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 17):
The non-transitory computer-readable medium of  claim 16 , wherein the program code further comprises:\n code to classify the received artificially generated deformed characters as new personalized data; and code to update the current signature model based at least on the new personalized data, based on the classifying.

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): 0.3380737361845493
- 35 USC 102 Novelty (BERT): 0.484113232079157
- Combined Prediction Score: 0.35267768577401
- Mean Citation Score: 161.121726
- Max Citation Score: 170.47821000000005
- Similarity Product: 116.37341898274366

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