Abstract: Computer servers configured to perform digital image processing are discloses herein. In one embodiment, upon receiving a transcription command, a computer server performs text recognition based on patterns in a digital image to generate digital text data corresponding to the digital image. The computer server can then determine a content format of the digital text data based on the imported digital image and automatically apply the determined content format to the generated digital text data. The digital data can then be inserted into an electronic document.

Abstract: A method for data structure modification is described. First data structures that represent user interactions with first user content by a user of a client device are obtained. The first data structures are labeled using content features of the first user content. A neural network model is trained using the labeled first data structures to obtain user-specific weights for the user of the client device. A second data structure that represents user interactions with second user content by the user is received. A predicted value for the second data structure is obtained based on an output of the trained neural network model using content features of the second user content and the user-specific weights as inputs to the trained neural network model. The second data structure is modified to include the predicted value when the second data structure is inconsistent with the predicted value.

Abstract: Computer servers configured to perform digital image processing are discloses herein. In one embodiment, upon receiving a transcription command, a computer server performs text recognition based on patterns in a digital image to generate digital text data corresponding to the digital image. The computer server can then determine a content format of the digital text data based on the imported digital image and automatically apply the determined content format to the generated digital text data. The digital data can then be inserted into an electronic document.

Abstract: A messaging system configured for recommending messages is provided. The messaging system machine trains a prediction model associated with a user based on past review and response times for past messages. During runtime, the messaging system generates a predicted user action time for each new message based on the prediction model and tags each new message with a corresponding predicted user action time. Based on an amount of available time, the predicted user action time for each new message, and importance for each new message, the messaging system determines a recommended set of new messages that the user should read and respond to during the available time, whereby a sum of the predicted user action time of the recommended set of new messages is equal to or less than the available time. The recommended set of new messages are visually distinguished for the user.

Abstract: Computer servers configured to perform digital image processing are discloses herein. In one embodiment, upon receiving a transcription command, a computer server performs text recognition based on patterns in a digital image to generate digital text data corresponding to the digital image. The computer server can then determine a content format of the digital text data based on the imported digital image and automatically apply the determined content format to the generated digital text data. The digital data can then be inserted into an electronic message and transmitted with the inserted typeset digital text data to a destination.

Abstract: Computer servers configured to perform digital image processing are discloses herein. In one embodiment, upon receiving a transcription command, a computer server performs text recognition based on patterns in a digital image to generate digital text data corresponding to the digital image. The computer server can then determine a content format of the digital text data based on the imported digital image and automatically apply the determined content format to the generated digital text data. The digital data can then be inserted into an electronic message and transmitted with the inserted typeset digital text data to a destination.

Abstract: A messaging system configured for recommending messages is provided. The messaging system machine trains a prediction model associated with a user based on past review and response times for past messages. During runtime, the messaging system generates a predicted user action time for each new message based on the prediction model and tags each new message with a corresponding predicted user action time. Based on an amount of available time, the predicted user action time for each new message, and importance for each new message, the messaging system determines a recommended set of new messages that the user should read and respond to during the available time, whereby a sum of the predicted user action time of the recommended set of new messages is equal to or less than the available time. The recommended set of new messages are visually distinguished for the user.

Abstract: Various embodiments of the present technology generally relate to systems and methods for self-healing services and automatic recovery of distribute systems. Some embodiments of the present technology leverage all the available synthetic, customer, client, server, support signals from various sources to intelligently and in real-time detect outages, root cause outages to recoverable targets (e.g., for auto recovery actions), identify the right engineering teams (e.g., for faster manual mitigation), and perform the appropriate recovery action (such as recycle service, reboot server, switch out a faulty rack) or other mitigation actions such as routing, collecting debug information, alerting to the right team, or alert suppression. Some embodiments separate signal monitoring and workflow coordination.

Abstract: Various embodiments of the present technology generally relate to systems and methods for self-healing services and automatic recovery of distribute systems. Some embodiments of the present technology leverage all the available synthetic, customer, client, server, support signals from various sources to intelligently and in real-time detect outages, root cause outages to recoverable targets (e.g., for auto recovery actions), identify the right engineering teams (e.g., for faster manual mitigation), and perform the appropriate recovery action (such as recycle service, reboot server, switch out a faulty rack) or other mitigation actions such as routing, collecting debug information, alerting to the right team, or alert suppression. Some embodiments separate signal monitoring and workflow coordination.