PATENT CLAIM ANALYSIS

Application Number: 15827263
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-09
Patent Classification: ["345", "426000"]

Abstract:
For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.

Claim (Index 1):
A method for three-dimensional rendering in a rendering system, the method comprising:\n acquiring, by a medical imaging system, a medical dataset representing a three-dimensional region of a patient; rendering, by a renderer using different combinations of rendering settings, a plurality of images from the medical dataset; applying, by an image processor, different pairs of the images of the plurality and a reference image to a machine-learnt model, the machine-learned model trained to generate representation vectors of the images and the reference image; selecting, by the image processor, one of the plurality of images based on the representation vectors; and displaying the selected one of the plurality of images.

Metadata:
- Claim Count in Document: 38.0
- Percentile: 86.0
- Lexical Diversity: 1.53623
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15191043', '15643973', '15677460', '15720317', '15661675']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6549350139886051
- 35 USC 102 Novelty (BERT): 0.492448182838883
- Combined Prediction Score: 0.6386863308736329
- Mean Citation Score: 232.033698
- Max Citation Score: 269.3774
- Similarity Product: 231.1613361807824

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

Dataset: test