公开(公告)号：US20230153680A1

公开(公告)日：2023-05-18

申请号：US17455536

申请日：2021-11-18

Applicant: Oracle International Corporation

Inventor： James Charles Rohrkemper ,  Kenneth Paul Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Guang Chao Wang ,  Anna Chystiakova ,  Richard Paul Sonderegger ,  Zhen Hua Liu

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Techniques for using machine learning model validated sensor data to generate recommendations for remediating issues in a monitored system are disclosed. A machine learning model is trained to identify correlations among sensors for a monitored system. Upon receiving current sensor data, the machine learning model identifies a subset of the current sensor data that cannot be validated. The system generates estimated values for the sensor data that cannot be validated based on the learned correlations among the sensor values. The system generates the recommendations for remediating the issues in the monitored system based on validated sensor values and the estimated sensor values.

公开(公告)号：US20230061280A1

公开(公告)日：2023-03-02

申请号：US17462592

申请日：2021-08-31

Applicant: Oracle International Corporation

Inventor： James Charles Rohrkemper ,  Richard Paul Sonderegger ,  Anna Chystiakova ,  Kenneth Paul Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu ,  Guang Chao Wang

IPC: G06N20/00 ,  G06F16/242

Abstract: Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a “fault” or “no-fault” in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.

公开(公告)号：US20220237509A1

公开(公告)日：2022-07-28

申请号：US17379937

申请日：2021-07-19

Applicant: Oracle International Corporation

Inventor： John Frederick Courtney ,  Kenneth Paul Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Guang Chao Wang ,  Anna Chystiakova ,  Richard Paul Sonderegger ,  Zhen Hua Liu

IPC: G06N20/00 ,  G06N5/04 ,  G06F11/32

Abstract: Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.