Microsoft Technology Licensing, LLC

Jaideep Sarkar

Olivier Colle

Jianfeng Gao

Xinying Song

Jinchao Li

Hui Su

Karan Srivastava

Vijay Mital

Yu Wang

Prabhdeep Singh

Radu Bogdan Gruian

Haiyuan Cao

Andrew Douglas Reddish

Nizam Anuar

Prithvi Raj Balaram

Arnaud Christian Flutre

Muralidhar Sathsahayaraman

Anthony Wah Lee

Moahemmed Amine Benmouffok

Andreea Bianca Spataru

Ah Young Kim

Shahina Ferdous

Radu B Gruian

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

Resource scheduling using machine learning

Systems and methods are disclosed for intelligent scheduling of calls to sales leads, leveraging machine learning (ML) to optimize expected results. One exemplary method includes determining, using a connectivity prediction model, call connectivity rate predictions; determining timeslot resources; allocating, based at least on the call connectivity rate predictions and timeslot resources, leads to timeslots in a first time period; determining, within a timeslot and using a lead scoring model, lead prioritization among leads within the timeslot; configuring, based at least on the lead prioritization, the telephone unit with lead information for placing a phone call; and applying a contextual bandit (ML) process to update the connectivity prediction model, the lead scoring model, or both. During subsequent time periods, the updated connectivity prediction and lead scoring models are used, thereby improving expected results over time.

Intelligent scheduling tool

Examples of the disclosure enable the recalculation of device chaining in a user interface. In some examples, a first element representing a first hardware device is received at a design surface of a computing device user interface. A second element representing a second hardware device is received at the design surface. A selection of an output associated with the first element is received. A function that binds a property of the second element to a property of the output associated with the first element is automatically generated. Aspects of the disclosure enable the automatically generated function to be displayed at the design surface.

Generation of a device application

The tracking of physical entities within a space such that computer-implemented computations (or ambient computing) may be performed on the features and/or activity of physical entities within that physical space. A space-time data structure corresponding to the physical space is set up. For at least one physical entity within that physical space, physical feature(s) of the physical entity is sensed at different instances in time. That sensed feature is stored in an entity data structure that is associated with the space-time data structure. This may be done for multiple physical entities within the location using multiple entity data structures that are each associated with the space-time data structure. A computing system may navigate such a space-time data structure to perform computation on physical entities, physical features and physical activity within that physical space.

Computer-aided tracking of physical entities

Examples of the disclosure enable one or more operations to be executed or implemented while managing computational resources. In some examples, an instruction to implement a first operation is received. The first operation is associated with a first node of a plurality of nodes. The plurality of nodes are arranged in a plurality of regions. A second node of the plurality of nodes that is related to the first node is identified. On condition that the second node is arranged in an active region of the plurality of regions, a second operation associated with the second node is implemented within a period of time. On condition that the second node is not arranged in the active region, the second operation is not implemented within the period of time. Aspects of the disclosure enable a computing device to defer the implementation of an operation to facilitate managing computational resources.

Generating a deferrable data flow

Generally discussed herein are devices, systems, and methods for task routing. A method can include receiving, from a resource, a request for a task, in response to receiving the request, determining whether to retrieve a new task of new tasks stored in a first queue or a backlog task of backlog tasks stored in a second queue based on a combined amount of backlog tasks and new tasks relative to a capacity of the resource or the resources, retrieving the new task or the backlog task from the determined first queue or second queue, respectively, based on the determination, and providing the retrieved task to the resource.

Dynamic, load balanced task routing

The compilation of a transformation chain of a recalculation user interface that displays an electronic canvas that contains one or more displayed result of a transformation chain. The transformation chain includes transforms between a respective data source and data sink. User editing of the recalculation user interface could cause one or more of the transforms to be re-executed, thereby causing recalculation. The compilation involves analyzing the transformation chain of the recalculation user interface for dependencies to create a dependency graph of dependencies between entities. For instance, some dependencies might be between entities so as to indicate that if one entity is evaluated, then the other should be also. The dependency graph is then used to create a lower level of execution steps. The dependency graph is further provided to a runtime for the program, so that the dependency graph may be available during operation of the recalculation user interface.

Compilation of transformation in recalculation user interface

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Redmond, WA, United States of America

Jaideep Sarkar