PATENT CLAIM ANALYSIS

Application Number: 16220499
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-05
Patent Classification: ["726", "023000"]

Abstract:
According to some embodiments, a method for training a malware detector comprising a deep learning algorithm is described, which comprises converting a set of malware files and non malware files into vectors by using a feature based dictionary, and/or by using a conversion into an image, and providing prospects that the files constitute malware. Various features and combinations of features are described to build a feature based dictionary and adapt its size. According to some embodiments, a method for detecting a malware by using a malware detector comprising a deep learning algorithm is described, which comprises converting a file into a vector by using a feature based dictionary, and/or by using a conversion into an image, and providing prospects that the file constitutes malware. Methods for providing a plurality of prospects and aggregating these prospects are provided. Additional methods and systems in the field of malware detection are also described.

Claim (Index 16):
The system according to  claim 9 , wherein the malware files and non-malware files are operable on different operating systems and the features are independent of the operating systems so that the first size dictionary and the second size dictionary are built irrespective of the operating systems.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 98.0
- Lexical Diversity: 2.4697
- Patent Class: 726.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14929902', '13163010', '14038682', '14985944', '15639805']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2949847459527101
- 35 USC 102 Novelty (BERT): 0.5796576259397174
- Combined Prediction Score: 0.3234520339514108
- Mean Citation Score: 261.399308
- Max Citation Score: 537.14636
- Similarity Product: 382.01275967846624

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

Dataset: test