PATENT CLAIM ANALYSIS

Application Number: 16256624
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-06
Patent Classification: ["375", "260000"]

Abstract:
According to an exemplary embodiment, a method of making a Hermetic transform to mitigate noise comprises: receiving over a channel signal frames comprising predetermined data and gaps comprising noise; framing the predetermined data; constructing a set of linear equations which relate a transfer function matrix of the channel to the predetermined data; determining the transfer function matrix by inverting the linear equations using a first pseudo inverse matrix; incorporating transfer function matrix into linear equations for a hermetic transform; and determining the hermetic transform using a second pseudo inverse matrix based on the predetermined data and the noise.

Claim (Index 1):
A method for spectral compacting, the method comprising:\n generating a set of orthogonal basis signals; modulating each of the basis signals with a complex modulation based on a constellation of complex symbols to produce modulated basis signals; summing the modulated basis signals to produce a frame of signal data; applying a matrix transformation to the frame of signal data to produce a reduced bandwidth frame of signal data; and transmitting the reduced bandwidth frame of signal data.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 99.0
- Lexical Diversity: 2.0
- Patent Class: 375.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15443109', '13927086', '14709377', '15407941', '13927089']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4726580212124743
- 35 USC 102 Novelty (BERT): 0.5123321901825029
- Combined Prediction Score: 0.4766254381094771
- Mean Citation Score: 196.30203
- Max Citation Score: 293.81018
- Similarity Product: 214.27056957655307

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

Dataset: test