Abstract: A system and method for distributed node-based data compaction. The system uses machine learning on data chunks to generate codebooks which compact the data to be stored, processed, or sent with a smaller data profile than uncompacted data. The system uses a data compaction in an existing blockchain fork or implemented in a new blockchain protocol from which nodes that wish to or need to use the blockchain can do so with a reduced storage requirement. The system uses network data compaction across all nodes to increase the speed of and decrease the size of a blockchain's data packets. The system uses data compaction firmware to increase the efficiency at which mining rigs can computationally validate new blocks on the blockchain. The system can be implemented using any combination of the three data compaction services to meet the needs of the desired blockchain technology.

Abstract: A system and method for implementing a Persistent Cognitive Machine (PCM) 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.

Abstract: This invention presents an optimized approach for training and operating Large Language Models (LLMs) using codewords. By converting traditional token-based LLMs to codeword-based systems, the method achieves significant efficiency gains. The process involves tokenizing training data and assigning codewords to tokens. LLMs are then trained and operated using these compact codewords instead of conventional tokens. During operation, prompts are converted to codewords, processed by the LLM, and the outputs are converted back to text. This approach reduces the overall cost of training and operating LLMs by approximately, offering a more efficient solution for large-scale language processing tasks.

Abstract: A system and method for adaptive geometric diffusion projection enables mapping of heterogeneous high-dimensional representations onto a shared low-dimensional manifold without neural network training. The system maintains landmark points in source spaces and computes their spectral coordinates through graph Laplacian eigen decomposition based on semantic similarities. New input points are projected via harmonic extension, computing weighted interpolations of nearby landmark spectral coordinates. A geometric optimization process refines positions while maintaining manifold constraints through tangent space projections. The system continuously monitors geometric invariants including principal angles, spectral gaps, and curvature distributions. When invariants exceed thresholds, targeted adaptations occur: spectral basis updates using warm-started iterations, landmark set augmentation in high-residual regions, or parameter adjustments.

Abstract: A system and method for event-driven data communication using codebooks with protocol adaption. The system initiates with a request for propagation information from an application to a first transaction manager. The first transaction manager configures a packet describing its location, potentially containing one or more protocol appendices, or encoded data using a codebook. This packet is provided to the application for transmission to another application with a second transaction manager. Upon receiving a protocol request from the second transaction manager, the first transaction manager communicates using a selected protocol decoded from the protocol appendix. If the selected protocol is supported, the transaction proceeds, completing successfully. This system enables transparent encoding, negotiation, and selection of communication protocols, allowing efficient transactions between different transaction managers.

Abstract: A system and method for multi-level compaction of floating-point numbers and associated metadata within datasets. The system identifies floating-point numbers and their associated metadata, pre-encodes numbers into binary string representations, and encodes metadata into compact binary form. These encoded elements are linked together and indexed to indicate they represent floating-point numbers with metadata. The dataset is organized into multiple compaction levels based on semantic relationships between metadata elements. The system creates specialized indices for metadata-based retrieval, maintains relationship maps, and implements inheritance policies across hierarchical levels. During retrieval, the system reconstructs both the original floating-point values and their associated metadata, preserving hierarchical relationships.

Abstract: A computer system for compacting video data. The system acquires a video stream, reduces redundancy through pre-processing, and analyzes the stream to identify patterns and irregularities. It detects spatial or temporal anomalies in the video and produces three outputs: a conditioned video stream based on statistical analysis, an error stream reflecting adjustments made during conditioning, and an anomaly meta-stream containing metadata about detected anomalies. The system communicates with one or more remote systems to synchronize and negotiate a compatible compression codebook, optionally exchanging compact updates that represent differences between local and remote codebooks. The conditioned video stream is then compressed using the agreed codebook.

Abstract: A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions.

Abstract: A system and method for adaptive data processing that combines statistical analysis, distribution transformation, and dynamic technique selection. The system analyzes input data characteristics, transforms the data into a target distribution using a transformation matrix, and generates separate data streams for transformed data and transformation information. Processing techniques are dynamically selected and applied based on data characteristics and performance metrics. At least one data stream is compressed using entropy coding. The system monitors the effectiveness of applied techniques and adjusts subsequent selections accordingly. Different operating modes allow for lossless reconstruction, efficient transmission, or enhanced security. The approach provides a unified solution for data processing challenges, simultaneously addressing compression, encryption, and adaptation to changing data characteristics.

Abstract: A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system preprocesses raw data at edge computing devices, compresses the data into latent space vectors using distributed encoders within a variational autoencoder spanning edge and cloud components, decompresses the vectors using decoders, and processes them through a resource-aware neural upsampler to generate enhanced reconstructed outputs. The system dynamically adapts compression based on available computing resources and network conditions, while enabling secure distributed processing through homomorphic operations on compressed data. Edge-cloud coordination layers manage data flow, compression parameters, and workload distribution, while maintaining system reliability through intelligent failover handling and resource optimization.

Abstract: A system and method are disclosed for low-light image enhancement using denoising preprocessing with wavelet decomposition AI-based techniques to enhance image quality of low-light images. Subsampled images are created from a raw input image. A wavelet decomposition process is performed on each subimage to create multiple frequency domain subimages. Each frequency domain subimage is input into a corresponding neural network. The output of each corresponding network is input to an inverse wavelet module. The output of the inverse wavelet module is a denoised image that is input to an image signal processing pipeline, where additional processing may be performed on the denoised image.

Abstract: 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 system supports goal-conditioned traversal, recycling generated outputs back into the latent representation, and iterative refinement from coarse to fine scales. The architecture enables real-time deployment on resource-constrained devices through efficient latent decoding.

Abstract: A system for adaptive data compression uses content-aware analysis and dynamic feedback to optimize compression quality. An adaptive quantization subsystem analyzes content characteristics of input data and determines appropriate quantization parameters. A bit allocation engine distributes available bits across different portions of the input data based on the analyzed characteristics. A quality assessment subsystem monitors the compressed output and generates parameter adjustment signals based on measured quality metrics. A feedback control subsystem then modifies the quantization parameters in response to these signals. The modified parameters are used to create optimized compressed output data from the input dataset. This dynamic, content-aware approach enables improved compression quality while maintaining efficient data reduction.

Abstract: A system and method are disclosed for encoding and decoding QR codes using multiple security levels through proprietary compression codebooks to increase information density and provide graduated data security. Input data is classified into public and multiple private security levels. Public data is encoded using a standard codebook while private data uses corresponding level-specific proprietary codebooks. The encoded data is combined into a single QR code with security level markers. Decoding extracts and processes each portion according to its security level using appropriate codebooks, enabling fine-grained access control while maintaining data compression benefits. The system supports complex security requirements while maximizing QR code capacity.

Abstract: A system comprises hierarchically organized microcells arranged in zones, tiles, and panels for energy regulation applications. Each microcell includes a substrate with surface features, an opposing membrane defining a maintained nanoscale gap, activation electrodes, and gap-control elements. Zone controllers coordinate subsets of microcells. Tile controllers manage multiple zones with local electronics. Panel controllers regulate system operation through coordinated control. Fault detection isolates malfunctioning elements while maintaining system operation. Panels are configured for architectural integration into walls, ceilings, or floors. The hierarchical architecture enables scalable deployment through tile replication and modular expansion. The system operates through controlled activation of distributed microcells across maintained gaps. Specific performance depends on system configuration, operating conditions, and environmental factors.

Abstract: A system and method for generating synthetic video from diverse sensor inputs within a unified computational framework. The system receives heterogeneous data such as acoustic, thermal, and textual streams, encodes each into modality-specific latent representations, and projects them into a shared geometric manifold. Within this manifold, convergence points known as multimodal landmarks are established and used to compute geodesic trajectories that describe relationships among the inputs. The trajectories are verified for reversibility to ensure that forward and reverse mappings remain consistent. A Lorentzian autoencoder then decodes the validated trajectories into temporally coherent video sequences derived from the multimodal evidence rather than reconstructed imagery.

Abstract: A system and method for compressing and restoring data using hierarchical autoencoders and Lorentzian autoencoders for video processing. For general data, the system employs hierarchical autoencoders operating at multiple scales. For video data, Lorentzian autoencoders preserve three-dimensional tensor structure where spatial and temporal relationships remain intact throughout compression and decompression. A correlation network, trained on cross-correlated data sets, enhances restoration by leveraging relationships between compressed representations, recovering information lost during compression. The Lorentzian approach enables advanced video features including temporal prediction and infinite zoom, where users can examine regions beyond original resolution with synthesized yet plausible details.

Abstract: A system and methods for upsampling of decompressed transformed time-series data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The recurrent layers extract multi-dimensional features from the two or more correlated datasets, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving decompressed data quality. The model's outputs enable effective data reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: A system and method learning-based lossless data compression. 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 coding which provides faster encoding times and higher compression ratios when paired with a long short-term memory system for data compression.

Abstract: 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.