Amirali Kia
23Patents
6h-index
27Co-inventors
61Inventor score
Filing activity: Apr 15, 2015 → Jul 13, 2023
Most-cited inventions
| Patent | Title | Area | Cited by | Status |
|---|---|---|---|---|
| US9790476B2 | Modified transposases for improved insertion sequence bias and increased DNA input tolerance | Chemistry; Metallurgy | 79 | Active |
| US10035992B2 | Modified transposases for improved insertion sequence bias and increased DNA input tolerance | Chemistry; Metallurgy | 72 | Active |
| US10544403B2 | Modified transposases for improved insertion sequence bias and increased DNA input tolerance | Chemistry; Metallurgy | 70 | Active |
| US11436429B2 | Artificial intelligence-based sequencing | Physics | 17 | Active |
| US10385323B2 | Modified transposases for improved insertion sequence bias and increased DNA input tolerance | Chemistry; Metallurgy | 14 | Active |
| US11347965B2 | Training data generation for artificial intelligence-based sequencing | Physics | 14 | Active |
| US11210554B2 | Artificial intelligence-based generation of sequencing metadata | Physics | 5 | Active |
| US12217831B2 | Artificial intelligence-based quality scoring | Physics | 4 | Active |
| US11783917B2 | Artificial intelligence-based base calling | Physics | 3 | Active |
| US11288576B2 | Predicting quality of sequencing results using deep neural networks | Physics | 2 | Active |
| US11590505B2 | System and method for storage | Physics | 1 | Active |
| US11676685B2 | Artificial intelligence-based quality scoring | Physics | 1 | Active |
| US12119088B2 | Deep neural network-based sequencing | Physics | 1 | Active |
| US11908548B2 | Training data generation for artificial intelligence-based sequencing | Physics | 1 | Active |
| US12354008B2 | Knowledge distillation and gradient pruning-based compression of artificial intelligence-based base caller | Physics | 0 | Active |
| US12106829B2 | Artificial intelligence-based many-to-many base calling | Physics | 0 | Active |
| US12073922B2 | Deep learning-based framework for identifying sequence patterns that cause sequence-specific errors (SSEs) | Physics | 0 | Active |
| US12277998B2 | Artificial intelligence-based base calling | Physics | 0 | Active |
| US12180468B2 | Transposase compositions for reduction of insertion bias | Chemistry; Metallurgy | 0 | Active |
| US11749380B2 | Artificial intelligence-based many-to-many base calling | Physics | 0 | Active |
| US12201985B2 | System and method for storage | Physics | 0 | Active |
| US11961593B2 | Artificial intelligence-based determination of analyte data for base calling | Physics | 0 | Active |
| US10815478B2 | Method of sequential tagmentation with transposase compositions for reduction of insertion bias | Chemistry; Metallurgy | 0 | Active |
Source: USPTO / EPO open patent data. Inventor disambiguation is heuristic; counts are objective bibliographic measures.