Published Technical Disclosure Archive

Advanced Plant Yield Gene Manipulation Technology

Publication ID: 24-11856903_0008_PTD
Published: October 26, 2025
Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Advanced Plant Yield Gene Manipulation Technology,” Published Technical Disclosure No. 24-11856903_0008_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856903_0008_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. 11,856,903.

Background and Problem Solved

The original patent disclosed methods for manipulating yield of plants and identifying yield genes, but these methods had limitations in terms of scalability, accuracy, and transparency. The present invention addresses these limitations by integrating cutting-edge technologies to create a more powerful system.

Novelty and Inventive Step

The integration of AI-driven climate control, IoT-enabled sensors, blockchain-based seed tracking, and machine learning algorithms for gene identification constitutes a novel and non-obvious combination of technologies that provides a synergistic effect on plant yield optimization. The use of a biodegradable polymer matrix infused with micronutrients in the plant growth medium is also a novel and inventive step.

Alternative Embodiments and Variations

Other ways the invention could be implemented include using different types of AI algorithms, integrating with autonomous farming equipment, or incorporating additional sensors and data analytics tools. The system could also be adapted for use in different types of crops or plant growth environments.

Potential Commercial Applications and Market

The invention has significant commercial potential in the agricultural industry, particularly in the areas of precision agriculture, seed production, and crop optimization. The system could be marketed as a comprehensive solution for farmers and agricultural companies looking to increase yields and improve efficiency.

CPC Classifications

SectionClassGroup
A A01 A01H3/02
A A01 A01H5/10
A A01 A01H6/542

Field of Art

Agricultural biotechnology and plant breeding, specifically focused on yield optimization techniques for short-day plants like soybeans, involving plant growth manipulation, environmental control, and genetic analysis

Person of Ordinary Skill (PHOSITA) Profile

A plant scientist or agricultural engineer with advanced degrees in plant genetics, biotechnology, or agricultural sciences, possessing expertise in crop optimization, environmental control systems, and molecular biology techniques

Obviousness Rationale

A PHOSITA would recognize that the PTD's integration of AI-driven climate control, sensor technologies, and machine learning represents a predictable application of modern technological approaches to the fundamental plant yield optimization methods disclosed in the source patent. The proposed variations systematically extend the source patent's core teachings about manipulating plant growth conditions by incorporating contemporary digital and data-driven technologies. These technological enhancements would be considered obvious improvements that leverage standard engineering practices and readily available technological solutions in agricultural biotechnology.

Obvious Combinations & Variations

Source Patent Element
Method of manipulating soybean plant yield through controlled light and temperature conditions
PTD Variation
AI-driven climate control system mimicking the specified long day and short day growing conditions
Obviousness Reasoning
Implementing computer-controlled environmental systems is a known technique for precise agricultural management, representing a predictable technological extension of existing plant growth control methods
Source Patent Element
Inducing flowering through specific light and temperature manipulations
PTD Variation
Machine learning algorithms for analyzing transcriptional profiling data to identify yield-related genes
Obviousness Reasoning
Using computational analysis to understand genetic responses to environmental conditions is a standard approach in modern plant breeding, representing an obvious application of computational techniques to genetic research
Source Patent Element
Growing soybean plants under restricted soil volume conditions
PTD Variation
IoT-enabled sensors for real-time monitoring of plant growth and soil conditions
Obviousness Reasoning
Integrating sensor technologies for precise agricultural monitoring is a predictable technological advancement that enhances existing plant growth management techniques
Source Patent Element
Method of manipulating plant yield through environmental conditions
PTD Variation
Blockchain-based platform for tracking seed origin and quality
Obviousness Reasoning
Implementing blockchain for supply chain verification is a known technique for enhancing traceability in agricultural systems, representing an obvious technological extension of existing plant breeding methods
Source Patent Element
Controlling plant growth through environmental manipulation
PTD Variation
Biodegradable polymer matrix with micronutrient release mechanism
Obviousness Reasoning
Developing advanced growth media with controlled nutrient release is a predictable innovation in agricultural materials science, representing an obvious improvement to standard growth techniques
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856903 and the disclosed technological variations, a person having ordinary skill in the art would find the proposed methods and systems for plant yield optimization obvious and lacking inventive step. The systematic integration of AI, IoT, machine learning, and advanced materials technologies represents a predictable application of contemporary technological approaches to the fundamental plant growth manipulation techniques disclosed in the prior art.

Original Patent Information

Patent NumberUS 11,856,903
TitleMethods for manipulating yield of plants and identifying yield genes
Assignee(s)MONSANTO TECHNOLOGY LLC