Abstract: Techniques relating to using machine learning (ML) with a point of sale (POS) system. These techniques include identifying one or more images, captured at a POS system, of one or more items for purchase. The techniques further include predicting a checkout issue relating to the one or more items for purchase, including determining the checkout issue using a trained ML model, based on the one or more images. The techniques further include generating one or more instructions for a purchaser of the one or more items, based on the predicted checkout issue, and presenting the one or more instructions at a user interface of the POS system.

Abstract: Systems and methods of performing item detection at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system device having a terminal station apparatus and a bagging station apparatus. The terminal station apparatus includes an optical scanner. The bagging station apparatus includes a bagging area. Further, the POS system device is operationally coupled to an optical sensor device having a field of view that includes a region about the POS system device with the POS region having a set of POS subregions. The method includes applying an artificial intelligence model to a set of interacted object track characteristics to enable a determination that one of a set of detected objects is transferred to the POS subregion associated with the bagging area without being scanned.

Abstract: Systems and methods of performing item detection at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system device having a terminal station apparatus and a bagging station apparatus. The terminal station apparatus includes an optical scanner. The bagging station apparatus includes a bagging area. Further, the POS system device is operationally coupled to an optical sensor device having a field of view that includes a region about the POS system device with the POS region having a set of POS subregions. The method includes applying an artificial intelligence model to a set of interacted object track characteristics to enable a determination that one of a set of detected objects is transferred to the POS subregion associated with the bagging area without being scanned.

Abstract: Systems and methods of performing item detection at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system device having a terminal station apparatus and a bagging station apparatus. The terminal station apparatus includes an optical scanner. The bagging station apparatus includes a bagging area. Further, the POS system device is operationally coupled to an optical sensor device having a field of view that includes a region about the POS system device with the POS region having a set of POS subregions. The method includes applying an artificial intelligence model to a set of interacted object track characteristics to enable a determination that one of a set of detected objects is transferred to the POS subregion associated with the bagging area without being scanned.

Abstract: The present disclosure provides techniques for improved animal recognition and recommendations. One or more images of an animal associated with a user are captured at a check-in kiosk of an enterprise. A unique identifier of the animal is determined by processing the one or more images using an animal recognition machine learning model. One or more records indicating characteristics of the animal are accessed based on the unique identifier, and one or more recommended items for the animal are generated based on processing the one or more records using an artificial intelligence system. One or more physical locations of the one or more recommended items in the enterprise are determined, and one or more indications of the one or more physical locations are output to facilitate user navigation to the one or more physical locations of the one or more recommended items in the enterprise.

Abstract: Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having first and optical scanner devices. The optical sensor device is positioned on or about the POS system with a field of view directed towards a first spatial region of the POS system and operable to capture successive images. The optical scanner device is positioned on or about the POS system with a field of view directed towards a second spatial region disposed within the first spatial region and associated with scanning visual object identifier codes disposed on objects and operable to scan those visual object identifier codes. The method includes receiving, from the optical sensor device, a set of successive images associated with the first spatial region to enable detection of activity in or about the second spatial region.

Abstract: A self-service computing device detects a first weight for an item positioned on a sensory platform and determines a stabilized weight and a weight-based item count. Responsive to determining the stabilized weight, the device causes image capture devices to capture images of the item, identifies a visual characteristic of the item, and retrieves, from memory, a second weight of the item. To generate an image-based item count, the device compares the identified visual characteristic of the item with the second weight of the item and validates the presence of the item. The device causes or blocks a self-service transaction operation at the self-service computing device depending respectively on whether or not the image-based item count and the weight-based item count correspond.

Abstract: Techniques relating to using machine learning (ML) with a point of sale (POS) system. These techniques include identifying one or more images, captured at a POS system, of one or more items for purchase. The techniques further include predicting a checkout issue relating to the one or more items for purchase, including determining the checkout issue using a trained ML model, based on the one or more images. The techniques further include generating one or more instructions for a purchaser of the one or more items, based on the predicted checkout issue, and presenting the one or more instructions at a user interface of the POS system.

Abstract: Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having an optical sensor device, an optical scanner device and a load sensor device. The optical sensor device has a field of view that includes a first region about the POS system and the load sensor device is operable to measure a load of an object while positioned in a bagging area of the POS system. Further, the first region includes a second region associated with a certain object type that can be positioned in the bagging area without having to be scanned or weighed. The method includes obtaining a successive image captured by the optical sensor device to enable a determination that the target object corresponds to the certain object type that is exempt from being scanned or weighed based on the successive image.

Abstract: Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having an optical sensor device, an optical scanner device and a load sensor device. The optical sensor device has a field of view that includes a first region about the POS system and the load sensor device is operable to measure a load of an object while positioned in a bagging area of the POS system. Further, the first region includes a second region associated with a certain object type that can be positioned in the bagging area without having to be scanned or weighed. The method includes obtaining a successive image captured by the optical sensor device to enable a determination that the target object corresponds to the certain object type that is exempt from being scanned or weighed based on the successive image.

Abstract: Techniques for predicting items for purchase at a point of sale (POS) system. These techniques include identifying one or more images, captured at a POS system, of one or more items for purchase. The techniques further include identifying a measured characteristic of the one or more items for purchase, the measured characteristic including at least one of: (i) a first weight measured using a weight sensor associated with the POS system or (ii) a footprint measured using one or more pressure or weight sensors associated with the POS system. The techniques further include predicting the items for purchase based on analyzing the one or more images using a machine learning (ML) model, and validating the predicted items for purchase using the measured characteristic.

Abstract: Systems and methods of providing navigation information associated with a retail space are provided. In one exemplary embodiment, a method comprises, a network node operationally coupled to a set of optical sensor devices positioned throughout a retail space, the network node receiving a sequence of images from each optical sensor device. The method comprises, obtaining a current position of a certain subject in the retail space based on one or more sequence of images received. The method comprises, obtaining a current position of an obstacle in the retail space based on one or more sequence of images received. The method comprises, sending to a wireless device associated with the certain subject, navigation information associated with the current positions of the certain subject and the obstacle in the retail space.

Abstract: Methods and techniques for generating a composite visual representation that integrates visual data of a pet with potential purchased items are provided. Visual data of a pet is received. Features of the pet are extracted by analyzing the visual data. A first mapping for the pet is generated based on the extracted features using one or more computer vision algorithms. Measurement data of a pet-related item selected by a user is collected. A second mapping for the pet-related item is generated based on the measurement data. A third mapping that corresponds to one or more dimensional differences between the pet in the first mapping and the pet-related item in the second mapping is generated. A composite mapping is created by combining the first, second and third mappings. The composite mapping is displayed in an augmented reality environment.

Abstract: Techniques to reconfigure self-checkout kiosks based on workflow interaction metrics. A measure of interaction is determined for at least a portion of a workflow during a transaction at a self-checkout kiosk. The self-checkout kiosk is configured to output one or more communications via one or more graphical user interface (GUI) screens associated with the workflow. A communication level is set for the workflow for the transaction. The communication level is determined based on the measure of interaction and without requiring an intervention. A next communication to output is determined based on the communication level. The transaction is executed at the self-checkout kiosk.

Abstract: Systems and methods of detecting activity at point of sale are provided. In one exemplary embodiment, a method is performed by a POS system having first and optical scanner devices. The optical sensor device is positioned on or about the POS system with a field of view directed towards a first spatial region of the POS system and operable to capture successive images. The optical scanner device is positioned on or about the POS system with a field of view directed towards a second spatial region disposed within the first spatial region and associated with scanning visual object identifier codes disposed on objects and operable to scan those visual object identifier codes. The method includes receiving, from the optical sensor device, a set of successive images associated with the first spatial region to enable detection of activity in or about the second spatial region.

Abstract: Techniques to reconfigure self-checkout kiosks based on computer vision or interaction metrics. One or more graphical images are received. The one or more graphical images at least partially depict a physical area associated with a self-checkout kiosk during a transaction involving one or more items and a form of payment. The one or more graphical images include a first item. The self-checkout kiosk is configured to output one or more communications via one or more graphical user interface (GUI) screens associated with a workflow. An item type or a payment type is predicted for the transaction and using one or more classifiers. The predicted item type or payment type is provided as input to the workflow without requiring an intervention. The transaction is executed.

Abstract: Computer vision grouping recognition is provided by receiving training images that include unpackaged items; identifying, by a computer vision model, a candidate identities for unpackaged items in a given training image; receiving, from a human user, a selected identity for the unpackaged item as feedback for the candidate identity; constructing a confusion matrix tallying matches and mismatches between candidate identities and the selected identities as analyzed across the training images for each unpackaged item; identifying at least one product category that includes at least a first unpackaged item and a second unpackaged item that the confusion matrix indicates as being misidentified for each other by the computer vision model; and reconfiguring the computer vision model to identify the product category instead of the first unpackaged item or the second unpackaged item when analyzing a given image including one or more of the first unpackaged item or the second unpackaged item.

Abstract: The present disclosure provides techniques for improved animal recognition and recommendations. One or more images of an animal associated with a user are captured at a check-in kiosk of an enterprise. A unique identifier of the animal is determined by processing the one or more images using an animal recognition machine learning model. One or more records indicating characteristics of the animal are accessed based on the unique identifier, and one or more recommended items for the animal are generated based on processing the one or more records using an artificial intelligence system. One or more physical locations of the one or more recommended items in the enterprise are determined, and one or more indications of the one or more physical locations are output to facilitate user navigation to the one or more physical locations of the one or more recommended items in the enterprise.

Abstract: Techniques for animal identification based on animal behavior. A first video depicting a first animal associated with a user during a first visit to an enterprise location is received. One or more attributes pertaining to the first animal or to the user are received. A first behavior of the first animal as depicted in the first video is extracted and encoded into a first behavioral representation. One or more machine learning models are trained to identify the first animal based on the first behavioral representation and the one or more attributes.

Abstract: Techniques to reconfigure self-checkout kiosks based on computer vision or interaction metrics. One or more graphical images are received. The one or more graphical images at least partially depict a physical area associated with a self-checkout kiosk during a transaction involving one or more items and a form of payment. The one or more graphical images include a first item. The self-checkout kiosk is configured to output one or more communications via one or more graphical user interface (GUI) screens associated with a workflow. An item type or a payment type is predicted for the transaction and using one or more classifiers. The predicted item type or payment type is provided as input to the workflow without requiring an intervention. The transaction is executed.