Changeflow GovPing Banking & Finance Machine Learning Reservation Data Management Sy...
Routine Rule Added Final

Machine Learning Reservation Data Management System

Email

Summary

The USPTO published patent application US20260099775A1 for a machine learning system that renders combined views of static contracted inventory and dynamic shared reserve inventory using time-based decay logit outputs. The system generates constrained views of available offers for re-allocating reservation data objects. The application was filed on October 7, 2025, by six inventors and published on April 9, 2026.

Published by USPTO on changeflow.com . Detected, standardized, and enriched by GovPing. Review our methodology and editorial standards .

What changed

The USPTO published a patent application for a machine learning-based computing system that manages reservation data. The system maintains a machine learning data architecture trained to generate time-based decay logit outputs populated into an extended data structure representing available offers for re-allocating reservation data objects in a dynamic shared reserve inventory. At run-time, the graphical user interface renders a combined view utilizing these outputs to show a constrained view of offers for potential re-allocation.

Patent applicants and technology companies developing reservation management systems may benefit from reviewing this published application to understand the scope of claimed subject matter. The CPC classification G06Q 10/02 indicates this falls within reservation management applications.

Archived snapshot

Apr 17, 2026

GovPing 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.

← USPTO Patent Applications

SYSTEM AND METHODS FOR DATA MESSAGING FOR GLOBALLY MANAGING SEGREGATED RESERVATION DATA

Application US20260099775A1 Kind: A1 Apr 09, 2026

Inventors

Avraam ZOSIMADIS, Dharmesh DAYABHAI, Jonathan BRUCE, Jan Gabriel ONA, Peter SYMANIW, Paul Andrew BIRKBECK

Abstract

A machine learning based computing system that is configured for rendering, at run-time, improved graphical user interfaces showing a combination of static contracted inventory and dynamic shared reserve inventory. A machine learning data architecture is maintained and trained to generate time-based decay logit outputs that are populated into an extended data structure representing available offers for re-allocating reservation data objects in the dynamic shared reserve inventory. At run-time, the graphical user interface renders a combined view that utilizes the time-based decay logit outputs to generate a rendering showing a constrained view of available offers for potential re-allocation of the reservation data objects.

CPC Classifications

G06Q 10/02

Filing Date

2025-10-07

Application No.

19352420

View original document →

Get daily alerts for USPTO Patent Applications - Business Methods (G06Q)

Daily digest delivered to your inbox.

Free. Unsubscribe anytime.

About this page

What is GovPing?

Every important government, regulator, and court update from around the world. One place. Real-time. Free. Our mission

What's from the agency?

Source document text, dates, docket IDs, and authority are extracted directly from USPTO.

What's AI-generated?

The summary, classification, recommended actions, deadlines, and penalty information are AI-generated from the original text and may contain errors. Always verify against the source document.

Last updated

Classification

Agency
USPTO
Instrument
Rule
Legal weight
Binding
Stage
Final
Change scope
Minor
Document ID
US20260099775A1
Docket
19352420

Who this affects

Applies to
Patent applicants
Industry sector
5112 Software & Technology
Activity scope
Patent filing Machine learning systems
Geographic scope
United States US

Taxonomy

Primary area
Intellectual Property
Operational domain
Legal
Topics
Artificial Intelligence Data Management

Get alerts for this source

We'll email you when USPTO Patent Applications - Business Methods (G06Q) publishes new changes.

Free. Unsubscribe anytime.

You're subscribed!