Intelligent Meal Bolus Detection and Recommendation System
Legal Citation
Summary of the Inventive Concept
A novel system integrating AI, IoT, blockchain, and continuous glucose monitoring to detect un-bolused meals and provide personalized meal bolus recommendations, enhancing diabetes therapy and improving patient outcomes.
Background and Problem Solved
The original patent addressed the need for accurate and controlled fluid delivery in diabetes therapy, but it did not fully leverage advancements in AI, IoT, and blockchain to optimize meal bolus timing and patient engagement. This new inventive concept solves the problem of inadequate meal bolus detection and recommendation by integrating these distinct technologies.
Detailed Description of the Inventive Concept
The system comprises an ambulatory infusion pump, a continuous glucose monitoring system, and a machine learning module that analyzes CGM data and user input to predict meal times and provide personalized reminders for meal boluses. The system can be integrated with IoT-enabled devices, blockchain-based secure data storage, and AI-powered meal detection algorithms to optimize meal bolus timing and enhance patient outcomes.
Novelty and Inventive Step
The new claims introduce the novel combination of AI, IoT, blockchain, and continuous glucose monitoring to create a more powerful system for detecting un-bolused meals and providing personalized meal bolus recommendations. This integration of distinct technologies represents a non-obvious improvement over the original patent.
Alternative Embodiments and Variations
Alternative embodiments may include integrating the system with wearable devices, mobile apps, or cloud-based services to enhance user experience and engagement. Variations may include using different machine learning algorithms, blockchain platforms, or IoT protocols to optimize system performance and scalability.
Potential Commercial Applications and Market
This inventive concept has significant commercial potential in the diabetes management and healthcare industries, with potential applications in insulin pump systems, continuous glucose monitoring devices, and personalized medicine platforms. The target market includes patients with diabetes, healthcare providers, and medical device manufacturers.
CPC Classifications
| Section | Class | Group |
|---|---|---|
| A | A61 | A61M5/1723 |
| A | A61 | A61M5/14244 |
| A | A61 | A61M5/1413 |
| A | A61 | A61M2005/14208 |
| A | A61 | A61M2005/14268 |
| A | A61 | A61M2205/52 |
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Medical Device Technology - Diabetes Management Systems, specifically ambulatory infusion pumps, continuous glucose monitoring, and automated medication delivery interfaces
Person of Ordinary Skill (PHOSITA) Profile
A biomedical engineer or medical device specialist with expertise in diabetes management technologies, machine learning applications in medical devices, and experience integrating sensor data with automated treatment systems
Obviousness Rationale
A PHOSITA would recognize that extending the source patent's un-bolused meal detection method with AI, IoT, and blockchain technologies represents a predictable technological enhancement using known integration techniques. The core functional elements of meal bolus detection remain consistent, with the PTD merely introducing additional data processing and communication layers. These technological additions would be considered routine optimization strategies within the medical device technology domain.
Obvious Combinations & Variations
Original Patent Information
| Patent Number | US 11,857,764 |
|---|---|
| Title | Automatic detection of un-bolused meals |
| Assignee(s) | Tandem Diabetes Care, Inc. |