PATENT CLAIM ANALYSIS

Application Number: 16414584
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2020-01
Patent Classification: ["706", "012000"]

Abstract:
There are disclosed devices, system and methods for a machine learning binary classifier automatically tolerating training data that is incorrect by determining a correct and an incorrect likelihood ratio that each training data entry has a correctly and an incorrectly labeled output. The correct and an incorrect likelihood ratio are combined with a correct and an incorrect priori odds ratio that the set of training data entries have correctly and incorrect labeled output labels. These two combinations are a correct probability and an incorrect probability that each entry of the set of entries has a correctly and an incorrect labeled output. A logistic regression model if fit to a combination of the correct probability and the incorrect probability for each training data entry to complete the training.

Claim (Index 2):
The method of  claim 1 , wherein:\n the tolerant logistic regression model includes a first and second logistic regression model; determining the correct likelihood ratio comprises fitting the first logistic regression model to each entry of the training data and determining the incorrect likelihood ratio comprises fitting the second logistic regression model to each entry of the training data; and wherein fitting comprises minimizing a likelihood function that each entry of the training data fits a logistic regression model to estimate model parameters of a logistic regression model.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 100.0
- Lexical Diversity: 2.23729
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15139807', '15878113', '12128947', '12789292', '13620668']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.393858938896967
- 35 USC 102 Novelty (BERT): 0.4881420737227238
- Combined Prediction Score: 0.4032872523795426
- Mean Citation Score: 211.59983000000005
- Max Citation Score: 226.26741
- Similarity Product: 168.98518838103357

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