Semantic Communication Method for AI Model Transmission
Summary
USPTO published patent application US20260099699A1 disclosing a semantic communication method for AI model transmission. The method encodes a target AI model using a preset semantic encoder to output semantic information, which is then transmitted through a wireless channel to a semantic decoder that reconstructs a corresponding model. The semantic encoder and decoder are trained using a semantic contrastive loss function to minimize semantic distance between target and enhanced samples while maximizing distance with remaining samples.
What changed
USPTO published patent application US20260099699A1 titled 'Semantic Communication Method and Apparatus, Device, and Storage Medium.' The application discloses a method for transmitting AI models efficiently by encoding models into semantic information using a trained semantic encoder, transmitting through a wireless channel, and reconstructing the model at the receiver using a trained semantic decoder. The training process uses a semantic contrastive loss function to optimize encoder-decoder pairs by minimizing semantic distance between target and enhanced training samples while maximizing distance to remaining samples.
For companies developing AI systems, this patent application indicates a potential future direction in AI model distribution using semantic communication rather than traditional data transmission. Inventors include Ying Sun, with CPC classifications covering neural network architectures (G06N 3/0455, G06N 3/0464, G06N 3/08).
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Apr 17, 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.
Semantic Communication Method and Apparatus, Device, and Storage Medium
Application US20260099699A1 Kind: A1 Apr 09, 2026
Inventors
Ying SUN
Abstract
Provided are a semantic communication method and apparatus, a device, and a storage medium. The semantic communication method includes: inputting a to-be-transmitted target model into a preset semantic encoder to output semantic information of the target model; and transmitting the semantic information of the target model to a preset semantic decoder through a wireless channel to output a reconstructed model corresponding to the target model. When the semantic encoder and the semantic decoder are trained, a training sample is selected from a training sample set as a target training sample; and a semantic contrastive loss function of the training sample set is determined with a goal of minimizing a first semantic distance between the target training sample and a corresponding enhanced sample and maximizing a second semantic distance between a remaining training sample and the target training sample to train the semantic encoder and the semantic decoder.
CPC Classifications
G06N 3/0455 G06N 3/0464 G06N 3/08
Filing Date
2024-10-25
Application No.
18926370
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