-
公开(公告)号：US11720808B2
公开(公告)日：2023-08-08
申请号：US16886316
申请日：2020-05-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yen-Jung Chang , Yunsong Meng , Tie Wang , Yang Yang , Bo Long , Boyi Chen , Yanbin Jiang , Zheng Li
Abstract: The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.
-
公开(公告)号：US10380624B2
公开(公告)日：2019-08-13
申请号：US14585863
申请日：2014-12-30
Applicant: Microsoft Technology Licensing, LLC
Inventor： Deepak Agarwal , Shaunak Chatterjee , Liang Zhang , Bee-Chung Chen , Yang Yang
Abstract: This disclosure relates to systems and methods that include a member activity database including data indicative of interactions with content items on a social network by a population of users of the social network. A processor is configured to obtain an optimization criterion based on at least two constraints related to a performance of the social network, obtain, for a subset of the population of users, at least some of the data indicative of interactions with content items from the member activity database, determine, based on the at least some of the data as obtained, an operating condition for the social network that is estimated to meet the optimization criterion, and provide, to at least some of the user devices via the network interface, the social network based, at least in part, on the operating condition.
-
公开(公告)号：US12169523B2
公开(公告)日：2024-12-17
申请号：US17920215
申请日：2021-03-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Siming Tian , Xin Zhang , Yi Zeng , Yang Yang , Daxin Jiang
IPC: G06F7/00 , G06F16/901 , G06F16/9032
Abstract: The present disclosure provides method and apparatus for query conversion. A first query for a first graph database may be obtained. A syntax tree may be generated through parsing the first query. A query graph may be created based on the syntax tree. The query graph may be converted into a second query for a second graph database.
-
公开(公告)号：US20160247070A1
公开(公告)日：2016-08-25
申请号：US15145563
申请日：2016-05-03
Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor： Shipeng Li , Yang Yang , Bin Benjamin Zhu , Rui Guo , Linjun Yang
CPC classification number: G06N5/022 , G06F2221/2133 , G06N3/12 , G06N3/126 , G06N5/04 , H04L63/1416
Abstract: Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.
-
公开(公告)号：US11204847B2
公开(公告)日：2021-12-21
申请号：US16228761
申请日：2018-12-21
Applicant: Microsoft Technology Licensing, LLC
Inventor： Kexin Nie , Yanbin Jiang , Yang Yang , Boyi Chen , Shilpa Gupta , Zheng Li
Abstract: Technologies for monitoring performance of a machine learning model include receiving, by an unsupervised anomaly detection function, digital time series data for a feature metric; where the feature metric is computed for a feature that is extracted from an online system over a time interval; where the machine learning model is to produce model output that relates to one or more users' use of the online system; using the unsupervised anomaly detection function, detecting anomalies in the digital time series data; labeling a subset of the detected anomalies in response to a deviation of a time-series prediction model from a predicted baseline model exceeding a predicted deviation criterion; creating digital output that identifies the feature as associated with the labeled subset of the detected anomalies; causing, in response to the digital output, a modification of the machine learning model.
-
Patent Agency Ranking
公开(公告)号：US20210374562A1
公开(公告)日：2021-12-02
申请号：US16886316
申请日：2020-05-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yen-Jung Chang , Yunsong Meng , Tie Wang , Yang Yang , Bo Long , Boyi Chen , Yanbin Jiang , Zheng Li
Abstract: The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.
-
公开(公告)号：US10600003B2
公开(公告)日：2020-03-24
申请号：US16024809
申请日：2018-06-30
Applicant: Microsoft Technology Licensing, LLC
Abstract: Techniques for auto-tuning anomaly detection are provided. In one technique, training data is stored that comprises training instances, each of which comprises a severity-duration pair and a label that indicates whether the severity-duration pair represents an anomaly. A model is trained based on a first subset of the training data. A second subset of the training data is identified where each training instance includes a positive label that indicates that that training instance represents an anomaly. Based on the second subset of the training data, the model generates multiple scores, each of which corresponds to a different training instance. A minimum score is identified that ensures a particular recall rate of the model. In response to receiving a particular severity-duration pair, the model generates a particular score for the particular severity-duration pair. A notification of an anomaly is generated if the particular score is greater than the minimum score.
-
公开(公告)号：US20230342396A1
公开(公告)日：2023-10-26
申请号：US17920215
申请日：2021-03-24
Applicant: Microsoft Technology Licensing, LLC
Inventor： Siming Tian , Xin Zhang , Yi Zeng , Yang Yang , Daxin Jiang
IPC: G06F16/9032 , G06F16/901
CPC classification number: G06F16/9032 , G06F16/9027
Abstract: The present disclosure provides method and apparatus for query conversion. A first query for a first graph database may be obtained. A syntax tree may be generated through parsing the first query. A query graph may be created based on the syntax tree. The query graph may be converted into a second query for a second graph database.
-
公开(公告)号：US10983856B2
公开(公告)日：2021-04-20
申请号：US16457727
申请日：2019-06-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Yi Zhen , Yung-Yu Chung , Yang Yang , Bo Long
Abstract: The disclosed embodiments provide a system for identifying root causes of performance issues. During operation, the system obtains a call graph containing a set of call paths for a set of services. Next, the system determines, based on a load test of the set of services, severity scores for the set of services, wherein the severity scores represent levels of abnormal behavior in the set of services. The system then groups the severity scores by the set of call paths and identifies, based on the grouped severity scores, one or more services as potential root causes of performance issues in the set of services. Finally, the system outputs the identified one or more services as the potential root causes of the performance issues.
-
公开(公告)号：US20200005193A1
公开(公告)日：2020-01-02
申请号：US16024809
申请日：2018-06-30
Applicant: Microsoft Technology Licensing, LLC
Abstract: Techniques for auto-tuning anomaly detection are provided. In one technique, training data is stored that comprises training instances, each of which comprises a severity-duration pair and a label that indicates whether the severity-duration pair represents an anomaly. A model is trained based on a first subset of the training data. A second subset of the training data is identified where each training instance includes a positive label that indicates that that training instance represents an anomaly. Based on the second subset of the training data, the model generates multiple scores, each of which corresponds to a different training instance. A minimum score is identified that ensures a particular recall rate of the model. In response to receiving a particular severity-duration pair, the model generates a particular score for the particular severity-duration pair. A notification of an anomaly is generated if the particular score is greater than the minimum score.
-
-
-
-
-
-
-
-
-
Search Results
11
records
(took 0.018 seconds)
