Taesik Na
14Patents
2h-index
24Co-inventors
50Inventor score
Filing activity: Mar 13, 2014 → May 22, 2024
Most-cited inventions
| Patent | Title | Area | Cited by | Status |
|---|---|---|---|---|
| US10474174B2 | Programmable supply generator | Physics | 2 | Active |
| US9473149B1 | Temperature-compensated signal generator for supply voltage monitoring | Electricity | 2 | Active |
| US9147452B2 | Synchronous semiconductor memory device having dual delay locked loop circuit and method of managing dual delay locked loop circuit | Physics | 1 | Active |
| US11526761B2 | Neural network training with decreased memory consumption and processor utilization | Physics | 1 | Active |
| US12266006B2 | Providing and displaying search results in response to a query | Physics | 0 | Active |
| US11853897B2 | Neural network training with decreased memory consumption and processor utilization | Physics | 0 | Active |
| US12259894B2 | Accounting for item attributes when selecting items satisfying a query based on item embeddings and an embedding for the query | Physics | 0 | Active |
| US12373446B1 | Search engine for recommending search queries based on user interactions using a transformer-based language model | Physics | 0 | Active |
| US12222937B2 | Training a machine learned model to determine relevance of items to a query using different sets of training data from a common domain | Physics | 0 | Active |
| US11676003B2 | Training neural network accelerators using mixed precision data formats | Physics | 0 | Active |
| US12026180B2 | Clustering data describing interactions performed after receipt of a query based on similarity between embeddings for different queries | Physics | 0 | Active |
| US12367220B2 | Clustering data describing interactions performed after receipt of a query based on similarity between embeddings for different queries | Physics | 0 | Active |
| US12314286B2 | Distributed approximate nearest neighbor service architecture for retrieving items in an embedding space | Physics | 0 | Active |
| US12287819B2 | Machine learned models for search and recommendations | Physics | 0 | Active |
Source: USPTO / EPO open patent data. Inventor disambiguation is heuristic; counts are objective bibliographic measures.