公开(公告)号：US20190108486A1

公开(公告)日：2019-04-11

申请号：US15725983

申请日：2017-10-05

Applicant: Microsoft Technology Licensing, LLC

Inventor： Navendu Jain ,  Shane Hu

IPC: G06Q10/10 ,  H04L29/06

Abstract: Methods for automatic and intelligent electronic communication support, including using machine learning, are performed by systems and apparatuses. The methods intelligently and automatically route electronic communication support requests and intelligently and automatically provide senders with information related to their support requests. The methods generate feature vectors from cleaned request information via featurization techniques, and utilize machine-learning algorithms/models and algorithm/model outputs based on the input feature vectors. Based on the algorithm/model outputs and personalized to the specific sender, relevant support information is automatically provided to the sender. The methods also determine a set of prior communications related to the support request based on a similarity measure, and provide prior communication information to the sender. The methods also include routing support requests to correct feature owner recipients based on the algorithm/model outputs.

公开(公告)号：US11743139B2

公开(公告)日：2023-08-29

申请号：US17537387

申请日：2021-11-29

Applicant: Microsoft Technology Licensing, LLC

Inventor： Gal Tamir ,  Rachel Lemberg ,  Zakie Mashiah ,  Shane Hu ,  Tamar Agmon ,  Navendu Jain

IPC: H04L41/5009 ,  G06N20/00 ,  H04L41/16

CPC classification number: H04L41/5009 ,  G06N20/00 ,  H04L41/16

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

公开(公告)号：US20220107858A1

公开(公告)日：2022-04-07

申请号：US17060835

申请日：2020-10-01

Applicant: Microsoft Technology Licensing, LLC

Inventor： Navendu Jain ,  Phuong Ngoc Viet Pham ,  Shane Hu

IPC: G06F11/07 ,  G06N20/00

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are described for detecting a common root cause for a multi-resource outage in a computing environment. For example, incident reports associated with multiple resources and that are generated by a plurality of monitors are featurized and provided to a classification model. The classification model detects whether a multi-resource outage exists based on the featurized incident reports and identifies a subset of the incident reports upon which the detection is based. Upon detecting a multi-resource outage, an analysis is performed to determine a potential common root cause of the multi-resource outage.

公开(公告)号：US11212195B1

公开(公告)日：2021-12-28

申请号：US17019187

申请日：2020-09-11

Applicant: Microsoft Technology Licensing, LLC

Inventor： Gal Tamir ,  Rachel Lemberg ,  Zakie Mashiah ,  Shane Hu ,  Tamar Agmon ,  Navendu Jain

IPC: H04L12/24 ,  G06N20/00

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.