PATENT CLAIM ANALYSIS

Application Number: 15941175
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-01
Patent Classification: ["703", "002000"]

Abstract:
Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.

Claim (Index 2):
The system according to  claim 1 , wherein:\n in response to receiving a request for a digital threat score for a digital event or for a digital actor, the machine learning service provides equivalent digital event data as machine learning input into both the incumbent digital threat ML model and the successor digital threat ML model.

Metadata:
- Claim Count in Document: 38.0
- Percentile: 90.0
- Lexical Diversity: 3.18421
- Patent Class: 703.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15653373', '15653354', '15922746', '15842379', '15396273']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2440641408521408
- 35 USC 102 Novelty (BERT): 0.509559141102983
- Combined Prediction Score: 0.270613640877225
- Mean Citation Score: 215.505092
- Max Citation Score: 250.36577000000003
- Similarity Product: 181.99735014676097

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