PATENT CLAIM ANALYSIS

Application Number: 15920936
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["382", "103000"]

Abstract:
A system, method, and apparatus to detect bio-mechanical geometry in a scene using machine vision. The invention provides accurate and dynamic data collection using machine learning and vision coupled with augmented reality to continually improve the process with each experience. It does not rely upon external sensors or manual input.

Claim (Index 10):
A system to detect bio-mechanical geometry in a scene, comprising:\n a camera having a digital video output; a computer having a user interface; and a program product comprising machine-readable program code for causing, when executed, the computer to perform the following process steps:\n receiving a plurality of image frames from the digital video output of the camera, the plurality of image frames having one or more target objects captured within the plurality of image frames; \n processing the plurality of image frames through a scene processor to identify the one or more target objects within each image frame, wherein the image frame is subdivided into a plurality of sub-images based on a target scale corresponding to the one or more target objects; \n converting each sub image into a neural input set for each target scale, \n concatenating the neural input set for each target scale into a single input array; \n processing the single input array through a conditioned neural network to compute a value for hidden neuron and computing a neural network output based on the hidden values.

Metadata:
- Claim Count in Document: 44.0
- Percentile: 90.0
- Lexical Diversity: 1.22222
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14513497', '14733472', '10755946', '15449614', '14844849']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3686058201885711
- 35 USC 102 Novelty (BERT): 0.4758568380802477
- Combined Prediction Score: 0.3793309219777387
- Mean Citation Score: 175.49955400000005
- Max Citation Score: 177.77393999999995
- Similarity Product: 127.04286761881822

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