PATENT CLAIM ANALYSIS

Application Number: 15970846
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-09
Patent Classification: ["715", "863000"]

Abstract:
Threat detection instrumentation is simplified by providing and updating labels for computing objects in a context-sensitive manner. This may include simple labeling schemes to distinguish between objects, e.g., trusted/untrusted processes or corporate/private data. This may also include more granular labeling schemes such as a three-tiered scheme that identifies a category (e.g., financial, e-mail, game), static threat detection attributes (e.g., signatures, hashes, API calls), and explicit identification (e.g., what a file or process calls itself). By tracking such data for various computing objects and correlating these labels to malware occurrences, rules can be written for distribution to endpoints to facilitate threat detection based on, e.g., interactions of labeled objects, changes to object labels, and so forth. In this manner, threat detection based on complex interactions of computing objects can be characterized in a platform independent manner and pre-processed on endpoints without requiring significant communications overhead with a remote threat management facility.

Claim (Index 17):
The method of  claim 5  further comprising changing a label for one of the plurality of files from out to in.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 93.0
- Lexical Diversity: 1.76238
- Patent Class: 715.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14485769', '15360591', '14485774', '14485782', '15963718']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4354747352142255
- 35 USC 102 Novelty (BERT): 0.5574146764094843
- Combined Prediction Score: 0.4476687293337514
- Mean Citation Score: 439.64857200000006
- Max Citation Score: 496.70993
- Similarity Product: 347.2527629760885

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

Dataset: test