ML-based Furniture Layout Generation in Virtual Spaces
Summary
USPTO published patent application US20260091318A1 on April 2, 2026, disclosing machine learning techniques for generating recommended furniture layouts in virtual spaces within online games. The system processes space configurations, user criteria such as furniture style and budget, and characteristics of proximate spaces to output AI-generated furnished layouts. Inventors include Anushka Nair, Nitish Victor, Caedmon Somers, Ashwin Nathan, Han Liu, Jesse Hans Stokes Harder, and Marjan Moodi.
What changed
Patent application US20260091318A1 (Application No. 18900372, filed September 27, 2024) describes techniques for instantiating objects in virtual spaces using machine learning. The system receives requests to generate furnished spaces, identifies current configurations including existing furniture, doors, apertures, and dimensions, receives criteria describing preferred furniture style, player budgets, and furniture priorities, and considers characteristics of proximate spaces. A machine learned model processes these inputs to output recommended furnished layouts.
Patent applications do not impose compliance obligations on regulated entities. Technology companies, game developers, and virtual world creators may reference this publication for competitive intelligence, prior art searches, or potential licensing considerations. The patent remains pending and has not yet been granted by the USPTO.
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.
INSTANTIATING OBJECTS IN A VIRTUAL SPACE USING MACHINE LEARNING
Application US20260091318A1 Kind: A1 Apr 02, 2026
Inventors
Anushka Nair, Nitish Victor, Caedmon Somers, Ashwin Nathan, Han Liu, Jesse Hans Stokes Harder, Marjan Moodi
Abstract
Techniques for instantiating objects in a virtual space are described herein. For example, the techniques may include generating recommended furniture layouts in online games. The game (e.g., an online game) may receive a request to generate a furnished space. The game may identify the current configuration of the space to furnish (e.g., identify existing furniture, door(s), aperture(s), dimension(s) of the space and/or furniture, etc.). Further, the game may receive criteria that describe how to furnish the space (e.g., preferred furniture style, player budget(s), furniture priority, rule(s), etc.). The game may identify characteristics (e.g., furniture types, furniture style, etc.) of other spaces that are proximate to the space to furnish. The game may generate the recommended furnished space by inputting the current configuration of the space, the criteria, and/or the characteristics of the proximate space(s) into a machine learned model which may output a recommended furnished space.
CPC Classifications
A63F 13/65 G06N 20/00
Filing Date
2024-09-27
Application No.
18900372
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