PATENTS AND APPLICATIONS
as of May, 2024

1. US-04792696-A
   Method and an apparatus for determining surface shape utilizing object self-shadowing


2. US-06111983-A
   Determination of image shapes using training and sectoring


3. US-07277574-B2
   Methods and systems for feature selection


4. US-07489727-B2
   Method and device for online dynamic semantic video compression and video indexing


5. US-07539934-B2
   Computer-implemented method, system, and program product for developing a content annotation lexicon


6. US-07599554-B2
   Method and apparatus for summarizing a music video using content analysis


7. US-07643597-B2
   Methods for selecting a subsequence of video frames from a sequence of video frames


8. US-08126262-B2
   Annotating video segments using feature rhythm models


9. US-08391355-B2
   Method and device for online dynamic semantic video compression and video indexing


10. US-08407164-B2
    Data classification and hierarchical clustering


11. US-08798402-B2
    Using near-duplicate video frames to analyze, classify, track, and visualize evolution and fitness of videos


12. US-08798400-B2
    Using near-duplicate video frames to analyze, classify, track, and visualize evolution and fitness of videos


13. US-09177229-B2
    Kalman filter approach to augment object tracking


14. US-09552442-B2
    Visual meme tracking for social media analysis


15. US-09710760-B2
    Multi-facet classification scheme for cataloging of information artifacts


16. US-10303801-B2
    Visual meme tracking for social media analysis


17. US-20190354850-A1
    Identifying transfer models for machine learning tasks


18. US-20210174191-A1
    Automated fine-tuning of a pre-trained neural network for transfer learning


19. US-11151410-B2
    Generating and augmenting transfer learning datasets with pseudo-labeled images


20. US-20230326186-A1
    Automated data labeling using a geometric approach


21. US-11853877-B2
    Training transfer-focused models for deep learning


22. US-11934922-B2
    Predictive data and model selection for transfer learning in natural language processing