Microsoft Technology Licensing, LLC

Shahin Shayandeh

Steven Wayne Ickman

Christopher Clayton McConnell

William L Portnoy

Lars Hasso Liden

Jitendra Dattatraya Padhye

Sharad Agarwal

Ratul Mahajan

Lenin Ravindranath Sivalingam

Swadheen Kumar Shukla

Ian Obermiller

Matthew D Mazzola

Robert Lovejoy Goodwin

John Anthony Taylor

Weipeng Liu

Jianfeng Gao

Aleksandar Uzelac

Hai Liu

Ronnie Chaiken

Adina Magdalena Trufinescu

Khuram Shahid

Thomas Park

Eslam K Abdelreheem

Adarsh Sridhar

Janice Von Itter

Sirius Kuttiyan

Christopher M Moore

This document relates to creating and/or updating a chatbot using a graphical user interface. For example, training dialogs for a chatbot can be displayed in a tree form on a graphical user interface. Based at least on interactions between a developer and the graphical user interface, the training dialogs can be modified in the tree form, and training dialogs can be updated based on the modifications provided on the tree form via the graphical user interface.

Visualization of training dialogs for a conversational bot

Methods and systems are disclosed for improving dialog management for task-oriented dialog systems. The disclosed dialog builder leverages machine teaching processing to improve development of dialog managers. In this way, the dialog builder combines the strengths of both rule-based and machine-learned approaches to allow dialog authors to: (1) import a dialog graph developed using popular dialog composers, (2) convert the dialog graph to text-based training dialogs, (3) continuously improve the trained dialogs based on log dialogs, and (4) generate a corrected dialog for retraining the machine learning.

Training a user-system dialog in a task-oriented dialog system

Systems and methods are disclosed for transitioning communication sessions across services. In one implementation, a first communication is received from a user via a first service. The first communication is associated with a communication session. The first communication is processed to identify a request with respect to a second service. The second service is initiated in response to the request.

Transitioning communication sessions across services

Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes ('sub POIs') can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Efficient denormalization of data instances

Methods, systems, and computer program products for dispatching an incoming event are described. A scoring stack is accessed, the scoring stack comprising an identity of one or more tasks, each task corresponding to one or more scorable functions. A scorable tree is generated based on the one or more scorable functions corresponding to the one or more tasks of the scoring stack and the incoming event is processed using the scorable tree to generate one or more scores. One or more actions identified in the scorable tree are performed based on the one or more scores.

Dynamic event processing

Methods, systems, and computer program products for dispatching an incoming event are described. A scorable tree is obtained and the incoming event is submitted to a root node of the scorable tree for processing. The incoming event is augmented with scoring data generated by the root node to create a scoring event and the scoring event is iteratively passed from each parent node of the scorable tree to each corresponding child node of the scorable tree for further processing until each leaf node of each branch of the scorable tree is reached, wherein each child node augments an incoming scoring event with additional scoring data and each leaf node generates an intermediate scoring event. Each intermediate scoring result is iteratively passed from each child node of the scorable tree to each corresponding parent node, where each child node produces a scoring result, the scoring result comprising an identification of a path to one or more selected child nodes and final scoring information based on data received from one or more corresponding child nodes.

Event processing using a scorable tree

Aspects of the subject disclosure are directed towards monitoring application performance during actual use, particularly mobile application performance. Described is instrumenting mobile application binaries to automatically identify a critical path in user transactions, including across asynchronous-call boundaries. Trace data is logged by the instrumented application to capture UI manipulations, thread execution, asynchronous calls and callbacks, UI updates and/or thread synchronization. The trace data is analyzed to assist developers in improving application performance.

Monitoring mobile application performance

Methods, computer systems, and computer-storage media are provided for connecting devices. Shared spaces may be created among one or more users and/or devices. Based on various factors including locations, histories among users, time of day, etc., spaces may be created among users such that content may be shared within the shared space. The spaces may be created using multiple technologies such that one user may connect to the space via a first identifier format while a second user may connect to the space via a second identifier format. Once created, content may be shared with one or more users of the space. Depending on privacy and/or security settings of the space, any user may invite other users to join the space. The content may be available to any participant once the space has terminated.

Creating connections and shared spaces

Various technologies pertaining to computing data that is indicative of a location of a client computing device are described herein. A client computing device is configured to capture an audio signal, the audio signature being indicative of acoustics of surroundings of the client computing device. A signature is generated based upon a high frequency portion of the captured audio signal, and the signature is compared with other signatures. The other signatures are generated based upon high frequency portions of audio signals captured by other computing devices. A determination regarding the client computing device being co-located with a second client computing device is made based upon the comparison of the signature with the other signatures.

Proximity discovery using audio signals

Methods and systems for dynamically providing application analytic information are provided herein. The method includes inserting instrumentation points into an application file via an application analytic service and dynamically determining desired instrumentation points from which to collect application analytic data. The method also includes receiving, at the application analytic service, the application analytic data corresponding to the desired instrumentation points and analyzing the application analytic data to generate application analytic information. The method further includes sending the application analytic information to a client computing device.

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Seattle, WA, United States of America

Shahin Shayandeh