Microsoft Technology Licensing, LLC

Blackout, Inc.

Ahmed Hassan Mohamed

David Matthew Kruse

Balan Sethu Raman

Ke Wang

Bo Liu

Anthony Frederick Voellm

Sundar Subbarayan

Mathew George

Pradeep Madhavarapu

Christopher Wellington Brumme

Jinsong Yu

F Soner Terek

Danilo Landucci Benzatti

Jay Paulus

John Joseph Duffy

Yun Lin

Sarosh Cyrus Havewala

Martin Taillefer

Shishir P Pardikar

Navjot Virk

Guhan Suriyanarayanan

David C Steere

Andrew Sean Watson

Jason Todd Hunter

Sean Erik McAteer

Matthew William Lee

Dustin G Friesenhahn

Thomas A Langan

Guihong Cao

Sukhdeep S Sodhi

Christophe Franck Robert

Andrew E Goodsell

Tiancong Zhou

Fetiye Karabay

Gregory Michael Neverov

Dmitry Kakurin

Dave M Kruse

Dustin G Fresenhahn

Lihao Zhang

Tahsin Murat Boduroglu

Kushal Manohar Aurangabadkar

Anirudhan Vijayakanthan

Victor Renjie Fu

Pol Llado

Victor Macêdo Alexandrino

V Kishore Chintalapati

Sean Watson

V R Kishore Chintalapati

User data is extracted from a user data shard and copied into a user-isolated tenant shard. Instructions for accessing the user data are stored in a separate data store. A tenant administrator computing system retrieves and runs the instructions for accessing and exporting the user data from the user-isolated tenant shard.

Exporting customer data using a compliant tenant shard

The present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. In particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. The system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. The personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.

Personalized service scheduling

Systems and methods for efficiently querying distributed data are disclosed. An example method includes receiving a query from a device; transmitting the query to multiple distributed data stores; receiving, from the data stores, first data items satisfying the query; generating first-page results from the first data items; based on the first-page results, generating a first-page watermark; transmitting the first-page results and the first-page watermark to the device; receiving a second-page request from the device, the second-page request including the query and the first-page watermark; transmitting the query and at least a portion of the first-page watermark to the data stores; receiving, from the data stores, second data items satisfying the query and the portion of the first-page watermark; generating second-page results from the received second data items; and transmitting the second-page results to the device.

Distributed query technique to efficiently retrieve and merge data from multiple shards

Techniques of interaction data processing in a distributed computing system are disclosed herein. One example technique includes upon receiving a user interface output having a content item to be surfaced on a client device and a script configured to detect an interaction with the content item at the client device, surfacing the user interface output having the content item on a display of the client device and automatically executing the script embedded in the user interface output to detect an interaction with the surfaced content item at the client device. Upon detecting an interaction with the content item on the surfaced user interface output, the example technique also includes generating and transmitting interaction data representing the detected interaction to a remote server.

Interaction signal processing in distributed computing systems

A method for signal request caching is described. Signal requests are received at a signal processor from a plurality of computing devices. The received signal requests are routed to a signal data store. An ingestion rate of the received signal requests is monitored by the signal processor. When the ingestion rate meets a signal request rate threshold of the signal data store, overflow signal requests of the received signal requests are automatically routed to an intermediate cache instead of the signal data store. The overflow signal requests within the intermediate cache are aggregated into one or more signal packages, each of the one or more signal packages containing a plurality of overflow signal requests. The one or more signal packages are stored at the signal data store.

Overflow signal caching and aggregation

In a computing system, data is ingested into a primary row of shards in a stamp data structure. The stamp data structure includes a primary row of data shards and a set of replica rows of data shards so the data shards are arranged in rows and columns in the stamp structure. The ingested data is replicated from the primary row into the replica rows of data shards. Each of the data shards, in each row, is evaluated to identify a particular data shard in each column of shards to generate a logical row of data shards. Queries against the data shards are serviced from the logical row of data shards. The system dynamically controls expansion and contraction of the number of data shards in a row and of the number of replica rows.

Generating and controlling an elastically scalable stamp data structure for storing data

A string, identifying an item to be assigned to a physical resource, is hashed to obtain a numeric hash value. The numeric hash value is downscaled to obtain a bucket identifier that identifies a bucket that will hold the numeric hash value. The bucket is then deterministically mapped to a physical resource so that it can be retrieved without accessing a stored data structure representative of the mapping.

Deterministic mapping and uniform routing of items to physical resources using hash values

Methods, computer systems, and computer-storage media are provided for generating natural language outputs. Sets of triples may be used to map voice queries and answers to sentence structures that may be used as an output answer to the voice query. Sentence structures are only appropriate with certain sets of triples. One or more constraints may be associated with the set of triples to make sure sentence structures are only applied in correct situations. In order to be a valid sentence structure, each constraint associated therewith must be satisfied. If each constraint is satisfied, the sentence structure is valid and may be used as the format for an output answer. If each constraint is not satisfied, additional sentence structures associated with the set of triples may be evaluated until a valid sentence structure is identified. If no sentence structure is valid, no output is generated.

Generating natural language outputs

Described is a data communication protocol, in which a client and server negotiate in a manner that does not require the client to retry negotiation when servers are not capable of the client-desired protocol. In one example implementation, the desired protocol is SMB 2.0 or greater. The protocol describes a create command with possibly additional context data attached for built-in extensibility, and a compound command comprising a plurality of related commands or unrelated commands. A multi-channel command requests data transfer on a separate data channel, a signed capability verification may be used to ensure that a secure connection is established, and the protocol provides the ability to transfer extended error data from the server in response to a request.

Data communication protocol

Described are sequence numbers for client-server communication, to control a client's use of server resources. A server grants the client credits, and the client consumes a credit for sending each command to the server. Each credit corresponds to a sequence number, with the set of sequence numbers forming a valid command window. The server enforces that for each received command, the command includes a sequence number that is within the valid command window and that the sequence number has not been used with another command. The server may also maintain a maximum window size, such that clients with credits cannot send a command with a sequence number that beyond a maximum sequence number. When incorporated into a data communication protocol, quality of service, combating denial of service, detection of message loss, division of server resources, secure message signing, and other numerous benefits result.

Data communication coordination with sequence numbers

Caching of an immutable buffer that has its data and address prevented from changing during the lifetime of the immutable buffer. A first computing entity maintains a cache of the immutable buffer and has a strong reference to the immutable buffer. So long as any entity has a strong reference to the immutable buffer, the immutable buffer is guaranteed to continue to exist for the duration of the strong reference. A second computing entity communicates with the first computing entity to obtain a strong reference to the immutable buffer and thereafter read data from the immutable buffer. Upon reading the data from the cache, the second computing entity demotes the strong reference to a weak reference to the immutable buffer. A weak reference to the immutable buffer does not guarantee that the immutable buffer will continue to exist for the duration of the weak reference.

Zero-copy caching

The environment and use of an immutable buffer. A computing entity acquires data or generates data and populates the data into the buffer, after which the buffer is classified as immutable. The classification protects the data populated within the immutable buffer from changing during the lifetime of the immutable buffer, and also protects the immutable buffer from having its physical address changed during the lifetime of the immutable buffer. As different computing entities consume data from the immutable buffer, they do so through views provided by a view providing entity. The immutable buffer architecture may also be used for streaming data in which each component of the streaming data uses an immutable buffer. Accordingly, different computing entities may view the immutable data differently without having to actually copy the data.

Immutable sharable zero-copy data and streaming

Growing community of inventors

Bellevue, WA, United States of America

Ahmed Hassan Mohamed