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 13):
The classifier of  claim 12 , wherein:\n the tolerant logistic regression model includes a first and second logistic regression model; the correct likelihood ratio is the first logistic regression model fit to each entry of the training data to minimize a likelihood function that each entry of the training data fits the first logistic regression model; and the incorrect likelihood ratio is the second logistic regression model fit to each entry of the training data to minimize a likelihood function that each entry of the training data fits the second 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): 0.3914203622321513
- 35 USC 102 Novelty (BERT): 0.4988941568730526
- Combined Prediction Score: 0.4021677416962415
- Mean Citation Score: 211.59983000000005
- Max Citation Score: 226.26741
- Similarity Product: 147.47986531485202

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