PATENT CLAIM ANALYSIS

Application Number: 16132103
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-10
Patent Classification: ["398", "202000"]

Abstract:
A high-speed and high-precision photonic analog-to-digital conversion device capable of realizing intelligent signal processing. Learning ability of deep learning technology is utilized to learn the nonlinear response and channel mismatch effect of the system and configure optimal parameters of the deep network. Deterioration of photonic analog-to-digital conversion system performance caused by nonlinear distortion and channel mismatch distortion is eliminated in real time, and performance indicators thereof are improved. By using the induction and deduction ability of deep learning technology, intelligent signal processing of the input signal is realized, and users are provided with digital signals that meet the requirements. It's important for improving the performance of microwave photonic systems that require high sampling rate, high time precision, and high sampling accuracy, such as microwave photonic radar and optical communication systems, and also critical to improve the signal processing ability of such systems under complex conditions.

Claim (Index 12):
A method for high-speed and high-precision photonic analog-to-digital conversion using the device of  claim 1 , comprising in a training phase, inputting standard sinusoidal signals with known frequency and amplitude information provided by the signal source into the high-speed photonic analog-to-digital conversion system,\n sampling the standard sinusoidal signals by the high-reputation-rate pulse light source, and sequentially passed the standard sinusoidal signals through the multi-channel demultiplexer module and the parallelization photoelectric conversion module, obtaining digital signals affected with distortion by the high-speed photonic analog-to-digital conversion system at the output ports of the parallelization electronic-quantization module, inputting the distorted digital signals into the deep network as a first part of a training sample set, generating standard sinusoidal digital signals according to the known frequency and amplitude information by the digital signal processor, inputting the standard sinusoidal digital signals to the deep network as a second part of the training sample set, combining the distorted digital signals and the standard sinusoidal digital signals by the deep network to train parameters of the deep network, and resetting the parameters of the deep network, and establishing an inverse response of distortion effect of the high-speed photonic analog-to-digital conversion system; and in an application phase, inputting sampled signals into the high-speed photonic analog-to-digital conversion system by the second input port of the photon sampling gate, sampling the sampled signals by the high-reputation-rate pulse light source, sequentially passing the sampled signals through the multi-channel demultiplexer module and the parallelization photoelectric conversion module to obtain digital signals, inputting the digital signals into the deep network by the parallelization electronic-quantization module, and using the inverse response of the distortion effect of the high-speed photonic analog-to-digital conversion system by the deep network to perform intelligent signal processing on the digital signals and obtains digital signals after distortion elimination.

Metadata:
- Claim Count in Document: 29.0
- Percentile: 97.0
- Lexical Diversity: 1.81609
- Patent Class: 398.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['13990161', '13386422', '14698087', '16006101', '13478255']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6522210308433527
- 35 USC 102 Novelty (BERT): 0.4891196137489851
- Combined Prediction Score: 0.6359108891339159
- Mean Citation Score: 168.974198
- Max Citation Score: 183.94719
- Similarity Product: 142.88442773713052

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

Dataset: test