# of Displayed Technologies: 11 / 11

Applied Category Filter (Click To Remove): Artificial Intelligence & Machine Learning


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A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
TS-063238 — A novel regularization technique applicable for medical imaging applications that leverages conditional generative adversarial networks (cGANs) to generate reconstructed images in significantly shorter timeframes. The Need Several techniques are used for image reconstruction in the medical aren…
  • College: College of Engineering (COE)
  • Inventors: Bendel, Matthew; Ahmad, Rizwan; Schniter, Philip
  • Licensing Officer: Hampton, Andrew

Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
  • College: College of Engineering (COE)
  • Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
  • Licensing Officer: Hampton, Andrew

An information extraction, enrichment, and caching framework for augmented reality applications
TS-063109 — The Need In the age of augmented reality (AR), there's a growing opportunity for a comprehensive solution that facilitates the seamless exploration of real-world data through camera-based AR applications. These applications require the ability to extract, cache, and enrich information, enhancin…
  • College: College of Engineering (COE)
  • Inventors: Nandi, Arnab; Burley, Codi; Sarkhel, Ritesh "Ritesh"
  • Licensing Officer: Hampton, Andrew

Auditing Fairness Online through Interactive Refinement
TS-063038 — The Need In the era of machine learning, high-stakes decisions are increasingly being made by black box models, leading to concerns about accountability and fairness. These models can exhibit inherent biases, raising the need for a system that ensures accountability and fairness in decision-making …
  • College: College of Engineering (COE)
  • Inventors: Maneriker, Pranav; Burley, Codi; Parthasarathy, Srinivasan
  • Licensing Officer: Hampton, Andrew

Method and system for generating data-enriching augmented reality applications from a domain-specific language
TS-063032 — The Need In the age of augmented reality (AR), there's a growing opportunity for a comprehensive solution that facilitates the seamless exploration of real-world data through camera-based AR applications. These applications require the ability to extract, cache, and enrich information, enhancin…
  • College: College of Engineering (COE)
  • Inventors: Nandi, Arnab; Burley, Codi; Sarkhel, Ritesh "Ritesh"
  • Licensing Officer: Hampton, Andrew

Novel Deep Learning Model for Reconnaissance of Infrastructure on Drones
TS-063007 — The Need In disaster-stricken areas, timely and accurate reconnaissance is paramount for effective response and recovery efforts. Traditional methods of assessing damage to critical infrastructure, such as power distribution poles, often involve time-consuming manual inspections, leading to delays …
  • College: College of Engineering (COE)
  • Inventors: Shafieezadeh, Abdollah; Bagheri Jeddi, Ashkan
  • Licensing Officer: Hampton, Andrew

Concept Discovery from Text via Knowledge Transfer
TS-050856 — A better way for systems to organize, file, or index documents or content based on actual or anticipated information needed in the form of a user query or natural language question.
Data Processing and (IT)-related activities, ranging from web hosting to automated data entry services are more important than ever due to the large amounts of data collected through technology. According to IBIS World, "Companies will increasingly capture more data, requiring the outside exp…
  • College: College of Engineering (COE)
  • Inventors: Das, Manirupa; Fosler-Lussier, Eric; Ramnath, Rajiv
  • Licensing Officer: Hampton, Andrew

Emergency Response Tool for Industrial Facilities
TS-041844 — Artificial intelligence software for making risk-informed decisions to prevent and mitigate emergencies.
No solutions exist for predicting the likelihood of future undesirable consequences across various industrial settings. The lack of these solutions subjects personnel, the public, and the environment to potentially catastrophic consequences. Essential services such as power plants, pump stations,…
  • College: College of Engineering (COE)
  • Inventors: Yilmaz, Alper; Ajam Gard, Nima; Aldemir, Tunc; Denning, Richard; Lee, Ji Hyun
  • Licensing Officer: Zinn, Ryan

Learning Optimal Empirical Reward iNtelligence (LOERN) with Cyber Maintenance Applications
TS-040341 — A software solution designed to optimize cyber security decision making and reduce maintenance costs.
As the world becomes more and more connected, our systems become more and more vulnerable. If cyber security can become more automated, risk-based decisions can be made more quickly and rationally. Organizations can then leverage this decision-making to improve their security without increasing co…
  • College: College of Engineering (COE)
  • Inventors: Allen, Theodore; Hou, Chengjun
  • Licensing Officer: Zinn, Ryan

DIAMOND
TS-037930 — Data-Driven Inspection, Alerts, Maintenance, Observable Network Decision Control System with Cyber Action Taker to Address Less Inspected Hosts Including Cell Phones
Cyber security is an important facet of many major corporations and government entities. The implementation of effective cyber security protocols is an arduous process because the most targeted individuals, C-suite and boardroom level personnel, understand the protocols the least. A lack of adhere…
  • College: College of Engineering (COE)
  • Inventors: Allen, Theodore; RoyChowdhury, Sayak
  • Licensing Officer: Zinn, Ryan

Subject Matter Expert Refined Topic Models
TS-015175 — Human-Assisted Modeling (HAM), Subject Matter Expert Refined Models (SMERM), and Subject Matter Expert Refined Topic (SMERT) Models.
Currently, unstructured data represents up to 80% of the data within an organization. This means the traditional data, such as sales figures or other statistics, are separated into different documents without a meaningful and effective way to aggregate the data. Due to this there is now an opportu…
  • College: College of Engineering (COE)
  • Inventors: Allen, Theodore; Xiong, Hui
  • Licensing Officer: Zinn, Ryan

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