Microsoft Technology Licensing, LLC

Siqing Chen

Zhang Li

Christine Lauren Mayer

Bhavuk Jain

Cheng Yong Yang

Ransom Lloyd Richardson

Rui Hu

Domenic J Cipollone

Tomasz Lukasz Religa

Kaushik Ramaiah Narayanan

Max Wang

Michael M Santos

Huitian Jiao

Michael Wilson Daniels

Olivier Gauthier

Jesse Alexander Freitas

Enrico Cadoni

Alice Yingming Lai

Mahaveer Bhavarlal Kothari

Yimeng Li

Warren Anthony Aldred

Alyssa Rachel Mayo

Cheng Yang

Jichen Yang

Tashfeen Ahmed

Tejprakash Singh Gill

William Nain Saez

Pramod Kumar Gupta

Sidhika Varshney

Weixin Cai

Christie Chan

Joe A Herzog

Utkarsh Garg

Tracy A Childers

Saket Kumar

Jinnie Park

Olivier Michel Nicolas Gauthier

Elijah John Scherrer

Yash Ravi Punjabi

Aleksey Aleksandrovich Sokolov

Raymond C Li

Juan Antonio Karmy Tacla

Priya Tushar Nakhre

Anshul Basia

David Milićević

A data processing system includes: a processor; a memory containing programming instructions for execution by the processor; and a network interface for communicating with an Artificial Intelligence (AI) engine. The programming instructions include an application for generating written content, the application having a function to generate and submit a structured query regarding the written content to the AI engine to generate feedback on an assessed quality of the written content, the structured query structured to prompt for feedback in a variety of specified categories for the written content. The application further includes a user interface to display the feedback on the written content and provide an option to a user to implement the feedback to revise the written content.

Systems and methods for writing feedback using an artificial intelligence engine

A method and system for providing tone detection for a content may include receiving a request to detect a tone for a content, retrieving user data and data about the content, detecting a content environment for the content based on at least one of the user data and the data about the content, detecting the tone for the content based on the content and the content environment, inputting the content and the detected tone into a machine-learning (ML) model for modifying the tone from the detected tone to a modified tone, obtaining at least one rephrased content segment as an output from the ML model, the rephrased content segment modifying the tone of the content from the detected tone to the modified tone, and providing at least one of the detected tone or the at least one rephrased content segment for display.

Automatic tone detection and suggestion

An editor service receives a textual input. The editor service provides the textual input to a rule-based grammar checker to obtain a grammar checker result. The editor service also provides the textual input to a machine learning (ML) fluency model that checks the textual input for errors and provides a ML model result. The editor service aggregates the grammar checker result and the ML model result and generates an editor service output based upon the aggregated results. A representation of the editor service result is provided to the client computing system for surfacing through a user interface.

Using editor service to control orchestration of grammar checker and machine learned mechanism

Methods, systems, and computer storage media for providing action-recommendations (e.g., save action, collaboration action, and upload action) using cloud system operations in a cloud system. For example, action-recommendations are predicted using usage data of applications and communicated to a user (e.g., via a prompt) such that the user elects to perform or not perform the action-recommendation. In operation, usage data for an application and an action-recommendation profile are accessed. Determining that an action-recommendation should be generated is based on the usage data and the action-recommendation profile. The determination is further based on action scores and thresholds (e.g., save action, collaboration action, upload action). When the action scores meet certain thresholds, the action-recommendation is generated as a save-action-recommendation, a collaboration-action-recommendation, or an upload-action-recommendation. Based on generating the action-recommendation, an action recommendation interface element (e.g., an action prompt or action dialog) is generated to perform a corresponding action.

Prediction-based action-recommendations in a cloud system

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Siqing Chen