PATENT CLAIM ANALYSIS

Application Number: 16215491
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-06
Patent Classification: ["726", "023000"]

Abstract:
Systems and methods for utilizing statistical relational learning techniques in order to predict factors for nodes of a node graph, such as a node graph that represents attacks and incidents to a computing system, are described. In some embodiments, the systems and methods identify certain nodes (of a node graph) as representing malicious attributes of an email or other threat artifact received by a computing system or network and utilize relational learning to predict the maliciousness of attributes represented by other nodes (of the node graph).

Claim (Index 11):
A method performed by an incident response system for mitigating attacks to a computing system, the method comprising:\n generating node graphs for threat artifacts received by the computing system,\n wherein the node graphs include nodes representing indicators derived from the threat artifacts and edges that represent relationships between the indicators; and \n wherein at least one of the nodes representing the indicators is assigned a predicted maliciousness based on a known maliciousness of multiple other nodes of the node graphs; and \n performing an action based on an analysis of the node graphs to dynamically adjust security operations of the computing system.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 1.72727
- Patent Class: 726.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15848337', '15236575', '15236595', '15236582', '16029964']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2593697120875793
- 35 USC 102 Novelty (BERT): 0.4582860369471192
- Combined Prediction Score: 0.2792613445735333
- Mean Citation Score: 148.58066000000005
- Max Citation Score: 151.89603
- Similarity Product: 119.34233316563844

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

Dataset: test