Microsoft Technology Licensing, LLC

Hyperproof Inc.

David John Brennan

Hubert Van Hoof

David Gray

John M Sanders

Vidyaraman Sankaranarayanan

Ruchita Bhargava

Nicholas Wrem

Roberto Sonnino

Michael T Daly

Murali R Krishnan

Darren Mitchell

Grégory Christopher John Vandenbrouck

Ram Viswanathan

Jun Yin

Bharat Shyam

Scott E Seiber

Brett A Tanzer

James Francis Gilsinan, Iv

Andres Carlo Petralli

Naveen K Kachroo

Philip Peter Reunert

Todd D Ostermeir

Carol R Rall

Rajesh Ramanathan

Andrew Robert Miller

Neeraj Garg

Andrew James Peacock

Brent Gilbert

Vlad Riscutia

Indrojit Deb

Daniel Escapa

Robert Marcus Girling

Aditi Desai

Igor V Grebnev

Anoop Anantha

Stefan Krzysztof Gawrys

Se-Wai Lee

Jennifer Ruth Mead

Nicholas Paul Duane

Craig Howard Unger

Alina Mihaela Stanciu

Robert Moodie Heddle

Eric L Van Doren

Stuart H Marshall

Anson Ho

Patrick James Gorman

Stéphane Morichére-Matte

Aryamman Jain

Priya Murali

Yurui Zhou

Alexander Vorobiev

Michael Gene Leu

Aaron Lo

Isaac Supeene

John R Sanders

David M Gray

David Michael Gray

Andrew J Peacock

Described herein is a system and method of processing data of a digital twins object model. Data associated with a node of the digital twins object model is parsed to identify metadata associated with the node. The data can comprise telemetry data received from an IoT device associated with the node. User-defined function(s) are determined that match the identified metadata. The data and metadata can be provided to the user-defined function(s). The determined user-defined function(s) are executed. Also described herein is a method of creating a user-defined function for processing data of a digital twins object model. Information regarding business logic is received from a user defining a user-defined function. Information regarding telemetry condition(s) to which the user-defined function applies is received defining a matcher. The user-defined function and matcher are stored as objects within a spatial intelligence graph associated with the digital twins object model.

Individualized telemetry processing leveraging digital twins property(ies) and topological metadata

Computer-implemented functionality is described herein for assisting a user in interacting with an environment. In one implementation, the functionality operates by: determining that a particular user is within a prescribed proximity to an electronic sign, or will be in prescribed proximity to the electronic sign, based on sensor information provided by one or more sensors within the environment; determining an identity of the user; determining at least one destination of the user based at least on calendar information; providing directions to the destination(s), to provide unfiltered direction information; optionally filtering the unfiltered direction information with respect to restriction information (e.g., privacy information, security information, etc.), to produce filtered direction information; composing sign information based on the filtered direction information; and sending the sign information to the electronic sign for presentation by the sign.

Presenting context-based guidance using electronic signs

A natural language query can be received at a main natural language processor inside a data protection compliance boundary in a computer system. The natural language query can be processed via the main natural language processor. The data from the query can be transmitted to a data protection computer component. It can be determined via the data protection component whether the data from the query includes protected data whose transmission to a computerized extension would violate a computer-readable data protection rule. The extension is outside the compliance boundary in the computer system and is configured to participate in responding to natural language queries received by the main natural language processor. In response to determining that the data from the query includes the protected data, transmission of the protected data from the main natural language processor to the extension as part of responding to the query can be inhibited.

Natural language processor extension transmission data protection

A receiver receives a request to schedule a meeting from a user and in response, prompts the user to provide meeting information including time information and attendee information for scheduling the meeting. A meeting scheduler identifies potential dates and potential times based on the meeting information. The meeting scheduler further detects locations of the attendees based on the potential dates and identifies potential meeting locations based on attendees' location. The meeting scheduler also computes date scores for the potential dates, time scores for the potential times, and location scores for the meeting locations to select a date, a time, and a meeting location based on scores to determine a meeting schedule. The meeting scheduler further selects a conferencing mechanism for the meeting and provides the meeting schedule comprising the selected date, time, and meeting location, and the selected conferencing mechanism as a meeting invite option to the user.

Automated meeting scheduler

Isolating resources between sub-entities. A method includes receiving data from a particular connected device. A memory storing a hierarchical graph that defines a topology for an entity is accessed. Branches or leaves in the graph include a node that represents a connected device, such as a sensor, controller, or computing system. Each connected device is configured to provide data or receive control signals. Each of the branches or leaves can be indicated as belonging to a particular sub-entity. A particular branch from the hierarchical graph having the particular connected device is identified using the graph. A sub-entity to which the particular branch belongs is identified. The method identifies that the particular sub-entity should be isolated from other sub entities. The data from the particular connected device is provided to a set of resources specifically allocated for the particular sub-entity.

Nested tenants

A program model that includes requirements and control objects associated with requirements, workflows, proof assets. Workflow tasks associated with the workflows may generate requests to collect each proof asset associated with the control objects. Workflow tasks may validate each collected proof asset based on the plurality of control objects associated with each collected proof asset. Workflow tasks may determine completed control objects based on the validated proof assets. Workflow tasks may employ an amount of the completed control objects that is equivalent to the amount of the control objects associated with the program model to provide a report indicating that the requirements associated with the program model may be satisfied. Workflow tasks may employ detection of modifications to the validated proof assets to determine a portion of the workflow tasks based on the modified proof assets such that the portion of the workflow tasks may be re-executed.

Managing proof assets for validating program compliance

A natural language query can be processed via a main natural language processor. A request to produce an intent of the query can be dispatched from the main natural language processor to an extension natural language processor. An intent of the query can be generated via the extension natural language processor in response to the dispatched request, with the generating of the intent being performed independently of the main natural language processor. The intent of the natural language query can be passed from the extension natural language processor to the main natural language processor in response to the request to produce the intent of the query. A selection of a conversation query processor for responding to the intent can be produced via the main natural language processor. Additionally, an instruction to respond to the intent of the query can be passed to the selected conversation query processor.

Computerized natural language query intent dispatching

A technique is described for efficiently detecting a wireless beacon signal emitted from a beacon-emitting mechanism, and thereby reducing consumption of power. The technique operates by selectively increasing detection activity when a detection event is likely to occur, and decreasing it otherwise. The technique leverages calendar information as one factor in determining when a detection event is likely to occur. The technique can also vary the detection activity based on location information (identifying the location of a user) and/or motion information (describing the motion of the user).

Detecting a wireless signal based on context

Systems and methods for generating and broadcasting a dynamic identifier are presented. Based on the current context and rules established for accessing personal information, a dynamic identifier corresponding to a computer user is dynamically generated and provided to a computing device corresponding to the computer user. The computing device may broadcast the dynamic identifier to others. Upon receipt of a dynamic identifier, a party wishing to determine information regarding the broadcasting party must query an information service with the dynamic identifier. According to a set of rules and/or permissions established for the target computer user (broadcasting the dynamic identifier), accessible information is identified and returned to the requesting party. Additionally, a user is provided with the ability to edit, add to, and/or delete information that is stored by the identification service regarding the user on behalf of a submitting party.

User-management of third-party user information

Policies are applied to specific data rather than to an entire computing device that contains the specific data. Access to the specific data is controlled by the policies utilizing various password or other authentication credential requirements, selective data caching, data transmission, temporary data storage, and/or pre-defined conditions under which the specific data is to be erased or rendered inaccessible. Policies may be defined by an administrator and pushed to a mobile computing device, whereat the policies are enforced.

Securing data usage in computing devices

Broadcasts from beacon devices each of which is disposed in a known different physical location within a venue are controlled. Scheduling information for events scheduled to take place at the venue may be collected from calendar management sources and analyzed. Notification of an emergent event associated with the venue or its geographic location may also be received from emergent event notification sources and analyzed. The result of these analyses and the knowledge of the physical location of each of the beacon devices are used to generate a set of broadcast rules for each of the beacon devices, where these rules specify one or more beacon signals to be periodically broadcast by the beacon device and the timing by which these signals are to be broadcast. For each of the beacon devices, the set of broadcast rules for the beacon device is downloaded to the beacon device.

Event-triggered control of beacon broadcasts

The described technology provides a system and method for dynamically managing a listen list for beacon aware devices. A mobile device may be configured to listen for one or more beacon UUIDs based on a listen list. A contextual change detected by the mobile device may trigger a collection of additional contextual information and update to the listen list based on the additional contextual information. Using the described technology, the mobile device may use a limited set of beacon UUIDs to improve micro-location granularity and enhance the mobile device's resources.

Dynamically managing a listen list of beacon aware devices

A system for periodic and real-time processing of accounts. The system operates in a bulk mode and an error mode. A bulk component concurrently processes a set of eligible accounts with a set of dependent tasks, and a removal component removes one or more accounts from the eligible accounts if an error is associated therewith. In bulk mode, each task fetches only the required set of accounts/subscriptions/payment instruments for processing. In error mode (retry/reprocess mode), if an account encounters an error for a task in the bulk mode, the account drops is removed from bulk mode and becomes ineligible for fetching in any future bulk-mode task processing. Error-mode processing tests and processes all tasks sequentially for an errored account until the account state is in par with the accounts being processed by bulk mode. Then the account is removed from error mode processing and is picked up appropriately by bulk-mode processing.

Account related task processing

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Redmond, WA, United States of America

David John Brennan