Automated High Throughput Protorheology
Summary
USPTO published patent application US20260092850A1 disclosing methods and systems for automatic, high-throughput estimation of rheological properties of fluidic materials using videography and neural-network processing. The invention enables parallel testing to achieve economical property predictions without expensive rheometric equipment.
What changed
This patent application (US20260092850A1) discloses a machine learning system for automated protorheology using neural networks (G06N 3/0464) to analyze visual observables from video recordings of fluidic material tests. The system achieves high throughput via parallel testing configurations and provides rheological property estimations without traditional rheometric equipment.
This is a patent publication notice only—no regulatory compliance actions are required. Inventors include researchers from the University of Illinois. Entities developing or using rheological testing systems should monitor this application for potential licensing implications or freedom-to-operate considerations.
Archived snapshot
Apr 2, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Automated High Throughput Protorheology
Application US20260092850A1 Kind: A1 Apr 02, 2026
Inventors
Randy Ewoldt, Sameh Tawfick, Ignacio Arretche, Nancy R. Sottos, Jeffrey S. Moore, Phillippe Geubelle, Mohammed Tanver Hossain, Connor Armstrong, Jacob J. Lessard, Ramdas Tiwari, Michael Zakoworotny
Abstract
This disclosure generally relates to rheological property measurements/estimation for fluidic materials and is specifically directed to methods and systems for automatic and high-throughput estimation of rheological properties via videography of visually observable tests and neural-network processing. The high throughput may be achieved via parallel testing. The disclosed methods and systems provide an economical approach to estimating rheological properties with reasonable prediction accuracy based on visual observables without relying on expensive and complex rheometric setup and equipment.
CPC Classifications
G01N 11/00 G06N 3/0464 G01N 2011/008
Filing Date
2025-10-01
Application No.
19347242
Named provisions
Related changes
Get daily alerts for ChangeBridge: Patent Apps - AI & Computing (G06N)
Daily digest delivered to your inbox.
Free. Unsubscribe anytime.
Source
About this page
Every important government, regulator, and court update from around the world. One place. Real-time. Free. Our mission
Source document text, dates, docket IDs, and authority are extracted directly from USPTO.
The plain-English summary, classification, and "what to do next" steps are AI-generated from the original text. Cite the source document, not the AI analysis.
Classification
Who this affects
Taxonomy
Browse Categories
Get alerts for this source
We'll email you when ChangeBridge: Patent Apps - AI & Computing (G06N) publishes new changes.