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 1):
A method of automatically tolerating training data that is incorrect when training a machine learning binary classifier, the method comprising:\n training the machine learning binary classifier using a set of training data entries, each training data entry having known inputs and a known output label, wherein the set of training data entries includes a subset of training data entries that have incorrectly labeled known output labels for both a binary true state and a binary false state of the output labels; wherein training comprises:\n determining a correct likelihood ratio that each training data entry of the set of training data entries has a correctly labeled output label and an incorrect likelihood ratio that each training data entry of the set of training data entries has an incorrectly labeled output label; \n identifying a correct priori odds ratio that the set of training data entries have correctly labeled output labels and an incorrect priori odds ratio that the set of training data entries have incorrectly labeled output labels; \n calculating a correct probability that each entry of the set of entries has a correctly labeled output label using the correct likelihood ratio for that entry and the correct prior odds ratio; \n calculating an incorrect probability that each entry of the set of entries has an incorrectly labeled output label using the incorrect likelihood ratio for that entry and the incorrect prior odds ratio; and \n training the machine learning binary classifier using a tolerant logistic regression model that combines the correct probability and the incorrect probability.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 100.0
- Lexical Diversity: 2.23729
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15139807', '15878113', '12128947', '12789292', '13620668']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3653399846358722
- 35 USC 102 Novelty (BERT): 0.488219095679467
- Combined Prediction Score: 0.3776278957402316
- Mean Citation Score: 211.59983000000005
- Max Citation Score: 226.26741
- Similarity Product: 164.23606153214698

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