Intelligent Medical Imaging Scheduling

Publication ID: 24-11857804_0005_PTD

Published: October 28, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Intelligent Medical Imaging Scheduling,” Published Technical Disclosure No. 24-11857804_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857804_0005_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. 11,857,804.

Summary of the Inventive Concept

A next-generation medical imaging scheduling system that leverages artificial intelligence, machine learning, and digital twin technology to provide personalized treatment monitoring and optimize treatment outcomes.

Background and Problem Solved

The original patent addressed the need for determining a medical imaging schedule for a subject receiving treatment at a target site. However, the patent's limitations included reliance on initial imaging data, clinical information, and treatment information. The new inventive concept overcomes these limitations by incorporating real-time treatment response data, artificial intelligence, and machine learning algorithms to adapt the imaging schedule accordingly, enabling more accurate and personalized treatment monitoring.

Detailed Description of the Inventive Concept

The new inventive concept consists of a system and method for determining a medical imaging schedule that utilizes artificial intelligence to analyze real-time treatment response data and adapt the imaging schedule accordingly. The system includes a real-time data analytics module, a machine learning algorithm module, and an imaging protocol generator. The method involves utilizing machine learning algorithms to identify patterns in treatment response data and predict optimal imaging times, and generating a customized imaging schedule based on the predicted optimal imaging times. Additionally, the system can utilize a digital twin model of the subject's immune system to simulate treatment responses and predict optimal imaging times, enabling data-driven decision making.

Novelty and Inventive Step

The new inventive concept introduces the use of artificial intelligence, machine learning, and digital twin technology to adapt the medical imaging schedule in real-time, providing a paradigm shift in personalized treatment monitoring. The inventive step lies in the integration of these technologies to enable more accurate and effective treatment outcomes.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include the use of natural language processing to analyze clinical notes and identify relevant treatment response data, or the integration of other AI-powered tools to enhance the accuracy of the imaging schedule. Variations of the system could include the use of different machine learning algorithms or the incorporation of additional data sources, such as genomic data or wearable device data.

Potential Commercial Applications and Market

The new inventive concept has significant commercial potential in the medical imaging and healthcare industries, particularly in the areas of personalized medicine, precision oncology, and radiology. The system's ability to optimize treatment outcomes and reduce healthcare costs makes it an attractive solution for hospitals, clinics, and pharmaceutical companies.

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Medical imaging and diagnostic technologies, with expertise in treatment response monitoring, data analytics, and medical informatics. Requires advanced knowledge of medical imaging modalities, statistical analysis, and computational methods for clinical decision support

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced degrees in biomedical engineering, medical imaging, or computational medicine, possessing expertise in machine learning, medical data analysis, and understanding of treatment response monitoring techniques

Obviousness Rationale

The PTD represents an obvious extension of the source patent's medical imaging scheduling methodology by introducing artificial intelligence and machine learning techniques to enhance the existing decision support framework. A PHOSITA would recognize that applying advanced computational techniques to medical imaging scheduling is a predictable solution for improving treatment monitoring precision. The core methodology of determining optimal imaging times remains fundamentally consistent with the source patent, with the primary difference being the introduction of more sophisticated analytical tools.

Obvious Combinations & Variations

Source Patent Element

Method for determining medical imaging schedule based on blood panel information and initial imaging data

PTD Variation

Utilizing machine learning algorithms to predict optimal imaging times and generate customized imaging schedules

Obviousness Reasoning

Applying machine learning to existing medical scheduling methodologies represents a known technique for enhancing predictive capabilities, with predictable results in improving treatment monitoring accuracy

Source Patent Element

Obtaining clinical information and treatment details for imaging schedule determination

PTD Variation

Implementing a digital twin model of the subject's immune system to simulate treatment responses

Obviousness Reasoning

Computational modeling of biological systems is a well-established technique in medical informatics, representing an obvious extension of existing clinical data analysis methods

Source Patent Element

Selecting appropriate imaging modalities for treatment monitoring

PTD Variation

Integrating real-time data analytics and AI-powered tools to dynamically adapt imaging protocols

Obviousness Reasoning

Adaptive imaging scheduling using computational techniques is a predictable solution for addressing variability in treatment responses, leveraging known machine learning and data analysis approaches

Source Patent Element

Method for capturing and analyzing medical imaging data for treatment monitoring

PTD Variation

Utilizing natural language processing to analyze clinical notes and extract relevant treatment response data

Obviousness Reasoning

Applying natural language processing to medical documentation represents a standard technique for extracting structured insights from unstructured clinical information

Source Patent Element

Initial imaging data collection and analysis approach

PTD Variation

Incorporating additional data sources like genomic data and wearable device information for enhanced predictive modeling

Obviousness Reasoning

Expanding data sources for medical decision support is a predictable approach for improving diagnostic and monitoring capabilities, representing an obvious technological progression

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857804 and the disclosed technical variations, a person having ordinary skill in the art would find the proposed medical imaging scheduling techniques involving artificial intelligence, machine learning, and digital twin modeling to be obvious extensions of existing medical imaging decision support methodologies. The incremental computational enhancements represent predictable applications of known techniques to improve treatment monitoring precision and personalization.

Original Patent Information

Patent Number	US 11,857,804
Title	Determining a medical imaging schedule
Assignee(s)	Koninklijke Philips N.V.