PATENT CLAIM ANALYSIS

Application Number: 15939893
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["717", "128000"]

Abstract:
A multivariate path-based anomaly detection and prediction service (“anomaly detector”) can generate a prediction event for consumption by the APM manager that indicates a likelihood of an anomaly occurring based on path analysis of multivariate values after topology-based feature selection. To predict that a set of metrics will travel to a cluster that represents anomalous application behavior, the anomaly detector analyzes a set of multivariate date slices that are not within a cluster to determine whether dimensionally reduced representations of the set of multivariate data slices fit a path as described by a function.

Claim (Index 12):
The apparatus of  claim 12 , wherein the machine-readable medium comprises program code executable by the processor to cause the apparatus to identify a series of at least n dimensionally reduced representations of time-series multivariate datasets that precede the first dimensionally reduced representation and that were not in any of the plurality of clusters, based on the determination that the first dimensionally reduced representation is not in one of the plurality of clusters, wherein the series includes the first set of dimensionally reduced representations.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 90.0
- Lexical Diversity: 1.48529
- Patent Class: 717.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15894647', '11557584', '14596151', '14586381', '15827663']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3387200643343133
- 35 USC 102 Novelty (BERT): 0.5097515542503038
- Combined Prediction Score: 0.3558232133259124
- Mean Citation Score: 142.90302880000004
- Max Citation Score: 224.49077000000003
- Similarity Product: 168.39620371584775

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test