Microsoft Technology Licensing, LLC

Enrico Cadoni

James Cogley

Olivier Gauthier

Shashank Shekhar Gupta

Domenic J Cipollone

Kaushik Ramaiah Narayanan

Zhang Li

Michael Wilson Daniels

Alessandro Curzi

Siqing Chen

Alice Yingming Lai

Andrew Donohoe

Davide Turcato

Bhavuk Jain

Colin Laird

Aman Singh

A computer implemented method includes receiving text corresponding to a span of text. Language detection is performed on the received text to generate multiple language candidates. The received text is also proofed over multiple languages to generate multiple language proofing critiques. A most likely language is generated for the received text as a function of the language candidates and the multiple language proofing critiques.

Multilingual text proofing

Systems and methods are directed to providing personalized text proofing. A user model that is used to personalize generic critiques for text proofing a document is generated based on user signals indicating past user actions. During runtime of an application used to create the document, the user model is accessed and locally cached. User inputs comprising typed components used to create the document are received and a set of one or more generic critiques for the user inputs is accessed from an editor system. The user model is applied to the set which may modify a generic critique of the set. The modifying of the generic critique can cause the generic critique to be automatically applied or suppressed at the client device. The set including the modified generic critique is transmitted to a user device, whereby the user device applies the set to the document including automatically applying or suppressing the modified generic critique.

Personalized text proofing using distributed services

A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Context adaptive writing assistant

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

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Dublin, Ireland

Enrico Cadoni