Abstract: 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.

Abstract: 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.

Abstract: A cloud-based service is provided that receives conditions for obtaining an object of value from an enterprise. A blockchain-based media of value is created on a blockchain for the object of value. A smart contract is generated an initiated on the blockchain to enforce the conditions with control over the blockchain-based media of value. When a customer provides actions or information that satisfies the conditions to the smart contract the smart contract returns the blockchain-based media of value to a customer wallet. The customer exchanges the blockchain-based media of value for the object of value from the enterprise. In an embodiment, the blockchain-based media of value is a non-fungible token (NFT).

Abstract: Systems, machines, methods, and computer-readable media are disclosed for detecting an event at a self-checkout (SCO) machine, determining that the event is an item-identifying event, triggering capture of image data of the item identified by the item-identifying event responsive to detecting the event and/or responsive to determining that the event is an item-identifying event, and generating enhanced item identification data that associates the captured image data with identifying information of the item. The enhanced item identification data may be stored in one or more datastores, which may include a local datastore in a retail environment in which a self-checkout machine is located or a remote datastore such as cloud-based data storage.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus is provided that executes instructions to move an item within a scan zone to specific X-Y coordinates of the scan zone. The item is placed on a platform and is rotated 360 degrees at each X-Y coordinate within the scan zone. Item images are captured by cameras at each X-Y coordinate and for each rotation at the corresponding X-Y coordinate. The item images are labeled and retained. The item images are used as input to a Machine-Learning Model (MLM) to train the MLM to recognize item codes for the items when subsequent images are captured for the item during a checkout. In an embodiment, during a checkout unknown item images are flagged and labeled with the corresponding item code when the corresponding item's barcode is scanned during the checkout; the labeled item images are also retailed for training the MLM for item recognition.

Abstract: 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.

Abstract: Depth camera settings are adjusted based on characteristics of items presented in a scan zone and based on depth values returned for the items in depth images. Red-Green-Blue (RGB) images and depth images are captured of items within the scan zone. Quality of the depth values are accessed. Bad depth values are replaced with good known depth values. When the depth values are not replaced, one or more depth value interpolation algorithms are selectively processed to enhance the depth values. The depth values are processed to place each item within a specific location of the scan zone and map that location to pixel values in the corresponding RGB images. The pixel values from the RGB images are passed to a classification model and an item code is returned. The item codes are provided to checkout a customer without any scanning of item barcodes of the items.

Abstract: A single item image is captured of an item situated within a given zone of a transaction area is captured, each different zone for each given item is associated a plurality of single item images captured by different cameras at different angles and perspectives of the transaction area. The single item images are passed to an existing segmentation Machine-Learning Model (MLM) and accurate masks for the items produced by the existing MLM are retained. A background image of an empty transaction area is obtained, each retained single item image is cropped and superimposed into the background image with one or more different cropped and superimposed singe item images creating a composite multi-item image. The composite multi-item images are labeled to identify the boundaries of each single item image and the existing segmentation MLM is trained on the composite multi-item and labeled images producing an enhanced segmentation MLM.

Abstract: Images of a transaction area comprising items are received during a checkout. Red-Green-Blue (RGB) data and Depth data are received with each image. Pixels captured in any given image by a given camera are pre-aligned with physical locations of a transaction area. Depth data provided by each camera and that camera's pre-alignment to the area are processed to map pixels in each image taken to X-Y coordinates within the area. X-Y coordinates for each item and for each image are grouped together as a single item within the area. RGB data for each image and item is used as a set of image patches per item. For each item, the corresponding patches are passed to a classification Machine-Learning Model (MLM) that returns an item code for each patch. A particular item code is selected for each set and the item codes are used to process the checkout.

Abstract: A first Machine-Learning Model (MLM) is processed on multiple images of a scene, each scene comprising a different perspective view of each of a plurality of items. The first MLM produces masks for the items within each image, each mask representing a portion of a given item within a given image. Depth information associated with the images and the masks are processed to isolate each portion of each item within each image. A single scene image is generated from the images by stitching each image’s pixel data from each portion or each image into a composite item image within the single scene image. Each item’s composite item image is passed to a second MLM and the second MLM returns an item code for the corresponding item associated with the corresponding composite item image. The item codes for each item is passed to a transaction manager to process a transaction.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A user engages in spoken voice or messaging platform-based natural language dialogues with an automated bot. The bot identifies actions and subjects of the actions from the dialogues and processes Application Programming Interfaces (API) to perform travel-related queries or process travel-related transactions with travel services. Real-time responses to queries or completed transaction notifications are provided back to the user by the bot during the dialogues.

Abstract: A store having an automated checkout can automatically associate multiple shoppers with a single purchase. For example, a mother and her children can walk independently through the store and select items. Upon checkout, the items selected by the mother and children can all be charged to the mother's credit card in a single purchase. The system and method discussed herein can capture images of multiple shoppers from one or more video streams of a store area, can associate the multiple shoppers with one another from the captured images when the multiple shoppers appear together in a check-in area of the store, can track the movement of the multiple shoppers within the store from the captured images, can track items selected by the multiple shoppers from the captured images, and can initiate a transaction that charges the selected items to an account associated with one of the multiple shoppers.

Abstract: Disclosed are systems and methods for mapping multiple views to an identity. The systems and methods may include receiving a plurality of images that depict an object. Attributes associated with the object may be extracted from the plurality of images. An identity of the object may be determined based on processing the attributes.