PATENT CLAIM ANALYSIS

Application Number: 15961454
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-11
Patent Classification: ["706", "012000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned compact representations of radio frequency (RF) signals. One of the methods includes: determining a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; calculating a measure of compression in the compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal.

Claim (Index 9):
The method of  claim 1 , wherein the encoder machine-learning network and the decoder machine-learning network are jointly trained as an auto-encoder to learn compact representations of RF signals, and\n wherein the auto-encoder comprises at least one regularization layer that comprises at least one of: weight regularization on network layer weights, activity regularization on network layer activations, or stochastic impairments on network layer activations or network layer weights.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 91.0
- Lexical Diversity: 2.50769
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15961465', '13476862', '15424711', '13343636', '15782725']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3477002625103304
- 35 USC 102 Novelty (BERT): 0.5142108655715185
- Combined Prediction Score: 0.3643513228164492
- Mean Citation Score: 193.806652
- Max Citation Score: 276.59918
- Similarity Product: 194.2506614853156

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