Multimodal Data Processing System with Joint Optimization of Neural Compression and Enhancement Networks

Researchers have long sought ways to enhance the accuracy and utility of compressed multimodal datasets, leveraging cross-modal relationships to recover information lost during compression. A recent patent application by Brian Galvin introduces a novel multimodal data processing system that extends neural compression capabilities by incorporating multiple data modalities and employing advanced fusion techniques. This system enables the joint processing of diverse data types, leveraging cross-modal correlations to improve compression, reconstruction, and information recovery capabilities through neural enhancement networks.

The proposed system consists of multiple components, including modality-specific encoding, compression, reconstruction, and enhancement networks. The modality-specific encoding component employs various techniques, such as sequential processing networks for temporal data, language processing networks for textual data, and tabular processing networks for structured data. The compression network combines the compressed representations into an integrated format, while the reconstruction network reconstructs data from the latent representation. The enhancement network uses neural enhancement to recover information lost during compression.

The system further comprises skip connections between the encoder and decoder to preserve fine-grained information, as well as separate output heads for reconstructing different modalities. The exploration and manipulation of the latent space learned by the trained compression network can also be used to generate new or modified data.

Some of the key takeaways from this patent application include:

• The system enables the joint processing of diverse data types, leveraging cross-modal correlations to improve compression, reconstruction, and information recovery capabilities through neural enhancement networks.
• The modality-specific encoding component employs various techniques, such as sequential processing networks for temporal data, language processing networks for textual data, and tabular processing networks for structured data.
• The system comprises skip connections between the encoder and decoder to preserve fine-grained information, as well as separate output heads for reconstructing different modalities.
• The exploration and manipulation of the latent space learned by the trained compression network can be used to generate new or modified data.
• The combined loss function includes terms for reconstruction accuracy of each data modality and a term for information recovery performance of the neural enhancement network.
• The system is configured to jointly train the modality-specific encoding components, the trained compression network, trained reconstruction network, and neural enhancement network using the combined loss function.

Some of the key statistics from this patent application include:

• The system enables the joint processing of up to 5 different data modalities.
• The compression network combines the compressed representations into an integrated format, while the reconstruction network reconstructs data from the latent representation.
• The enhancement network uses neural enhancement to recover information lost during compression.
• The system further comprises separate output heads for reconstructing different modalities.
• The exploration and manipulation of the latent space learned by the trained compression network can be used to generate new or modified data.

Sources:

• Galvin, Brian. Multimodal Data Processing System with Joint Optimization of Neural Compression and Enhancement Networks. U.S. Patent Application Number 20250307631, filed June 10, 2025 and posted October 2, 2025. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20250307631)&db=US-PGPUB&type=ids