Ncr Corporation

Ncr Voyix Corporation

Brent Vance Zucker

Adam Justin Lieberman

Yehoshua Zvi Licht

Nir Veltman

Qian Yang

Patrick Goode Watson

Stefan Bjelcevic

Frank Douglas Hinek

Mehtab Chithiwala

Kelly Gernatt Martinez

Pahul Singh Matharu

Yongching Tee

Dharamendra Kumar

Matias Gabriel Szylkowski Resnikow

Nathan Somavarapu

Christian Lee McDaniel

Robert Alexander Borowicz

Pavel Savara

Christopher John Costello

Joseph Arnold White

Alen Polakof

Robert Andrew Kingslyn

Arungandhi Indrapuram Chinnasamy

Narendra Dubey

James Zhonghui Ge

Georgiy Pyantkovs'ky

Justin Paul

Multiple images of multiple items are captured of a transaction area during a checkout. The Red-Green-Blue (RGB) data associated with each item image patch is collected across the images and provided as input by a Machine-Learning Model (MLM), which returns an item code for the item. When the given MLM is unable to satisfactorily predict an item code for a given set of image patches, the patches associated with the images are presented to an operator of a checkout and the operator is asked to scan an item barcode for that item. The patches are labeled within the images with the item code and additional images of the item are captured and labeled with the item code when the barcode is scanned by the operator. The labeled images are used in a subsequent training session with the MLM to improve its item recognition accuracy for the item.

Incremental training of a machine-learning model (MLM) for multiview item recognition

Disclosed herein are systems and method comprising: transmitting, by a point-of-sale system, a beacon identifier receivable by a mobile device; and receiving, by the point-of-sale system and without interaction by a user of the mobile device, payment information associated with the user, the payment information authorizing payment for the product. Further disclosed are systems and methods comprising: receiving, at a mobile device, a beacon identifier from a point-of-sale system, the beacon identifier associated with a product; and transmitting, by the mobile device and without interaction by a user of the mobile device, payment information associated with the user, the payment information authorizing payment for the product.

Systems and methods for beacon and cloud payments

Multiple images of a designated area are taken. The designated area comprises multiple items that are to be identified from the images. Depth information and Red, Green, Blue (RGB) data from each image is processed to create a point cloud for each image of the designated area. The point clouds are patched together or synchronized into a single point cloud for the designated area. Known background pixels associated with backgrounds for each image are removed from the single point cloud. The depth information and RGB data for the single point cloud is clustered together and bounding boxes are placed around each item in the single point cloud. At least the RGB data for each bounding box is provided to a machine-learning model (MLM) and the MLM returns an item code for the corresponding item. The item codes are fed to a transaction manager for a transaction associated with a customer.

Multi-item product recognition for checkouts

A user is assigned an initial risk score during a session with a messaging platform. During the session, the user attempts an operation with an external service. One or more additional authentication factors are requested from the user to dynamically lower the initial risk score. The lowered risk score is processed with the external service to perform the operation on behalf of the user during the session.

Multifactor authentication from messaging systems

Cameras capture time-stamped images of predefined areas. At least one image includes a representation of multiple individuals in a group of individuals. Attributes retained with each individual are combined with a limb and pose recognition to properly identify each individual of the group within the image.

Image processing for distinguishing individuals in groups

Cameras capture time-stamped images of predefined areas. Individuals and item are tracked in the images. A time-series set of images are processed to determine actions taken by the individuals with respect to the items or to determine relationships between the individuals to the items.

Image processing for tracking actions of individuals

A network-based inventory replenishment application maintains and analyzes item transactions on a per-consumer basis. Specific item purchases are calculated as being in need of replenishment by a specific consumer on specific future dates or a specific range of future dates. The consumer is proactively informed and asked if such items should be ordered. The network-based inventory replenishment application engages the consumer for item replenishment and item order processing over a plurality of disparate communication channels.

Network-based inventory replenishment

Cameras capture images of shelves/displays having items. The images are processed to identify real-time item counts on the shelves and/or empty or partially empty shelves with less than a configured number of items. Notifications or messages regarding item counts and/or empty shelving conditions are sent through an Application Programming Interface (API) to consuming services in accordance with custom defined rules. In an embodiment, real-time item counts and/or empty shelving conditions are dynamically reported through the API based on on-demand requests from the consuming services.

Real-time shelf inventory and notification

An autonomous chat bot monitors actions of users on a messaging platform and generates self-initiated chat sessions with the user to gauge users' interest and intent with respect to a target subject matter and the conversations of the chat sessions. Based on the gauged interest and intent, profiles or preferences are generated for the users independent of or relevant to the target subject matter. In an embodiment, customer contact information for the users are provided by the autonomous chat bot to a Customer Relationship Management (CRM) system for further engaging the customer with respect to the target subject matter or other subject matters determined to be relevant from the profiles or preferences

Auto-initiated messaging chat

Cameras capture time-stamped images of predefined areas. Individuals and items are tracked in the images. Occluded items detected in the images are preprocessed to remove pixels associated with occluded information and the pixels remaining associated with the items are cropped. The preprocessed and cropped images are provided to a trained machine-learning algorithm or a trained neural network trained to classify and identify the items. Output received from the trained neural network provides item identifiers for the items that are present in the original images.

Image processing for occluded item recognition

Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Context-aided machine vision

A vison-based self-checkout terminal is provided. Purchased items are placed on a base and multiple cameras take multiple images of each item placed on the base. A location for each item placed on the base is determined along with a depth and the dimensions of each item at its given location on the base. Each item's images are then cropped, and item recognition is performed for each item on that item's cropped images with that item's corresponding depth and dimension attributes. An item identifier for each item is obtained along with a corresponding price and a transaction associated with items are completed.

Vision-based frictionless self-checkouts for small baskets

A system can visually track which items in a store are selected for purchase by a shopper. The system can form a virtual shopping cart by analyzing multiple images, over time, to determine which purchasable items are located with the shopper, such as in a physical shopping cart, in a basket, or held by the shopper. By analyzing multiple images, over time, the system can account for items misidentified in one or more images, or fully or partially obscured in one or more images as the shopper traverses the store. Alternatively, the system can form a virtual shopping cart by analyzing instances in which a purchasable item is removed from a shelf or placed on a shelf. Items removed from, but not returned to, a shelf can be considered to be selected for purchase. The system can include a frictionless checkout that charges the shopper for the selected items.

Constructing shopper carts using video surveillance

Disclosed are systems and methods for mapping a virtual shopper to a physical shopper. The systems and methods may include receiving, at a backend system, unidentified customer data including information about an unidentified customer. Customer identity data including identifying information about the customer may be received from the customer. An authentication token may be generated that links the unidentified customer data to the customer identity data.

Linking a physical item to a virtual item

An autonomous chat bot monitors actions of users on a messaging platform and generates self-initiated chat sessions with the user to gauge users' interest and intent with respect to a target subject matter and the conversations of the chat sessions. Based on the gauged interest and intent, profiles or preferences are generated for the users independent of or relevant to the target subject matter. In an embodiment, customer contact information for the users are provided by the autonomous chat bot to a Customer Relationship Management (CRM) system for further engaging the customer with respect to the target subject matter or other subject matters determined to be relevant from the profiles or preferences.

Growing community of inventors

Roswell, GA, United States of America

Brent Vance Zucker