Published Technical Disclosure Archive

Adaptive Parameter Modeling for Diverse Applications

Publication ID: 24-11857765_0002_PTD
Published: November 07, 2025
Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Adaptive Parameter Modeling for Diverse Applications,” Published Technical Disclosure No. 24-11857765_0002_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857765_0002_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. 11,857,765.

Summary of the Inventive Concept

This inventive concept extends the personalized parameter modeling technology to new industries, enabling accurate predictions and optimized decision-making in fields such as agriculture, energy management, product recommendations, traffic management, and supply chain logistics.

Background and Problem Solved

The original patent's personalized parameter modeling technology has revolutionized therapy management. However, its potential applications extend far beyond healthcare. This inventive concept addresses the limitations of existing solutions in various industries, where inaccurate predictions and inefficient decision-making hinder progress. By applying the core technology to these new fields, this concept solves the problem of inadequate modeling and prediction capabilities.

Detailed Description of the Inventive Concept

The inventive concept comprises a system and method for adapting the personalized parameter modeling technology to diverse applications. It involves obtaining current operational context information, obtaining an expected parameter model associated with a specific industry or region, and calculating an expected value based on the expected parameter model and the current operational context information. This technology can be applied to various fields, such as predicting crop yields, optimizing energy consumption, providing personalized product recommendations, predicting traffic patterns, and optimizing supply chain logistics.

Novelty and Inventive Step

The novelty of this inventive concept lies in its application of the personalized parameter modeling technology to entirely new industries, addressing previously unexplored problems. The inventive step is the recognition of the potential for this technology to be adapted and applied to diverse fields, enabling accurate predictions and optimized decision-making.

Alternative Embodiments and Variations

Alternative embodiments of this inventive concept may include variations in the type of operational context information obtained, the specific parameter models used, and the industries or regions targeted. For example, the technology could be applied to environmental monitoring, financial forecasting, or smart city infrastructure management.

Potential Commercial Applications and Market

The potential commercial applications of this inventive concept are vast, with opportunities in agriculture, energy management, e-commerce, transportation, and logistics. The target market includes companies and organizations seeking to improve their predictive capabilities and decision-making processes, as well as industries looking to optimize their operations and reduce costs.

CPC Classifications

SectionClassGroup
A A61 A61M5/1723
A A61 A61B5/14532
A A61 A61B5/4839
G G16 G16H10/60
G G16 G16H20/17
G G16 G16H40/40
G G16 G16H40/63
G G16 G16H50/20
G G16 G16H50/50
A A61 A61B5/0022
A A61 A61B5/02438
A A61 A61B5/7239
A A61 A61B5/7242
A A61 A61B2560/0242
A A61 A61B2562/0219
A A61 A61M5/14244
A A61 A61M2205/3569
A A61 A61M2205/502
A A61 A61M2205/52
A A61 A61M2205/70
A A61 A61M2230/005
A A61 A61M2230/06
A A61 A61M2230/201
G G16 G16H15/00

Field of Art

Medical and computational systems involving personalized parameter modeling, sensor data processing, and predictive analytics across domains including healthcare, environmental monitoring, and management systems

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in data science, machine learning, sensor technologies, and interdisciplinary system design, capable of translating computational modeling techniques across different technical domains

Obviousness Rationale

The source patent's core methodology of personalized parameter modeling using operational context and predictive models provides a generalizable framework that a skilled practitioner would recognize as adaptable to multiple domains. The technical core of obtaining context information, generating a domain-specific parameter model, and calculating predictive values represents a transferable computational approach. A PHOSITA would understand that the fundamental algorithmic structure can be readily applied to diverse fields by substituting domain-specific sensors, context variables, and prediction targets.

Obvious Combinations & Variations

Source Patent Element
Obtaining current operational context information and generating a patient-specific calibration factor model
PTD Variation
Obtaining agricultural environmental data and generating a crop yield prediction model for specific farms
Obviousness Reasoning
Substituting medical sensor inputs with agricultural sensor data represents a predictable application of a known computational technique, involving merely replacing domain-specific input variables while maintaining the core predictive modeling approach
Source Patent Element
Calculating expected calibration values based on historical operational context and identified predictive variables
PTD Variation
Calculating expected traffic patterns by analyzing historical transportation context and identifying relevant predictive variables
Obviousness Reasoning
The source patent's method of correlating historical data to generate predictive models is a known technique that would be obvious to apply across different contextual domains with minimal technical adaptation
Source Patent Element
Transmitting calibrated measurement values to an autonomous system for decision-making
PTD Variation
Transmitting predicted energy consumption values to a building management system for automated optimization
Obviousness Reasoning
Extending the autonomous decision support framework from medical devices to building management represents a straightforward application of a known system architecture with predictable results
Source Patent Element
Patient-specific measurement offset modeling to improve prediction accuracy
PTD Variation
Supply chain logistics parameter modeling to predict and optimize logistics performance
Obviousness Reasoning
Applying domain-specific offset or error correction techniques to improve predictive accuracy is a standard engineering approach that would be obvious to a skilled practitioner
Source Patent Element
Personalized parameter modeling using computational methods
PTD Variation
Generating personalized product recommendation models based on user context and historical interaction data
Obviousness Reasoning
Transferring personalized predictive modeling techniques from medical monitoring to recommendation systems represents a known and predictable extension of computational modeling principles
35 U.S.C. § 103 Summary: Based on US Patent 11857765's disclosure of personalized parameter modeling methods, the present publication demonstrates that a Person Having Ordinary Skill In The Art would find the disclosed variations obvious through straightforward technological translation, involving mere substitution of domain-specific variables within a well-established computational framework for context-aware predictive modeling.

Original Patent Information

Patent NumberUS 11,857,765
TitlePersonalized parameter modeling methods and related devices and systems
Assignee(s)MEDTRONIC MINIMED, INC.