Microsoft Technology Licensing, LLC

Jenna Hong

Benjamin Gustav Wilde

Ransom Lloyd Richardson

Michael Adam Scarpati

Courtney Sarah Cochrane

Jyh-Han Lin

Vivek Gupta

Sheng Yi

Biyi Fang

Aleksandr Polyakov

Paul Anthony Scudieri

Theo Lorrain-Hale

Natalie Ann Mionis

Ehimwenma Nosakhare

Gilbert Antonius

Lucas Rosenblatt

Mawulolo Koku Ameko

Yang He

Jichen Yang

Sean Gormley T Kelley

Anish Yatin Pimpley

Katie Nicole Rotella

Ricardo Reyna Fernandez

Susanne Duswald

Ryan Serrao

Ashish Neupane

Apurva Sandeep Gandhi

Kabir Walia

Abhijith Asok

Yiquan Xu

Tra My Nguyen

Moiz Shabbir Ghadiyali

Amit Dinesh Gupte

Yang Wang

Omar Basim Tuffaha

Alexey Romanov

Ryan Michael Lawrence

Christina Rose Long

Emili Koui-miclette

A computer-implemented method for proactively detecting and remediating anomalous devices includes accessing, via a network, device attributes corresponding to enterprise devices within an enterprise network, providing the device attributes to a supervised machine learning model, and predicting, via the supervised machine learning model, whether each enterprise device is healthy or anomalous, where the enterprise device is predicted to be healthy unless the supervised machine learning model determines that the probability of the enterprise device being anomalous exceeds a specified confidence threshold. The method includes, for each enterprise device that is predicted to be anomalous, perturbing a portion of the corresponding device attributes via an automated counterfactual generator to generate synthetic data representative of counterfactual healthy devices. The method includes generating recommended remedial action(s) that will cause each enterprise device to approximate each counterfactual healthy device and causing surfacing, via a user interface, of the recommended remedial action(s).

Proactively detecting and remediating anomalous devices using supervised machine learning model and automated counterfactual generator

A data processing system implements and iterative process for dynamically creating a template including receiving a request for a template suggestion for an electronic content item for an application on a client device. The request includes an identifier of the application and textual content from the first electronic content item. The system further implements providing the identifier of the first application and the textual content to a language model to obtain a template suggestion, the language model being trained to analyze the identifier of the application and the textual content and to output the template suggestion, the template suggestion comprising a template identifier and a first subsection identifier identifying a first subsection of the first template predicted to be relevant to the user for creating the electronic content item, sending the template suggestion to the client device, and causing the client device to present the template suggestion in the application.

Real-time artificial intelligence powered dynamic selection of template sections for adaptive content creation

The disclosed technology is generally directed to the conversion of text to tables. In one example of the technology, input text that includes at least three rows is received. A plurality of characteristics of the input text is determined. Each characteristic of the plurality of characteristics is associated with a uniformity between the rows of the input text. The plurality of characteristics includes at least one characteristic that is associated with a delimiter count. A determination is made as to whether the input text is suitable for conversion to table based on the plurality of characteristics. Upon determining that the input text is suitable for conversion to a table, a machine learning model is used to convert the input text into a table.

Intelligent table suggestion and conversion for text

A data processing system implements receiving a first textual prompt from a first user via a first user interface of a first client device, the first textual prompt includes a textual description of content to be generated by a generative model; providing the first textual prompt as a first input to the generative model to obtain as an output first collaborative content based on the first textual prompt; receiving an edited version of the first collaborative content from the first client device that includes edits to the first collaborative content provided by the first user; providing the edited version of the first collaborative content as a second input to the generative model to obtain as an output second collaborative content based on the edited version of the first collaborative content; and causing the first client device to present the second collaborative content on the first user interface.

Collaborative coauthoring with artificial intelligence

A computer system is provided that includes one or more processors configured to receive user input for inked content to a digital canvas, and process the inked content to determine one or more writing regions. Each writing region includes recognized text and one or more document layout features associated with that writing region. The one or more processors are further configured to tokenize a target writing region of the one or more writing regions into a sequence of tokens, process the sequence of tokens of the target writing region using a task extraction subsystem that operates on tokens representing both the recognized text and the one or more document layout features of the target writing region, segment the target writing region into one or more sentence segments, and classify each of the one or more sentence segments as a task sentence or a non-task sentence.

Sequence labeling task extraction from inked content

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Acton, MA, United States of America

Jenna Hong