# US 2019/0317480 A1 · Determining failure modes of devices based on text analysis

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- Number: US 2019/0317480 A1
- Document ID: US20190317480A1
- Status: Published application
- Kind: A1
- Application number: 15/927,068
- Filed: 2018-03-20
- Published: 2019-10-17
- Earliest claimed: 2017-10-24
- Assignees: Sap SE
- Applicants: Sap SE
- Inventors: Rashmi B. Shetty; Simon Lee
- Web page: https://patentgrep.com/patent/US20190317480A1

## CPC

- G06Q 10/20: Administration of product repair or maintenance
- G05B 19/4184: {characterised by fault tolerance, reliability of production system}
- G06F 16/35: Clustering; Classification
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/24: Classification techniques
- G06F 40/20: Natural language analysis (semantic analysis of natural language G06F40/30)
- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06V 30/413: Classification of content, e.g. text, photographs or tables
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Some embodiments provide a program that retrieves a set of notifications describing failures that occurred on a set of monitored devices. The program further determines a set of topics based on the set of notifications. The program also determines failure modes associated with the set of topic from a plurality of failure modes defined for the set of monitored devices. The program further determines failure modes associated with the set of notifications based on the set of topics and the failure modes associated with the set of topics. The program also receives a particular notification that includes a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices. The program further determines a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

## Claims

### Claim 1

A non-transitory machine-readable medium storing a program executable by at least one processing unit of a device, the program comprising sets of instructions for:
retrieving a set of notifications, each notification comprising a set of words describing a failure that occurred on a monitored device in a set of monitored devices;
determining a set of topics based on the sets of words of the set of notifications;
for each topic in the set of topics, determining a failure mode associated with the topic from a plurality of failure modes defined for the set of monitored devices;
for each notification in the set of notifications, determining a failure mode associated with the notification based on the set of topics and the failure modes associated with the set of topics;
receiving a particular notification comprising a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices; and
determining a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

### Claim 2 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein a topic in the set of topics comprises a set of related words or phrases that occur in the set of words of the set of notifications.

### Claim 3 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein determining the set of topics is further based on a Latent Dirichlet allocation (LDA) model.

### Claim 4 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein determining, for each topic in the set of topics, the failure mode associated with the topic from the plurality of failure modes defined for the set of monitored devices is based on a Latent Dirichlet allocation (LDA) model.

### Claim 5 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein determining, for each notification in the set of notifications, the failure mode associated with the notification comprises determining a topic in the set of topics associated with the notification and determining the failure mode associated with the notification based on the failure modes associated with the set of topics.

### Claim 6 (depends on 5)

The non-transitory machine-readable medium of claim 5, wherein determining, for each notification in the set of notifications, the topic in the set of topics associated with the notification is based on a Latent Dirichlet allocation (LDA) model.

### Claim 7 (depends on 1)

The non-transitory machine-readable medium of claim 1, wherein determining, for each topic in the set of topics, the failure mode associated with the topic from the plurality of failure modes defined for the set of monitored devices comprises determining a device model associated with the set of monitored devices, determining that the plurality of failure mode definitions are defined for the device model, and retrieving the plurality of failure modes from a storage configured to store failure mode definitions.

### Claim 8

A method comprising:
retrieving a set of notifications, each notification comprising a set of words describing a failure that occurred on a monitored device in a set of monitored devices;
determining a set of topics based on the sets of words of the set of notifications;
for each topic in the set of topics, determining a failure mode associated with the topic from a plurality of failure modes defined for the set of monitored devices;
for each notification in the set of notifications, determining a failure mode associated with the notification based on the set of topics and the failure modes associated with the set of topics;
receiving a particular notification comprising a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices; and
determining a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

### Claim 9 (depends on 8)

The method of claim 8, wherein a topic in the set of topics comprises a set of related words or phrases that occur in the set of words of the set of notifications.

### Claim 10 (depends on 8)

The method of claim 8, wherein determining the set of topics is further based on a Latent Dirichlet allocation (LDA) model.

### Claim 11 (depends on 8)

The method of claim 8, wherein determining, for each topic in the set of topics, the failure mode associated with the topic from the plurality of failure modes defined for the set of monitored devices is based on a Latent Dirichlet allocation (LDA) model.

### Claim 12 (depends on 8)

The method of claim 8, wherein determining, for each notification in the set of notifications, the failure mode associated with the notification comprises determining a topic in the set of topics associated with the notification and determining the failure mode associated with the notification based on the failure modes associated with the set of topics.

### Claim 13 (depends on 12)

The method of claim 12, wherein determining, for each notification in the set of notifications, the topic in the set of topics associated with the notification is based on a Latent Dirichlet allocation (LDA) model.

### Claim 14 (depends on 8)

The method of claim 8, wherein determining, for each topic in the set of topics, the failure mode associated with the topic from the plurality of failure modes defined for the set of monitored devices comprises determining a device model associated with the set of monitored devices, determining that the plurality of failure mode definitions are defined for the device model, and retrieving the plurality of failure modes from a storage configured to store failure mode definitions.

### Claim 15

A system comprising:
a set of processing units; and
a non-transitory machine-readable medium storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to:
retrieve a set of notifications, each notification comprising a set of words describing a failure that occurred on a monitored device in a set of monitored devices;
determine a set of topics based on the sets of words of the set of notifications;
for each topic in the set of topics, determine a failure mode associated with the topic from a plurality of failure modes defined for the set of monitored devices;
for each notification in the set of notifications, determine a failure mode associated with the notification based on the set of topics and the failure modes associated with the set of topics;
receive a particular notification comprising a particular set of words describing a failure that occurred on a particular monitored device in the set of monitored devices; and
determine a failure mode associated with the particular notification based on the particular set of words and the determined failure modes associated with the set of notifications.

### Claim 16 (depends on 15)

The system of claim 15, wherein a topic in the set of topics comprises a set of related words or phrases that occur in the set of words of the set of notifications.

### Claim 17 (depends on 15)

The system of claim 15, wherein determining the set of topics is further based on a Latent Dirichlet allocation (LDA) model.

### Claim 18 (depends on 15)

The system of claim 15, wherein determining, for each topic in the set of topics, the failure mode associated with the topic from the plurality of failure modes defined for the set of monitored devices is based on a Latent Dirichlet allocation (LDA) model.

### Claim 19 (depends on 15)

The system of claim 15, wherein determining, for each notification in the set of notifications, the failure mode associated with the notification comprises determining a topic in the set of topics associated with the notification and determining the failure mode associated with the notification based on the failure modes associated with the set of topics.

### Claim 20 (depends on 19)

The system of claim 19, wherein determining, for each notification in the set of notifications, the topic in the set of topics associated with the notification is based on a Latent Dirichlet allocation (LDA) model.