PATENT CLAIM ANALYSIS

Application Number: 15771056
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-11
Patent Classification: ["347", "019000"]

Abstract:
A method identifies at least one malfunctioning nozzle in a digital printing press, the digital printing press including a plurality of nozzles. The method includes printing a design on a substrate, acquiring at least one image of the printed design and identifying at least one artifact in the acquired image. The method further includes identifying the malfunctioning nozzle and classifying the at least one malfunctioning nozzle according to the at least one of the acquired image of the printed design, at least a portion of a nozzle pattern and at least a portion of a uniformity pattern.

Claim (Index 17):
The method according to  claim 16 , wherein nozzle is classified as intact when a nozzle mark is identified in an expected nozzle mark location within a determined tolerance,\n wherein a nozzle is classified as missing when an expected nozzle mark location is not associated with a detected nozzle mark, wherein a nozzle is classified as deviated when the distance between said actual nozzle mark location and said expected nozzle mark location associated with the detected nozzle mark is above an x-deviation threshold, wherein a nozzle is classified as inconsistent when the respective detected nozzle mark thereof exhibits a strength score below a determined strength score threshold, wherein a nozzle is classified as redundant when a nozzle mark is detected between two expected nozzle mark locations with detected nozzle marks associated therewith.

Metadata:
- Claim Count in Document: 19.0
- Percentile: 91.0
- Lexical Diversity: 2.64103
- Patent Class: 347.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16092738', '14019929', '12413817', '13351007', '12391618']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7656127943238478
- 35 USC 102 Novelty (BERT): 0.5344402740722961
- Combined Prediction Score: 0.7424955422986926
- Mean Citation Score: 238.288516
- Max Citation Score: 386.2516
- Similarity Product: 223.86679079811577

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

Dataset: test