PATENT CLAIM ANALYSIS

Application Number: 16082103
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-07
Patent Classification: ["382", "141000"]

Abstract:
A method of board lumber (Table 2) grading is performed in an industrial environment on a machine learning framework ( 12 ) configured as an interface to a machine learning-based deep convolutional network ( 20 ) that is trained end-to-end, pixels-to-pixels on semantic segmentation. The method uses deep learning techniques that are applied to semantic segmentation to delineate board lumber characteristics (Table 1), including their sizes and boundaries.

Claim (Index 1):
A computer-implemented method of detecting defining characteristics of wood material to facilitate one or both of grading and optimizing board lumber by machine learning techniques, comprising:\n creating a definition of wood characteristics learned from images of multiple wood specimens, the multiple wood specimen images identifying the wood characteristics and represented by input layer pixel data derived from raw image data produced by automatic scanning of the multiple wood specimens, the input layer pixel data corresponding to sets of pixels representing regions of each of the multiple wood specimen images, the input layer pixel data representing classes of the wood characteristics within specified boundaries of the regions at identified locations; receiving, by a machine learning framework, the input layer pixel data, the machine learning framework supporting a training processing unit in which is performed a set of deep learning algorithms developed to train a machine learning-based convolutional neural network on semantic segmentation, the set of deep learning algorithms performing semantic segmentation on the input layer pixel data to determine edges in and network learned weights for collections of pixels in the sets of pixels, the collections of pixels encompassed by the edges and corresponding to the regions of each of the multiple wood specimens; providing, to the machine learning framework, milled board image data representing a milled board of wood, the milled board image data including input layer pixel data produced by multiple sensor channels from each one of which are derived milled board pixels of images defining wood characteristics of the milled board of wood; applying the milled board image data to the convolutional neural network operating on an inference processing unit, the convolutional neural network performing semantic segmentation on the milled board image data to determine, for the milled board pixels, probability values for use in forming a series of probability maps, each probability map in the series corresponding to a different one of the classes of wood characteristics so that each milled board pixel of the milled board pixels has a probability value for each of the classes of wood characteristics; and the inference processing unit deriving, from the probability values determined for the milled board pixels in the series of probability maps, a solution identifying which ones of the milled board pixels belong to classes of the wood characteristics and specifying the classes to which the identified milled board pixels belong.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 97.0
- Lexical Diversity: 1.46154
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10854930', '15352821', '11314852', '15690037', '11316046']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2748552451489132
- 35 USC 102 Novelty (BERT): 0.4748662619836657
- Combined Prediction Score: 0.2948563468323885
- Mean Citation Score: 157.11545200000003
- Max Citation Score: 166.22716
- Similarity Product: 126.95927574269771

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