Deere Location Embeddings AI Patent, Apr 2026
Summary
USPTO granted Patent US12608590B2 to Deere & Co. on April 21, 2026, covering methods for generating location embeddings that capture both spatial dependence and spatial heterogeneity of geospatial measures. The patent, filed March 12, 2021, names Hongxu Ma, Gengchen Mai, and Bin Ni as inventors and contains 13 claims. The technology enables geographic coordinates to be processed through dual encoders to produce combined embeddings suitable for downstream statistical analysis and machine learning applications.
“A combined embedding corresponding to the geographic location may be generated based on the first and second location embeddings.”
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GovPing monitors USPTO Patent Grants - AI & Computing (G06N) for new telecom & technology regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 32 changes logged to date.
What changed
USPTO granted US Patent US12608590B2 to Deere & Co. for methods of generating location embeddings using spatial dependence and spatial heterogeneity encoders to process geographic position coordinates. The combined embeddings enable predictions for geospatial measures and support downstream machine learning processing.
Manufacturers and technology companies developing geospatial AI systems should review the patent claims for potential licensing considerations or freedom-to-operate analysis. The 13 claims broadly cover processing position coordinates through dual encoding mechanisms to capture spatial patterns in geospatial data.
Archived snapshot
Apr 23, 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.
Generation and application of location embeddings
Grant US12608590B2 Kind: B2 Apr 21, 2026
Assignee
DEERE & CO.
Inventors
Hongxu Ma, Gengchen Mai, Bin Ni
Abstract
Implementations are described herein for generating location embeddings that capture spatial dependence and heterogeneity of data, making the embeddings suitable for downstream statistical analysis and/or machine learning processing. In various implementations, a position coordinate for a geographic location of interest may be processed using a spatial dependence encoder to generate a first location embedding that captures spatial dependence of geospatial measure(s) for the geographic location of interest. The position coordinate may also be processed using a spatial heterogeneity encoder to generate a second location embedding that captures spatial heterogeneity of the geospatial measure(s) for the geographic location. A combined embedding corresponding to the geographic location may be generated based on the first and second location embeddings. The combined embedding may be processed using a function to determine a prediction for one or more of the geospatial measures of the geographic location of interest.
CPC Classifications
G06N 3/044 G06N 3/045 G06N 3/08 G06F 16/29
Filing Date
2021-03-12
Application No.
17200097
Claims
13
Parties
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