公开(公告)号：US10331479B2

公开(公告)日：2019-06-25

申请号：US15406290

申请日：2017-01-13

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Ying Yan ,  Yanjie Gao ,  Yang Chen ,  Thomas Moscibroda ,  Narayanan Ganapathy ,  Bole Chen ,  Zhongxin Guo

IPC: G06F9/48 ,  G06F11/14

Abstract: Aspects of the technology described herein can facilitate computing on transient resources. An exemplary computing device may use a task scheduler to access information of a computational task and instability information of a transient resource. Moreover, the task scheduler can schedule the computational task to use the transient resource based at least in part on the rate of data size reduction of the computational task. Further, a checkpointing scheduler in the exemplary computing device can determine a checkpointing plan for the computational task based at least in part on a recomputation cost associated with the instability information of the transient resource. Resultantly, the overall utilization rate of computing resources is improved by effectively utilizing transient resources.

公开(公告)号：US12248525B1

公开(公告)日：2025-03-11

申请号：US18243883

申请日：2023-09-08

Applicant: Microsoft Technology Licensing, LLC

Inventor： Guillaume Didier Jean-Marc Dufour ,  Yang Chen ,  Lukasz Janusz Karolewski

IPC: G06F16/00 ,  G06F16/9032 ,  G06F16/9535 ,  G06F40/30

Abstract: In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

公开(公告)号：US11961334B2

公开(公告)日：2024-04-16

申请号：US17331574

申请日：2021-05-26

Applicant: Microsoft Technology Licensing, LLC

Inventor： William Louis Thomas ,  Jinyu Li ,  Yang Chen ,  Youyou Han Oppenlander ,  Steven John Bowles ,  Qingfen Lin

IPC: G06K9/36 ,  G06F16/28 ,  G06K9/62 ,  G06V10/75 ,  G06V40/16 ,  G06V40/50

CPC classification number: G06V40/50 ,  G06F16/285 ,  G06V10/751 ,  G06V40/168 ,  G06V40/172

Abstract: The disclosure herein describes systems and methods for object data storage. In some examples, the method includes generating a profile for an object in a directory, the profile including a first feature vector corresponding to the object and a global unique identifier (GUID) corresponding to the first feature vector in the profile; generating a search scope, the search scope including at least the GUID corresponding to the profile; generating a second feature vector from a live image scan; matching the generated second feature vector from the live image scan to the first feature vector using the generated search scope; identifying the GUID corresponding to the first feature vector that matches the second feature vector; and outputting information corresponding to the object of the profile identified by the GUID corresponding to the first feature vector.

公开(公告)号：US11792167B2

公开(公告)日：2023-10-17

申请号：US17219482

申请日：2021-03-31

Applicant: Microsoft Technology Licensing, LLC

Inventor： Haifeng Zhao ,  Yang Chen ,  Jiashuo Wang ,  Xiaojing Chen ,  Chencheng Wu ,  Souvik Ghosh ,  Ankit Gupta ,  Jing Wang ,  John Patrick Moore ,  Henry Heyburn Pistell ,  Mira Thambireddy ,  Haowen Cao ,  Keyi Yu

IPC: H04L9/40 ,  G06N20/00

CPC classification number: H04L63/0428 ,  G06N20/00

Abstract: Techniques for a flexible data security and machine learning system for merging third-party data are provided. In one technique, the system receives a data set from a third-party entity and receives selection data that indicates that the third-party entity selected a set of data security policies that includes an encryption option and a data mixing option from among multiple data mixing options. In response to receiving the selection data, the system stores data that associates the set of data security policies with the data set, encrypts the data set according to the encryption option, and persistently stores the encrypted data set. Later, the system decrypts the encrypted data set in volatile memory, generates, based on the data mixing option, training data based on the decrypted version of the data set, trains a machine-learned model based on the training data, and stores the machine-learned model in association with the data set.

公开(公告)号：US11032359B2

公开(公告)日：2021-06-08

申请号：US16839357

申请日：2020-04-03

Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor： Thomas Moscibroda ,  Yang Chen ,  James E. Johnson ,  Ajay Mani ,  Mark Eugene Russinovich

IPC: H04L29/08 ,  G06F9/50

Abstract: In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a quality metric associated with each candidate node to which a service instance could be allocated is determined. An eviction cost or a survival metric associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the quality metrics and either the eviction costs or the survival metrics are used to select a node from the candidate nodes to which to allocate the service instance.