Techniques for self-checkout alert reduction based on packaging detection. A deflection in a first bag reservoir associated with a self-checkout kiosk is determined. The first bag reservoir is positioned adjacent to a packing area and includes a first type of bags. The self-checkout kiosk includes a terminal scale and further includes a platform scale in the packing area. That an empty bag is retrieved from the first bag reservoir is determined based on the deflection in the first bag reservoir. A bag type and a weight range are identified for the empty bag. An allowed weight range of a combination of the empty bag and a desired item is then determined. Presence of the empty bag on the platform scale is prevented from causing a loss prevention alert to be generated when the desired item has been placed in the empty bag on the platform scale.

A point-of-sale device including an adjustable screen, an accelerometer, and a processor is provided. The adjustable screen is arranged in a cashier position or a customer position. The accelerometer generates orientation data corresponding to the adjustable screen. The processor determines, via an accelerometer driver, an adjustable screen position based on the orientation data. The processor then generates, via the accelerometer driver, a position change notification if the adjustable screen changes position. The processor then provides the position change notification and the adjustable screen position to an application selector. The processor then launches, via the application selector upon receiving the position change notification, an application based on the adjustable screen position. In one example, the launched application is a point-of-sale application if the adjustable screen position is the cashier position. In another example, the launched application is a self-checkout application if the adjustable screen position is the customer position.

Method and systems are provided for processing actions in a store. One example method includes capturing sensor output from two or more sensors in a shopping environment. One sensor includes a first camera to capture scene data where the scene includes movement of a shopper in the store performing interaction with an item in the store. Another sensor includes a second camera capturing at least part of the scene from a different perspective. The method includes processing, by a processing entity associated with the store, at least one of the camera's output to generate feature data. The feature data is processed by one or more machine learning models to produce engineered feature data. The engineered feature data includes data relating to tracking skeletal movement of shopper. The method includes processing, by a processing entity associated with the store, the feature data including engineered feature data using said one or more machine learning models to produce a prediction that a take of the item has occurred by the shopper. The prediction is based on a characterization that said feature data or engineered feature data is labeled to infer that the movement of the shopper is interaction with the item that is classified as the take as having occurred.

Various examples of the invention conduct a purchase transaction with a first sensor that senses removal or return of a first item from a first region; a computer vision sensor that senses removal or return of a second item from the first region; a transaction detector that determines an accuracy that the sensed removal or return of the first item by the first sensor and the sensed removal or return of the second item by the computer vision sensor correspond to a single event of removal or return of an item by a consumer and that forwards, to a machine learning tool, information associated with the sensed removal or return of the first item by the first sensor and information associated with the sensed removal or return of the second item by the computer vision sensor when the accuracy is less than an accuracy threshold; the machine learning tool that verifies or corrects the information associated with the sensed removal or return of the first item or the information associated with the sensed removal or return of the second item and provides verified or corrected information to the transaction detector; and an automated billing processor, coupled to the transaction detector, that applies a purchase price of the item against an account of the consumer for the item based on the verified or corrected information, thereby completing the purchase transaction.

In some implementations, a system for verifying items in a retail environment includes a physical shopping cart including a first set of sensors, and an automated checkout station including a second, different set of sensors. The physical shopping cart receives item verification data for verifying an item, detects the item as it enters the physical shopping cart, and performs a primary verification of the item. The automated checkout station obtains a virtual shopping cart that corresponds to the physical shopping cart. The virtual shopping cart includes a list of items that have been placed in the physical shopping cart, and a verification status of each item. The second, different set of sensors generate station sensor data that represents the physical shopping cart and the items in the physical shopping cart. A secondary verification of the physical shopping cart and its contents is performed by the automated checkout station.

In some implementations, a method performed by data processing apparatuses includes receiving, by an item verification engine, a scan notification indicating that a mobile computing device has scanned an item. In response to receiving the scan notification, the item verification engine provides an instruction for a cart computing system to activate item sensors on a shopping cart. The item verification engine receives sensor data collected by the item sensors as a result of the item having been placed in the shopping cart. The item verification engine receives item verification data for verifying the scanned item from the mobile computing device, performs a verification of the item based on the sensor data and the item verification data, and provides verification results for presentation by the mobile computing device.

A method of ordering a new optical article definable based on features of an initial optical article including at least a lens, the method including the following steps: —acquiring an identifier by capturing a marking carried by the lens of the initial optical article; —sending, by an electronic device, the identifier and an order for the new optical article. A corresponding apparatus and a corresponding method for launching production of the new optical article are also described.

A medication dispensing cabinet provides controlled access to medications and supplies stored in it. The cabinet may include at least one lockable storage compartment, and a controller that controls access to the at least one lockable storage compartment. The cabinet may include multiple printers integrated into the cabinet. The cabinet may include a camera operably coupled to the controller. The cabinet may include a set of cabinet electronics, and a power distribution and communications circuit board. The cabinet may include a radio frequency identification (RFID) reader, wherein the controller conditions access to the at least one lockable storage compartment on receipt of information from the RFID reader. Data may be stored in the controller according to an implementation of RAID technology. The controller may include multiple electronic communications network interfaces, and may include an out of band network communication channel. A dispensing cabinet may facilitate printing of labels for medications.

Systems and methods for self-checkout at a point-of-sale are provided. The system and method includes using a plurality of radio frequency identification (RFID) transceivers within a store, and an RFID reader configured to receive an RFID code from an RFID tag activated by the plurality of radio frequency identification (RFID) transceivers. The system and method also includes using a classifier configured to determine whether the RFID tag is inside or outside a designated area, wherein the classifier is trained in a manner that a number of items incorrectly identified as being purchased is below a threshold to minimize customer dissatisfaction (CDS) determined as the ratio of the value of items charged to the customer but not purchased by a customer to the total charge to the customer.

A method apparatus are directed to identify items disposed on the bottom shelf of a shopping cart bottom of basket (BoB). Certain aspects envision a distance measurement sensor and computing system connected to the shopping cart. A first set of distance measurements of the bottom shelf when empty is obtained via the distance measurement sensor. Next, at a checkout stand, a second set of distance measurements of the shelf are taken, which can be used to compare with the first set of distance measurements to identify if there is an object on the BoB. An alert can be provided to a checkout attendant if there is an object on the BoB.