Next-Gen Soybean Cultivar: AI-Enhanced Genetic Engineering

Publication ID: 24-11856917_0010_PTD

Published: October 26, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Next-Gen Soybean Cultivar: AI-Enhanced Genetic Engineering,” Published Technical Disclosure No. 24-11856917_0010_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856917_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. 11,856,917.

Background and Problem Solved

The original soybean cultivar 02220303 has limitations in terms of its yield potential, disease susceptibility, and environmental stress tolerance. The present invention addresses these limitations by leveraging recent breakthroughs in genetic engineering, genome editing, and artificial intelligence to create next-generation soybean cultivars that can thrive in diverse environmental conditions.

Novelty and Inventive Step

The new claims introduce several novel and non-obvious features, including the use of genetic engineering to confer abiotic stress resistance, machine learning-based phenotyping for yield potential, and genome editing for disease tolerance. These advancements represent a significant departure from the original patent and demonstrate a new level of sophistication in soybean breeding.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include the use of different biotechnologies, such as RNA interference or gene silencing, to achieve similar traits. Additionally, the invention could be adapted for use in other crop species, such as corn or wheat, to create a broader range of enhanced cultivars.

Potential Commercial Applications and Market

The next-generation soybean cultivars disclosed in this invention have significant commercial potential in the agricultural industry, particularly in regions with challenging environmental conditions. The integration of machine learning and precision agriculture could also create new opportunities for data-driven services and precision farming platforms.

CPC Classifications

Section	Class	Group
A	A01	A01H6/542
A	A01	A01H5/10

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Plant Biotechnology and Agricultural Genetics, focusing on soybean cultivar development and genetic engineering, requiring advanced degrees in plant science, molecular biology, or related fields with expertise in genomics, breeding techniques, and biotechnological manipulation

Person of Ordinary Skill (PHOSITA) Profile

A plant geneticist with PhD-level training, experienced in crop improvement techniques, familiar with CRISPR genome editing, machine learning applications in agriculture, and advanced breeding methodologies

Obviousness Rationale

A PHOSITA would recognize that the disclosed variations represent predictable extensions of existing soybean breeding technologies, leveraging known genetic engineering techniques and computational tools to enhance crop performance. The proposed methods systematically apply established biotechnological approaches to address known agricultural challenges. The variations demonstrate incremental improvements using standard techniques available in the field of plant genetic engineering.

Obvious Combinations & Variations

Source Patent Element

Soybean cultivar breeding methodology described in original patent

PTD Variation

Machine learning-based phenotyping for yield potential selection

Obviousness Reasoning

Computational selection techniques are a known and predictable method for accelerating plant breeding, representing an obvious technological progression in crop improvement strategies

Source Patent Element

Genetic manipulation of soybean plant characteristics

PTD Variation

CRISPR-Cas9 genome editing for disease tolerance

Obviousness Reasoning

Genome editing is a standard technique in plant biotechnology, with CRISPR being a well-established method for precise genetic modifications, making such an approach an obvious extension of existing genetic engineering practices

Source Patent Element

Soybean plant cell and tissue culture techniques

PTD Variation

Generative adversarial networks for predicting breeding strategies

Obviousness Reasoning

Integration of machine learning with existing breeding techniques represents a predictable application of computational technologies to agricultural research, utilizing known data analysis approaches

Source Patent Element

Genetic variation and trait selection in soybean cultivars

PTD Variation

Abiotic stress resistance gene derived from non-soybean species

Obviousness Reasoning

Interspecies gene transfer is a standard biotechnological approach for enhancing crop resilience, representing a known and predictable method of genetic improvement

Source Patent Element

Plant breeding methodologies

PTD Variation

Precision agriculture system integrating sensor data with genomic information

Obviousness Reasoning

Combining sensor technologies with genomic data is a logical and predictable approach to agricultural management, representing an obvious technological convergence

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856917 and the comprehensive biotechnological approaches disclosed herein, a Person Having Ordinary Skill In The Art would find the claimed variations obvious and lacking inventive step. The systematic application of known genetic engineering, machine learning, and precision agriculture techniques to soybean cultivar improvement represents a predictable progression of existing technological capabilities, thereby rendering the proposed innovations obvious to one skilled in the art of plant biotechnology.

Original Patent Information

Patent Number	US 11,856,917
Title	Soybean cultivar 02220303
Assignee(s)	M.S. Technologies, L.L.C.