PATENT CLAIM ANALYSIS

Application Number: 16257979
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-07
Patent Classification: ["345", "633000"]

Abstract:
Methods and apparatus for calibrating performance of one or more statistical models used to generate a musculoskeletal representation. The method comprises controlling presentation of instructions via a user interface to instruct the user to perform the at least one gesture and updating at least one parameter of the one or more statistical models based, at least in part on a plurality of neuromuscular signals recorded by a plurality of neuromuscular sensors during performance of the at least one gesture by the user.

Claim (Index 28):
A method of calibrating performance of one or more statistical models used to generate a musculoskeletal representation, the method comprising:\n instructing, via a user interface, a user to perform at least one gesture while wearing a wearable device having a plurality of neuromuscular sensors arranged thereon; controlling presentation of instructions via the user interface to instruct the user to perform the at least one gesture; updating at least one parameter of the one or more statistical models based, at least in part on a plurality of neuromuscular signals recorded by the plurality of neuromuscular sensors during performance of the at least one gesture by the user; identifying, based on an output of the one or more statistical models, a plurality of gesture characteristics that the one or more statistical model is poor at estimating; selecting a new gesture for the user to perform that includes the identified plurality of gesture characteristics; controlling presentation of instructions via the user interface to instruct the user to perform the new gesture; and updating at least one parameter of the one or more statistical models based, at least in part on the plurality of neuromuscular signals recorded by the neuromuscular sensors during performance of the new gesture by the user.

Metadata:
- Claim Count in Document: 74.0
- Percentile: 99.0
- Lexical Diversity: 1.77083
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16258409', '16258232', '16258442', '15974384', '15659018']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6370828981172927
- 35 USC 102 Novelty (BERT): 0.5384869745801519
- Combined Prediction Score: 0.6272233057635787
- Mean Citation Score: 308.838978
- Max Citation Score: 372.13522
- Similarity Product: 315.8719471881176

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