Average Co-Inventor Count = 4.53
ph-index = 15
The patent ph-index is calculated by counting the number of publications for which an author has been cited by other authors at least that same number of times.
Company Filing History:
1. Veveo, Inc. (66 from 123 patents)
2. Anumana, Inc. (22 from 44 patents)
3. Nference, Inc. (7 from 27 patents)
4. Adeia Guides Inc. (6 from 498 patents)
5. Rovi Guides, Inc. (2 from 2,118 patents)
6. Mayo Foundation for Medical Education and Research (1 from 1,732 patents)
104 patents:
1. 12436983 - Method for adaptive conversation state management with filtering operators applied dynamically as part of a conversational interface
2. 12430378 - Apparatus and method for note data analysis to identify unmet needs and generation of data structures
3. 12430375 - Method for adaptive conversation state management with filtering operators applied dynamically as part of a conversational interface
4. 12424334 - Apparatus and method for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data
5. 12399932 - Apparatus and methods for visualization within a three-dimensional model using neural networks
6. 12387365 - Apparatus and method for object pose estimation in a medical image
7. 12387847 - Method and an apparatus for detecting a level of cardiovascular disease
8. 12381008 - System and methods for observing medical conditions
9. 12376777 - Systems and methods for transforming electrocardiogram images for use in one or more machine learning models
10. 12374438 - Apparatus and methods for prediction of repeat ablation efficacy
11. 12369814 - Methods and systems for determining position signals of electrodes using a retrained machine-learning model
12. 12367392 - Apparatus (and/or method) of training a machine-learning model to generate determinations using mismatched-channel signals
13. 12367953 - Apparatus and a method for the generation of a medical report
14. 12362048 - Systems and methods for signal digitization
15. 12361327 - Systems and methods for training machine learning models using unlabeled electrocardiogram data