An out-of-stock detection system notifies store management that a product is out of stock. The system captures images of a shelf and determines the position product labels thereon. For each product label, a bounding box is generated based on the position of each product label on the shelf. The system then identifies a product for each product label based on information within each product label and, for each product label, stores a product identified for each bounding box. Accordingly, the system performs an out-of-stock detection process that includes capturing additional image data of the shelf periodically that includes each bounding box, providing a portion of the additional image data for each bounding box to a model trained to determine whether the bounding box contains products, sending a notification for a product determined to be out of stock to a store client device based on output from the model.

The disclosure includes a system and method for correlation of signals including determining, using one or more processors, a first position in a user journey; determining, using the one or more processors, a first set of signal data identified for capture at the first position in the user journey, the first set of environmental signal data including a representation, at a first time, of one or more of an environment of a user device and the user device in relation to the environment; obtaining, using the one or more processors, the first set of signal data, the first set of signal data including first sensor data obtained from a first sensor at the user device; and determining, using the one or more processors, based at least in part on the first set of signal data including the first sensor data, a correlation to untrustworthiness.

Systems and methods for tracking inventory items in an area of real space are disclosed. The method includes receiving a signal generated in dependence on sensors. The signal indicates a change to a portion of an image of an area of real space. The method includes, in response to receiving the signal, implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. The method includes implementing a trained item classification model to determine a classification of the inventory item. The method includes updating an inventory database with inventory item data determined in dependence on the classification of the inventory item to provide an updated map of the area of real space as a result of the received signal indicating the change to the portion of the image.

Methods and systems for determining an identity of a subject based on a single-frame binary shape-capturing image extracted from distorted image of the subject and using a shape-based biometric image derived from the shape-capturing image. The shape-based biometric image includes a biometric feature of the subject and is generated by transforming the shape-capturing image to a distance transformed image and deriving a multi-scale representation of the distance transformed image. The identity of the subject can be further determined using an outfit regularizing biometric image derived from the distorted image using the shape-based biometric image. The outfit regularizing biometric includes biometric feature of the subject independent of an outfit of the subject and is generated by replacing a region of subject's boy covered by an outfit with corresponding region of the shape-based biometric image.

A method for reading an optical prescription on an optical prescription image. The method includes detecting a region comprising the optical prescription on the optical prescription image; extracting the optical prescription and converting the optical prescription into machine-encoded optical prescription data; classifying a portion of the optical prescription data into one or more predetermined categories, to generate an optical prescription value associated with a respective one of the one or more predetermined categories; and determining whether the optical prescription value associated with the respective one of the one or more predetermined categories contains an error, and, if the optical prescription value contains the error, correcting the error within the optical prescription value, to generate a corrected optical prescription value associated with the respective one of the one or more predetermined categories. A system for reading an optical prescription on an optical prescription is also disclosed.

Embodiments of the inventive subject matter are directed to AI systems that are designed to identify whether identification cards or driver's licenses issued to states from the United States are compliant with either Real ID or Enhanced ID laws. When a driver's license or ID card is Real ID compliant, it includes a visual indicator in the form of a small start on the front of the card (e.g., a start located generally at the top right of the card). When a driver's license is Enhanced ID compliant, it will include a small image of an American flag somewhere on the front of the card. AI systems of the inventive subject matter are trained to identify these visual indicators to determine whether an ID is Real ID compliant or Enhanced ID compliant.

A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Systems and methods for tracking inventory items in an area of real space are disclosed. The methods can include receiving a signal generated in dependence on sensors, and detecting an item in the area of real space based on the received signal. One method includes implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. Another method includes implementing a trained size determination model to determine, based on inputs, the size of an inventory item detected in the area of real space.

To provide an optical information reading device capable of narrowing down to the character strings to be output from multiple character strings attached to a target object. The imaging unit 31 of the optical information reading device 10 captures an image of the target object 11 with multiple character strings attached as a symbol 20, and generates an input image containing the character strings. The character string processing unit of the optical information reading device 10 obtains multiple character string candidates from the input image. Then, the character string processing unit narrows down to the output character string from the multiple character string candidates based on at least one of the character string likelihood indicating the plausibility as a character string, the distance from the aiming position, and the degree of matching with a predetermined format pattern for each of the multiple character string candidates.