PATENT CLAIM ANALYSIS

Application Number: 15994461
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-12
Patent Classification: ["716", "110000"]

Abstract:
A computer-implemented method, computer program product, and computer processing system are provided for generating synthetic layout patterns. The method includes receiving, by a processor, a set of physical design layouts that include a variety of layout patterns for neural network training. The method further includes generating, by the processor, a set of training layout pattern images for the neural network training by performing automatic image capturing on the set of physical design layouts with scripts. The method also includes training, by the processor, a feedforward neural network (FFNN)-based Variational Autoencoder (VAE) with the set of training layout pattern images. The method additionally includes generating, by the processor using the FFNN-based VAE, new synthetic layout images.

Claim (Index 17):
A computer processing system for generating synthetic layout patterns, comprising:\n a memory for storing program code; and a processor, operatively coupled to the memory, for running program code to\n receive a set of physical design layouts that include a variety of layout patterns for neural network training; \n generate a set of training layout pattern images for the neural network training by performing automatic image capturing on the set of physical design layouts with scripts; \n train a feedforward neural network (FFNN)-based Variational Autoencoder (VAE) with the set of training layout pattern images; and \n generate, using the FFNN-based VAE, new synthetic layout images.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 2.28814
- Patent Class: 716.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15994396', '15994598', '15880339', '15609009', '15938897']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3436892122404518
- 35 USC 102 Novelty (BERT): 0.4801850691137508
- Combined Prediction Score: 0.3573387979277818
- Mean Citation Score: 224.380102
- Max Citation Score: 274.06577000000004
- Similarity Product: 224.13134209009

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

Dataset: test