Microsoft Technology Licensing, LLC

Docugami, Inc.

Hydramaster Corporation

Michael Brian Palmer

Kenneth Fern

Andrew Paul Begun

Marcin Sawicki

Jean Paoli

Taqi Jaffri

Michael Taron

Zubin Rustom Wadia

Christina Pavlopoulou

Xiaoquan Zhou

David Watson

Yue Zhang

Zhaofeng Wu

Jerome George Paliakkara

Zhanlin Liu

Andrew Minh Hoang

Kush Gupta

Joshua Yongshin Fan

Eric White

Bhaven Toprani

Manar Shehadeh

Steven DeRose

Swagatika Sarangi

Elena Pricoiu

David Paul Limont

Luis Marti Orosa

Todd D Newman

Joseph Patrick Masterson

Pamela Bhattacharya

Jeremy De Souza

Elena Catrinescu

Nathan Waddoups

Jin Ma

Justin Brooks Cranshaw

Mayerber Loureiro De Carvalho Neto

Benjamin Gene Cheung

Charles Yin-che Lee

Alessandro Catorcini

Anand Ramagopalrao

David Lloyd Meyers, Jr

Jean-Marie H Larcheveque

Russell L Simpson, Jr

Laurent Mollicone

Michael A Smuga

Charles Yin-Che Lee

Betsy McIntyre

David L Meyers

Mirela D S Correa

Erik Bergman

Weiye Zhang

Mirela Correa

Betsy Yu-pui McIntyre

Oscar Papel

Wayne Eric Boone

Nima Mirzad

Emad Mohamed Hamdy Elwany

Avrum Stephen Laurie

Betsy Y McIntyre

Todd Daniel Newman

David Lloyd Meyers

Russell L Simpson

Mirela DS Correa

Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called 'context', to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set). Similarity is not limited to exact or fuzzy string or property comparisons, but may include similarity of natural language grammatical structure, ML (machine learning) techniques such as measuring similarity of word, chunk, and other embeddings, and the datatypes and semantic roles of previously-identified chunks.

Cross-document intelligent authoring and processing, with arbitration for semantically-annotated documents

Enabling flexible processing of semantically-annotated documents

Automatically identifying chunks in sets of documents

A software agent, that is used to assist in providing a service, receives communications from a set of users that are attempting to use the software agent. The communications include communications that are interacting with the software agent, and communications that are not interacting with the software agent. The software agent performs natural language processing on all communications to identify such things as user sentiment, user concerns or other items in the content of the messages, and also to identify actions taken by the users in order to obtain a measure of user satisfaction with the software agent. One or more action signals are then generated based upon the identified user satisfaction with the software agent.

Signal analysis in a conversational scheduling assistant computing system

Automatically assigning semantic role labels to parts of documents

Assisting authors via semantically-annotated documents

Cross-document intelligent authoring and processing, including format for semantically-annotated documents

Inline document collaboration workflows are provided. When a user receives an electronic communication that includes an attached content item, the receiving user may select the attached content item and cause the attached content item to be displayed in an immersive view in proximity to an electronic communication pane in which an electronic communication may be conducted about any topic including the content item that is displayed in proximity to the electronic communication pane. During editing of a content item, an electronic communication may be generated from a user interface in which the content item is being generated, or alternatively a new content item may be generated out of an electronic communications user interface.

Generating content items out of an electronic communication workflow

Electronic communication-based storage of content items to support multiple workflows is provided. When an attached content item is received with an electronic communication, a receiving user may select the attached content item for review and editing in an immersive view pane that may be launched adjacent to or in proximity to an electronic mail viewing pane. When the received content item is edited, a copy of the edited content item is temporarily stored with the electronic communication with which the content item was received at an associated electronic communication server. When the receiving user then desires to dispose of the edited content item, for example, by replying back to the sending user with the edited content item as an attachment, the edited content item may be retrieved from the electronic communication server automatically for attachment to the responsive communication.

Electronic communication-based storage of documents to support multiple workflows

A set of nodes are organized into a graph to represent a workflow to enable the dynamic and directed management of that workflow in a decentralized system. Each node maintains a value necessary for execution of the workflow, and includes code to populate that value. A workflow agent manages the population of the values according to an identified dependency structure for the nodes relative to the workflow. As changes are made to the workflow, the workflow agent ensures that values and dependencies of the nodes stay up-to-date. Each node retains historic values, which enables the workflow agent to query several states of the workflow throughout time as changes are made thereto. The dynamic management of the nodes improves the responsiveness of the system to changes, thereby improving computational efficiency.

Dynamic directed graph workflows

Enhanced links in a message composition surface are described. A uniform resource locator (URL) may be identified while a user is composing a message in a message composition surface of an electronic message such as an email. A source of the URL and the file type of the document linked to by the URL can be determined and a preview of the document can be rendered in the message composition surface. When the source is a cloud storage location, access permissions for the document can be adjusted from within email&#x2014;without the user navigating to a file management site of the cloud storage. When the file type is a productivity application, a link rendering element can call a productivity application service to provide interaction functionality for the productivity application file and facilitate the rendering of the productivity application file.

Document link previewing and permissioning while composing an email

An adaptive layout and workflow engine. The adaptive layout and workflow engine allows various applications to dynamically render scaled views of content based on the characteristics of the display on the device being used to consume the content. The adaptive layout and workflow engine may select an appropriate view of the content to render based on the size, resolution, or aspect ratio of the display. The rendered size of the content and/or the user interface of the content application is evaluated relative to the characteristics of the display. Embodiments may provide different controls via the user interfaces based on the characteristics of the display and/or content. The adaptive layout and workflow engine may provide customized work flows via the user interface to improve the user experience depending on the display capabilities of the device used to consume the content.

Immersive document interaction with device-aware scaling

Growing community of inventors

Edmonds, WA, United States of America

Michael Brian Palmer