecting location features data, for example, a back side of a door, a ceiling, one or more windows (e.g., inside windows), artificial lighting, etc. For example, the user device102may use a trained machine learning model to output a determination as to whether the user device102is inside the building based on the input of the image data and/or parsing of the location features data from the image data. The trained machine learning model may have been trained to learn relationships between (i) training real-time image data that includes prior location features data and (ii) prior inside or outside indications data associated with the prior location features data. For example, the training set of data for the machine learning model may include location features data for inside or outside locations and locations data that identifies the location. In this way, the machine learning model may be trained such that the trained machine learning model is configured to use the learned relationships to determine whether the user device102is inside the building of the location based on input of the real-time image data.

In some embodiments, the user device102may use sequential processing to determine the location. Sequential processing may describe processing data that occurs in the order that the data is received. For example, the user device102may detect a front door then the interior of a store. Based on this sequence, it may be determined that the location may be interior of the location with the front door.

At step208, the method200may include detecting, based on the real-time image data, a terminal at the location. For example, the user device102may detect, based on the real-time image data, a terminal at the location. The user device102may detect the terminal based on processing the image data, based on determining that the user device102is at a certain type of location (e.g., a merchant), based on receiving a command from a user of the user device102, and/or the like.

The user device102may detect the terminal based on parsing location features data from the image data in a manner similar to that described at step204. For example, after determining that the user device102is located at a certain location (e.g., a merchant), the user device102may process the image data as the user device102is moved about by the user to detect the terminal. Similar to that described elsewhere herein, the user device102may use one or more machine learning models to extract features from the image data and may process the features from the image data to detect objects in the image data. After detecting the objects in the image data, the one or more machine learning models may classify the objects and determine a score for how similar the detected objects are to the classification. When the one or more machine learning models determine that an object in the image data is classified as a terminal with a score higher than a threshold, the one or more machine learning models may output an indication of this classification. Based on this output, the user device102may detect the terminal at the location.

The one or more machine learning models may further determine, in connection with detecting the terminal, whether the terminal is in the foreground of the image data rather than in the background of the image data. In this case, the one or more machine learning models may just classify objects detected in the foreground of the image data. This may help the user device102detect the terminal when the user of the user device102is located at or using the terminal, rather than, e.g., walking past the terminal while shopping. This may conserve computing resources of the user device102by reducing or eliminating irrelevant detections of the terminal in the image data.

As noted above, in some examples, the user device102may detect the terminal based on receiving a command from a user of the user device102. In examples in which the user device102comprises AR eyeglasses, the user may interact with an interaction element of a GUI included in an output augmented image. For example, the user may interact with the augmented image to identify the terminal via any appropriate input device or method (e.g., manual selection controller, retinal control, and/or audio control).

At step210, the method200may include generating virtual certification number data for a user specific to the location, or requesting virtual certification number data to be generated for the specific location. For example, the user device102may generate virtual certification number data specific to the location for a user, or transmit a request to server device104to generate the same. The user device102or server device104may generate the virtual certification number data based on detecting the terminal at the location, based on receiving a command from the user of the user device102, based on establishing a connection with the terminal (e.g., the user device102and the terminal may establish a connection or otherwise communicate when within a distance of each other), and/or the like.

The virtual certification number data may include a number (or alphanumeric information) that can be used to complete an interaction. For example, the user device102or server device104may generate a virtual interaction number. The user device102or server device104may generate virtual certification number data that represents (or can be decoded to identify) the user, an account associated with the user, the user device102, the location, the terminal, and/or the like. For example, the user device102may input alphanumeric information identifying the user, the account, or the user device102into a cryptographic element of the user device102, and the cryptographic element may output the virtual certification number data.

The virtual certification number data may be specific to the location. For example, the user device102or server device104may generate virtual certification number data for the specific location at which the user device102is located (e.g., generate different virtual certification numbers for different merchants). Additionally, or alternatively, the user device102or server device104may generate virtual certification number data specific for the terminal. Generating virtual certification number data specific to the location and/or the terminal may increase security of the virtual certification number data as the virtual certification number data, if stolen by a malicious actor, may not be usable at different locations and/or different terminals at the location.

The method200may include, at step212, causing the user device to store the virtual certification number data for the user on a memory device associated with the user device102. For example, the user device102may store the virtual certification number data in the memory112A. The user device102may store the virtual certification number data after generating the virtual certification number data, based on receiving a command from the user of the user device102to store the virtual certification number data, after receiving a transmission from the server device104including the virtual certification number data or a command relating thereto, and/or the like. The user device102may store the virtual certification number data in a memory device accessible by an application installed on the user device102and/or another user device102. For example, the user device102may transmit the virtual certification number data to a server device104or a cell phone associated with the user for storage. The user device102may store the virtual certification number data in temporarily. For example, the user device102may store the virtual certification number data for an amount of time, until an interaction is completed at the terminal using the virtual certification number, until the user leaves the location and/or a connection with the terminal is disconnected, and/or the like.

The method200may include, at step214, transmitting the virtual certification number data to the terminal at the location. For example, the user device102may transmit the virtual certification number data to the terminal via a wireless antenna, using a near-field communication (NFC) connection, using a Bluetooth connection, and/or the like. In some embodiments, the user device102may cause another user device102to transmit the virtual certification number data. For example, the user device102may provide the virtual certification number data to a cell phone or an interaction certification mechanism (e.g., payment card) of the user with a command to use the virtual certification number data to complete an interaction with the terminal.

In some embodiments, the terminal may communicate with a server device104to complete the interaction. For example, the terminal may provide the virtual certification number data to the server device104to record the interaction, to confirm that the user has a sufficient balance for completing the interaction, and/or the like. Additionally, or alternatively, the user device102(or another user device102) may communicate with the server device104to complete the interaction. For example, the user device102may confirm the interaction, the server device104may prompt the user, e.g., via a cell phone of the user, to confirm the interaction, and/or the like.

In some embodiments, after transmitting the virtual certification number data to the terminal, the user device102may receive an approval indication associated with the interaction and/or the virtual certification number data. For example, the approval indication may indicate that the interaction has been approved, that the virtual certification number data has been accepted for the interaction, and/or the like. The user device102may then present, via a display of the user device102or another user device102, a graphical representation of the approval indication. For example, when the user device102is a pair of smart eyeglasses, the user device102may present the graphical representation via a lens or other display of the pair of eyeglasses or may provide the approval indication to a cellphone of the user for display.

The user device102may determine whether the location of the user device102has changed. For example, and in a merchant context, the user device102may determine whether the user device102has been moved to a different store or to a different terminal in the store. The user device102may determine whether the location has changed at any time after determining the location and/or generating the virtual certification number data. Upon determining that the location of the user device has changed, the user device102may delete the virtual certification number data from the memory device associated with the user device102.

The example method200is provided merely as an example, and may include additional, fewer, different, or differently arranged aspects than depicted inFIG.2.

FIGS.3A-3Fdepict an example300of using location-related data for generating virtual certification number data for an interaction, according to one or more embodiments. As illustrated inFIG.3A, the example300includes eyeglasses302as an example user device102. The eyeglasses302may be, e.g., a pair of smart eyeglasses that include a camera304that has a field of view306and one or more other components308. The other components308may include, e.g., a processor, a memory, a sensor (e.g., a GPS receiver), etc. As illustrated at310, the eyeglasses302may receive real-time image data from the camera304of the eyeglasses302. For example, the eyeglasses302may receive the real-time image data as a user of the eyeglasses302wears the eyeglasses302while moving about, e.g., in a manner similar to that described above at step202ofFIG.2.

Turning toFIG.3B, the image data may include one or more images312. For example, the image data may include an image312A of an exterior of a building that the user was approaching, an image312B of a terminal when the user is checking out, and/or the like. In this way, the eyeglasses302may gather image data as the user moves about over time. Turning toFIG.3C, and as illustrated at314, the eyeglasses302may parse location features from the real-time image data. For example, the eyeglasses302may parse text, objects, and/or the like from the images312, e.g., in a manner similar to that described above at204.

FIG.3Dillustrates an example of parsing the images312. For example, for the image312A, the eyeglasses302may parse text (e.g., the text “SUPERSTORE” shown at316) from the image312A. As another example, and for the image312B, the eyeglasses302may parse objects (e.g., a terminal at318) from the image312B. Turning toFIG.3E, the eyeglasses302may, at320, determine a location of the eyeglasses302. For example, the eyeglasses302determine that the wearer of the eyeglasses302is at a merchant based on the text at316parsed from the image312A, e.g., in a manner similar to that described above at206ofFIG.2.

As illustrated at322, the eyeglasses302may detect a terminal at the location. For example, the eyeglasses302may detect the terminal based on the object at318parsed from the image312B, e.g., in a manner similar to that described above at208ofFIG.2. As illustrated at324, the eyeglasses302may generate virtual certification number data for the user specific to the location. For example, the eyeglasses302may generate the virtual certification number data specific to the merchant, e.g., in a manner similar to that described above at210ofFIG.2.

Turning toFIG.3F, and as illustrated at328, the eyeglasses302may transmit the virtual certification number data to the terminal (terminal326), e.g., in a manner similar to that described above at214ofFIG.2. For example, the eyeglasses302may, at330, transmit the virtual certification number data to the terminal326, e.g., via an NFC connection, a Bluetooth connection, and/or the like. As illustrated at332, the terminal326may complete an interaction using the virtual certification number data, in a manner similar to that described elsewhere herein.

The example300described above is provided merely as an example, and may include additional, fewer, different, or differently arranged aspects than depicted inFIGS.3A-3F.

In this way, the above referenced systems and methods may increase interaction security for merchants and for customers. Communication as described herein between the hardware and software components may allow for confirmation of a customer's virtual certification numbers with respect to a given merchant and the user device's location at the time of use of their virtual certification numbers. This may reduce the amount of fraudulent activity, e.g., fraudulent use of the virtual certification numbers, and/or prevent fraudulent activity, which is often detected only after the fraudulent activity has occurred and can no longer be prevented.FIG.4depicts an example of a computer400, according to certain embodiments.FIG.4is a simplified functional block diagram of a computer400that may be configured as a device for executing processes or operations depicted in, or described with respect to,FIGS.1-3F, according to exemplary embodiments of the present disclosure. For example, the computer400may be configured as the user device102, server device104, and/or another device according to exemplary embodiments of this disclosure. In various embodiments, any of the systems herein may be a computer400including, e.g., a data communication interface420for packet data communication. The computer400may communicate with one or more other computers400using the electronic network425. The network interfaces114A and114B inFIG.1may include one or more communication interfaces420. The electronic network425may include a wired or wireless network similar to the network106depicted inFIG.1.

The computer400also may include a central processing unit (CPU), in the form of one or more processors402, for executing program instructions424. The processors110A and1106depicted inFIG.1may include one or more processors402. The computer400may include an internal communication bus408, and a drive unit406(such as read-only memory (ROM), hard disk drive (HDD), solid-state disk drive (SDD), etc.) that may store data on a computer readable medium422, although the computer400may receive programming and data via network communications. The computer400may also have a memory404(such as random access memory (RAM)) storing instructions424for executing techniques presented herein, although the instructions424may be stored temporarily or permanently within other modules of the computer400(e.g., processor402and/or computer readable medium422). The memories112A and112B depicted inFIG.1may include one or more memories404. The computer400also may include user input and output devices412and/or a display410to connect with input and output devices412such as keyboards, mice, touchscreens, monitors, displays, etc. In some examples, the user input and output devices412may include tracking inputs and/or audio inputs. These inputs may be configured to capture AR device functions. The displays108A and1088may include one or more displays410. The various system functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the systems may be implemented by appropriate programming of one computer hardware platform.

While the disclosed methods, devices, and systems are described with exemplary reference to processing image data for generating virtual certification number data, it should be appreciated that the disclosed embodiments may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainment system, a home entertainment system, etc. Also, the disclosed embodiments may be applicable to any type of Internet protocol.

It should be understood that embodiments in this disclosure are exemplary only, and that other embodiments may include various combinations of features from other embodiments, as well as additional or fewer features. For example, while some of the embodiments above pertain to processing image data, any suitable activity may be used. In an exemplary embodiment, instead of or in addition to processing image data, certain embodiments include processing location-related data (e.g., GPS data).