Data for interactions performed by users through a portal page is collected. The data comprising a plurality of sequences of interactions performed by a user on representations of products displayed in the portal page. The plurality of sequences of interactions are input to train a neural network on temporal dependencies between interactions within a sequence from the plurality of sequences. The plurality of sequences are processed by the neural network through a plurality of learning layers to generate a model for product identification. Upon receiving an interaction by a first user at the portal page in relation to a product from the products and based on the model, identifying a first product from the products to be displayed in the portal page for the first user.

In one implementation, a system for providing product suggestions to provide to a customer during a checkout process at a retail store includes a checkout configuration that includes a point of sale terminal, a scanner to scan products, a scanning preparation area where products to be scanned are positioned, and a customer-facing device. The system can further include one or more product sensors to capture and transmit product signals. The system can also include a product identification system that is programmed to identify the products contained in the scanning preparation area. The system can further include a product recommendation engine that is configured to identify contents of a customer cart from the products contained in the scanning preparation area; determine product recommendations based, at least in part, on the contents of the customer cart; and transmit the product recommendations to the customer-facing device for display to the user.

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for performing operations comprising: receiving a request from a client device of a first user to engage in a shared augmented reality (AR) shopping experience with a second user; generating, for display on respective client devices of the first and second users, the shared AR shopping experience comprising a plurality of AR items that represent real-world products overlaid on a shared real-world environment of the first and second users; determining that the second user is interacting with a first AR item of the plurality of AR items; and in response to detecting that the second user is depicted in an image captured by the client device of the first user, overlaying the first AR item on the second user in the image.

A method, system, and computer program product for automated collection of training data and training object detection models is provided. The method generates a set of reference images for a first set of products. Based on the set of reference images, the method identifies a subset of products within an image stream. Based on the subset of products, a second set of products is determined within the image stream. The method identifies a set of product gaps based on the subset of products and the second set of products. The method generates a product detection model based on the set of reference images, the subset of products, the second set of products, and the product gaps.

A computer readable medium for sizing a product includes instructions, that when executed by at least one processor, cause a computing device to: retrieve from a webpage information on a product including product dimensions; present on a display of a client device a graphical button that upon access by a user activates a camera for capturing an image of an object positioned at a focal distance from the camera, the object having a surface; prompt the user to enter boundary information of an imaginary housing to be placed on the surface; generate the imaginary housing dimensions in two dimensions (2D) based on the boundary information and the focal distance; and determine whether the product fits within the imaginary housing by comparing the product dimensions against the imaginary housing dimensions.

A mixed reality system for displaying virtual holograms using a head mounted display and a tracking device. The HMD has one or more cameras mounted on its exterior connected to a portable computing device. The cameras record what a user's eyes would see in the environment and project the images onto the screen panel display. The cameras also transmit tracking device data to a software application installed on a portable computing device. Light emitting diodes serve as tracking points for the detection algorithm. The tracking device can be configured to transmit data to the portable computing device via optical modulation. Less private radio frequency technologies can be employed such as Bluetooth or WiFi to enable one-way or two-way communication. In some embodiments, multiplexing can be implemented to enable identifying and differentiating multiple tracking devices in a single frame.

A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

A method for collaborative instantiation of remote session objects and interactive video-based modification thereof includes receiving a uniform resource locator (URL) via a first graphical user interface (GUI). A request is generated based on the URL, and is sent to a remote server. In response to the request, a data object including product data is returned, and the product data is modified, so as to generate normalized product data. A host server causes playback of a video within a second GUI that is associated with a mobile device of a user. A product selection is received at the host server during playback of the video and in response to a user interaction with the video. Normalized product data associated with the product selection is identified, and a message including the identified normalized product data is sent to the remote server to cause instantiation of a software shopping cart.

Methods and apparatus are described for producing a computer-generated image by monitoring one or more kinds of activity of a user to identify at least one item of interest; and creating a computer-generated image including a representation of the item of interest overlaid onto an image of a model, environment or contextually relevant scenario. The process begins at 102 with monitoring a user's online activity. Software tracks and stores which purchasable items on the website/mobile application the user appears to be most interested. In a particular example, a user's abandoned basket feed is tracked and stored. In another example, cookies information is stored. Another indicator of items that a user is most interested in is length of time browsing a webpage associated with a particular item. Dwell time and cursor tracking software may also be used to identify which items the user viewed most. At 114, a model simulation is created. The object to be modelled relates to the items of interest. In the example where the items of interest are wearable items such as clothing garments, the model to be simulated is a human person said human person resembling the user as closely as possible based on the available personal data.

The present disclosure relates to a system, method, and computer program for automatically providing similar product recommendations for sold-out products promoted on non-merchant publisher sites, wherein the similar product recommendations are filtered for publisher preferences. A computer system with a product database provides a platform that enables non-merchant publishers to search products in the database, create call-to-actions on publisher sites for products in the database, and automatically receive similar product recommendations when end users of the publisher sites attempt to purchase promoted products that are now sold out. Once a publisher creates a call-to-action for a product that subsequently becomes sold out, similar product recommendations are provided without requiring any action on the publisher's part to change the publisher's site. Similar product recommendations are tailored to each publisher to account for publisher preferences. Publisher preferences may be learned by the system based on publisher behavior or inputted by each publisher.