PATENT CLAIM ANALYSIS

Application Number: 16121293
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-01
Patent Classification: ["375", "232000"]

Abstract:
A receiver (e.g., for a 10G fiber communications link) includes an interleaved ADC coupled to a multi-channel equalizer that can provide different equalization for different ADC channels within the interleaved ADC. That is, the multi-channel equalizer can compensate for channel-dependent impairments. In one approach, the multi-channel equalizer is a feedforward equalizer (FFE) coupled to a Viterbi decorder, for example, a sliding block Viterbi decoder (SBVD); and the FFE and/or the channel estimator for the Viterbi decoder are adapted using the LMS algorithm.

Claim (Index 20):
A method for equalizing a received signal, the method comprising:\n generating, by an interleaved analog-to-digital converter (ADC) having more than one channel, signal samples from the received signal having combined non-Gaussian noise and Gaussian noise; applying, by a multi-channel equalizer, an equalization to the signal samples from the interleaved ADC to generate equalized samples; determining, by a decoder coupled to an output of the multi-channel equalizer, detected symbols from the equalized samples and a channel model by minimizing a cumulative metric that compensates the combined Gaussian and non-Gaussian noise in the received signal; and generating, by a channel estimator, the channel model and an error feedback signal to adaptively update coefficients of the multi-channel equalizer based on the error feedback signal.

Metadata:
- Claim Count in Document: 23.0
- Percentile: 97.0
- Lexical Diversity: 1.73214
- Patent Class: 375.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15839380', '11559850', '15387246', '14480085', '13013149']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5249079721579012
- 35 USC 102 Novelty (BERT): 0.570553767050655
- Combined Prediction Score: 0.5294725516471765
- Mean Citation Score: 421.3349
- Max Citation Score: 441.83072
- Similarity Product: 430.34184707946775

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