PATENT CLAIM ANALYSIS

Application Number: 16180790
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-10
Patent Classification: ["726", "023000"]

Abstract:
A system and method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity. The method produces a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations, adjudicates the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems, determines a minimum number of adjudicated samples required to initiate the in-situ retraining process, creates a new training and test set using samples from one or more in-situ systems, blends a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets, conducts machine learning over the blended training set, evaluates the new and parent models using the blended test set and additional unlabeled samples, and elects whether to replace the parent classifier with the retrained version.

Claim (Index 1):
A method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity, the method comprising:\n a. producing a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations; b. adjudicating the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems; c. determining a minimum number of adjudicated samples required to initiate the in-situ retraining process; d. blending a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets or subset thereof; e conducting machine learning over the blended training set, f evaluating the new and parent models using the blended test set and additional unlabeled samples; and g electing whether to replace the parent classifier with the retrained version.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 98.0
- Lexical Diversity: 2.25676
- Patent Class: 726.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15176784', '15895072', '15076073', '14038682', '13949974']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2230434104654668
- 35 USC 102 Novelty (BERT): 0.5380283850972359
- Combined Prediction Score: 0.2545419079286437
- Mean Citation Score: 190.906708
- Max Citation Score: 369.07825
- Similarity Product: 341.97738343134523

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

Dataset: test