An electronic apparatus for recognizing a user and a method therefor are provided. The electronic apparatus includes a communication interface, a dynamic vision sensor (DVS), a memory including a database in which one or more images are stored, and at least one processor. The at least one processor is configured to generate an image, in which a shape of an object is included, based on an event detected through the DVS, control the memory to store a plurality of images generated under a specified condition, in the database, identify shapes of the user included in each of the plurality of images stored in the database, and generate shape information for recognizing the user based on the identified shapes. The plurality of images may include a shape of a user.

A self-learning robot, according to one embodiment of the present invention, comprises: a data receiving unit for sensing video data or audio data relating to an object located within a predetermined range; a data recognition unit for matching data received from the data receiving unit and data included in a database in the self-learning robot; a result output unit for outputting a matching result from the data recognition unit; a recognition result verifying unit for determining the accuracy of the matching result; a server communication unit for transmitting data received from the data receiving unit to a server, when the accuracy of the matching result determined by the recognition result verifying unit is lower than a predetermined level; and an action command unit for causing the self-learning robot to perform a pre-set object response action, when the accuracy of the matching result determined by the recognition result verifying unit is at least the predetermined level.

This disclosure can provide a display device and a display method. The display device includes at least one camera configured to capture an environmental scenario image; a display configured to display a user interface; a controller in communicated with the display, configured to receive a command, input by a user, for obtaining a content recommendation resource associated with content currently displayed in the user interface; determine whether an application corresponding to the content currently displayed in the user interface is an application invoking the at least one camera, and if yes, display a first user interface, where the first user interface displays a first image captured by the at least one camera.

A system and method for detecting annotation errors are disclosed. A processor receives an image that includes a first annotation, and identifies a first classifier associated with the first annotation. The processor invokes the first classifier to classify the first annotation, where the first annotation is classified with a first label. The processor transmits a message in response to classifying the first annotation with the first label, where the message is for prompting an update to the first annotation. The processor receives the image with an updated first annotation, and saves the image with the updated first annotation in a data storage device. The image may be for training an artificial intelligence machine for conducting an automated task.

An apparatus including a sensor communication circuit structured to interpret a plurality of sensor data values from input sensors operationally coupled to a component of an industrial environment, a sensor data storage profile to determine a data storage profile in response to a processing capability associated with each of a plurality of data storage devices, and a data storage implementation circuit, communicatively coupled to each of the plurality of data storage devices, to store at least a portion of the sensor data values in response to the data storage profile is disclosed. The data storage profile may include a data storage plan wherein the data storage plan includes a storage location and a storage time for sensor data values.

Systems and methods utilizing noise analysis to analyze conveyor performance are disclosed. An example system for monitoring a plurality of components of a conveyor in an industrial environment may include a data acquisition circuit to interpret a plurality of detection values, each detection values corresponding to at least one of a plurality of input sensors operationally coupled to the conveyor. A data processing circuit may utilize at least one of the detection values to perform a noise processing operation on at least a portion of the detection values. A signal evaluation circuit may determine a conveyor performance parameter in response to the noise processed portion of the detection values and a response circuit may then perform at least one operation in response to the conveyor performance parameter.

Systems and methods for data communication over a plurality of data paths are disclosed. Messages between a first node and a second node may be transmitted, wherein some of the messages are transmitted over a first data path using a first communication protocol, and some of the messages are transmitted over a second data path using a second communication protocol. A determination may be made that the first data path is altering a flow of messages due to the messages being transmitted using the first communication protocol and a number of messages sent over the different data paths adjusted in response, wherein adjusting comprises decreasing a number of the messages transmitted over the first data path and increasing a number of messages transmitted over the second data path, wherein altering the flow of messages is performed automatically under control of an expert system.

Methods of determining, and using, a process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

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.

An apparatus, methods and systems for monitoring data collection in an industrial environment are disclosed. The system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels, and a data analysis circuit structured to analyze the collected data and determine an aggregate rate of data being collected, wherein if the aggregate rate exceeds a current bandwidth allocation rate associated with the network infrastructure, then the data analysis circuit requests an increase to the current bandwidth allocation rate from the network infrastructure.