A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.

A shelf bracket assembly mounted upright on a vertically disposed shelf has: a load cell; an anchor; and a cantilever supporting a shelf panel. The cantilever projects from the shelf upright in a substantially horizontal direction. The cantilever has a vertically disposed frame or a vertically disposed metal plate and a mount for the load cell. The load cell has a monolithic measuring body that has: a force-supporting section; a force-introduction section; and a linkage section between the force-supporting section and the force-introduction section. The force-supporting section of the monolithic measuring body is laterally attached to the mount. The monolithic measuring body has at least one mounting hole through which the monolithic measuring body is attached to the mount with a screw extending horizontally through the monolithic measuring body.

A shelf for storing bottles in a wine cabinet has a rectangular frame bounded by two side beams and two crossbeams, respectively front and rear, and being equipped with individualized reception locations for a plurality of bottles. Each slot has a cradle for the neck of a bottle, the cradle being equipped with a sensor to detect the presence of a bottle neck. The front crossbeam of the shelf can include, for each individualized location, an electronic device to visualize the presence of a bottle.

Live inventory management environment system and methods are disclosed. A shelf for items has bins each with an associated sensor. A processor receives a detection signal from the sensor which indicates placement or removal an item(s) from an associated bin. An entry device sends an entered request for placement or removal of items to the processor, and an indicator strip has lights which uniquely identify each one of the bins when lit. The processor has information relating to which bin each subset of lights is associated, and causes activation of a subset of lights associated with a bin of the item(s) in the request and records in memory a type of action, a time stamp and a quantity of the item(s) placed or removed from the bin having the subset of lights lit and indicated in the detection signal subsequently received from the sensor of the associated bin.

An imaging system capture images of products on shelves in a minimally invasive way. The images can be processed to assess restocking needs and other information. The imaging system includes multiple shelf-mountable imaging devices that can be positioned throughout one or more retail locations. Certain types of imaging devices may be self-contained units having replaceable or rechargeable internal power sources and/or wireless communication interfaces. Certain types of imaging device are configured to take images only when the field of view is not blocked by traffic (e.g., consumers, employees, etc.). Certain types of imaging devices are low-profile and/or are camouflageable to enhance the user experience.

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.

Apparatuses, components and methods are provided herein useful to provide assistance to customers and/or workers in a shopping facility. In some embodiments, a shopping facility personal assistance system comprises: a plurality of motorized transport units located in and configured to move through a shopping facility space; a plurality of user interface units, each corresponding to a respective motorized transport unit during use of the respective motorized transport unit; and a central computer system having a network interface such that the central computer system wirelessly communicates with one or both of the plurality of motorized transport units and the plurality of user interface units, wherein the central computer system is configured to control movement of the plurality of motorized transport units through the shopping facility space based at least on inputs from the plurality of user interface units.

A shelf bracket assembly is mounted on a vertically disposed shelf upright, and has: at least one weighing device; an anchoring device; and a cantilever for supporting a shelf panel. In a condition where the shelf bracket assembly is mounted on the shelf upright, the cantilever projects from the shelf upright in a substantially horizontal direction. The cantilever has a vertically disposed metal plate. In a Cartesian coordinate system, an extent of the cantilever in a horizontal plane defines a Y-direction, a vertical direction defines a Z-direction, and a direction perpendicular to the Y-direction and the Z-direction defines an X-direction. The at least one weighing device includes: a force-introduction section; a linkage section; and a force-supporting section. The linkage section comprises two weighing plates extending horizontally and parallel to each other. The force-supporting section and the force-introduction section are at least partially formed of vertically extending metal plates.

In some embodiments, apparatuses, systems, and methods are provided herein for front-facing merchandise in a shopping facility. A system for front-facing products includes a product display surface having a customer-facing portion, wherein a plurality of products are positioned on the product display surface, a control unit, and a product movement unit coupled to and under control of the control unit, wherein the product movement unit is configured to provide a force sufficient to induce movement of at least one product relative to the product display surface, the product display surface being substantially stationary during the movement, wherein the movement is in a direction toward the customer-facing position, and wherein the control unit is configured to receive a signal instructing activation of the product movement unit and output an activation signal to the product movement unit.

Sensor-equipped display management systems and methods that may be used to calculate a number of products removed from a display management system based upon motion of one or more mechanisms within the display management system. Additionally, the systems and methods may be used to detect patterns from the sensor data, which may be indicative of attempted theft of products stored within the display management system.