PATENT CLAIM ANALYSIS

Application Number: 15912355
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["382", "159000"]

Abstract:
Embodiments of the technology discussed herein address problems of traditional electronic character recognition training by artificially generating handwriting in a unique way according to machine learning techniques that transform handwriting samples according to generative rules and discriminative rules. Solutions provided herein produce a wide range of artificially generated handwriting that appears to be human generated handwriting. As such, embodiments herein provide additional characters for a system's character bank that are obtained more efficiently, as compared to traditional techniques. Further, embodiments herein are designed to be suitable for machine learning, and as such, the techniques grow ever more efficient as the techniques are performed. In short, the solutions provided herein improve the computing technology itself in a manner that makes robust electronic Chinese character recognition feasible.

Claim (Index 20):
The non-transitory computer-readable medium of  claim 12 , wherein the program code further comprises:\n code to determine that the artificially generating deformed characters is recognizable as the seed character; and code to add the artificially generating deformed characters to a character bank that stores handwritten characters.

Metadata:
- Claim Count in Document: 8.0
- Percentile: 90.0
- Lexical Diversity: 1.75641
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['11305968', '10353102', '12848173', '15648710', '13734197']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3384276074853985
- 35 USC 102 Novelty (BERT): 0.4826048912567341
- Combined Prediction Score: 0.3528453358625321
- Mean Citation Score: 161.121726
- Max Citation Score: 170.47821000000005
- Similarity Product: 118.01835960158232

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