PATENT CLAIM ANALYSIS

Application Number: 16274781
Application Type: Utility
Filing Date: 2019-02
Publication Date: 2019-06
Patent Classification: ["709", "224000"]

Abstract:
Mechanisms for anomaly detection in a network management system are provided. The mechanisms collect metric data from a plurality of network devices and determine metric types for the metric data using metric type reference data. The mechanisms determine and apply properties from the metric type reference data to metrics of the determined metric types. The mechanisms monitor subsequent metric data for anomalies that do not conform to the applied properties.

Claim (Index 37):
A computer program product for monitoring network devices, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code, when executed on a computing device, causes the computing device to:\n generate a metric type reference data structure comprising a plurality of entries of metric type reference data, wherein one entry of the plurality of entries in the metric type reference data structure comprises:\n a metric type identifier identifying a first predetermined metric type, \n a corresponding standard metric property upon which a first plurality of metrics of first predetermined metric type are measured, \n a good metric property identifying a potential behavior of each of the first plurality of metrics of the first predetermined metric type, as measured based on the standard metric property, that would be stable and expected, and \n a bad metric property identifying a potential behavior of each of the first plurality of metrics of the first predetermined metric type, as measured based on the standard metric property, that would be unusual, and wherein a different entry of the plurality of entries in the metric type reference data structure comprises: \n a different metric type identifier identifying a second predetermined metric type different from the first predetermined metric type, \n a corresponding second standard metric property upon which a second plurality of metrics of the second predetermined metric type are measured, \n a second good metric property identifying a potential behavior of each of the second plurality of metrics of the second predetermined metric type, as measured based on the second standard metric property, that would be stable and expected, and \n a second bad metric property identifying a potential behavior of each of the second plurality of metrics of the second predetermined metric type, as measured based on the second standard metric property, that would be unusual; \n collect metric data from a plurality of managed network devices; determine, for a collected metric in the collected metric data, that the collected metric is one of the first predetermined metric type or the second predetermined metric type, based on a matching of the collected metric to one of the first predefined metric type or the second predefined metric type in the metric type reference data structure; automatically apply, based on a measurement of the collected metric in accordance with the standard metric property, a property corresponding to one of the first good metric property, the first bad metric property, the second good metric property, or the second bad metric property based results of determining that the collected metric is one of the first predetermined metric type or the second predetermined metric type; and responsive to the applied property being one of the first bad metric property or the second bad metric property, automatically generate a notification of an anomaly.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 99.0
- Lexical Diversity: 1.80488
- Patent Class: 709.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['14476959', '14929982', '13851618', '14030652', '15425906']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2116253581715659
- 35 USC 102 Novelty (BERT): 0.5202356912851276
- Combined Prediction Score: 0.2424863914829221
- Mean Citation Score: 180.888056
- Max Citation Score: 330.6219
- Similarity Product: 291.12277741112706

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test