Disclosed is an effective domain name defense solution in which a domain name string may be provided to or obtained by a computer embodying a visual domain analyzer. The domain name string may be rendered or otherwise converted to an image. An optical character recognition function may be applied to the image to read out a text string which can then be compared with a protected domain name to determine whether the text string generated by the optical character recognition function from the image converted from the domain name string is similar to or matches the protected domain name. This visual domain analysis can be dynamically applied in an online process or proactively applied in an offline process to hundreds of millions of domain names.

A meter monitoring system (200) includes: a wireless gateway (202), a monitoring device (201), and at least one meter recognition apparatus (100). The meter recognition apparatus (100) includes: an image acquirer (1), a processor (2), and a wireless transceiver (3). The image acquirer (1) is configured to acquire images of a display side of a monitoring meter (4) at set time intervals. The processor (2) is coupled to the image acquirer (1) and configured to determine, according to an image of the images acquired by the image acquirer (1) and based on an image processing algorithm, monitoring data displayed by the monitoring meter (4). The wireless transceiver (3) is coupled to the processor (2) and configured to send the monitoring data determined by the processor (2) to the wireless gateway (202). The wireless gateway (202) is configured to transmit the received monitoring data to the monitoring device (201).

In some implementations, a device may receive data identifying products and encoded data identifying smart tags of the products. The device may map the data and the encoded data to generate encoded product data. The device may receive encoded data provided by smart tags of products received by a store. The device may receive images of the products. The device may compare the encoded data and the encoded product data to identify a set of the products received by the store. The device may correlate the images with the set of the products. The device may process the correlated data to identify locations of the set of the products in the store. The device may generate an instruction to relocate a product to a new location and may provide the instruction to a device, associated with the store, to cause the product to be relocated to the new location.

A system for digitizing gauges, lights and other human-readable machine gauges and functions and status without interfering with the operation of the machine or requiring re-working or interfering with the existing machine wiring, signaling, electrical or mechanical elements or operating modes, or adding new digitizing equipment to the machine.

A system (300) and a related method (100) for controlling an optical output of at least one electronic label in a retail environment is provided. The system comprises a camera (310) comprising a transmitter for transmitting a control signal (380, 381) and an electronic label (320) comprising a receiver (325) for receiving the control signal and wherein the electronic label is adapted to change its optical output in response to the control signal, the electronic label is arranged within the field of view of the camera. The camera is adapted to receive an identifier of the electronic label and transmit a control signal comprising the identifier and wherein the electronic label is adapted to receive the control signal comprising the identifier and change its optical output based on that the identifier comprised in the control signal matches an identifier of the electronic label.

An acquisition unit (15a) detects an operation event of a user to acquire an occurrence position of the operation event in an operation screen and a captured image of the operation screen. An extraction unit (15b) extracts images that are able to become candidates for a GUI part from the acquired captured image, identifies which image the operation event has occurred on from the occurrence position of the operation event, and records an occurrence clock time of the operation event and the identified image in an associated manner. A classification unit (15c) classifies a group of recorded images into clusters in accordance with similarities of the images. A determination unit (15d) adds up the number of times the operation event has occurred in the images for each classified cluster, and in a case in which the aggregated value is equal to or greater than a predetermined threshold value, determines an image included in the cluster as an image of the GUI part that is an operation target at the occurrence clock time of the operation event.

A video game may include a virtual representation of a game player created object. The game player created object may be imaged by an imaging device of a smartphone, with the smartphone determining a virtual representation for use in the video game based on information of the image. In some embodiments the game player may control the virtual representation during game play.

In some embodiments the game player created object may be an origami object.

A medical device monitoring system and method extract information from screen images from medical device controllers, with a single OCR process invocation per screen image, despite critical information appearing in different screen locations, depending on which medical device controller's screen image is processed. For example, different software versions of the medical device controllers might display the same type of information in different screen locations. Copies of the critical screen information, one copy from each different screen location, are made in a mosaic image, and then the mosaic image is OCR processed to produce text results. Text is selectively extracted from the OCR text results, depending on contents of a selector field on the screen image, such as a software version number or a heart pump model identifier.

System(s) and method(s) that analyze image data associated with a computing screen operated by a user, and learns the image data (e.g., using pattern recognition, historical information analysis, user implicit and explicit training data, optical character recognition (OCR), video information, 360°/panoramic recordings, and so on) to concurrently glean information regarding multiple states of user interaction (e.g., analyzing data associated with multiple applications open on a desktop, mobile phone or tablet). A machine learning model is trained on analysis of graphical image data associated with screen display to determine or infer user intent. An input component receives image data regarding a screen display associated with user interaction with a computing device. An analysis component employs the model to determine or infer user intent based on the image data analysis; and an action component provisions services to the user as a function of the determined or inferred user intent. In an implementation, a gaming component gamifies interaction with the user in connection with explicitly training the model.

A test support method includes a step of obtaining a pre-change image and a post-change image to be displayed on a monitoring and control system, a step of extracting, from the post-change image, multiple symbols that have changed from corresponding symbols in the pre-change image, a step of adding order information to the multiple symbols extracted, and a step of outputting a test image in which the order information is added to the multiple symbols.