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 13):
The apparatus of  claim 12 , wherein the program code to determine whether the selected first set of dimensionally reduced representations and the first dimensionally reduced representation occur near the first path comprises program code executable by the processor to cause the apparatus to determine whether at least a subset of the first set of dimensionally reduced representations and the first dimensionally reduced representation occurs within a defined distance of a curve described by the first time-dependent multivariate function.

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): 0.3376461255002826
- 35 USC 102 Novelty (BERT): 0.5147726991324478
- Combined Prediction Score: 0.3553587828634992
- Mean Citation Score: 142.90302880000004
- Max Citation Score: 224.49077000000003
- Similarity Product: 158.54833509798408

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