Atombeam Technologies Inc.

Genesys Telecommunications Laboratories, Inc.

Virtual Hold Technology Solutions, LLC

Other

Newvoicemedia Limited

Michelli Capital Limited Liability Company

Brian R Galvin

Zhu Li

Paras Maharjan

Herbert Willi Artur Ristock

S Michael Perlmutter

Matthew DiMaria

Shannon Lekas

Nicholas James Kennedy

Kurt Nelson

Charles Yeomans

Matthew Donaldson Moller

Joshua Cooper

Andriy Vladimirovich Ryabchun

Nikolay A Anisimov

Vladimir Mezhibovsky

Daniel Bohannon

Alan McCord

Dave Sneyders

Sergey A Fedorov

Grant Fickes

Etienne Coulon

Edward Woolen

Alan Mccord

Ashley Unitt

Alan Wayne McCord

Donald R Boys

Erin Johnston

Alec Perry

A system and method for a federated deep learning platform utilizing homomorphically-compressed and encrypted data. The system comprises multiple client devices, each with a local dataset, and a central server hosting a deep learning core. Client devices convert local data into codewords, which are also homomorphically encrypted. The central server processes these encrypted codewords without decryption, preserving data privacy. The platform supports at least two architectural variants: a conventional Transformer trained on codewords, and a Latent Transformer operating on latent space vectors. Both variants eliminate the need for embedding and positional encoding layers. The system aggregates encrypted model updates from clients, enabling collaborative learning while maintaining data confidentiality. Additional features comprise differential privacy implementation and adaptive federated optimization techniques. This innovative approach allows for efficient, privacy-preserving distributed learning across diverse datasets, addressing key challenges in federated learning such as data heterogeneity, non-IID distributions, and communication efficiency.

Federated latent transformer deep learning core

A system and method for implementing a Persistent Cognitive Machine (PCMs) that extends beyond the traditional prompt-response paradigm of artificial intelligence are disclosed. A PCM maintains persistent cognitive processes regardless of external interaction, stores and organizes thoughts in a thought cache, retrieves relevant thoughts based on current stimuli, generates new thoughts through reasoning processes, and curates stored thoughts during periods of reduced external interaction. The PCM includes language and reasoning model components, a thought cache, an executive component, and an embedding system. The PCM remains continuously active, remembers previous experiences, learns from these experiences, creates new thought experiences independently, and initiates interactions without waiting for external prompts. The PCM enters sleep-like states during which it curates its thought cache, generalizes experiences, and performs other memory management functions. Applications may include but are not limited to synthetic cognitive colleagues, strategic war gaming platforms, and personal cognitive assistants.

System and method for persistent cognitive machines using a digital thought architecture

A system and method for complex-valued radar image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the complex-valued radar image, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective complex-valued radar image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

System and methods for upsampling of decompressed data after lossy compression using a neural network

Compressing and re-securing blockchain data using a large codeword model (LCM) with deep learning. The LCM tokenizes the blockchain into sourceblocks, assigns unique codewords to each sourceblock, and processes the codewords through a deep learning core, enabling efficient compression, semantic understanding, and generation of blockchain data. In the event of a compromised block, the system re-encodes and rehashes the entire compressed chain, generating a new secured chain while preserving the original chain as metadata for backward compatibility. The LCM-based approach enhances security, efficiency, and resilience of blockchain networks, offering significant advantages over existing techniques.

Secure, robust, and efficient blockchain management using large codeword models

A system and method for Light Detection and Ranging (LIDAR) image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the LIDAR, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective LIDAR image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

System and method for adaptive quality driven compression of genomic data using neural networks

A system and method for federated two-stage compression with federated joint learning. The system and method proposed allow for fast and efficient lossless data compression of a large variety of data types. The system and method have a variety of real-world applications, including deep learning solutions for telemetry, tracking, and command subsystems for satellites. Satellites and their control centers are incredibly spaced apart which makes data compression an extremely important process to transmit large sets of information in a low-latency, high-efficiency environment. The proposed system and method utilize probability prediction driven arithmetic long short-term memory system for data compression.

System and method for federated two-stage compression within a persistent cognitive machine

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

System and methods for multimodal series transformation for optimal compressibility with neural upsampling

Federated large codeword model deep learning architecture

A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.

System and methods for adaptive bandwidth-efficient encoding of genomic data

A unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. The platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. Multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. A neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. The platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. By integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. The system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

Unified system for multi-modal data compression with relationship preservation and neural reconstruction

A system and method for multimodal latent hyperspace navigation that enables efficient compression and interactive exploration of spatiotemporal and spectral media content. The system encodes video data into a structured seven-dimensional hyperspace spanning spatial coordinates, temporal progression, viewing orientation, scale, and spectral wavelength using variational autoencoders that generate locally Lorentzian latent patches. Navigation through the hyperspace is achieved via learned geodesic transition functions guided by a latent-space metric tensor, while generative fill-in modules synthesize content for sparsely populated regions. The architecture supports real-time deployment on resource-constrained devices such as set-top boxes through efficient latent decoding and optional generative refinement. Applications include immersive film exploration with continuous zoom and viewpoint control, surveillance systems with anomaly detection capabilities, and hyperspectral environmental monitoring with real-time spectral analysis across multiple wavelength bands.

Multimodal latent hyperspace navigation incorporating spectral, spatial, temporal, and scale dimensions

A system and method for compressing and restoring multi-modal data utilizing a variational autoencoder to enable homomorphic compression techniques is disclosed. Multi-modal input data, comprising at least two different data types, is compressed into a unified latent space using an encoder network of a multi-modal variational autoencoder. Homomorphic operations are performed on compressed data in the latent space. The latent space compressed data is decompressed using a decoder network of the multi-modal variational autoencoder. The system utilizes modality-specific layers and cross-modal attention mechanisms to effectively process diverse data types. The homomorphic operations enable performing computations while the data is in a compressed form, preserving results of those operations in the decompressed output. This approach allows for efficient storage, transmission, and analysis of multi-modal data while maintaining privacy and data integrity across different modalities.

System and method for edge based multi-modal homomorphic compression

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

System and methods for upsampling of decompressed genomic data after lossy compression using a neural network

A system and method for efficient natural language processing combines large and small language models with a thought caching architecture. The system includes a router that directs prompts either to a large language model for thought generation or to a thought cache containing previously generated thoughts. When using the large model, generated thoughts are combined with the original prompt and routed through a smaller language model to produce responses. The thought cache stores reasoning patterns that can be retrieved and reused, eliminating the need to regenerate similar thoughts for related prompts. The system supports both local and cloud-based caching, enabling personal and enterprise-wide thought storage and retrieval. This architecture reduces computational overhead while maintaining reasoning capabilities, effectively extends context windows beyond traditional limits, and enables efficient scaling across different deployment scenarios. The system can operate with reduced resources by leveraging cached thoughts without requiring constant access to the large model.

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Silverdale, WA, United States of America

Brian R Galvin