PATENT CLAIM ANALYSIS

Application Number: 15883359
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-02
Patent Classification: ["708", "322000"]

Abstract:
A method is explained for any adaptive processor processing digital signals by adjusting signal weights on digital signal(s) it handles, to optimize adaptation criteria responsive to a functional purpose or externalities (transient, temporary, situational, and even permanent) of that processor. Adaptation criteria for the adaptive algorithm may be any combination of a signal or parameter estimation, and measured quality(ies). This method performs a linear transformation adapting parameters from M to (M 1 +L) dimensions in each adaptation event, such that M 1  weights are updated without constraints and M 0 =M−M 1  weights are forced by soft constraints into an L-dimensional subspace they spanned at the beginning of the adaptation period. The same dimensionality reduction, using the same linear transformation, is applied to the input data. The reduced-dimensionality weights are then adapted using the identical optimization strategy employed by the processor, except with input data that has also been reduced in dimensionality.

Claim (Index 10):
The method of  claim 9 , wherein the step of combining the update-set data and the held-set output data to form enhanced data further comprises:\n a) providing a single-port adapt-path; b) extracting single port held-set weights; c) computing the single-port held set weights and the held-set data to form held-set output data; and d) combining the held-set output data and the update-set data to form the enhanced data.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 86.0
- Lexical Diversity: 1.67619
- Patent Class: 708.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14121895', '15219145', '10587701', '09878789', '14309332']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4266723332591963
- 35 USC 102 Novelty (BERT): 0.5612663162079901
- Combined Prediction Score: 0.4401317315540757
- Mean Citation Score: 223.99368
- Max Citation Score: 444.13138
- Similarity Product: 320.5406350532496

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