Abstract: An example electronic device includes a display device, and a sensor to detect position data of a user of the electronic device. The position data indicates a position and orientation of the user relative to the display device. In addition, the electronic device includes a controller coupled to the sensor and the display device. The controller is to: receive the position data from the sensor; use a machine learning model and the position data to classify an interaction of the user with the electronic device in a first ergonomic category or a second ergonomic category; and adjust an angular position of a display device or an output from the display device responsive to a classification of the interaction in the first ergonomic category.

Abstract: Techniques for providing location identifiers of devices to render image data comprising faces are described. According to an example of the present subject matter, a face present in image data is detected. A contact identifier corresponding to the face is obtained and a location identifier of a device to render image data is sent to the contact identifier.

Abstract: Techniques for providing location identifiers of devices to render image data comprising faces are described. According to an example of the present subject matter, a face present in image data is detected. A contact identifier corresponding to the face is obtained and a location identifier of a device to render image data is sent to the contact identifier.

Abstract: An image forming apparatus may include a receiving unit to receive a print request to print a document. Further, the image forming apparatus may include an extraction unit to extract content from the document. Furthermore, the image forming apparatus may include a categorization unit to determine a type of the content by applying a machine learning model to the extracted content. Further, the image forming apparatus may include a controller to manage the print request based on the type of the content.

Abstract: In an example, a non-transitory machine-readable storage medium storing instructions executable by a processor of a computing device to receive device telemetry data associated with an electronic device. The device telemetry data may include data that affects a performance of a graphics processor of the electronic device. Further, instructions may be executed by the processor to predict health of the graphics processor by applying a machine learning model to the device telemetry data. Furthermore, instructions may be executed by the processor to generate an alert notification based on the predicted health of the graphics processor.

Abstract: In an example, a non-transitory machine-readable storage medium storing instructions executable by a processor of a computing device to receive device telemetry data associated with an electronic device. The device telemetry data may include data that affects a performance of a graphics processor of the electronic device. Further, instructions may be executed by the processor to predict health of the graphics processor by applying a machine learning model to the device telemetry data. Furthermore, instructions may be executed by the processor to generate an alert notification based on the predicted health of the graphics processor.

Abstract: Techniques for providing location identifiers of devices to render image data comprising faces are described. According to an example of the present subject matter, a face present in image data is detected. A contact identifier corresponding to the face is obtained and a location identifier of a device to render image data is sent to the contact identifier.

Abstract: In an example, a non-transitory machine-readable storage medium may include instructions that, when executed by a processor of a computing device, cause the processor to receive device usage data associated with an electronic device. Further, instructions may be executed by the processor to determine a touch-related contamination state of a surface of the electronic device by applying a machine learning model to the device usage data. Furthermore, instructions may be executed by the processor to send an alert notification to the electronic device based on the touch-related contamination state.

Abstract: In an example in accordance with the present disclosure, a system is described. The system includes a data collector to collect usage data for the electronic device over a first period of time. The system also includes a model generator. The model generator extrapolates usage data for the electronic device over a second period of time that is longer than the first period of time and predicts a state of the electronic device based on extrapolated usage data for the electronic device over the second period of time.

Abstract: A method of electronically supporting a meeting includes, with a computerized agent having a mailbox, receiving a meeting invite from a meeting organizer, the meeting invite including meeting participants, meeting attendance information and a meeting agenda; and with the agent, in response to receiving the meeting invite, automatically communicating with the meeting participants in advance of the meeting to prepare for the meeting. The agent may answer questions during the meeting. A computerized agent includes a bot for supporting a meeting using artificial intelligence to process an electronic recording of the meeting to extract information about the meeting for a meeting organizer or participant. The hot is deployed as a docker containerized service.

Abstract: In an example implementation according to aspects of the present disclosure, a system, method, and storage medium to identify and score security vulnerabilities is disclosed. A memory and a processor receive security-related data from a plurality of client computing devices, create a security score for each the client computing devices. The processor identifies a subset of the client computing devices with security scores surpassing a threshold and remediates a security vulnerability on each of the subset.