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 1):
An online system for calibrating a machine learning model, the system comprising:\n a machine learning service comprising:\n a digital threat score reservoir that:\n collects incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by an uncalibrated successor digital threat machine learning (ML) model; \n generates an incumbent threat score distribution based on the incumbent digital threat scores and generates an uncalibrated successor threat score distribution based on the successor digital threat scores; \n captures quantiles data from the incumbent digital threat score distribution and the uncalibrated successor score distribution; \n \n a remapping module that generates a calibrated successor digital threat machine learning model by:\n applying the quantiles of the incumbent digital threat score distribution to the uncalibrated successor digital threat score distribution; \n remapping the successor digital threat scores of the successor digital threat score distribution based on the incumbent digital threat scores of the incumbent digital threat score distribution; \n using the remapping of the successor digital threat scores and the quantiles of the incumbent digital threat scores to transform the successor digital threat scores to calibrated digital threat scores of the calibrated digital threat score distribution; and \n \n wherein response to transforming the successor digital threat scores to calibrated successor digital threat scores, returning a calibrated successor digital threat score in response to a request for a digital threat score for a digital event or a digital actor.

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): 0.2224937529103015
- 35 USC 102 Novelty (BERT): 0.5092842263726354
- Combined Prediction Score: 0.2511728002565349
- Mean Citation Score: 215.505092
- Max Citation Score: 250.36577000000003
- Similarity Product: 176.6339179382134

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