IBM AI Patent, Real-Time Air Temperature Forecasting
Summary
The USPTO granted US Patent 12,607,771B2 to International Business Machines Corporation on April 21, 2026. The patent covers a system and method for dynamically forecasting high spatial resolution air temperature in real-time using multiple data sources, generative adversarial networks for spatial downscaling, and physics-informed neural networks with extended Kalman filters for temporal forecasting. The patent contains 19 claims.
What changed
The USPTO issued Patent 12,607,771B2 to International Business Machines Corporation on April 21, 2026, covering a dual-module system for air temperature forecasting. The spatial downscaling module uses a generative adversarial network conditioned on atmospheric variables to produce high-resolution temperature maps. The temporal forecasting module applies a physics-informed neural network with an extended Kalman filter, incorporating real-time sensor data to generate forecasts with both high temporal and spatial resolution.
Patent grants are administrative IP events that establish enforceable exclusivity rights for the assignee. The filing date of May 22, 2023 suggests approximately three years of prosecution. No compliance obligations are created for third parties by this grant alone; however, parties developing similar real-time weather forecasting AI systems should consider whether the claimed methods fall within their own product designs, as IBM now holds enforceable rights under 35 U.S.C.
Archived snapshot
Apr 21, 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.
Dynamically forecasting high resolution air temperature in real-time using multiple sources
Grant US12607771B2 Kind: B2 Apr 21, 2026
Assignee
International Business Machines Corporation
Inventors
Ismail Yunus Akhalwaya, Etienne Eben Vos, Sibusisiwe Audrey Makhanya, Tamara Rosemary Govindasamy, Zubeida Patel, Dedricks Monyai Morake, Craig Mahlasi, Muaaz Bhamjee
Abstract
Techniques for dynamically forecasting high spatial resolution air temperature in real-time using a multiplicity of data sources and advanced machine learning models are provided. In one aspect, a system for generating air temperature forecasts includes: a spatial downscaling module for providing air temperature data from a multiplicity of sources as input to a generative adversarial network, conditioning the input to the generative adversarial network on at least one conditioning variable, and generating high resolution air temperature maps; and a temporal forecasting module for providing the high resolution air temperature maps along with real-time air temperature data from a sensor network as input to a physics-informed neural network with extended Kalman filter, and generating high temporal and spatial resolution air temperature forecasts. A method for generating air temperature forecasts using the present system is also provided.
CPC Classifications
G01W 1/10 G06F 18/214 G06F 18/24 G06F 16/2474 G06F 18/2136 G06F 18/2148 G06F 2218/08 G06F 17/18 G06F 30/27 G06N 3/045 G06N 3/0475 G06N 3/08 G06N 3/088 G06N 3/094 G06N 3/048 G06N 3/084 G06N 20/10 G06N 3/04 G06N 3/0455 G06Q 10/04 G06T 3/4046 G06T 3/4053 G06T 3/40 G06T 11/206 G06T 2207/10004 G06T 2207/20081 G06T 2207/20084 G06T 5/50 H04L 41/145
Filing Date
2023-05-22
Application No.
18200050
Claims
19
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