6132results about "Cash registers" patented technology

The invention relates to the technical field of encryption communication, in particular to a data encryption communication method and system applied to an intelligent cash register. The method comprises the following steps: collecting user touch data and equipment hardware fingerprints, and carrying out high-dimensional feature fusion coding to obtain a joint identity certificate; obtaining an original key negotiation protocol and transmission metadata of an opposite-end server, and executing multi-logic path mapping and channel encryption configuration to obtain distributed ciphertext channel parameters; encrypting the distributed ciphertext channel parameters and the corresponding transmission metadata, and then performing block content packaging and node hash calculation to obtain an audit chain hash block; performing homomorphic encryption processing on the obtained real-time transaction data to obtain a to-be-transmitted homomorphic ciphertext; and performing communication transmission anomaly detection on information transmission data acquired in real time, and triggering dynamic key re-negotiation so as to improve the communication feedback capability. According to the invention, the overall security and stability of data encryption communication between the cash register and the back-end server can be improved.

A checkout terminal is provided. The checkout terminal comprises a camera array, a display device, a weight scale, and a control circuit. The control circuit is configured to identify, based at least on images captured by the camera array, product identifiers associated with a plurality of different items placed in a placement area and on the weight scale, determine a combined weight of the plurality of different items based on product weight information stored in a product database, retrieve a weight measurement measured by the weight scale, detect for unaccounted items based on comparing the combined weight the plurality of different items and the weight measurement, and in the event that an unaccounted item is detected, display, via the display device, instructions to move one or more items to identify the unaccounted item.

The present invention relates to a system (100) and method (300) for automated product validation, identification, and transaction management in shopping environments. The system (100) integrates a smart shopping cart (102) equipped with at least one camera (106), a load cell (108), and a user interface (110). An AI model trained on a dataset of high-resolution, 360° product images generate unique image embeddings for each product. The system performs multimodal validation by comparing the extracted image embeddings, measured weight data, and scanned barcode information with reference data stored in a vector database. Further, discrepancies, such as unscanned products or mismatches, are flagged, and payment is blocked until resolved.

A self-service checkout system includes a processor and a non-transitory computer-readable storage medium storing executable instructions. The executable instructions when executed by the processor cause the processor to perform operations including: receiving a message from a vision system that includes a list of identified items on a tray positioned adjacent to the vision system for a current transaction and one or more issues related to an identification of the items on the tray, processing the message to identify the one or more issues related to the identification of the items on the tray, providing a user interface for a customer to correct the one or more issues related to the identification of the items on the tray, and completing the current transaction once the issues related to the identification of the items on the tray are corrected.

A self-service POS terminal includes a tag reader, a camera, and a processor configured to: start a registration process to register items each of which has a wireless tag attached thereto, control the wireless tag reader to read tag information from wireless tags and store the tag information in memory, determine a number of the tags based on the tag information, control the camera to capture images and generate image data from each of the images, perform object recognition on the image data to determine a number of items shown in the images, compare the number of the tags with the number of the items, when the number of the tags is identical to the number of the items, perform a checkout process, and when the number of the tags is not identical to the number of the items, issue a notification signal.

Systems and methods include extracting item parameters from images of items positioned at a POS system. Item parameters associated with each item when mapped into a feature vector for each item are indicative as to an identification of the item. The feature vectors are analyzed to determine whether item parameters when combined and mapped into the feature vectors match a corresponding feature vector stored in a database. The database stores different combinations of item parameters as mapped into different stored feature vectors with different stored feature vectors associated with different items thereby identifying each item based on each different combination of item parameters as mapped into each stored feature vector associated with each item. Each item positioned at the POS system is identified when the feature vectors associated with each item match a corresponding stored feature vector as stored in the database.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property.

Assisted checkout devices, including point-of-sale stations, can use computer vision and machine learning to speed the checkout process while maintaining human verification, assistance, and customer interaction provided by human clerks. A plurality of optical sensors, including cameras, can be arranged with different views of a checkout plane upon which items being purchased by a buyer are placed. Moreover, one or more support towers can be utilized to elevate the optical sensors to vertical heights at which the checkout plane, and items placed thereon, is within the field of view of the optical sensors. The information captured by the plurality of optical sensors can be analyzed using machine learning models to detect and identify the items placed on the checkout plane.

The invention relates to the technical field of self-service, and particularly discloses a non-inductive payment method and device, and the method comprises the steps: collecting goods taking pictures of a plurality of clients through a binocular camera and an infrared array which are disposed at the top and side surfaces of a cabinet body, extracting hand key points through posture estimation, and distinguishing the identity through multi-target tracking; a gravity sensor under a shelf board detects the weight change of each goods allocation, and combines with a visual track to construct the space-time correlation of goods movement, so as to realize the cross validation of goods taking behaviors; the convolutional neural network performs abnormal behavior detection on the multi-modal data and generates a risk score; creating a virtual shopping basket containing a space-time mark for each customer, and judging commodity attribution by adopting a decision tree algorithm; and executing distributed bill generation and multi-channel fee deduction according to the risk level, and completing real-time settlement in a multi-client concurrent scene. According to the method, the commodity affiliation accuracy and the payment security are remarkably improved, and efficient and stable unmanned retail operation can be realized.

A radio frequency identification (RFID) system includes an array of antennas to distinguish line-of-sight (LOS) paths from non-line-of-sight (NLOS) paths. The distance between adjacent antennas in the array of antennas is less than half the wavelength of the radio frequency (RF) signal of the system. Each antenna in the antenna array is also digitally controlled to change relative phase difference among the antennas, thereby allowing digital steering of the array of antennas across angles of arrival (AOAs) between 0 and π. The digital steering generates a plot of signal amplitudes as a function of AOAs. LOS paths are distinguished from NLOS paths based on the shapes (e.g., depth, gradient, etc.) of local extremes (e.g., maxima or minima) in the plot.

Implementations of this application provide a checkout device, including a housing, a display screen, and a near-field communication (NFC) module. The housing includes a support portion for support of the housing on a countertop. The display screen is fastened to the housing, and when the checkout device is used, the display screen faces a consumer. The NFC module is located in space enclosed by the housing, and located to permit an electronic device to make an electronic payment.

A wireless tag reading apparatus includes a housing having an accommodation space in which a shopping cart is placeable, the shopping cart including an upper rack and a lower rack on which one or more items or one or more shopping baskets containing the one or more items are placeable; one or more antennas movable along one or more surfaces of the accommodation space; a communication interface; and a processor configured to when the shopping cart is placed in the accommodation space, control the antennas to move along the surfaces, and read identification information of one or more items in the shopping cart from one or more wireless tags attached thereto, generate item registration information based on the read identification information, and control the communication interface to output the item registration information to a payment device for payment of the items.

An information processing apparatus (10) is used in a store, and includes a display (110) and a computation processing unit (142). The display (110) is capable of rotating about an axis of rotation in a direction intersecting a display surface. The computation processing unit (142) changes processing modes according to an angle of the display (110). The processing modes include a first mode relevant to a first application and a second mode relevant to a second application different from the first application. One example of the first application is settlement processing for a product being sold in a store.

A self-checkout monitoring system including self-checkout terminals, a site controller, and point-of-sale devices, is provided. A self-checkout terminal receives a transaction event, generates a transaction status update based on this transaction event, and transmits the transaction status up-date to a site controller, which routes the transaction status update to a point-of-sale device. The point-of-sale device generates a transaction status icon based on the transaction status update and displays a self-checkout monitoring panel with a status tile displaying the status icon corresponding to the transaction status update most recently received from the self-checkout terminal. The cashier selects the self-checkout terminal by entering a self-checkout terminal selection input causing the point-of-sale device to display transaction control commands. The cashier chooses a transaction control command to apply to the self-checkout terminal. The transaction control command is then transmitted, via the site controller, to the self-checkout terminal to configure the self-checkout terminal accordingly.

An information processing system according to an aspect of the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: estimate an age of a customer from a customer image; receive input of identification information about a product; display a first age verification screen requesting the customer to express his / her age in response to the input of the identification information about an age-restricted product in a case where the estimated age of the customer meets a predetermined condition; and display a second age verification screen requesting the customer for proof of age in response to the input of the identification information about an age-restricted product in a case where the estimated age of the customer does not meet the predetermined condition.

Systems and methods include extracting item parameters from images of items positioned at a POS system. The item parameters associated with each item are indicative as to an identification of each item thereby enabling the identification of each item based on the item parameters. The item parameters are analyzed to determine whether the item parameters match item parameters stored in a database. The database stores different combinations of item parameters to thereby identify each item based on each different combination of item parameters associated with each item. Each item positioned at the POS system is identified when the item parameters for each item match item parameters as stored in the database and fail to identify each item when the item parameters fail to match item parameters. The item parameters associated with the items that fail to match are streamed to the database thereby enabling the identification of each failed item.

The invention discloses a method and a device for realizing key import and key use. When a secret key import interface of the SDK is called by an upper-layer application, a certificate chain generated by the secret key library is sent to the push service, and a first encryption result, a first random number, a second encryption result and label data returned by the push service are sent to the secret key library for verification; if the verification is passed, the master key identifier and a master key obtained by decrypting the second encryption result through the key library are correspondingly stored in the key library; when a key encryption interface of the SDK is called by an upper-layer application, encrypting data to be processed by using a first working key returned by a key library to obtain a first data ciphertext to be processed, and sending the first data ciphertext to be processed, preset data and a second random number to a push service; receiving a processing result ciphertext and a third random number returned by the push service; and decrypting the processing result ciphertext by using a fourth working key returned by the key library, and performing subsequent operation according to a processing result obtained by successful decryption.

A computer system for predicting an anomaly behavior (fraudulent behavior) for a user of a smart shopping cart (physical receptacle). The computer system gathers data associated with a physical activity of the user, the user being physically present at a location of a retailer and adding one or more items from the location of the retailer to the physical receptacle. The computer system applies a computer model to detect, based on the gathered data, one or more anomalies during the physical activity of the user, the one or more anomalies being indicative of the fraudulent behavior. In response to determining that the one or more detected anomalies satisfy a threshold condition for the fraudulent behavior, the computer system sends a communication to a management system of the retailer to cause at least one remedial action to be performed before the user physically leaves the location of the retailer.

Disclosed is a storage cabinet. An embodiment has one or more shelves, one or more doors for enclosing the cabinet, and an electromagnetic lock for locking the door or doors. The shelves have one or more cameras, an opened- or closed-door state sensor, and a weight sensor arranged to measure weight of products placed on said shelves. An electronic data processor carries out a machine learning computer vision method for processing data received from cameras and sensors to identify products removed from said shelves. The processor detects when a cabinet door is in an open state, acquires image and weight data from the associated camera and weight sensor. When a weight change at a shelf is detected, processing of image data acquired including when the weight change occurred occurs, with said machine learning computer vision method.

To reduce man-hours in a work for returning articles on deposit.SOLUTION: An article on deposit information processing device according to the present disclosure includes: identification information acquisition means which acquires identification information of articles on deposit from an information reading device which reads identification information of articles on deposit for identifying an article on deposit written or stored on a receipt from an information reading device for reading identification information of articles on deposit for identifying an article on deposit written or stored on a receipt; information of article on deposit acquisition means which acquires signature handing information indicating whether the article on deposit is handed or not without receiving the receipt but using the signature on the basis of the identification information; and information output means which outputs display information for displaying the identification information of articles on deposit and the signature handing information.SELECTED DRAWING: Figure 1

A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

A product checkout device operable to couple to a shopping cart and facilitate a checkout process for items placed in the shopping cart.

A kiosk apparatus capable of mounting or detaching a printer is described. In an embodiment, the printer is fixed by coupling with a stand unit, so the printer can be easily and stably mounted on the kiosk apparatus by simply inserting the printer into the housing. In addition, the embodiment allows the printer to be easily attached and detached from the kiosk apparatus by disconnecting the printer from the stand unit simply by opening operation of the door of the printer without using a separate tool. Therefore, for the maintenance of the printer, the manager can easily mount or detach the printer.

Techniques for intelligent system optimization are provided. Capacity data of one or more exit devices at a physical location is received. Demand data that indicates a current demand for exit service at the physical location is received from one or more monitoring devices. A decision is generated based on the capacity data and the demand data using a machine learning (ML) model. Commands corresponding to the decision are transmitted to the one or more exit devices for implementation.

A school food and beverage recognition and processing system includes a self-checkout system that attaches to a school meal line. The self-checkout system uses multiple cameras to obtain images of a student for identification and images of a meal, identify contents of the meal, and determine whether the meal qualifies for reimbursement through a health program or agency. In addition to determining meal reimbursement qualifications, the self-checkout system tracks the meals and items sold for inventory and purchasing purposes. The self-checkout system sends reports to the school or administration agency including reimbursement information, inventory information, and customer information. This information is used to update a student's prepaid school meal account and track transactions.

A method for service location optimization may include methods for deriving service location utilization data from video, predicting an operational characteristic of a service location, optimizing an operational characteristic of a service location, and presenting a user interface for user-directed optimization of an operational characteristic of a service location.

Implementations of this application provide a checkout device, a checkout assembly, and a checkout method. An example checkout device includes a housing, a display screen, and a Near Field Communication (NFC) module. The display screen is fastened to the housing, and is configured to display at least one of a to-be-paid amount of a product, payment method guidance, or a payment result. The NFC module is located in a space enclosed by the housing to be in close proximity to an electronic device to make an electronic payment. The display screen displays the payment method guidance to direct a consumer to quickly make an electronic payment. The display screen displays the to-be-paid amount of the product and the payment result.

This invention presents a high-tech shopping cart designed to revolutionize the retail shopping experience. The cart has four independently controlled wheels, multiple storage compartments, and a controller managing item selection and pricing. Equipped with sensors for obstacle detection, a user-friendly interface, and wireless communication capabilities, the cart offers real-time budget tracking and eliminates the need for traditional checkout queues. Shoppers can interact with the cart via a mobile app, making shopping more convenient and efficient. With autonomous movement and advanced features, this high-tech shopping cart enhances shopping and checkout processes within stores or retail establishments.

A self-service checkout system has a processor and a non-transitory computer-readable storage medium storing executable instructions. The executable instructions, when executed by the processor, cause the processor to perform operations including: receiving a message from a vision system that includes a current list of identified items on a tray positioned adjacent to the vision system for a current transaction, comparing the current list of identified items to a previous list of identified items, and adding any items on the current list that are not on the previous list to a transaction list for a current transaction, determining that the current transaction is complete when the current list of identified items has no items, enabling a customer to review the transaction list and remove any duplicate entries, and completing the current transaction.

Disclosed are systems and methods for determining whether an unknown product matches a scanned barcode during checkout. The system includes a checkout lane having a flatbed scanning area with scanning devices and a point of sale (POS) terminal that scans a product identifier of an unknown product, identifies a product associated with the scanned product identifier, and transmits, to a computing system, product information. An overhead camera idnentifies, based on detecting an optical signal from the POS terminal, that a scanning event occurred, captures image data of the unknown product, and transmits, to the computing system, the image data. The computing system generates machine learning product identification models for identifying unknown products, identifies candidate product identifications for the unknown product based on applying the models to the image data, and determines, based on the candidate product identifications and the information about the product, whether the unknown product matches the product.