Published Technical Disclosure Archive

Advanced Vegetative Health Mapping System

Publication ID: 24-11856883_0003_PTD
Published: October 26, 2025
Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Advanced Vegetative Health Mapping System,” Published Technical Disclosure No. 24-11856883_0003_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856883_0003_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. 11,856,883.

Background and Problem Solved

The original patent, 'Moisture and Vegetative Health Mapping', addresses the need for accurate moisture measurement to ensure optimal vegetative health. However, it has limitations in terms of scalability, data integration, and treatment recommendation. This new invention overcomes these limitations by combining the original patent's technology with distinct technologies, enabling a more powerful and efficient system for vegetative health monitoring and improvement.

Novelty and Inventive Step

The new invention's synergistic combination of distinct technologies, including AI, IoT, blockchain, and new materials, provides a non-obvious and novel solution for vegetative health monitoring and improvement. The integration of these technologies enables a more efficient, scalable, and accurate system that overcomes the limitations of the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include varying the type of AI algorithm used for vegetative health prediction, utilizing different IoT sensor networks, or incorporating additional technologies such as computer vision or robotics. Variations could also include adapting the system for different types of vegetation or environments.

Potential Commercial Applications and Market

The Synergistic Vegetative Health Mapping System has significant commercial potential in various industries, including agriculture, landscaping, and environmental conservation. The system's ability to optimize vegetative health and reduce water waste could lead to increased crop yields, reduced costs, and improved environmental sustainability.

CPC Classifications

SectionClassGroup
A A01 A01C21/007
A A01 A01B69/004
A A01 A01C7/06
A A01 A01C7/102
A A01 A01C23/007
A A01 A01M7/0089
G G05 G05D1/0246
G G05 G05D1/0257
G G06 G06N20/00
G G06 G06T7/0012
G G06 G06T7/70
G G06 G06T17/05
G G05 G05D2201/0201
G G06 G06T2207/10004
G G06 G06T2207/10028
G G06 G06T2207/10044
G G06 G06T2207/20081
G G06 G06T2207/30188

Field of Art

Agricultural technology, precision agriculture, remote sensing, and environmental monitoring systems with a focus on vegetation health assessment and moisture mapping

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in agricultural engineering, remote sensing technologies, machine learning, IoT sensor networks, and data analysis techniques with knowledge of geospatial mapping and environmental monitoring systems

Obviousness Rationale

A person having ordinary skill in the art would recognize that integrating machine learning, IoT sensor networks, and blockchain technologies with existing moisture mapping techniques represents a predictable extension of known agricultural monitoring approaches. The source patent's foundational teachings of vegetative health monitoring provide a clear technical framework that would naturally suggest incorporating emerging digital technologies to enhance data collection, analysis, and treatment recommendation processes. These technological integrations represent incremental improvements using standard engineering design choices within the field of precision agriculture.

Obvious Combinations & Variations

Source Patent Element
Moisture mapping device with coordinate-based region of interest detection
PTD Variation
Adding AI-powered vegetative health prediction module and blockchain-based treatment recommendation system
Obviousness Reasoning
Predictable combination of known machine learning techniques with existing sensor-based monitoring systems to enhance data interpretation and decision-making capabilities
Source Patent Element
Three-dimensional mapping of vegetative health indicators
PTD Variation
Integrating IoT sensor networks and machine learning for real-time health prediction and treatment recommendations
Obviousness Reasoning
Obvious application of contemporary data collection and analysis technologies to improve spatial resolution and predictive accuracy of vegetation monitoring
Source Patent Element
Moisture level detection and health assessment methods
PTD Variation
Implementing blockchain-based supply chain management for optimizing treatment delivery
Obviousness Reasoning
Routine engineering design choice to enhance data tracking, verification, and treatment implementation using distributed ledger technologies
Source Patent Element
Vegetative health degradation detection techniques
PTD Variation
Incorporating new material-based water retention systems with AI-powered analysis
Obviousness Reasoning
Obvious technological evolution combining materials science, machine learning, and moisture management to improve agricultural intervention strategies
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856883 and the disclosed technological variations, a person having ordinary skill in the art would find the claimed innovations obvious and lacking inventive step. The incremental technological integrations represent predictable combinations of known techniques in precision agriculture, machine learning, and environmental monitoring, thereby rendering subsequent patent claims obvious and unpatentable under 35 U.S.C. Section 103.

Original Patent Information

Patent NumberUS 11,856,883
TitleMoisture and vegetative health mapping
Assignee(s)Scythe Robotics, Inc.