A system and methods for upsampling compressed data using a jointly trained Vector Quantized Variational Autoencoder (VQ-VAE) and neural upsampler. The system compresses input data into a discrete latent space using a VQ-VAE encoder, reconstructs the data using a VQ-VAE decoder, and enhances the reconstructed data using a neural upsampler. The VQ-VAE and neural upsampler are jointly trained using a combined loss function, enabling end-to-end optimization. The system allows for efficient compression and high-quality reconstruction of various data types, including financial time-series, images, audio, video, sensor data, and text. The learned discrete latent space can be explored and manipulated using techniques such as interpolation, extrapolation, and vector arithmetic to generate new or modified data samples. The system finds applications in data storage, transmission, analysis, and generation across multiple domains.
A system and method for extending mobile-optimized multi-stage language model processing with autonomous reasoning capabilities. Building upon the three-tier thought caching architecture from the parent invention, the system implements a cognitive dyad framework that continues reasoning operations in cloud environments when mobile devices are inactive. The system enters a dream-state processing mode during periods of user inactivity, performing memory consolidation, thought cache optimization, and novel thought generation without consuming mobile device resources. Through persistent cognitive operation, the system maintains reasoning continuity across user interactions and devices while preserving mobile optimization benefits including battery-aware execution, offline functionality, and privacy protection. The cognitive dyad functions as a thinking partner rather than merely a responsive tool, generating novel insights through autonomous exploration while maintaining strict boundaries between private and shared thought spaces.
A system and method for data compaction and encryption of anonymized data records. A dataset may be pre-processed by dividing into a plurality of sourceblocks at all reasonable sourceblock lengths, and then counting how many times each sourceblock occurs in the dataset, resulting in a tally record of tokens and their count value. This tally record may then be anonymized and transmitted to a data deconstruction engine which combined with a library manager creates a codebook and performs optimization techniques on the codebook. The received anonymized tally record may be parsed into individual tokens by identifying the tokens with the highest count value. The tokens may then be sent, in descending order of count value, to the library manger where each token may be assigned a codeword. A half-backed codebook is then created using the tokens and each token's unique codeword, before sending the half-backed codebook to a system user.
Systems and methods for persistence of memory on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. A means for providing variable resistance to change of thoughts on the cognitive manifold is provided in a manner analogous to accretion disk and gravitational hardening in astrophysics by a layered cognitive manifold in which outer layers represent more transient thoughts and inner layers represent more permanent thoughts, with increasing hardening against change occurring in the direction from outer layers to inner layers.
A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.
A system and method are provided for implementing a pulse-regulated temporal architecture in a multiscale persistent cognitive fabric. The system maintains fast, medium, and slow pulse layers coupled through adaptive curvature-based feedback to sustain coherent timing across cognitive processes. An elastic temporal manifold adjusts its internal rhythm in response to cognitive load, contracting during novelty and expanding during stability. Spectral diagnostics monitor a global order parameter and spectral entropy to classify operating states of coherence, adaptation, and desynchronization, while automated controllers correct pathologies such as starvation, storm, and phase drift. A closed feedback loop regulates temporal curvature through sensing, comparison, control, and actuation to maintain equilibrium. In distributed configurations, multiple persistent cognitive machines align their intrinsic time geometries through curvature-diffusion coupling across a shared communication manifold, achieving synchronized persistence and scalable, energy-efficient artificial cognition.
A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.
A system and method for efficient natural language processing combines large and small language models with a reasoning cache architecture. Input data is processed by a first large language model to generate structured thoughts with associated latent representations, which are cached for future use. Specialized agents perform domain-specific operations on cached thoughts and collaboratively evolve them using genetic algorithms. When new input is received, similar cached or evolved thoughts are retrieved based on latent representation similarity. The input and retrieved thoughts are then routed to a second, smaller language model to generate a response. This architecture reduces computational overhead while preserving response quality, enables reuse of reasoning across sessions and devices, and extends effective context beyond traditional sequence limits. By leveraging prior reasoning, the system minimizes redundant computation and supports scalable deployment across diverse hardware environments.
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.
Systems and methods for persistence of memory on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Persistence of memory is reflected on the cognitive manifold through relative displacements between geodesics after a reasoning trajectory has been calculated in a manner analogous to gravitational wave echoes in general relativity physics.
A system and method for data compaction optimization which leverages a neural network to predict optimal block sizes for data encoding, enhancing efficiency and adaptability in various applications. It begins with data preprocessing, extracting features, and creating labeled datasets for training. The neural network architecture is carefully designed, allowing it to learn complex relationships between data characteristics and optimal block sizes. During training, the network is fine-tuned and optimized using appropriate loss functions and regularization techniques. Once deployed, it continuously monitors incoming data streams for shifts in data patterns and adapts predictions accordingly. By predicting multiple block sizes, the system accommodates diverse compression needs. This versatile system offers real-time adaptability, ensuring optimal encoding performance as data patterns evolve over time.
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 coding which provides faster encoding times and higher compression ratios when paired with a long short-term memory system for data compression.
Systems and methods for latent slice budgeting on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for latent slice budgeting on the cognitive manifold are disclosed that foliation of the cognitive manifold into time slices and budgeting change between the time slices.
A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.
A system and method are disclosed for implementing a multiscale persistent cognitive fabric that achieves scalable, long-term cognition through geometric coupling of structure and time. The system includes interconnected cognitive manifolds-fast, mesoscale, and slow-linked by fibers that transmit curvature, bias, and coherence information across scales. The architecture regulates curvature and compression pressure according to activity level, enabling logarithmic scaling of computational cost and transitions between noise, flow, coherence, generative, and doctrinal regimes. A curvature-exchange field couples cognitive and temporal manifolds to maintain equilibrium between cognitive and temporal processes, sustaining cognition across restarts and temporal discontinuities. Curvature regulators maintain stability and form shielded core domains that localize curvature into persistent attractors representing long-term memory and schema structures. Through continuous internal activity and feedback, the system achieves autonomous metabolic maintenance, scalable reasoning, and enduring cognitive persistence.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
A video compression system and method integrates geometric compression with cognitive understanding through a persistent cognitive machine interface. The system employs a hierarchical encoder generating multi-scale compressed representations organized within a Lorentzian manifold structure. A geometric processor maintains temporal causality through time-like geodesics and light cone constraints while organizing video content according to semantic relationships. A cognitive interface creates thought bundles as navigable submanifolds, enabling semantic access to compressed content beyond traditional temporal indexing. The system supports real-time processing through progressive refinement, streaming coarse representations immediately while adding detail in parallel. Symbolic anchors mark semantically significant points, enabling concept-based navigation through compressed video. Federated learning capabilities allow distributed systems to share geometric patterns while preserving content privacy. The architecture enables improved compression ratios while maintaining both temporal causality and semantic navigability, transforming video from sequential media into an intelligently accessible information space.
A system and method for scriptable selective obfuscation of records comprising a data obfuscation module configured to identify and perform data anonymization on personal identifiable information (PII) contained within a plurality of records to create a partially-blurred dataset, and further comprising an encoder which receives the partially-blurred dataset and performs data compaction on the partially-blurred dataset before storing the compacted dataset in a data storage system. In some implementations, the data storage system is a blockchain database and the system functions as a clearinghouse to validate and monitor transactions involving data access rights between record owners and third-party entities. The system can further broker such transactions and direct payment form the third-party entity to the record owner when access rights have been purchased.
A system and method for filesystem data compression using codebooks, that measures in real-time the probability distribution of an encoded data stream, compares the probability distribution to a reference probability distribution, and uses one or more statistical algorithms to determine the divergence between the two sets of probability distributions to determine if an unusual distribution is the result of a data intrusion. The system comprises both encoding and decoding machines, an intrusion detection module, a codebook training module, and various databases which perform various analyses on encoded data streams. Further, the system comprises a system for integrating the compression into a filesystem for both system-wide compression on a per-file or filegroup basis, and intrusion or alteration detection of files.
A system and method for mobile-optimized natural language processing employs a three-tier thought caching architecture comprising a local device cache, a user-specific cloud cache, and a global generalized thought cache. The system processes prompts using a first large language model to generate thoughts, which are then processed with the prompt by a smaller model to produce responses. A thought generalizer identifies common reasoning patterns across users, removes personal information, and creates shareable abstracted thought structures. Mobile-specific optimizations include battery-aware execution scaling and predictive thought pre-caching. When offline, the system adapts existing cached thoughts to address new prompts. Hierarchical thought management organizes information at different abstraction levels, enabling effectively unlimited context while efficiently managing resources. This architecture provides sophisticated language processing on mobile devices with offline functionality while maximizing battery efficiency and maintaining privacy.
A scalable expert foundry system enables creation, management, and coordination of multiple specialized expert domains, each developing autonomous cognitive capabilities through geometric manifold formation while maintaining hierarchical oversight and cross-domain knowledge transfer. The system utilizes a Persistent Cognitive Machine architecture with hierarchical supervisory networks that provide multi-layered coordination, conflict resolution, and quality management across distributed expert domains. Cross-domain coordinators orchestrate communication and knowledge sharing between domains through geometric abstraction and manifold projection techniques that preserve semantic integrity while enabling beneficial knowledge propagation. Executive manifold supervisors implement second-order control architectures managing meta-cognitive capabilities and system-wide reasoning strategies. The system supports enterprise deployment across multiple geographic regions with distributed computing resources. Expert domains achieve operational readiness through statistical observables monitoring including cache hit rates, distance distribution shifts, and trajectory coherence measurements that validate manifold maturity. The architecture enables scalable expert-level performance across diverse knowledge domains while maintaining coordination effectiveness and quality standards.
A system and methods for enterprise hierarchical persistent cognitive machines (PCMs) that extends mobile-optimized multi-stage language model processing with organizational hierarchy awareness and enterprise-specific adaptations. The system comprises a CEO-PCM at the executive level coordinating multiple functional domain PCMs corresponding to organizational departments. A supervisory network layer dynamically adapts underlying language models to enterprise-specific dialect, terminology, and communication patterns through automated analysis of organizational knowledge sources. User prompts are routed through enterprise hierarchical pathways based on authority levels and sensitivity classification, enabling appropriate escalation and cross-functional coordination. The CEO-PCM synthesizes insights from multiple domain PCMs to generate enterprise-wide strategic intelligence while domain-specific compliance modules enforce regulatory requirements during cognitive processing. The system automatically adapts to organizational changes by redistributing knowledge and reconfiguring PCM architecture, while maintaining persistent cognitive state across enterprise operations through comprehensive state preservation and restoration mechanisms that ensure organizational knowledge continuity.
A system and method for zero-knowledge verifiable codebook compression receives an input data stream comprising data blocks and encodes the stream using codebook-based compression algorithms. Concurrently with encoding, the system generates zero-knowledge proofs that cryptographically attest that the encoded representation will decode to data having a specified digest and that policy appendices associated with the codebook were applied during encoding. The system generates codebook commitments comprising cryptographic commitments to codebook contents and policy metadata, then formats output packets containing the encoded representation, zero-knowledge proof, and public inputs including the specified digest and codebook commitment. The zero-knowledge proofs enable verification systems to validate encoding correctness and policy compliance without accessing plaintext content or codebook contents.
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.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.
G06F 18/2137 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
G06F 18/22 - Matching criteria, e.g. proximity measures
25.
System and Methods for Secure Deduplication of Compacted Data
A system and methods for secure deduplication of compacted data comprising a data deconstruction engine, a data reconstruction engine, a library manager, a reference codebook, and a codeword storage which performs simultaneous compaction and deduplication of data sets. A data set may be comprised of one or more sourcepackets which may be optimally deconstructed into a plurality of sourceblocks and wherein each sourceblock may be compared against a reference codebook that contains key-value pairs of a sourceblock and its associated reference code in order to determine if a received sourceblock is a duplicate of data already stored within the reference codebook. Non-duplicate sourceblocks can have a reference code algorithmically created and stored in the reference codebook, thereby ensuring that when a duplicate sourceblock is received, it will not be stored as duplicated data.
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.
A system and method for hierarchical PCM-controlled traversal across nested latent hyperspaces. Input data, including video, is encoded into coupled various granularity subspaces. A goal-conditioned controller computes geodesic routes within levels and defines cross-level lifts and projections to maintain semantic continuity. Symbolic anchors provide durable reentry and audit, while strategy caching abstracts recurrent decision motifs for reuse. A kernel-adaptation subsystem derives motion/recurrence/frequency/semantic features to reshape local metrics and traversal costs, enabling level-aware, reversible updates. During execution the system dynamically switches levels, records checkpoints for backtracking, and commits salient results to persistent memory. For video embodiments, a Lorentzian structure preserves temporal causality and supports continuous zoom, multiview alignment, and cross-temporal analysis. The architecture transforms navigation from frame- or token-based stepping to structured, goal-aligned movement through shaped latent space, improving efficiency, fidelity, and explainability across tasks.
A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes. Cross-team learning capabilities enable organizational intelligence development through pattern abstraction and context-aware adaptation of successful coordination strategies. The persistent cognitive architecture maintains coordination patterns across sessions and team composition changes, enabling continuous improvement through accumulated team experience.
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.
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
A codeword-native database management platform enables efficient processing of database operations by working directly with compressed data. The system implements multiple compression schemes including Huffman, alphabetic, and neural compression, with support for conditional variants and constraints. A hybrid architecture manages both compressed and uncompressed data formats, allowing for seamless operation across different data representations. The system includes specialized components for query processing, storage management, and client-server communication, all optimized for compressed data operations. Query execution plans are generated to minimize decompression requirements while maintaining performance efficiency. The system employs compression-aware buffer management and indexing strategies, enabling direct operations on compressed keys. This approach significantly reduces storage requirements and improves query performance by eliminating unnecessary decompression cycles, while maintaining full ACID compliance and supporting existing database functionalities.
A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.
A system and method for compressing synthetic aperture radar (SAR) images with enhanced phase recovery and unwrapping capabilities is disclosed. The system performs preprocessing on input SAR images, applies discrete cosine transform (DCT) to create subbands, and utilizes a multi-pass amplitude compression technique. A specialized neural network performs phase unwrapping using compressed amplitude information and interferogram wrapped phase data. The system employs a channel-wise transformer fusion block (CTFB) for feature fusion and a multi-stage context recovery subsystem with optimized loss functions for both amplitude and phase recovery. The method achieves improved compression efficiency and phase recovery accuracy, particularly beneficial for Interferometric SAR (InSAR) applications.
A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning. The latent transformer operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.
A system and method for compressing temporal stacks of synthetic aperture radar (SAR) images while preserving interferometric properties. The system receives multiple SAR images acquired over time, aligns them through coregistration, and maintains phase continuity across the temporal sequence. A three-dimensional discrete cosine transform processes both spatial and temporal dimensions, creating hybrid subbands organized by frequency content and temporal change characteristics. The system employs a change-aware encoder that selectively uses differential encoding for small changes between frames and full encoding at adaptive keyframe intervals. A temporal coherence network with separate pathways for amplitude and phase information ensures consistency across the image stack. The compressed output preserves interferometric coherence properties essential for applications such as ground deformation monitoring and change detection. The system achieves compression ratios from 10:1 to 50:1 for static content while maintaining higher quality for rapidly changing features.
H04N 19/13 - Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/142 - Detection of scene cut or scene change
H04N 19/154 - Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/182 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
H04N 19/625 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
35.
System and Method for Geometric Compression and Persistent Memory Management of Genomic Data Using Dynamic Latent Manifolds
A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.
A system and methods for latent contextual threading for personalized dialogue through geometric manifold-based conversation management. The system maintains a personalized cognitive manifold as a geometric manifold in latent space that encodes user-specific dialogue patterns as navigable geometric structures. Multiple dialogue contexts are maintained as geometric trajectories within the manifold, with dialogue responses generated through manifold traversal rather than discrete context retrieval. A bidirectional adaptation system modifies the manifold's geometric structure based on user interactions. The system preserves dialogue continuity across session boundaries by serializing manifold geometry during session termination and restoring geometric positioning during session resumption. Dialogue coherence is evaluated through geometric analysis including curvature calculations and geodesic deviation measurements. The system maintains conversations through real-time manifold geometry modifications, providing dialogue experiences across session boundaries while maintaining contextual threading and personalized interaction patterns through geometric principles.
For compressing synthetic aperture radar (SAR) images, preprocessing operations are performed on an input SAR image. A discrete cosine transform is performed on the image, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for SAR image data compression, including phase unwrapping for supporting interferometric SAR (InSAR) applications.
A system for multi-modal genomic data fusion with adaptive quality driven compression processes genomic data from multiple sequencing platforms. The system harmonizes heterogeneous data formats from different platforms into a unified representation, then evaluates genomic region importance by analyzing cross-platform correlations. A multi-modal quality assessor generates consensus quality scores across platforms using weighted voting algorithms, while a multi-modal rate control engine determines optimal compression rates based on quality scores and platform-specific characteristics. The system compresses genomic data while maintaining cross-platform relationships, then recovers lost information using a neural network comprising recurrent layers and channel-wise transformers that leverage cross-platform correlations. The neural network integrates complementary information from multiple sequencing technologies to reconstruct genomic data with improved quality compared to single-platform approaches, enabling efficient storage and analysis of multi-modal genomic datasets while preserving critical biological relationships.
A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.
A large language model system integrates persistent memory directly into inference operations through geometric manifold traversal rather than external retrieval. The system implements a memory-integrated inference engine that performs token generation with simultaneous memory access by navigating curved regions in a geometric memory manifold. Memories exist as navigable basins of increased curvature that are reinforced through usage rather than stored as discrete objects. An intent conditioning system formulates user queries as utility functions and generates vector fields that guide goal-directed memory traversal. A manifold geometry interface converts geometric memory coordinates into vectors compatible with language model attention mechanisms, augmenting standard key-value caches with memory-derived content. The system performs intentional remembering through path optimization that balances fidelity to prior cognitive trajectories with current intent guidance. Each memory access operation simultaneously retrieves information and strengthens accessed memory regions through bidirectional geometric shaping, enabling persistent cognitive evolution and cross-session memory continuity.
A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.
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.
An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.
A system and method for generation-augmented latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses media into latent representations while preserving geometric, temporal, and semantic relationships. A latent hyperspace manager organizes compressed data as geodesic trajectories, and a geodesic trajectory mapper computes navigation paths. Symbolic anchors provide persistent reference points, while spatiotemporal routing coordinates decisions across multiple scales. A strategy caching system preserves successful navigation patterns for reuse as procedural memory. A synthetic content generator including latent diffusion models, neural radiance fields, and context-aware refinement produces augmentation for continuous zoom, bidirectional traversal, and rotational reorientation. A user input interface and zoom controller enable interactive exploration and reconstruction, supporting applications in immersive media, visualization, and surveillance.
A system and method for latent geodesic traversal across multi-axis hyperspaces for real-time video reconstruction and augmentation. Spatiotemporal video data are compressed into navigable latent representations using hierarchical and Lorentzian autoencoders that preserve geometric and temporal structure. A geodesic traversal engine computes paths across spatial, temporal, spectral, and semantic axes, guided by symbolic anchors and spatiotemporal routing protocols. A correlation network restores fine detail, while an augmentation generator synthesizes additional or counterfactual content to enable infinite zoom, continuous multi-scale exploration, and temporally coherent augmentation. A strategy caching system preserves successful traversal patterns for reuse, supporting persistent learning and adaptive real-time performance.
A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
H04N 19/126 - Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
H04N 19/85 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
47.
Systems and methods for latent hyperspace navigation in spatiotemporal media
A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.
A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.
A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.
A federated system and method for data compression optimization in distributed device networks. The system comprises multiple edge devices that analyze local data patterns to generate device characteristic profiles while performing local compression optimization and maintaining data privacy. Edge devices contribute to collaborative learning by generating privacy-preserved updates without transmitting raw data. A central coordination system aggregates encrypted contributions using secure multi-party computation protocols, identifies device groups based on data pattern similarities, and generates optimized compression parameters for each group. The system coordinates collaborative training of data reconstruction models across device groups and deploys group-optimized reconstruction capabilities. Device grouping is performed by calculating similarity scores between device characteristic profiles and clustering devices with scores above predetermined thresholds. The system dynamically adapts compression and reconstruction parameters through federated learning while preserving individual device data privacy, enabling efficient data compression and near-lossless recovery across heterogeneous Internet-of-Things networks.
A system and method for a digital thought architecture, otherwise called a persistent cognitive machine (PCM), that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. Not only does the PCM with thought manifold maintain persistent cognitive processes regardless of external interaction, overcoming limitations of existing AI systems that operate within a prompt-response paradigm where they await input, generate output, and return to a waiting state, it also performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. In some embodiments, the thought manifold may be implemented as a neuromorphic platform.
G06N 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06F 18/2137 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
52.
Latent Cognitive Manifolds with Lensing Potentials
Systems and methods for guiding or steering thought processes on a persistent cognitive machine (PCM) that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for guiding or steering thought processes on the thought manifold are disclosed that involve mathematical manipulations of the geometric space of the thought manifold inspired by gravitational lensing.
A system and method for data compaction and encryption of anonymized data records. A dataset may be pre-processed by dividing into sourceblocks at reasonable intervals and tallying each sourceblock's frequency, creating a tally record of tokens and count values. This tally record may then be anonymized and transmitted to a data deconstruction engine which combined with a library manager creates a codebook and performs optimization techniques on the codebook. The data deconstruction engine and library manager may be distributed across multiple nodes or devices. The received anonymized tally record may be parsed into individual tokens by identifying the tokens with the highest count value. The tokens may then be sent descending order of count value to the library manger where each token may be assigned a codeword. A half-backed codebook is then created using the tokens and each token's unique codeword, before sending the half-backed codebook to a system user.
A system and method is provided for adaptive sharing of cached results in a distributed machine learning environment. The system receives compressed representations of input data from multiple devices and processes them through a model to generate responses. These responses are stored in both local caches and a shared global cache. Each response is evaluated for reuse and classified for privacy, allowing some to be shared widely, some only with select groups, and others to remain private. Usage patterns from different devices are combined to train models that guide which cached responses should be retained or synchronized. Synchronization is managed adaptively, adjusting when and how information is shared depending on network conditions, utility, and privacy budgets. Before sharing, privacy safeguards such as encryption or differential privacy are applied, and entries are distributed through a coordinating system that ensures consistency and avoids duplication across devices.
The codebook-based homomorphic compression system is a novel approach that combines data compression and homomorphic encryption to enable efficient and secure computation on compressed data. It involves quantizing the input data, generating an optimized codebook using techniques like Huffman coding or deep learning, and compressing the data by replacing each value with its corresponding codeword. The compressed data is then encrypted using a homomorphic encryption scheme, such as the Paillier cryptosystem, allowing computations to be performed directly on the encrypted compressed data without decryption. Homomorphic properties of the encryption scheme enable operations like addition and multiplication on the ciphertexts, while preserving the confidentiality of the underlying data. The system also incorporates error correction techniques to mitigate the impact of quantization and encryption on the accuracy of the computations. This approach combines the benefits of data compression and homomorphic encryption, enabling efficient storage, transmission, and secure computation on compressed data.
A system and method for extending mobile-optimized multi-stage language model processing with federated persistent cognitive architecture. The system processes prompts through a first large language model to generate “thoughts,” which are cached and processed with the original prompt through a smaller language model. Building upon the three-tier thought caching, the system implements a federated multi-tier hierarchy with local device, domain-specific branch, and global collective caches. A federated cognitive orchestrator coordinates operations across multiple domain-specialized instances, managing thought routing, state synchronization, and cross-domain knowledge sharing while maintaining domain boundaries. During user inactivity, autonomous reasoning continues in cloud environments, generating insights from existing thoughts and interaction history. The system performs memory consolidation, thought cache optimization, and cross-domain pattern recognition without consuming mobile device resources, while maintaining privacy boundaries. This persistent cognitive architecture functions as an evolving reasoning partner rather than merely a responsive tool.
A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.
A system and method for cross-stream asymmetric enhancement combines machine learning-driven asymmetric codebook generation with dyadic distribution algorithms to enable simultaneous optimization of compression efficiency, cryptographic security, and error correction capability. The system analyzes input data characteristics and initializes multiple specialized ML models to generate stream-specific asymmetric codebooks optimized for different objectives. Enhanced dyadic distribution processing creates three pre-conditioned data streams that are processed through parallel asymmetric transformation pipelines: compression-optimized for maximum data reduction, security-optimized for cryptographic strength, and error-correction-optimized for robust recovery capability. Cross-stream optimization coordinates the multiple processing paths to ensure overall system coherence while maintaining individual stream objectives. The system supports multiple operating modes including ultra-high compression using only the primary stream, broadcast quality using primary and secondary streams, and archival mode using all streams for lossless reconstruction. The system supports graduated access control that enables different reconstruction quality levels based on available stream combinations.
A system and method are disclosed for compressing and restoring data. The system includes a computing device comprising at least a memory and a processor, and a telemetry encoding module comprising programming instructions stored in the memory and operable on the processor. The instructions cause the computing device to compress telemetry data to create compressed telemetry data and generate a bitstream of the compressed telemetry data. A telemetry decoding module includes programming instructions stored in the memory and operable on the processor that cause the computing device to receive the bitstream of compressed telemetry data and apply the bitstream of compressed telemetry data as input to the telemetry decoding module. The bitstream can be decompressed to generate a reconstructed version of the telemetry data to conserve important resources such as network bandwidth and storage.
H04Q 9/00 - Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
A system is disclosed for encoding and decoding QR codes using proprietary compression codebooks to increase information density and provide data security. Public data is encoded using a standard codebook while private data uses a proprietary codebook. The encoded data is combined into a single QR code. Decoding extracts the public and private portions and decompresses them using the appropriate codebooks.
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
61.
System and Method for Dynamic Multi-Level Security in High-Capacity Optical Codes
A system and method for encoding and decoding optical codes with context-aware, multi-level security. Input data is classified into security levels with associated context sensitivity requirements. The system compresses data using public and private codebooks based on security classifications, then generates optical codes incorporating both compressed data and context requirements. When scanned, the system collects environmental contextual data (location, network environment, device security, user behavior), analyzes it against embedded context requirements, and dynamically determines which security levels are accessible in the current environment. Only authorized security levels are decoded using appropriate codebooks based on both user credentials and current contextual factors. This approach enables fine-grained, context-sensitive access control that adapts to changing environments while maintaining the compression benefits and capacity advantages of the multi-level security framework.
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
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.
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06K 19/06 - Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
63.
System and Methods for Adaptive Low-Light Image Enhancement Using Machine Learning
A system and method are disclosed for adaptive low-light image enhancement using machine learning-based frequency decomposition. The system analyzes raw input images captured under low-light conditions to determine image characteristics including brightness levels, contrast levels, noise estimation, and detail complexity. Based on this analysis, preprocessing parameters are determined that guide adaptive frequency decomposition, creating multiple frequency components from the raw input image. Each frequency component is processed by a machine learning model trained for denoising to generate enhanced components. The enhanced components are reconstructed to produce an enhanced image provided to an image processing pipeline. The adaptive system dynamically adjusts preprocessing parameters based on individual image characteristics, enabling optimized enhancement across diverse low-light scenarios. This approach effectively balances noise reduction, detail preservation, and overall image quality improvement while accommodating varying low-light conditions and image types.
A Large Codeword Model (LCM) with a latent transformer core is a deep learning architecture that operates on discrete, compressed representations of data called codewords. The latent transformer core incorporates a Variational Autoencoder (VAE) which allows for the removal of the embedding and positional encoding layers from the Transformer. Input data is compressed into a latent space representation using the VAE encoder, which is then processed by the Transformer. The VAE decoder generates outputs based on the processed latent vectors. This approach enables efficient handling of diverse data types beyond language, including time series, images, and audio.
An all-binary neural network system and method for processing and analyzing multi-source time series data is disclosed. The system employs a shared codebook to encode input streams into binary codewords, which are then processed through a series of binary convolutional layers, binary LSTM layers, and binary fully connected layers. The system maintains binary representations throughout, enabling efficient computation and reduced memory requirements while effectively capturing temporal and inter-source relationships in the data.
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.
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.
A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.
A system and method for biometric-authenticated personal health monitor data compaction with clinical trial optimization is disclosed. The system receives biometric signals from multiple sensor modalities associated with a patient and extracts distinctive biometric features using signal processing algorithms. Patient identity verification is performed by comparing extracted features against stored biometric templates, generating cryptographic keys derived from verified biometric characteristics. Health data is divided into sourceblocks and encoded using multiple compression codebooks enhanced with biometric-derived cryptographic keys. Optimal encoded sourceblocks are selected based on compression efficiency and statistical preservation requirements. A clinical trial data optimization engine classifies health data by type and endpoint significance, determines statistical preservation requirements for regulatory compliance, and validates that compressed data maintains required statistical properties for clinical analysis. The system implements multi-modal biometric fusion, liveness detection, emergency override capabilities, and security controls including role-based access control and audit logging for secure clinical trial data management.
A system and method for implementing Persistent Cognitive Machines (PCMs) for strategic simulation and analysis applications are disclosed. The PCM maintains persistent cognitive processes regardless of external interaction, enabling advanced strategic simulation capabilities through multi-instance coordination, autonomous scenario exploration, and continuous learning from accumulated experiences. The system includes game control and referee components, multi-domain operations interfaces, PCM orchestration for managing multiple cognitive instances, and strategic analysis engines. Unlike traditional simulation platforms that operate in isolated sessions, the PCM remembers previous simulations, develops strategic insights autonomously, and explores strategic spaces through self-directed learning. The system supports a plurality of operational modes including but not limited to referee-only for human teams, human-PCM collaborative teams, and autonomous PCM-versus-PCM exploration. Applications include but are not limited to military wargaming, business strategy simulation, crisis management, and policy analysis. The PCM enters sleep-like states for memory consolidation and strategic concept extraction from accumulated simulation experiences.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
71.
Data Compression With Quantum-Resistant Intrusion Detection
Data compression with quantum-resistant intrusion detection, that measures in real-time the probability distribution of an encoded data stream and analyzes entropy characteristics across multiple bit-scale windows to detect both classical and quantum-generated intrusions. The system compares the probability distribution to a reference probability distribution and uses statistical algorithms to determine divergence between distributions while simultaneously analyzing entropy cascade patterns characteristic of quantum computing sources. When divergence exceeds configured thresholds or quantum-generated characteristics are detected, the system generates intrusion alerts identifying the threat type. The system comprises encoding and decoding machines, an intrusion detection engine that performs multi-scale entropy analysis, a codebook training engine that creates quantum-resistant codebooks using entropy-stratified training algorithms, and databases including a quantum signature database storing compression patterns of known quantum algorithms. The codebook training engine adaptively retrains encoding algorithms upon detecting new quantum patterns, maintaining system effectiveness against evolving quantum threats.
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. The system outputs a compacted representation of the video that includes the compressed stream, the error stream, and the anomaly metadata, supporting efficient storage or transmission while maintaining the ability to detect, trace, and reconstruct key information within the video.
H04N 19/48 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using compressed domain processing techniques other than decoding, e.g. modification of transform coefficients, variable length coding [VLC] data or run-length data
H04N 19/119 - Adaptive subdivision aspects e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
H04N 19/139 - Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/46 - Embedding additional information in the video signal during the compression process
H04N 19/85 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
73.
SYSTEM AND METHOD FOR COMPRESSING AND RESTORING DATA USING MULTI-LEVEL AUTOENCODERS AND CORRELATION NETWORKS
A system and method for compressing and restoring data using multi-level autoencoders and a correlation network. The system compresses data, such as hyperspectral images, using a multi-level autoencoder. Data restoration employs a correlation network trained on image sets to leverage inter-image correlations. Latent space vector grouping may be used to enhance reconstruction accuracy. The approach achieves efficient compression while maintaining data quality through learned correlations.
H03M 7/30 - CompressionExpansionSuppression of unnecessary data, e.g. redundancy reduction
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
74.
System and Method for Low-Light Image Enhancement Using Hierarchical Adaptive Wavelet Decomposition with Cross-Scale Feature Fusion
A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/52 - Scale-space analysis, e.g. wavelet analysis
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
75.
Adaptive Real Time Image and Video Processing Using PCM-Enhanced Visual Strategy Caching and Multi-Stage Cognitive Routing
A system and method for adaptive image and video processing using a Persistent Cognitive Machine (PCM) architecture with visual strategy caching. The system receives degraded input media and extracts degradation fingerprints to query a PCM-based visual strategy cache containing previously successful processing strategies. When matching cached strategies are found above a relevance threshold, they are retrieved and applied directly. When no match exists, the input is processed through transform-domain networks to generate new strategies. A pattern synthesizer combines multiple strategies for complex degradation types. The system evaluates processing effectiveness using a feedback controller and stores successful strategies in the hierarchical cache. This cognitive approach enables real-time processing with continuously improving performance as the cache learns from successful patterns. The adaptive architecture eliminates redundant processing while maintaining high-quality output, making it suitable for diverse imaging and video applications requiring efficient enhancement capabilities with superior performance over traditional methods.
A Large Codeword Model (LCM) is a deep learning architecture that operates on discrete, compressed representations of data called codewords. Unlike traditional models that use raw tokens and dense embeddings, LCMs can efficiently process and generate data in various modalities, including text, images, audio, and time series. By capturing the inherent structure and patterns in the data, LCMs learn more generalizable and interpretable features, enabling transfer learning across different domains. The LCM architecture offers a scalable, flexible, and computationally efficient approach to building AI systems, with potential applications in natural language processing, speech recognition, and beyond.
A system and method for real-time financial data analysis and market prediction. The system processes diverse inputs, including financial news snippets and trading data, through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer core. The system's architecture enables efficient handling of multi-modal financial data, capturing complex relationships between news sentiment and market behavior. An adaptive codebook generation method ensures the system remains responsive to evolving market conditions. This approach aims to provide more accurate and timely market predictions by leveraging both textual and numerical financial data in a sophisticated, integrated manner.
A deep learning system for time series prediction comprising a preprocessor that receives time series input sequences, truncates them by removing terminal values, and appends padding values to maintain the original sequence length. An encoder compresses these padded sequences into latent space representations, while a decoder reconstructs predicted sequences matching the original length, specifically trained to reconstruct values matching the removed terminal values in positions corresponding to the padding values. A training system optimizes the encoder and decoder by minimizing differences between original sequences and predicted sequences. The system can process multiple time horizons simultaneously while maintaining statistical properties and providing uncertainty quantification through confidence intervals. This approach enables accurate short-term forecasting while preserving both temporal patterns and statistical relationships in the predicted sequences.
A system and method for an adaptive network architecture utilizing dynamically-encoded agents. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more agent layers composed of dynamically-encoded agents. These agents optimize encoding strategies, generate new agents, and prune inefficient agents based on network performance objectives. A telemetry layer continuously tracks network operations using adaptive kernel functions and topology-aware distance metrics. The system may dynamically adjust network structure and resource allocation, maintaining efficient operations through encoding optimization. By leveraging short-term and long-term memory systems, the system adapts over time, improving learning retention and responsiveness. Error detection and recovery mechanisms ensure network stability during agent generation and pruning. This approach enables real-time network adaptation, optimizing performance and efficiency across multiple layers while maintaining system resilience and stability.
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.
A computer system for persistent cognitive neural architecture implementing sophisticated state preservation and sleep-state optimization capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, and implements architectural changes. A meta-supervisory system tracks behavior patterns and extracts generalizable principles. A cognitive neural orchestrator manages operational states and coordinates decision-making across the network. The system maintains persistent neural network state through mechanisms that store and retrieve neural activation patterns and architectural configurations across operational sessions. During designated sleep states, the system executes optimization operations including memory consolidation and insight generation. This innovative architecture enables neural networks to maintain knowledge continuity across system restarts while implementing sophisticated optimization during periods of reduced demand, enhancing long-term performance through persistent cognitive capabilities.
A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.
A system and method for performing arithmetic operations on compacted data files. The system receives data queries containing arithmetic operations to be performed on compressed data. Using an estimation process, the system locates a starting position in the compacted file and refines this location by finding codeword boundaries in a reference codebook. The system then traverses the file to identify codewords corresponding to the queried data. Each codeword has associated arithmetic metadata including numeric values and data types stored in the reference codebook. The system performs arithmetic operations directly on these codewords using their metadata, without decompressing them back to their original form. Results of arithmetic operations are generated as new codewords. This approach enables mathematical computations on compressed data while maintaining the storage efficiency of data compaction.
A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.
Modality agnostic Large Codeword Model (“LCM”) is an advanced deep learning architecture that processes discrete, compressed data representations called codewords across multiple modalities. Unlike traditional models using raw tokens and dense embeddings, LCMs efficiently handle diverse input types including text, images, audio, and video. The system employs a modality agnostic encoder, unified codebook, and multimodal machine learning core to capture inherent data structures and patterns. This approach enables more generalizable and interpretable feature learning, facilitating transfer learning across domains. The LCM's scalable and flexible architecture includes components for modality-specific processing, cross-modal attention, and joint representation learning. With its computational efficiency and versatility, the Modality Agnostic LCM offers significant potential for various AI applications, including natural language processing, computer vision, and multimodal reasoning.
A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.
A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types for a latent transformer-based machine learning core. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors local neural network regions, performing real-time statistical analysis and implementing structural modifications. The system efficiently handles multi-modal data, capturing complex relationships between input types. An adaptive codebook generation method, coupled with the supervisory architecture, ensures responsiveness to evolving data patterns and task requirements. This approach provides accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure during operation to maintain optimal performance.
A system and method for adaptive neural network architecture with real-time neurogenesis capabilities during inference operations. The system processes data through a core neural network with integrated supervisory and neurogenesis control systems. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors network activity patterns and information flow. The neurogenesis control system maintains continuous activity maps, detects processing bottlenecks, and determines optimal placement of new neurons using geometric optimization. A modification subsystem implements controlled neurogenesis operations while maintaining network stability. The system handles data through adaptive codeword allocation and fusion of dissimilar data types. This sophisticated approach enables neural networks to dynamically expand their processing capacity during operation, responding to detected bottlenecks while maintaining operational stability through carefully managed integration of new neurons.
A system and method for adaptive neural network architecture implementing sophisticated supervision and signal transmission capabilities. The system comprises a layered neural network monitored by a hierarchical supervisory system that collects operational data and implements architectural modifications. A meta-supervisory system oversees the supervisory process, tracking adaptation patterns and extracting generalizable principles from successful modifications. The system implements novel signal transmission pathways that enable direct communication between non-adjacent network regions through adaptive transformation components and coordinated timing mechanisms. This multi-level approach enables dynamic network adaptation while maintaining operational stability through careful monitoring and controlled modification procedures. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled signal propagation.
A computer system for adaptive neural network architecture implementing sophisticated supervision, pruning, and signal transmission capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, implements architectural changes, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior patterns, stores successful modification and pruning patterns, and extracts generalizable principles from these patterns. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions through signal modification and temporal coordination. This multi-level approach enables dynamic network adaptation and efficient resource utilization through pruning while maintaining operational stability. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled modification.
A system and method for a hierarchical thought supervision network with adaptive processing capabilities. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more supervision layers composed of supervisory nodes. The base layer handles thought processing and management, while the telemetry layer continuously tracks operational metrics to evaluate processing efficiency. Supervisory nodes adapt network operations by optimizing thought encodings, generating new nodes when needed, and pruning inefficient nodes based on performance objectives. A telemetry layer continuously tracks processing efficiency using adaptive kernel functions and topology-aware distance metrics. The system maintains effective processing while dynamically adjusting to computational demands through coordinated supervision across multiple layers. This approach enables real-time network adaptation while optimizing performance and efficiency across the system.
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.
A computer system for adaptive operation of deep learning networks through hierarchical supervision, meta-level pattern tracking, cross-network signal coordination, and selective activation prioritization. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operational patterns, implements architectural modifications, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior, stores successful pruning and modification patterns, and extracts generalizable optimization principles. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions, with signal modification and temporal coordination. A greedy neural system selectively processes activation patterns based on utility metrics and includes a competitive bidding manager to allocate limited computational resources to high-value signals. This architecture enables real-time optimization of network behavior and resource usage while maintaining operational stability and responsiveness across diverse applications.
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.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable data compression software; computer chips and
semiconductor chips with embedded software for data
compression; computer hardware and recorded software for
data compression sold as a unit. Digital compression of computer data; providing software as
a service featuring software for data compression; providing
online, non-downloadable software for data compression.
96.
Adaptive Data Processing System with Real-Time Anomaly Detection and Self-Healing
A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.
A system and method are disclosed for processing hyperspectral image information. A set of hyperspectral images is obtained, and a spectrum compact representation is derived for each hyperspectral image in the set of hyperspectral images. The spectrum compact representation for each hyperspectral image in the set of hyperspectral images is encoded with a video encoder, such as an H.266 encoder, to generate an encoded spectrum compact representation for each hyperspectral image in the set of hyperspectral images. The encoded spectrum compact representation is a compressed representation of the original hyperspectral image information that can be transmitted via a communication channel. On a receiving side of the communication channel, the compressed representation can be decompressed utilizing a corresponding video decoder, in conjunction with a joint spatial-frequency (JSF) network.
H04N 19/132 - Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
H04N 19/136 - Incoming video signal characteristics or properties
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
H04N 19/625 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
98.
Classification of Image Data from Synthetic Aperture Radar Images and Electro-Optical Images with Multi-Modal Fusion
Systems and methods are disclosed for classifying objects using electro-optical and synthetic aperture radar images through multi-modal feature alignment and fusion. A computing system acquires and preprocesses image data, then aligns features across modalities using a multi-modal alignment engine. A cross-modal attention fusion network extracts and integrates complementary information using transformer-based attention mechanisms. A modality-specific feature extraction framework processes EO and SAR images through specialized branches, ensuring optimal feature representation. An adaptive fusion decision system dynamically determines the best fusion strategy based on image quality and confidence scores. A self-supervised consistency controller enforces alignment between EO and SAR features using contrastive learning. The fused representations are processed by a neural network to generate object classifications. This system improves accuracy and robustness in environments where one modality may be degraded or missing, enhancing applications such as remote sensing, surveillance, and autonomous navigation.
G06V 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/84 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
99.
SYSTEM AND METHODS FOR CLASSIFICATION OF IMAGE DATA FROM SYNTHETIC APERTURE RADAR IMAGES AND ELECTRO-OPTICAL IMAGES
Systems and methods are disclosed for image classification of electro-optical images and synthetic aperture radar images using training techniques that can include appearance labeling and triplet mining to train a neural network system. The training data can include image pairs of electro-optical images and synthetic radar aperture images. The training data can include anchor, positive, and negative images. The neural network can be trained using triplet loss and cross-entropy loss. The trained neural network can be used for object classification such as automatic target recognition of aerial images.
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
100.
Adaptive Data Compression and Encryption System Using Reinforcement Learning for Pipeline Configuration
A system and method for optimizing data compression and encryption using reinforcement learning. The system analyzes incoming data streams to extract statistical features and data characteristics, which are processed by a reinforcement learning engine to automatically configure a multi-stage compression pipeline. Each compression stage transforms data into optimized distributions, applies Huffman coding, and maintains full encryption using homomorphic operations. A performance monitor tracks compression efficiency, processing speed, and output quality in real-time, providing feedback to continuously improve the reinforcement learning model's decisions. The system can dynamically adjust between one to five compression stages and select appropriate compression methods, including traditional algorithms or neural network-based approaches, based on data characteristics and performance requirements. All processing occurs on encrypted data without requiring decryption, ensuring complete data security throughout the pipeline. The adaptive nature of the system enables optimal compression performance across diverse data types while maintaining encryption integrity.
