Published Technical Disclosure Archive

Personalized Spinal Reconstruction Platform

Publication ID: 24-11857264_0010_PTD
Published: November 07, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Personalized Spinal Reconstruction Platform,” Published Technical Disclosure No. 24-11857264_0010_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857264_0010_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,264.

Summary of the Inventive Concept

A comprehensive system for planning, executing, and adapting personalized spinal reconstruction surgeries, leveraging AI, real-time data, and collaborative tools to revolutionize patient outcomes.

Background and Problem Solved

The original patent, 'Systems and methods for physician designed surgical procedures', provided a foundation for patient-specific implant design. However, it was limited by its reliance on preoperative planning and static anatomical models. The new inventive concept addresses these limitations by integrating real-time data, AI-driven prediction, and intraoperative adaptation to ensure optimal implant configurations and minimize surgical complications.

Detailed Description of the Inventive Concept

The Personalized Spinal Reconstruction Platform consists of four interconnected modules: 1) a neural network-based prediction module for forecasting optimal implant configurations, 2) a real-time intraoperative implant adaptation module, 3) a cloud-based collaborative surgical planning platform, and 4) a machine learning-based framework for predicting and preventing surgical complications. These modules work in tandem to provide surgeons with a dynamic, data-driven approach to spinal reconstruction, enabling them to respond to changing patient needs and anatomical conditions during surgery.

Novelty and Inventive Step

The new inventive concept introduces a paradigm shift in spinal reconstruction by integrating AI, real-time data, and collaborative tools to create a truly personalized and adaptive surgical experience. The novel combination of neural network-based prediction, intraoperative adaptation, and machine learning-driven complication prevention sets this concept apart from the original patent and existing state-of-the-art solutions.

Alternative Embodiments and Variations

Alternative embodiments of the Personalized Spinal Reconstruction Platform could include: 1) integrating robotic-assisted surgery capabilities, 2) developing specialized modules for specific spinal disorders (e.g., scoliosis), 3) expanding the platform to accommodate other orthopedic procedures (e.g., joint replacements), or 4) incorporating virtual or augmented reality tools for enhanced surgical visualization and training.

Potential Commercial Applications and Market

The Personalized Spinal Reconstruction Platform has significant commercial potential in the orthopedic and spine surgery markets, with potential applications in hospitals, outpatient surgery centers, and medical device companies. The market for spinal reconstruction surgeries is projected to grow significantly in the coming years, driven by an aging population and increasing demand for personalized, minimally invasive procedures.

CPC Classifications

SectionClassGroup
A A61 A61B34/10
A A61 A61B5/743
A A61 A61B5/748
A A61 A61B5/7435
A A61 A61B34/20
A A61 A61B34/25
A A61 A61B90/37
G G06 G06N20/00
G G06 G06T7/73
G G06 G06T19/006
G G16 G16H20/40
G G16 G16H40/63
G G16 G16H50/00
A A61 A61B2034/102
A A61 A61B2034/105
A A61 A61B2034/107
A A61 A61B2034/108
A A61 A61B2034/256
A A61 A61B2090/365
G G06 G06N5/01
G G06 G06T2207/10072
G G06 G06T2207/10116
G G06 G06T2207/20084
G G06 G06T2207/30012

Field of Art

Medical informatics and surgical planning technologies, specifically focused on computer-assisted surgical procedures, virtual anatomical modeling, and personalized medical interventions involving spinal reconstruction and implant design

Person of Ordinary Skill (PHOSITA) Profile

A multidisciplinary professional with expertise in biomedical engineering, computer science, surgical technologies, and machine learning, possessing advanced knowledge of medical imaging, computational modeling, and surgical intervention optimization techniques

Obviousness Rationale

A PHOSITA would recognize that the PTD's neural network-based prediction and real-time adaptation modules represent predictable extensions of the source patent's virtual anatomical modeling framework, leveraging established machine learning techniques to enhance surgical planning and execution. The disclosed variations systematically address known limitations in personalized surgical interventions by introducing more dynamic, data-driven approaches to implant design and surgical navigation. These technical improvements would be considered obvious incremental advancements within the established technological domain of computer-assisted surgical systems.

Obvious Combinations & Variations

Source Patent Element
Virtual anatomical model generation for patient-specific surgical planning
PTD Variation
Neural network-based prediction module for forecasting optimal implant configurations
Obviousness Reasoning
Applying machine learning techniques to enhance existing virtual modeling approaches represents a known and predictable optimization strategy in medical informatics, with a reasonable expectation of improved surgical outcomes
Source Patent Element
Transmitting and displaying annotated anatomical images for surgical review
PTD Variation
Cloud-based collaborative surgical planning platform with secure data sharing and annotation capabilities
Obviousness Reasoning
Extending single-user annotation systems to collaborative, cloud-based platforms is a straightforward technological progression enabled by standard networking and information sharing technologies
Source Patent Element
Computer-implemented method for generating patient-specific implants
PTD Variation
Real-time intraoperative implant adaptation module dynamically updating virtual anatomical models
Obviousness Reasoning
Introducing dynamic, responsive modeling techniques represents an obvious enhancement to existing static surgical planning approaches, utilizing well-established computational modeling and machine learning techniques
Source Patent Element
Virtual implantation simulation data generation
PTD Variation
Machine learning-based framework for predicting and preventing surgical complications
Obviousness Reasoning
Expanding simulation capabilities to include predictive risk assessment is a natural technological progression utilizing established statistical and machine learning methodologies
Source Patent Element
Computer-implemented surgical assistance methods
PTD Variation
AI-powered surgical navigation tool with real-time instrument tracking and haptic feedback
Obviousness Reasoning
Integrating advanced sensing and feedback technologies into surgical navigation systems represents a predictable technological evolution with clear performance improvements
35 U.S.C. § 103 Summary: Based on the comprehensive teachings of US Patent 11857264 and the disclosed technological variations, a Person Having Ordinary Skill In The Art would find the claimed innovations of the Personalized Spinal Reconstruction Platform obvious and non-patentable, as the technical modifications represent predictable extensions of existing computer-assisted surgical planning methodologies, employing standard machine learning and computational techniques to incrementally enhance patient-specific surgical interventions.

Original Patent Information

Patent NumberUS 11,857,264
TitleSystems and methods for physician designed surgical procedures
Assignee(s)Carlsmed, Inc.