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 3):
The non-transitory computer-readable medium of  claim 1 , wherein the node graphs include nodes having weights associated with a determined maliciousness assigned to the indicators represented by the nodes, and wherein the predicted maliciousness for the node is based on the weights assigned to the indicators represented by the other nodes of the node graphs.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 1.72727
- Patent Class: 726.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15848337', '15236575', '15236595', '15236582', '16029964']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2833603359966316
- 35 USC 102 Novelty (BERT): 0.4582344672853182
- Combined Prediction Score: 0.3008477491255003
- Mean Citation Score: 148.58066000000005
- Max Citation Score: 151.89603
- Similarity Product: 121.43049869627596

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