PATENT CLAIM ANALYSIS

Application Number: 16057745
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-11
Patent Classification: ["345", "426000"]

Abstract:
Dynamic soft shadows may be generated without resorting to computationally-expensive multiple render passes and sampling, or lightmap generation. With disclosed systems and methods, a dynamic soft shadow may be rendered in a single pass, which is sufficiently efficient to run on an untethered virtual reality (VR) device, such as a head mounted device (HMD). Despite the efficiency, the shadow quality may be markedly superior to those generated with other methods. In some embodiments, a script may be used with a shader to render a shadow having a realistic size, shape, position, fading factor and sharpness, based on a position and size of a shadow casting element and a light vector.

Claim (Index 10):
A method of rendering a soft shadow, the method comprising:\n obtaining a position and a size of a shadow casting element; obtaining a direction of a light vector; based at least on a distance between the shadow casting element and a shadowed element, a relative direction between the shadow casting element and the shadowed element, and the direction of the light vector, determining a shadow position, a shadow size, and a shadow fading; and based at least on the shadow position, the shadow size, and the shadow fading, rendering a shadow for the shadow casting element on the shadowed element.

Metadata:
- Claim Count in Document: 30.0
- Percentile: 96.0
- Lexical Diversity: 1.62025
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['10022133', '11832296', '11355029', '16030510', '13263313']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6461857314170211
- 35 USC 102 Novelty (BERT): 0.4924136071623183
- Combined Prediction Score: 0.6308085189915509
- Mean Citation Score: 186.638354
- Max Citation Score: 241.53763
- Similarity Product: 182.2467575514597

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