Microsoft Technology Licensing, LLC

Oz Solomon

Keith Coleman Herold

Han Yee Mimi Fung

Sergey Aleksandrovich Doroshenko

Christopher Brian Quirk

Erich-Soren Finkelstein

Gary Caldwell

Nathan James Fish

Oussama Elachqar

Nima Mohajerin

Erich Søren Finkelstein

Badal Yadav

Technology is described herein for facilitating a user's interaction with a digital ink document. The technology internally represents the ink document using a data structure having a hierarchy of nodes. The nodes describe respective elements in the ink document. The technology leverages the data structure to identify a set of nodes that grows upon the user's repeated selection of a particular part of the ink document. At each stage of the selection, the technology highlights a set of elements in the ink document that correspond to the current set of identified nodes. According to another illustrative aspect, the technology produces the data structure by modifying an original data structure provided by a text analysis engine. The technology performs this task with the objective of accommodating structured interaction by the user with the ink document.

Selecting content in ink documents using a hierarchical data structure

Ink-processing technology is set forth herein for detecting a gesture that a user performs in the course of interacting with an ink document. The technology operates by identifying a grouping of ink strokes created by the user. The technology then determines whether the grouping expresses a gesture based on a combination of spatial information and image information, both of which describe the grouping. That is, the spatial information describes a sequence of positions traversed by the user in drawing the grouping of ink strokes using an ink capture device, while the image information refers to image content in an image produced by rendering the grouping into image form. The technology also provides a technique for identifying the grouping by successively expanding a region of analysis, to ultimately provide a spatial cluster of ink strokes for analysis.

Detecting ink gestures based on spatial and image data processing

Technology is described herein for parsing an ink document having a plurality of ink strokes. The technology performs stroke-level processing on the plurality of ink strokes to produce stroke-level information, the stroke-level information identifying at least one characteristic associated with each ink stroke. The technology also performs object-level processing on individual objects within the ink document to produce object-level information, the object-level information identifying one or more groupings of ink strokes in the ink document. The technology then parses the ink document into constituent parts based on the stroke-level information and the object-level information. In some implementations, the technology converts the ink stroke data into an ink image. The stroke-level processing and/or the object-level processing may operate on the ink image using one or more neural networks. More specifically the stroke-level processing can classify pixels in the input image, while the object-level processing can identify bounding boxes containing possible objects.

Parsing an ink document using object-level and stroke-level processing

A method for natural language interaction includes recording speech provided by a human user. The recorded speech is translated into a machine-readable natural language input relating to an interaction topic. An interaction timer is maintained that tracks a length of time since a last machine-readable natural language input referring to the interaction topic was translated. Based on a current value of the interaction timer being greater than an interaction engagement threshold, a message relating to the interaction topic is delivered with a first natural language phrasing that includes an interaction topic reminder. Based on the current value of the interaction timer being less than the interaction engagement threshold, the message relating to the interaction topic is delivered with a second natural language phrasing that lacks the interaction topic reminder.

Natural language interaction for smart assistant

A method for use with a computing device is provided. The method may include executing one or more programs of an intelligent digital assistant system at a processor and presenting a user interface to a user. At the processor, the method may include receiving natural language user input from the user, parsing the user input at an intent handler to determine an intent template with slots, populating the slots in the intent template with information from user input, and performing resolution on the intent template to partially resolve unresolved information. If a slot with missing slot information exists in the partially resolved intent template, a loop may be executed at the processor to fill the slots. The method may include, at the processor, determining that all required information is available and resolved and generating a rule based upon the intent template with all required information being available and resolved.

Intelligent assistant with intent-based information resolution

Intelligent assistant systems, methods and computing devices are disclosed for resolving alias identifiers. A method comprises receiving and parsing data comprising a current user input that includes an alias identifier. The data and/or other sensor data are analyzed to identify the user. Based at least on identifying the user and recognizing the alias identifier, usage pattern data comprising at least one previous user input that includes the alias identifier and corresponding context information is accessed. The usage pattern data is used to resolve the alias identifier to mean the alias identifier in an alias record of a known entity. Based at least on resolving the alias identifier, an output device is controlled to one or more of generate a message and perform an action with respect to the known entity.

Alias resolving intelligent assistant computing device

To address the issues of handling conversations with multiple users, an intelligent digital assistant system is provided. The system may include at least one microphone configured to receive an audio input, a speaker configured to emit an audio output, and a processor. The processor may be configured engage in a conversation with a first user, and, concurrent with the first user being engaged in the conversation with the system, recognize speech of one or more additional users in the audio input. The processor may process the recognized speech of the one or more additional users to determine a context for each additional user, and execute a conversation disentanglement module to select and perform one or more predetermined conversation disentanglement actions according to the context of the recognized speech of each additional user.

Intelligent digital assistant system

A system for intelligent cycling of a battery for improved lifetime longevity includes a charge interrupt predictor executable to predict a likelihood of a power source disconnect event interrupting a current flow to the battery within one or more segments of a future time interval. The system also includes a charge cycle model executable to model a charge cycle for the battery within the future time interval based on the charge interrupt prediction. The system further includes a charge cycling controller that controls battery circuitry to charge or discharge the battery in accord with the charge cycle model.

Intelligent battery cycling for lifetime longevity

Intelligent assistant systems, methods and computing devices are disclosed for training a machine learning-based parser to derive user intents. A method comprises analyzing with a feeder parser a surface form of a user input. A user intent underlying the surface form is derived by the feeder parser. The surface form and the user intent are provided to a machine learning-based parser and used to enhance a training set of the machine learning-based parser.

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Maple, Canada

Oz Solomon