General Electric Company

Ncr Voyix Corporation

Jerry Steven Massey

James Joseph Schmid

Mark Joseph Meyerhofer

Jaime Alberto Sierra

Bobby Antione Wilson

Kip Oliver Morgan

Gina Torcivia Bennett

Anthony Steven McCulley

Sthitaprajna Acharya

Vernon Meadows

Kamal Kumar Arvind

Chad Eric Knodle

Marc Karl Losee

John Kennedy

Ninad Vijaykumar Kulkarni

Julia Anne Ogilvy

A transaction terminal includes at least one top-down camera, side cameras, and at least one barcode scanner. Images captured by the cameras are processed by one or more computer vision applications for purposes of counting items placed on a tray of the terminal during a checkout. Each item is associated with a bounding box or region of the tray within the images. The computer vision applications and scanner are polled to provide a region identifier, a barcode, and a confidence value for each item on the tray. Duplicated item barcodes are removed and barcodes with the highest confidence values are retained. The final item barcodes are provided to a transaction manager of the terminal to complete the self-checkout with a customer at the terminal.

Interruption free vision checkout

A self-service checkout terminal for vision-based checkout includes a computing device having a processor and a memory. A camera coupled to the computing device has a predefined field of view focused on a scan zone and provides a video output signal of the scan zone to the computing device. A checkout tray has a tactile surface formed from a predefined number of rows and a predefined number of columns of raised dot. The checkout tray is positioned within the scan zone. The processor processes the video output signal from the camera to attempt to identify an item placed on the checkout tray by a user during a transaction, provides a description of the item to the user via the user interface when the item is identified, and provides the user of an indication of a location of the item via a user interface when the item is not identified.

Vision-based self-checkout system and method for the visually impaired

A device includes a network interface and a processor. The network interface is configured to receive one or more preferences of a customer related to the charging of at least one backup device of the consumer. The network interface is also configured to receive at least one notification of at least one predicted power disturbance. The processor is configured to utilize the one or more consumer preferences and the at least one notification to generate a charging schedule of the at least one backup device and to generate one or more charging notifications for charging the at least one backup device. The one or more charging notifications are based on the charging schedule.

End-user based backup management

A computing device for use with a demand response system is provided. The computing device includes a communication interface for receiving customer data of a plurality of customers, wherein the customer data includes a participation history and historical consumption values for each customer for participating in at least one demand response event. A processor is coupled to the communication interface and is programmed to select at least one customer from the plurality of customers by considering the participation history and the historical consumption values for each of the customers. The processor is also programmed to estimate a future reduction in energy consumption for the customer based on the customer data and to determine whether the estimated future reduction in energy consumption is substantially accurate.

Systems and methods to predict a reduction of energy consumption

A computing device for use with a demand response system is provided. The computing device includes an interface for receiving customer data that includes at least a location for each customer and/or a preference selection made by each customer associated with at least one demand response program. A processor coupled to the interface and programmed to select a plurality of participants from the customer data to receive a plurality of signals representative of at least one demand response event. The processor selects the participants based at least in part on the customer location and/or the preference selection associated with each customer.

Systems and methods to implement demand response events

A computing device for use with a demand response system is provided. The computing device includes a communication interface for receiving customer data of a plurality of customers, wherein the customer data includes at least a location, a policy limitation, a participation history, and an enrollment status for each customer. A processor is coupled to the communication interface and programmed to select at least one participant from the plurality of customers, based on the customer data, to participate in at least one demand response event. The processor is further programmed to verify that the selected participant, based on the customer data, satisfies at least one precondition to receive at least one signal representative of the demand response event.

Disclosed and described herein are embodiments of systems, methods and computer program for scheduling demand events over a time period based on differences between the estimated power availability and the estimated power consumption at various points during the time period. In one aspect, methods are described. One embodiment of a method comprises establishing a defined time period, estimating power availability over the time period, estimating power consumption over the time period, and scheduling, using a computing device, demand events over the time period based on differences between the estimated power availability and the estimated power consumption at various points during the time period.

Method, system and computer program product for scheduling demand events

Certain embodiments of the invention may include systems, methods, and apparatus for estimating demand response load change. According to an example embodiment of the invention, a method is provided for estimating energy usage change associated with a demand event. The method may include determining energy potential change during a demand event for one or more devices associated with a location; estimating energy usage patterns for the location over predefined time periods before and after the demand event; determining pre-demand event and post-demand event energy rebounds based at least in part on the estimated energy usage patterns; determining an energy change profile associated with the location based at least in part on the determined energy potential change, and the pre-demand event and post-demand event energy rebounds.

Growing community of inventors

Lawrenceville, GA, United States of America

Jerry Steven Massey