PATENT CLAIM ANALYSIS

Application Number: 15961465
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["706", "012000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals. One of the methods includes: determining an RF signal configured to be transmitted through an RF band of a communication medium; determining first classification information that is associated with the RF signal, and that includes a representation of a characteristic of the RF signal or a characteristic of an environment in which the RF signal is communicated; using at least one machine-learning network to process the RF signal and generate second classification information as a prediction of the first classification information; calculating a measure of distance between (i) the second classification information that was generated by the at least one machine-learning network, and (ii) the first classification information associated with the RF signal; and updating the at least one machine-learning network based on the measure of distance.

Claim (Index 26):
A system comprising:\n at least one processor; and at least one computer memory that is operably connectable to the at least one processor and that has stored thereon instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: determining at least one machine-learning network has been trained to classify RF signals configured to be transmitted through an RF band of a communication medium; setting at least one parameter of an RF receiver based on the at least one trained machine-learning network; using the RF receiver to receive an analog RF waveform from an RF spectrum of the communication medium, and to process the analog RF waveform to generate a discrete-time representation of the analog RF waveform as a received RF signal; and using the at least one trained machine-learning network to process the received RF signal and generate predicted RF signal classification information, wherein the predicted RF signal classification information comprises a representation of at least one of a characteristic of the received RF signal or a characteristic of an environment in which the received RF signal was communicated.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 91.0
- Lexical Diversity: 2.13924
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15961454', '15955485', '15688678', '15894774', '15938983']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3720459832061397
- 35 USC 102 Novelty (BERT): 0.5100848796891831
- Combined Prediction Score: 0.385849872854444
- Mean Citation Score: 209.449794
- Max Citation Score: 305.38593
- Similarity Product: 237.1897266193092

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test