Published Technical Disclosure Archive

Autonomous Plant Yield Optimization AI Technology

Publication ID: 24-11856903_0010_PTD
Published: October 26, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Autonomous Plant Yield Optimization AI Technology,” Published Technical Disclosure No. 24-11856903_0010_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856903_0010_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 are limited by their reliance on manual optimization and lack of precision. The present invention addresses these limitations by introducing autonomous plant yield optimization and genome-wide yield gene identification, enabling farmers to achieve unprecedented yields while reducing environmental impact.

Novelty and Inventive Step

The new claims introduce the use of machine learning, CRISPR-Cas9 genome editing, and precision agriculture to optimize plant yield, which is a significant departure from the original patent's manual optimization methods. The autonomous plant yield optimization and genome-wide yield gene identification capabilities are novel and non-obvious, and provide a paradigm shift in agricultural practices.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include the use of different machine learning algorithms, genome editing tools, or precision agriculture infrastructure. The invention could also be adapted for use with other crop types or in different environmental conditions.

Potential Commercial Applications and Market

The invention has significant commercial potential in the agricultural industry, particularly in the areas of precision agriculture, crop breeding, and farm management. The target market includes farmers, agricultural companies, and research institutions, with potential applications in soybean production, as well as other crops.

CPC Classifications

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

Field of Art

Agricultural biotechnology, plant genetics, and precision crop management, focusing on yield optimization techniques for short-day crops like soybeans

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced degrees in agricultural science, plant genetics, or biotechnology, having expertise in crop optimization, machine learning applications in agriculture, and genome editing techniques

Obviousness Rationale

A person of ordinary skill would recognize that the integration of machine learning, precision agriculture technologies, and genome editing represents a natural progression of plant yield optimization techniques disclosed in the source patent. The fundamental goal of enhancing soybean yield remains consistent, with the PTD introducing computational and genetic tools that are well-established in the agricultural biotechnology domain. The systematic approach of using data-driven methods to optimize plant growth conditions is a logical extension of the source patent's manual optimization strategies.

Obvious Combinations & Variations

Source Patent Element
Method for manipulating soybean plant yield under specific light and temperature conditions
PTD Variation
Machine learning module for predicting and adjusting optimal yield parameters based on environmental data
Obviousness Reasoning
Predictable application of known machine learning techniques to automate the manual yield optimization process disclosed in the source patent, representing a routine technological enhancement
Source Patent Element
Growing soybean plants under controlled conditions to enhance seed yield
PTD Variation
CRISPR-Cas9 genome editing for identifying and introducing yield-enhancing genetic traits
Obviousness Reasoning
Well-known genome editing technique applied to the specific goal of yield improvement, representing a standard approach in agricultural biotechnology for trait enhancement
Source Patent Element
Temperature and light condition manipulation for plant growth
PTD Variation
Real-time sensor network and cloud-based platform for continuous environmental monitoring and adjustment
Obviousness Reasoning
Predictable implementation of IoT and precision agriculture technologies to systematize the plant growth optimization process disclosed in the original patent
Source Patent Element
Methods for restricting vegetative growth and enhancing flowering
PTD Variation
Neural network-based yield prediction using comprehensive environmental and genetic data inputs
Obviousness Reasoning
Logical extension of existing plant growth optimization techniques using advanced computational methods that are standard in modern agricultural research
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856903 and the disclosed technological variations, a person of ordinary skill in agricultural biotechnology would find the claimed innovations obvious and non-patentable. The systematic application of machine learning, genome editing, and precision agriculture technologies to soybean yield optimization represents a predictable and routine technological progression that would be apparent to a skilled practitioner in the field.

Original Patent Information

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