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 18):
The device of  claim 12 , further comprising:\n a processor; and a memory; wherein the processor and the memory comprise circuits and software for performing the tolerating.

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): 0.3604190042092398
- 35 USC 102 Novelty (BERT): 0.510600204297825
- Combined Prediction Score: 0.3754371242180984
- Mean Citation Score: 211.59983000000005
- Max Citation Score: 226.26741
- Similarity Product: 119.47312991651415

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test