PATENT CLAIM ANALYSIS

Application Number: 16353998
Application Type: Utility
Filing Date: 2019-03
Publication Date: 2019-07
Patent Classification: ["345", "156000"]

Abstract:
Methods and apparatus for providing a dynamically-updated computerized musculo-skeletal representation comprising a plurality of rigid body segments connected by joints. The method comprises recording, using a plurality of autonomous sensors arranged on one or more wearable devices, a plurality of autonomous signals from a user, wherein the plurality of autonomous sensors include a plurality of neuromuscular sensors configured to record neuromuscular signals. The method further comprises providing as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals. The method further comprises determining, based on an output of the trained statistical model, musculo-skeletal position information describing a spatial relationship between two or more connected segments of the plurality of rigid body segments of the computerized musculo-skeletal representation, and updating the computerized musculo-skeletal representation based, at least in part, on the musculo-skeletal position information.

Claim (Index 28):
The method according to  claim 16 , wherein the wearable device further comprises an inertial measurement unit configured to generate inertial signals corresponding to movement of the wearable device and the user's arm, and wherein the method further comprises an act of determining, using information derived from the inertial signals with the machine learning model, the output that describes the hand position.

Metadata:
- Claim Count in Document: 12.0
- Percentile: 99.0
- Lexical Diversity: 2.29412
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16258232', '16258442', '16257979', '15974384', '16258409']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7137806538095189
- 35 USC 102 Novelty (BERT): 0.5226729111786792
- Combined Prediction Score: 0.6946698795464349
- Mean Citation Score: 316.366728
- Max Citation Score: 331.97498
- Similarity Product: 231.3423058447445

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

Dataset: test