View source for Unknown Organization (20240266074). Cognitive Communications, Collaboration, Consultation and Instruction with Multimodal Media and Augmented Generative Intelligence simplified abstract

=Cognitive Communications, Collaboration, Consultation and Instruction with Multimodal Media and Augmented Generative Intelligence=


==Organization Name==

[[:Category:Unknown Organization|Unknown Organization]]


[[Category:Unknown Organization]]

==Inventor(s)==

[[:Category:James Paul Smurro of San Clemente CA (US)|James Paul Smurro of San Clemente CA (US)]][[Category:James Paul Smurro of San Clemente CA (US)]]

==Cognitive Communications, Collaboration, Consultation and Instruction with Multimodal Media and Augmented Generative Intelligence - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240266074 titled 'Cognitive Communications, Collaboration, Consultation and Instruction with Multimodal Media and Augmented Generative Intelligence

The invention combines machine learning in medicine with videoconference networking technology to support rapid adaptive learning for medical professionals and machines.

* Integrates emerging applications, tools, and techniques for machine learning in medicine with videoconference networking technology.
* Supports rapid adaptive learning for medical minds and machines.
* Leverages domain knowledge and clinical expertise through networked cognitive collaboration.
* Enables multimodal clinical communications, collaboration, consultation, and instruction among heterogeneous networked teams.
* Facilitates cognitive-enriched annotation, tagging, encapsulation, saving, and sharing of collaborated imagery data streams.

Potential Applications:
- Medical education and training
- Telemedicine and remote consultations
- Clinical decision support systems
- Healthcare workflow optimization
- Research collaboration in healthcare

Problems Solved:
- Limited access to specialized medical expertise
- Inefficient communication and collaboration among healthcare professionals
- Lack of real-time clinical intelligence sharing
- Difficulty in leveraging machine learning in clinical practice
- Inadequate support for continuous learning in healthcare

Benefits:
- Enhanced collaboration and communication in healthcare
- Improved access to specialized medical knowledge
- Increased efficiency in clinical decision-making
- Facilitates continuous learning and adaptation in healthcare
- Supports the integration of machine learning into clinical workflows

Commercial Applications:
Title: "Enhancing Healthcare Collaboration with Machine Learning and Videoconferencing"
This technology can be utilized in telemedicine platforms, medical education institutions, healthcare organizations, and research facilities to improve collaboration, communication, and decision-making in healthcare settings.

Questions about the technology:
1. How does this innovation improve access to specialized medical expertise?
- The technology enables remote consultations and collaboration among healthcare professionals, allowing for the sharing of domain knowledge and clinical expertise.
2. What are the potential implications of integrating machine learning with videoconferencing in healthcare?
- The integration can lead to more efficient clinical workflows, improved patient outcomes, and enhanced research collaboration in healthcare.


==Original Abstract Submitted==

the invention integrates emerging applications, tools and techniques for machine learning in medicine with videoconference networking technology in novel business methods that support rapid adaptive learning for medical minds and machines. these methods can leverage domain knowledge and clinical expertise with networked cognitive collaboration, augmented clinical intelligence and cybernetic workflow streams for learning health care systems. the invention enables multimodal clinical communications, collaboration, consultation and instruction between and among heterogeneous networked teams of persons, machines, devices, neural networks, robots and algorithms, including augmented generative ai algorithms, models and systems. the invention enables cognitively-enriched, annotation and tagging, as well as encapsulation, saving and sharing of collaborated imagery data streams as packetized clinical intelligence.



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