Abstract: A point-of-sale system is provided. The point of sale system receives map data that includes a plurality of dining locations in a dining environment, collects image information captured by a mobile device of a user in which the image information includes an image associated with a dining location of the user within the dining environment, determines the dining location of the user based on the collected image information and the map data, and transmits, to the mobile device, payment information for the dining location of the user. A payment method is also provided.

Abstract: Inventive concepts disclosed herein relate systems, methods, and computer-readable media for detecting a leakage condition. A container integrity system can obtain sensor data associated with a sensor pad placed on or integrated with shelves or similar surfaces. Containers or items susceptible to leakage can be positioned on the sensor pad, and sensors distributed throughout the sensor pad can measure force profiles exerted by the items. Using the sensor data from the sensor pad, the container integrity system can identify the presence or absence of a leakage condition.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A movable shelf system is provided for a retail or grocery store. The system includes a movable shelf in a store location; an actuator for moving the movable shelf; a plurality of cameras in the store location, each of the plurality of cameras having a field of view; and a vision mesh network having a plurality of nodes in communication with each other, at least one of the plurality of cameras being a node within the plurality of nodes on the vision mesh network. The vision mesh network receives and processes information about the movable shelf and sends an instruction to the movable shelf based on the information.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A Radio Frequency Identification (RFID) enabled Self-Checkout (SCO) station includes an RF antenna that emits RF energy, such as RFID interrogation signals, through the variable apertures of one or more aperture layers into a scan zone. The size of the scan zone is selectively varied by electrically modifying the effective size of the variable aperture in the aperture layer.

Abstract: A Radio Frequency Identification (RFID) enabled Self-Checkout (SCO) station includes an RF antenna that emits RF energy, such as RFID interrogation signals, through a variable aperture of an aperture plate into a scan zone. The size of the scan zone is selectively varied by electrically modifying the effective size of the aperture in the aperture plate.

Abstract: Pay stations in a retail store initiate customer transactions for customers. When a first pay station is unable to complete a customer transaction, it hands the transaction off to a second pay station that is able to complete the transaction. During hand off, the first pay station determines a first set of biometric and contextual data associated with the customers and sends that information to the second pay station. The second pay station then determines, independently, a second set of biometric and contextual data associated with the customers and compares it against the first set. Depending on the results of the comparison, customers are either granted or denied access at the second pay station to complete the transaction. Additionally, all biometric and contextual data are permanently destroyed without being saved to memory.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: The present disclosure utilizes excess processing capacity of a point of sale (POS) system by dynamically managing assigned tasks based on an expected activity of an individual. A resource management system can use image data and a trained neural network to analyzes aspects of an appearance or a behavior of the individual as captured in the image data to quantify a likelihood of the individual participating in a checkout procedure. The resource management system can dynamically allocate resources across different POS systems in anticipation of expected activity. This allows for the efficient use of the POS systems' resources while maintaining good system performance and a positive experience.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: Method, computer program product, and system to prioritize the execution of processing task requests in a task request queue, where the processing task requests are related to an environment. The method includes adding processing task requests to a task request queue in response to the detection of predefined actions in an environment. The method also includes adding additional processing task requests or adjusting a priority level of not yet completed task requests in the task request queue, in response to detecting subsequent predefined actions in the environment.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Abstract: A method, computer program product, and system to perform a sale transaction are provided. The method includes identifying each item of a plurality of items, based on at least one image of the plurality of items, determining a cost for each item, optionally identifying a person based on an image of the person, adding each of the items and each of the costs to a sale transaction, and charging the person for the sale transaction.

Abstract: An energy generation system to generate energy from a fluid flow, the energy generation system including an extendable arm that extends into a fluid flow, a fin connected to one end of the extendable arm and causing, due to the fluid flow, the extendable arm to move from a first orientation within the fluid flow to a second orientation within the fluid flow, and a joint connected to a second end of the extendable arm that couples the extendable arm to an energy generator.

Abstract: Customers are identified as they enter a store and linked to one or more items previously ordered on-line. A route through the store to retrieve the ordered items is generated and sent to the customer's mobile device for display. The customer's movements are tracked as he/she goes through the store to retrieve products, and the location of the customer determined whenever he/she selects a product from the shelves. Based on whether the locations of the customer when he/she selected the products matches the predefined locations of the items ordered on-line, the customer is directed to go to one of an audit station or checkout lane, or alternatively, to bypass both audit station and the checkout lane and exit the store. In the latter case, the customer's account is automatically charged for the total cost of the ordered items.

Abstract: Method, computer program product, and system to provide efficient processing of image data representing an environment comprising a plurality of items available for selection by one or more persons are described. The method includes receiving image data from a plurality of visual sensors, segmenting the image data into a plurality of image segments and classifying the data into predefined image categories. The method also includes identifying an image processing task having a predefined association with the first image category and executing the identified image processing task.