PATENT CLAIM ANALYSIS

Application Number: 16213880
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-04
Patent Classification: ["702", "104000"]

Abstract:
The disclosed embodiments include a method, apparatus, and computer program product for generating a cross-sensor standardization model. For example, one disclosed embodiment includes a system that includes at least one processor; at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations comprising selecting a representative sensor from a group of sensors comprising at least one of same primary optical elements and similar synthetic optical responses and calibrating a cross-sensor standardization model based on a matched data pair for each sensor in the group of sensors and for the representative sensor. In one embodiment, the at least one memory coupled to the at least one processor and storing instructions that when executed by the at least one processor performs operations further comprises generating the matched data pair, wherein the matched data pair comprises calibration input data and calibration output data.

Claim (Index 12):
The non-transitory computer readable medium of  claim 11 , wherein the synthetic responses comprise synthetic optical responses associated with a design or fabrication batch that is the same.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 98.0
- Lexical Diversity: 2.32857
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15900679', '15514469', '15124282', '14436017', '14780780']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2097345623417652
- 35 USC 102 Novelty (BERT): 0.4905836010496598
- Combined Prediction Score: 0.2378194662125547
- Mean Citation Score: 171.82555399999995
- Max Citation Score: 194.3119
- Similarity Product: 129.15643900928498

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

Dataset: test