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 5):
A method of calibrating a machine learning model of a machine learning system, the method comprising:\n collecting digital threat scores of an incumbent digital threat machine learning (ML) model; identifying an incumbent digital threat score distribution of the digital threat scores of the incumbent digital threat ML model; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning (ML) model; identifying an uncalibrated successor digital threat score distribution of the digital threat scores of the successor digital threat ML model; identifying quantiles data of the uncalibrated successor digital threat score distribution. calibrating the uncalibrated successor digital threat ML model by 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, wherein the calibrating includes: remapping the digital threat scores of the uncalibrated successor digital threat score distribution based on the digital threat scores of the incumbent digital threat score distribution, wherein the remapping includes:\n applying the quantiles data to the remapping of the digital threat scores of the uncalibrated successor digital threat score distribution; \n configuring the digital threat scores of the uncalibrated successor digital threat score distribution to fit within a plurality of indices of the quantiles data; \n wherein response to the remapping of the digital threat scores of the uncalibrated successor digital threat score distribution, the machine learning system publishes calibrated successor digital scores in lieu of the incumbent digital threat scores based on one or more requests for digital threat scores.

Metadata:
- Claim Count in Document: 38.0
- Percentile: 90.0
- Lexical Diversity: 3.18421
- Patent Class: 703.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15653373', '15653354', '15922746', '15842379', '15396273']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.222427825700666
- 35 USC 102 Novelty (BERT): 0.5097162801572935
- Combined Prediction Score: 0.2511566711463288
- Mean Citation Score: 215.505092
- Max Citation Score: 250.36577000000003
- Similarity Product: 175.48795378004556

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