公开(公告)号：US11663544B2

公开(公告)日：2023-05-30

申请号：US16774822

申请日：2020-01-28

Applicant: salesforce.com, inc.

Inventor： Jiaping Zhang ,  Ana Bertran ,  Elena Novakovskaia ,  Zhanara Amans ,  Garren Bellew ,  Philip Dolle

IPC: G06Q10/06 ,  G06N20/00 ,  G06Q10/10 ,  G06Q10/0635 ,  G06Q10/0637

CPC classification number: G06Q10/0635 ,  G06N20/00 ,  G06Q10/06375 ,  G06Q10/10

Abstract: A method of early warning and risk assessment of incidents in a multi-tenant cloud environment is provided. The method includes: capturing a plurality of data metrics; automatically generating derived features from the plurality of captured data metrics; automatically selecting risk assessment features from the derived features and the captured data metrics; and predicting the risk of an incident in the multi-tenant cloud environment within a specified time window in the future and one or more possible root causes of the incident by applying the newly selected risk assessment features to a trained risk assessment model. The trained risk assessment model has been trained using machine learning techniques to predict the risk of an incident in the multi-tenant cloud environment within a specified time window in the future, provide an explanation of possible root causes of the incident, and assign a strength level to each possible root cause.

公开(公告)号：US20210232995A1

公开(公告)日：2021-07-29

申请号：US16774822

申请日：2020-01-28

Applicant: salesforce.com, inc.

Inventor： Jiaping Zhang ,  Ana Bertran ,  Elena Novakovskaia ,  Zhanara Amans ,  Garren Bellew ,  Philip Dolle

IPC: G06Q10/06 ,  G06N20/00 ,  G06Q10/10

Abstract: A method of early warning and risk assessment of incidents in a multi-tenant cloud environment is provided. The method includes: capturing a plurality of data metrics; automatically generating derived features from the plurality of captured data metrics; automatically selecting risk assessment features from the derived features and the captured data metrics; and predicting the risk of an incident in the multi-tenant cloud environment within a specified time window in the future and one or more possible root causes of the incident by applying the newly selected risk assessment features to a trained risk assessment model. The trained risk assessment model has been trained using machine learning techniques to predict the risk of an incident in the multi-tenant cloud environment within a specified time window in the future, provide an explanation of possible root causes of the incident, and assign a strength level to each possible root cause.