Technologies are shown for automatically registering items in an automated physical shopping cart involving receiving image data for items in the cart, detecting a change in a weight of items in the cart and, determining whether a correlated item was identified in the image data. If no correlated item was identified, a message is displayed indicating that no correlated item was detected and a user can be prompted. A user device may be used to scan items and display messages for the cart. Items may be scanned using image recognition to determine item information. An item weight may be determined based on the change in the weight of items in the cart and priced accordingly to weight. A notification for an item in the cart may be provided to an inventory or a shopping list application indicating that the correlated item was added to the cart.

A weigh platter assembly includes a weigh platter having a proximal edge and a lateral edge and an off-platter detection assembly. The off-platter detection assembly has an assembly field-of-view (field-of-view) extending from the proximal edge and constrained to have an assembly central field-of-view axis substantially parallel to the lateral edge and at least one lateral boundary substantially adjacent to the lateral edge. A light emission assembly has a light source and emits a light away from the proximal edge. A light detection assembly has a light sensor and detects at least a portion of the light reflected towards the proximal edge. A controller allows the measured weight to be recorded in response to a first value from the light detection assembly and prevents the measured weight from being recorded and/or provides an alert in response to a second value from the light detection assembly.

Methods for accurate object tracking are disclosed herein. An example the method includes receiving, from a first optical imaging assembly having a first field of view (FOV), a first image captured over the first FOV and based on a decode of an indicia associated with an object of interest, identifying the object of interest within the first image. The method further includes determining a location of the object of interest within the first image and mapping the location of the object of interest within the first image to a predicted location of the object of interest within a second image, the second image being received from a second optical imaging assembly having a second FOV and the second image being captured over the second FOV.

A store system includes a server and a store terminal. The server receives data based on purchased commodities respectively input in a plurality of mobile terminals. The server stores the received data based on the purchased commodities. The store terminal acquires, from the stored data, the data based on the purchased commodity input in any one of the mobile terminals. The store terminal instructs correction of the acquired data. The server executes the instructed correction concerning the stored data.

A self-checkout apparatus includes a display screen, a product scanner to obtain a commodity code from a product being registered in a sales transaction, a storage unit to store journal data including product information for registered. A controller is configured to record product information for products registered in the sales transaction and output a notification of an error in the sales transaction when detected. The notification is output to the display screen and includes information regarding resolution of the error. The controller executes a first countermeasure for resolving the error, then stops the sales transaction if the error is not resolved. The controller then executes a second countermeasure after the sales transaction has been stopped and starts a new transaction if the error is resolved by the second countermeasure.

A product identification method comprises instructing an antenna in a product verification area to collect first product identification data of identified products in the case where a user enters the product verification area of a product identification passage; instructing the antenna in the product verification area to collect product verification data of identified products in the case where a user enters a product settlement area of the product identification passage; and determining user product data based on the first product identification data and the product verification data. A product verification area is provided to eliminate a tag outside the passage being falsely read and reduce a tag false reading probability.

A method of simulating device state changes in an integrated system includes receiving a transaction request from a client device, storing the transaction request as a first event in an event log, transmitting the transaction request to a terminal device, storing the transmission of the transaction request as a second event in the event log, receiving a device response from the terminal device, storing the device response as a third event in the event log, and when the integrated system is under test, a simulator replays the stored events in the integrated system under test.

A method of simulating device state changes in an integrated system includes receiving a transaction request from a client device, storing the transaction request as a first event in an event log, transmitting the transaction request to a terminal device, storing the transmission of the transaction request as a second event in the event log, receiving a device response from the terminal device, storing the device response as a third event in the event log, and when the integrated system is under test, a simulator replays the stored events in the integrated system under test.

A system for detecting scan and non-scan events in a self-check out (SCO) process includes a a scanner for scanning objects and generating point of sale (POS) data, a video camera for generating a video of the scanning region, proximity sensors proximal to the video camera for defining an Area of Action (AoA), wherein the video camera starts capturing scanning region, when the objects enter the AoA, and the POS data includes non-zero values, an Artificial neural network (ANN) for receiving an image frame and generating one or more values, each indicating a probability of classification of the image frame into one or more classes respectively, and a processing unit for processing the POS data, and probabilities of one or more classes to detect a correlation between video data and POS data, and detect one of: scan and non-scan event in the image frame based on the correlation.

A weigh platter assembly includes a weigh platter having a proximal edge and a lateral edge and an off-platter detection assembly. The off-platter detection assembly has an assembly field-of-view (field-of-view) extending from the proximal edge and constrained to have an assembly central field-of-view axis substantially parallel to the lateral edge and at least one lateral boundary substantially adjacent to the lateral edge. A light emission assembly has a light source and emits a light away from the proximal edge. A light detection assembly has a light sensor and detects at least a portion of the light reflected towards the proximal edge. A controller allows the measured weight to be recorded in response to a first value from the light detection assembly and prevents the measured weight from being recorded and/or provides an alert in response to a second value from the light detection assembly.