Microsoft Technology Licensing, LLC

Docugami, Inc.

Andrew Paul Begun

Michael Brian Palmer

Jean Paoli

Marcin Sawicki

Taqi Jaffri

Michael Taron

Zubin Rustom Wadia

Christina Pavlopoulou

Manar Shehadeh

Zhanlin Liu

Jerome George Paliakkara

Zhaofeng Wu

Yue Zhang

Bhaven Toprani

Xiaoquan Zhou

Elena Pricoiu

Eric White

David Watson

Steven DeRose

Swagatika Sarangi

Joshua Yongshin Fan

Kush Gupta

Andrew Minh Hoang

Luis Marti Orosa

Laurent Mollicone

Alessandro Catorcini

Joseph Spencer King

Aaron Naoyoshi Sheung Yan Woo

Shawn M Thomas

Michael Burt Goulding

David A Braun

Alan Bennett Auerbach

Andrew Haon

Thomas R Lawrence

Todd Myles Derksen

Bernardo Iturriaga Dubost

Shai Guday

Joseph H Matthews, Iii

Sharad Agarwal

Raman Kumar Sarin

Ned Bearer Friend

Joseph Alton Schrader

Michael A Smuga

Scott M Roberts

John Yovin

Ted Tai-Yu Chen

Stephen John Mooney

John D Skovron

Joshua S Bell

Willson Kulandai Raj David

Jun Jin

Jennifer Anne Karr

Nima Mirzad

Willson Kulandai Raj

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

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

In embodiments of restricted execution modes, a mobile device can display a device lock screen on an integrated display device, and transition from the device lock screen to display a shared space user interface of a shared space. The transition to display the shared space user interface is without receiving a PIN code entered on the device lock screen. The mobile device implements a restricted execution service that is implemented to activate a restricted execution mode of the mobile device, and restrict access of a device application to device content while the restricted execution mode is activated. The restricted execution service can also allow a shared device application that is included in the shared space access to the device content while the restricted execution mode is activated.

Restricted execution modes

In embodiments of mobile device child share, a mobile device can display a default device lock screen on an integrated display device, and receive an input effective to transition from the default device lock screen to display a child lock screen without receiving a PIN code entered on the default device lock screen. The mobile device can receive a second input effective to transition from the child lock screen to display a child space. The mobile device implements a device share service that activates a child share mode of the mobile device, and restricts functionality of device applications and access to device content based on designated restriction limits.

Mobile device child share

Various systems and techniques are disclosed relating to dynamic storage of documents. According to one or more exemplary systems and techniques, a location may be dynamically determined from a group of locations to store a state associated with a document based on one or more criteria.

Dynamic storage of documents

Systems and/or methods that enable an electronic form to provide data external to the electronic form in response to a query while offline with respect to a data source from which the data originated are described. These systems and methods may also, in one embodiment, receive data from a data source that is capable of being inaccessible and associate that data with an electronic form, a user, and a query. These systems and methods may, in another embodiment, provide data in response to a query made by a user to an electronic form that was previously associated with the electronic form, the user, and the query.

Query to an electronic form

Systems and methods enabling creation and/or use of an electronic form capable of allowing a user to select from mutually exclusive options without the electronic form being in an invalid state are described. One of the described electronic forms remains valid to its schema by atomic swapping of data substructures corresponding to the form's mutually exclusive options. A method and a user interface for creating some of these electronic forms are also described.

Mutually exclusive options in electronic forms

Various embodiments are disclosed relating to a compression of documents. According to an example embodiment, a document may be compressed by replacing one or more language constructs in the document with a language-based replacement code. In addition, the document may be compressed by replacing one or more text strings in the document with a schema-based replacement code.

Compression of structured documents

An upgrade module is used to convert arbitrary XML documents into a form compatible with the processing expectations of a particular processing mechanism. In one implementation, the upgrade module can be used to convert XML documents pertaining to an earlier version of the processing mechanism to a form compatible with a later version of the processing mechanism. The processing mechanism can pertain to a solution module that transforms an input XML document into an electronic form. The upgrade module can be automatically generated when a user makes changes to the electronic form in a manner that affects the schema of the electronic form, or which affects other aspects of the electronic form. The upgrade module can be implemented as an Extensible Stylesheet Language Transformation (XSLT) file.

Conversion of structured documents

A first data source has a plurality of nodes each corresponding to a respective piece of a form template. Each piece of the form template has one of more dependencies to the correspond node of the first data source. Dependencies can be bindings or validation of data. A second data source has a plurality of nodes. Differences are found between the first and second data sources by comparing each node in the first data source with a corresponding node in the second data source. The differences can be as to type, cardinality, name, or a movement, removal or addition of a node. The differences are used to update the dependencies of each piece of the form template to each node of the first data source. Each of the first and second data sources can be a document expressed in a markup language or in a web service definition language.

Form template data source change

A designer uses a forms designer application to build electronic forms from hierarchical data. Displays of hierarchical data, facilitation of selection of a portion of the hierarchical data, and displays of one or more suggested transformation-language components associated with a selected portion of hierarchical data are described. From the transformation-language components selected by a designer, generation of an electronic form is also described.

Building a view on markup language data through a set of components

Growing community of inventors

Redmond, WA, United States of America

Andrew Paul Begun