Adaptive Multispectral Imaging Systems for Enhanced Surgical Visualization
Legal Citation
Summary of the Inventive Concept
A next-generation surgical imaging system that leverages machine learning, neural networks, and real-time feedback to optimize illumination settings for enhanced visual acuity during medical procedures.
Background and Problem Solved
The original patent's closed-loop surgical imaging optimization system, while innovative, has limitations in its ability to adapt to real-time changes in tissue characteristics and visual acuity requirements. The new inventive concept addresses these limitations by incorporating advanced machine learning algorithms and neural networks to adaptively adjust illumination wavelengths in real-time.
Detailed Description of the Inventive Concept
The new system comprises a neural network-based image processing module that analyzes multispectral image data and adjusts illumination settings in real-time based on tissue characteristics and visual acuity requirements. The system also includes a feedback loop to refine illumination settings during the medical procedure. Additionally, the system can be integrated with wearable devices, cloud-based platforms, and predictive modeling tools to provide real-time analytics and recommendations for optimal illumination settings.
Novelty and Inventive Step
The new claims introduce a paradigm shift in surgical imaging optimization by incorporating machine learning and neural networks to adaptively adjust illumination wavelengths in real-time. This is a significant departure from the original patent's closed-loop approach, which relied on pre-defined settings and annotations.
Alternative Embodiments and Variations
Alternative embodiments of the inventive concept could include the use of different machine learning algorithms, such as deep learning or transfer learning, or the integration of additional sensors, such as optical coherence tomography (OCT) or ultrasound. Variations could also include the use of different types of wearable devices or cloud-based platforms.
Potential Commercial Applications and Market
The inventive concept has significant commercial potential in the medical device industry, particularly in the areas of minimally invasive surgery, endoscopy, and laparoscopy. The system's ability to enhance visual acuity and reduce complications during medical procedures could lead to improved patient outcomes and reduced healthcare costs.
CPC Classifications
| Section | Class | Group |
|---|---|---|
| A | A61 | A61B1/000095 |
| A | A61 | A61B1/00009 |
| A | A61 | A61B1/00013 |
| A | A61 | A61B1/04 |
| A | A61 | A61B1/045 |
| A | A61 | A61B1/0638 |
| A | A61 | A61B1/0655 |
| A | A61 | A61B1/07 |
| A | A61 | A61B34/25 |
| A | A61 | A61B90/37 |
| A | A61 | A61B2090/306 |
| A | A61 | A61B2090/3614 |
| A | A61 | A61B2090/371 |
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Medical imaging systems, specifically surgical visualization technologies involving endoscopic and laparoscopic imaging with advanced processing techniques
Person of Ordinary Skill (PHOSITA) Profile
A biomedical engineer or medical imaging specialist with expertise in computer vision, image processing, machine learning, and surgical visualization technologies, holding advanced degrees in bioengineering or medical technology with 3-5 years of practical experience
Obviousness Rationale
A person having ordinary skill would recognize that the PTD's neural network-based image optimization represents a predictable technological evolution from the source patent's closed-loop imaging system. The fundamental concept of adaptive image processing remains consistent, with the primary difference being the incorporation of machine learning techniques to dynamically adjust illumination parameters. These modifications represent an incremental technological advancement that would be obvious to a skilled practitioner familiar with emerging medical imaging technologies.
Obvious Combinations & Variations
Original Patent Information
| Patent Number | US 11,857,154 |
|---|---|
| Title | Systems and methods for closed-loop surgical imaging optimization |