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 23):
A system that progressively trains machine learning to artificially generate recognizable handwritten characters, the system comprising:\n one or more memory; and one or more processor that receives a digitized seed character comprising pixels, chooses at least one feature of the seed character, determines a probability distribution of the pixels of the chosen feature, and artificially generates deformed characters at least by:\n performing physiognomy gridline adjusting based on positions of the pixels, \n defining alignment classifiers based at least on the gridline repositioning, and \n identifying deformation classifiers based at least on the alignment classifiers, and \n selecting one or more deformational rules from a deformational rule bank based at least on the deformation classifiers, and \n deforming the digitized seed character according to the selected one or more deformational rules, where \n the one or more processor further collects accuracy data, and alters the selecting step based at least on the accuracy data.

Metadata:
- Claim Count in Document: 8.0
- Percentile: 90.0
- Lexical Diversity: 1.75641
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['11305968', '10353102', '12848173', '15648710', '13734197']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3108046606752129
- 35 USC 102 Novelty (BERT): 0.4871486297143795
- Combined Prediction Score: 0.3284390575791295
- Mean Citation Score: 161.121726
- Max Citation Score: 170.47821000000005
- Similarity Product: 110.56082397884492

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