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NanoPlex: Rapid, Memory-Efficient Nanopore Data Solutions for Modern Labs
TS-070331 — Accelerate discovery and streamline workflows with a next-generation platform for high-throughput nanopore data extraction.
Overview Nanopore sensing is revolutionizing genomics, proteomics, and diagnostics, but the explosive growth in data volume has outpaced the capabilities of current analysis tools. Researchers often face bottlenecks due to slow processing speeds, memory limitations, and the need for specialized …
  • College: College of Arts & Sciences
  • Inventors: Bandara, Nuwan
  • Licensing Officer: Panic, Ana

Precision Soil Health: The Key to Sustainable, High-Yield Farming
TS-070310 — An integrated soil health assessment and management platform that empowers growers to boost yields and sustainability through data-driven decisions.
Soil health is the cornerstone of sustainable agriculture, yet traditional management often overlooks the complex interactions that drive crop performance and environmental outcomes. Many growers struggle to optimize yields and reduce input costs due to limited, fragmented soil data. This techno…
  • College: College of Food, Agricultural, and Environmental Sciences (CFAES)
  • Inventors: Dick, Richard; Renz, Peter
  • Licensing Officer: Panic, Ana

GPS Independent Lane Level Vehicle Localization
TS-069654 — Technology bundle containing T2025-148 and T2024-150.
The Need Reliable vehicle localization is critical for autonomous and human-driven vehicles, especially in GPS-denied environments such as dense urban areas, tunnels, and off-road farms and construction sites. Current localization methods relying solely on GPS are prone to signal loss and inaccurac…
  • College: College of Engineering (COE)
  • Inventors: Javed, Nur Uddin; Ahmed, Qadeer
  • Licensing Officer: Ashouripashaki, Mandana

SyMANTIC – Novel Symbolic Regression to Discover Accurate Models from Data
TS-069523 — The Need In many scientific and industrial fields, there is a critical need for interpretable and accurate models that can be derived from complex datasets. Traditional machine learning methods often produce black-box models that lack transparency and interpretability, making it difficult to unders…
  • College: College of Engineering (COE)
  • Inventors: Muthyala, Madhav Reddy; Paulson, Joel; Sorourifar, Farshud
  • Licensing Officer: Randhawa, Davinder

Vision System Using MDP-Based Tracking for Automated Dimensional Analysis
TS-068440 — Accurate measurement of physical attributes, such as dimensions, alignments, and surface features, is essential for ensuring quality and consistency across industries. Traditional measurement methods, often reliant on manual tools and human oversight, are time-intensive, prone to errors, and lac…
  • College: College of Engineering (COE)
  • Inventors: Allen, Theodore; Rodriguezbuno, Ramiro; Zhang, Yifei
  • Licensing Officer: Zinn, Ryan

Computational Design of Experiment Framework for Processing of Metal Alloys
TS-067792 — A software tool for optimizing the manufacturing process for metal alloys.
Metallic alloys are composed of a homogeneous mixture of two or more metals or of metals and nonmetal or metalloid elements to provide specific characteristics or structural properties. Alloys are used in many applications, such as aircraft, offshore drilling, automobiles, and others. Obtaining t…
  • College: College of Engineering (COE)
  • Inventors: Alexandrov, Boian; Forquer, Matthew; Jang, Eun; Luo, Yuxiang; Stewart, Jeffrey
  • Licensing Officer: Zinn, Ryan

Unlocking Hidden Opportunities: The Power of Multi-Solution Spatial Aggregation
TS-067434 — The Need Spatial aggregation is crucial in numerous industries where data from low-level spatial units, such as census blocks, must be grouped into larger, meaningful regions. Traditional approaches often struggle with the computational complexity of these tasks and tend to focus on finding a singl…
  • College: College of Arts & Sciences
  • Inventors: Xiao, Ningchuan
  • Licensing Officer: Dahlman, Jason "Jay"

AIDRIN (AI Data Readiness Inspector)
TS-067116 — AIDRIN (AI Data Readiness Inspector) is a system designed to comprehensively evaluate datasets through a diverse range of metrics, giving an overall perspective on their readiness for AI applications.
In the contemporary digital landscape, the explosive growth in data generation has created a pressing need for efficient and scalable database systems. Traditional databases struggle with the increasing volume, variety, and velocity of data, leading to performance bottlenecks, high operational cos…
  • College: College of Engineering (COE)
  • Inventors: Byna, Suren; Hiniduma, Kaveen
  • Licensing Officer: Zinn, Ryan

Unveiling the Nanoscale: Breakthrough Analytical Speed of Real-Time Super-Resolution Microscopy and Beyond
TS-066926 — This Ohio State University software innovation offers groundbreaking analytical speed and automation of Single-Molecule Localization Microscopy (SMLM) as well as analysis of 3D data and images extending beyond the field of microscopy. Our technology offers real-time spatial analysis in continuous and discrete space, enabling unprecedented speed and efficiency in data processing that translates into faster decision-making, high-throughput screening capabilities, and broad applicability beyond traditional microscopy.
Single-molecule localization microscopy (SMLM) describes a family of fast-evolving, powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. SMLM can now be performe…
  • College: College of Engineering (COE)
  • Inventors: Soltisz, Andrew; Veeraraghavan, Rengasayee
  • Licensing Officer: Zinn, Ryan

SimulationAI -- AI-Enabled Software Solution for Physics-Based Simulations
TS-066058 — By adopting our AI-driven solution, engineering teams can achieve more in less time, push the boundaries of innovation, and significantly cut down costs, all while maintaining or increasing the reliability and accuracy of their structural and material analysis. This is not just an evolution in FEM technology—it's a revolution.
In an era where precision and efficiency drive the success of engineering projects, the finite element method (FEM) remains indispensable but is burdened by high operational and computational costs. These costs often lead to overlooked uncertainty factors, suboptimal designs, and significant finan…
  • College: College of Engineering (COE)
  • Inventors: Soghrati, Soheil; vemparala, Balavignesh; Yang, Ming
  • Licensing Officer: Zinn, Ryan

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