A system and method are provided for predicting the future behavior of electric submersible pumps using sensor data collected from down-hole and surface equipment. The system processes this data to model the internal operating state of the pump as a geometric structure that reflects physical relationships and time-based changes. It compares current pump conditions to historical patterns from other installations and uses that information to forecast how the pump is likely to perform over time. These predictions are transformed into visual representations that show possible future flow patterns, mechanical stress, and signs of wear or failure. Based on this information, the system provides recommendations for adjusting pump operation or scheduling maintenance to reduce risk. By combining live data, past experience, and predictive modeling, the system helps operators make proactive decisions that improve pump reliability and field performance across multiple wells.
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.
A computer-implemented system generates predictive video representations of physical system behavior from non-visual telemetry data. Sensor inputs including vibration, acoustic, flow, pressure, thermal, chemical, and electromagnetic measurements are encoded into mathematical representations within a latent geometric manifold that preserves temporal and cross-modal relationships. The system forecasts future states by computing geometric trajectories through the manifold using geodesic forecasting, stochastic perturbations, and historical trajectory matching, and combines results through Bayesian fusion to form probabilistic predictions. These predictions are projected into visual coordinates and rendered as synthetic video sequences illustrating anticipated system evolution. Uncertainty is visually encoded using opacity gradients, branching trajectories, and probabilistic overlays to convey confidence levels. A manifold journaling framework maintains reversible correspondence between predictive video frames and originating telemetry data, enabling auditability, verification, and traceable reconstruction of predictions back to their sensor sources.
A system and method for a persistent cognitive machine that maintains two geometrically distinct latent manifolds in hardware memory. The first manifold encodes spatiotemporal or perceptually derived states under a geometric metric that governs causal ordering and predictive trajectory computation. The second manifold encodes cognitive states under epistemic conditioning that defines capacity constraints, admissibility boundaries, and forbidden regions for reasoning trajectories. A bidirectional geometric interface operator serves as the exclusive pathway for state exchange between the two manifolds. For transfers from the first manifold to the second, the operator performs structural reconciliation and evaluates capacity and admissibility constraints before permitting incorporation. For transfers from the second manifold to the first, the operator verifies epistemic eligibility and physical plausibility before permitting incorporation. States that fail either evaluation are not transferred. All cross-manifold transfers are recorded through journaling that maintains reversible mappings between source and target states within bounded error tolerances.
A computer-implemented system generates auditable visual representations of physical system states from non-visual sensor data through a persistent cognitive substrate maintaining a latent manifold. The system receives heterogeneous sensor inputs including acoustic, thermal, electromagnetic, chemical, and seismic data, encodes them using tensor-preserving Lorentzian autoencoders, and fuses the representations within a unified geometric manifold. Multimodal landmarks establish stable convergence points where correlated sensor features coalesce. The system computes geodesic trajectories through the manifold and applies domain-specific projection operators transforming sensor-space coordinates into visual manifold representations. A visual synthesis cortex generates temporally coherent video depicting physically inaccessible states such as reactor dynamics or subsurface processes. Manifold journaling maintains bidirectional transformations enabling reversible reconstruction from synthetic video to original sensor data with bounded error. The persistent cognitive substrate ensures continuity across sessions through sleep-state consolidation. This architecture provides mathematically grounded, physically constrained visual synthesis with complete audit trails for regulatory and scientific validation.
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.
A system and method for geometrically-grounded persistent cognitive architecture with invariant-enforced federated intelligence. The system implements a persistent cognitive substrate organized as an internal configuration space supporting path-dependent traversal, within which a federated multi-tier thought caching hierarchy operates as holonomy-based constraint memory achieving sublinear complexity growth. User prompts are processed through a dual-model architecture comprising a first reasoning large language model generating thoughts and a second smaller model generating responses, with thoughts cached across local device, domain-specific branch, and global collective tiers through successive stages of non-invertible holonomy compression. A federated cognitive orchestrator implements sectorized interface management coordinating domain-specialized instances across non-commuting operational projection boundaries. The system further implements variational stabilization of reasoning trajectories, multi-domain conflict arbitration through geometric consensus, cognitive regime monitoring with adaptive resource allocation, and hallucination suppression through geometric structural validation including traversal cost, homotopy class, and holonomy consistency validation.
Systems and methods for operation of persistent cognitive machines using a federated architecture of domain-specialized cognitive substrates. In an embodiment, the system navigates a plurality of cognitive trajectories through a primary cognitive substrate based on a prompt, then routes both the prompt and trajectories through a smaller secondary cognitive substrate to generate a response. Cognitive trajectories are associated with portions of the prompt and stored in a federated multi-tier hierarchy comprising a local device cache, a domain-specific branch cache, and a global collective cache. Embodiments herein further support autonomous cognitive operations in cloud environments, generating new trajectories from stored interaction history without user interaction. In some embodiments, a federated cognitive substrate orchestrator coordinates operations across multiple domain-specialized substrates, managing trajectory routing, substrate synchronization, and cross-domain curvature exchange while maintaining sector boundaries.
A computer system and method for dynamic interaction between human operators and persistent cognitive machines is disclosed. The invention enables adaptive collaboration in operational environments by integrating multimodal translation, cognitive processing, load balancing, trust calibration, operational learning, and team coordination. Human inputs such as voice, gestures, biometric signals, and contextual data are converted into prompts for a cognitive core that processes reasoning through multi-stage language models and thought caching. Operator cognitive load is quantified by combining physiological and behavioral indicators, and tasks are dynamically allocated between human and machine based on load, task complexity, and trust. Operational modes transition between advisory, collaborative, autonomous, and override states with safeguards to ensure stability and human primacy. Outputs are adapted in detail, modality, and timing according to operator state. Continuous learning captures interaction patterns and team dynamics, providing personalized adaptations, distributed knowledge sharing, and resilience to component failures.
A distributed cognitive middleware system enables natural human control of complex distributed computing environments. The system maintains a geometric manifold that represents system commands as nodes in space, with edges showing valid command sequences. When operators provide natural language input, a cognitive mediation engine maps their intent to paths through this command space, finding optimal routes to execute their desired operations. The system remembers successful command patterns, operator preferences, and interaction histories in a distributed cache that can be shared across multiple instances. When familiar patterns are recognized, the system adapts previous solutions to the current context. For novel situations, it synthesizes new command sequences by reasoning through the geometric space. The middleware coordinates command execution across multiple backend systems while keeping all instances synchronized. Individual operator patterns are tracked to personalize future interactions, making complex system control increasingly intuitive over time.
A system and method for PCM supervision of Lorentzian autoencoders providing adaptive zoom and focus operations through cognitive supervision. The system operates a Lorentzian autoencoder preserving spatiotemporal relationships, encoding video segments into mini-Lorentzian representations through 3D convolutional processing while maintaining tensor structure. A Persistent Cognitive Machine analyzes collaborative context and expertise distribution to generate control parameters modifying geometric properties including curvature and compression pressure. The system implements role-specific zoom behaviors: teacher mode provides structured sequences, student mode enables exploration, peer mode supports collaboration, and assistant mode optimizes tasks. Attention fusion combines human patterns with AI assessments to compute adaptive focus regions. Hierarchical processing provides transitions between scene-wide analysis, intermediate processing, and fine inspection. Enhanced output is decoded through 3D decoding augmented by latent diffusion and generative models for infinite zoom while enabling learning through privacy-preserving storage.
A system and method for generating synthetic video from diverse sensor inputs within a unified computational framework. The system receives heterogeneous data such as acoustic, thermal, and textual streams, encodes each into modality-specific latent representations, and projects them into a shared geometric manifold. Within this manifold, convergence points known as multimodal landmarks are established and used to compute geodesic trajectories that describe relationships among the inputs. The trajectories are verified for reversibility to ensure that forward and reverse mappings remain consistent. A Lorentzian autoencoder then decodes the validated trajectories into temporally coherent video sequences derived from the multimodal evidence rather than reconstructed imagery. The system records geometric states for auditability and persistently stores the resulting landmarks and trajectories for reuse, enabling reversible, verifiable generation of synthetic video that accurately reflects the integrated sensor data.
A system and method for compressing and curating memory structures in a cognitive computing system. The system maintains a memory manifold that represents memories as geometric objects with a metric defining their relationships. Memory structures are evaluated using a curation functional that combines semantic utility, geometric complexity, and additional factors such as usage frequency or storage cost. A compression flow transforms memory structures by following the gradient of this functional to reduce redundancy and complexity while preserving meaning. Additional compression operations may include entropy reduction of memory bundles, pruning of geometrically unstable regions, projection between spaces of different dimensions, or evolution of the metric itself based on usage patterns. Compressed structures are validated against identity constraints maintained in a separate cognitive hierarchy before being returned to memory for future retrieval. The approach enables efficient long-term memory management in persistent cognitive architectures.
A system and method for regulating temporal activity in persistent cognitive machines using intrinsic pulse mechanisms rather than external clocks. The system defines cognitive fields over a cognitive manifold stored in memory, where each field represents a distribution of cognitive state across the manifold. Temporal pulses are generated to authorize bounded execution intervals during which cognitive field updates occur, with each pulse corresponding to a discrete evolution step that advances the fields. A closed-loop control system monitors cognitive order parameters derived from the fields and adjusts pulse characteristics when deviations from a stability corridor are detected. Field updates are executed during authorized pulse intervals by applying update operators that modify values on the cognitive manifold. Completion of each pulse advances a cognitive time coordinate that operates independently of wall-clock time, enabling adaptive pacing and persistent cognition across varying computational conditions.
A system and method for adaptive navigation and control in compressed spatiotemporal media transforms static media navigation into a dynamic, self-improving process. The system compresses temporally-organized multidimensional data into a latent space with geometric structure, where compressed representations form navigable trajectories. During navigation through this latent space, the system continuously monitors performance to generate real-time metrics. These metrics are processed into feedback signals that drive an adaptive control engine, which generates control signals to modify navigation paths in real-time during execution. The system adapts its parameters—including encoder settings, latent space geometry, and navigation strategies—based on accumulated performance data, enabling continuous improvement of future navigation operations. This closed-loop architecture creates a learning system that becomes more efficient through use, optimizing both compression quality and navigation effectiveness while maintaining stable operation through coordinated feedback mechanisms.
A system and method for adaptive role-based human-AI collaboration through a persistent cognitive machine that maintains a latent manifold as a geometric substrate for collaborative cognitive operations. The system encodes inputs into dual geometric representations capturing both semantic content and human cognitive patterns, then detects expertise distributions across the manifold to identify regions of human or AI knowledge superiority. Based on detected expertise and task requirements, the system selects collaborative roles including teacher, student, peer, or assistant modes, and modifies manifold geometry to create role-specific cognitive pathways. The system computes collaborative geodesic paths that balance human cognitive constraints with AI computational capabilities, stores human patterns and collaborative interactions in privacy-preserving distributed caches, and enables bidirectional learning where both participants adapt through interaction. Outputs are generated by traversing computed paths and synthesizing responses that appropriately balance AI reasoning with human cognitive patterns according to the active collaborative role.
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 of video reconstruction compresses video into curved latent manifold representations using Lorentzian autoencoder processing that preserves temporal causality and spatial relationships. The system organizes compressed representations hierarchically across multiple resolution scales, then computes mathematically valid expansion trajectories extending beyond original encoded boundaries. Expansions follow geodesic paths determined by minimizing an action functional balancing kinetic energy, compression pressure from manifold curvature, and semantic goal potentials. Expanded trajectories are decoded into video content while maintaining coherence through geometric constraints. Energy budgeting manages computational resources to prevent uncontrolled generation. The system provides real-time reconstruction supporting infinite zoom beyond sensor resolution, counterfactual scenario synthesis, and cross-domain video generation, enabling coherent content generation beyond originally encoded material while preserving semantic consistency and temporal fidelity.
A system and method for real-time latent-based scene fusion across multiple camera feeds enables seamless navigation through unified visual representations. Video streams from multiple cameras are encoded into separate latent manifolds using Lorentzian autoencoders that preserve spatiotemporal coherence for each viewpoint. These individual manifolds are registered and fused through weighted geodesic interpolation into a unified representation where compression pressure fields reflect semantic density. Users navigate this fused space along geodesic trajectories that traverse both scale and viewpoint axes by minimizing a functional balancing kinetic energy, compression pressure, and goal potential. Cross-view correlations restore occluded regions while Bayesian fusion of geometric priors, simulated rollouts, and historical outcomes computes probabilities for reconstructing unobserved viewpoints. The system renders video by decoding latent representations along computed trajectories, synthesizing content for regions not captured by any camera when posterior probabilities exceed thresholds, enabling continuous zoom operations across multiple perspectives without perceptual discontinuities.
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 cognitive manifold in a continuous, differentiable, cognitive manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. A mechanism for increasing stability of thought in regions of the cognitive manifold in which curvature reaches a certain threshold is provided in a manner analogous to collapse of stars in astrophysics, as well as propagation of additional attractors based on a collapse in a region of the cognitive manifold.
Systems and methods for compaction-derived telemetry generation and analysis that transform anonymized encoding operations from passive data processing mechanisms into active sources of privacy-preserving analytical signals, enabling detection of encryption attempts, data exfiltration, dataset evolution, and other operationally significant conditions while maintaining full compliance with data protection requirements and preserving the integrity of anonymization guarantees. The compaction-derived telemetry generation and analysis systems and methods disclosed herein enable interpretation of telemetry signals to infer dataset evolution, security-relevant conditions, and anomalous behaviors, and the use of such interpretations to drive closed-loop control actions, all without reconstructing, inspecting, or accessing underlying plaintext data, thereby preserving privacy and regulatory compliance while enabling novel analytic and security capabilities.
Systems and methods that enable random access decoding within variable-length encoded bitstreams by initializing multiple decoder instances at staggered bit offsets and detecting convergence through agreement among decoded outputs. In an embodiment, given an arbitrary target offset N in a Huffman-encoded bitstream, decoder instances are initialized at consecutive positions N through N+L−1, where L is the maximum codeword length. Each instance decodes independently according to the codebook until a valid codeword boundary is identified. This agreement-based detection provides correctness guarantees without requiring synchronization markers, external indices, or modifications to the encoded data. The disclosed techniques preserve compression efficiency while enabling efficient random access, search operations, and partial decoding previously unavailable with variable-length encodings.
Systems and methods for managing temporal structure in persistent cognitive machines (PCMs) through holonomy-based time representation by distinguishing between event time, which measures raw occurrence of operations, and holonomy time, which measures irreversible accumulation of mismatch under constrained projection across interfaces. Clock bundles associated with cognitive sectors track local temporal evolution, while clock connections transport temporal information between sectors in a potentially lossy and non-invertible manner. Interfaces that compress or abstract information induce temporal holonomy, leading to sector-relative time and enabling temporal isolation of cognitive processes. A temporal fabric manager monitors holonomy accumulation, detects temporal defects through calibration loops, and provides temporal metrics to executive control systems. The framework supports robust cognitive operation despite irreversibility and long-term persistence without requiring global temporal synchronization.
G06F 16/353 - ClusteringClassification into predefined classes
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 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
24.
System and Method for Direct Operations on Compressed Data
A system and method for executing operations on compressed data enables efficient processing by performing operations directly on data in compressed formats. The system determines characteristics of compressed data that enable direct manipulation and identifies operations that can be performed without decompression. Multiple compression formats are supported, including order-preserving compression, fixed-length codeword formats, variable-length codeword formats, and learned compression models using machine learning. The system executes operations such as comparisons, aggregations, and joins directly on compressed data. When necessary, the system coordinates between operations performed on compressed data and those requiring decompression. Compression metadata including scheme identifiers and codebook version information is maintained, with compression efficiency monitored during operations. When efficiency falls below thresholds, codebook updates are initiated. This approach significantly reduces storage requirements and improves processing performance by eliminating unnecessary decompression cycles while maintaining data integrity and supporting a wide range of data operations beyond traditional database queries.
A system and method for geometric cognitive processing employs attractor-based manifold flow prediction to model and predict cognitive state evolution. The system represents cognitive states as points within a geometric manifold and identifies attractors comprising stable subsets toward which states naturally evolve. For each cognitive state, the system computes proximity scores quantifying relationships to identified attractors. These proximity scores weight attractor-influenced flow components that combine to generate predicted cognitive trajectories through the manifold. The system dynamically adapts manifold geometry, attractor parameters, and basin boundaries based on observed cognitive trajectories, enabling continuous refinement of predictive accuracy. The architecture supports multiple attractor types including point, limit cycle, strange, and hierarchical attractors, and accommodates multi-manifold configurations with cross-manifold information transfer. The system provides a unified framework for understanding and predicting complex cognitive dynamics across diverse implementation platforms.
G06F 16/353 - ClusteringClassification into predefined classes
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 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
26.
System and Method for Geometric Probability Estimation in Persistent Cognitive Machines
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 16/353 - ClusteringClassification into predefined classes
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 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
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 adaptive geometric diffusion projection enables mapping of heterogeneous high-dimensional representations onto a shared low-dimensional manifold without neural network training. The system maintains landmark points in source spaces and computes their spectral coordinates through graph Laplacian eigen decomposition based on semantic similarities. New input points are projected via harmonic extension, computing weighted interpolations of nearby landmark spectral coordinates. A geometric optimization process refines positions while maintaining manifold constraints through tangent space projections. The system continuously monitors geometric invariants including principal angles, spectral gaps, and curvature distributions. When invariants exceed thresholds, targeted adaptations occur: spectral basis updates using warm-started iterations, landmark set augmentation in high-residual regions, or parameter adjustments. The approach supports logarithmic computational scaling, enables streaming operation on continuous data, and handles multimodal inputs through reliability-weighted consensus. The system maintains projection quality indefinitely through continuous geometric monitoring and local adaptations.
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 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06F 18/2132 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on discrimination criteria, e.g. discriminant analysis
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
29.
System and Method for Spectral Learning in Cognitive Manifolds
A system and method for spectral learning in persistent cognitive machines implements learning through controlled evolution of a spectral decomposition of a cognitive manifold. The spectral decomposition, comprising eigenvectors and eigenvalues, encodes long-term memory as global geometric structure rather than as stored data or network parameters. The system performs inference operations by projecting incoming data onto the cognitive manifold using a fixed spectral decomposition without modification. Geometric invariants including principal angles, spectral gap ratios, projection residuals, and curvature statistics are continuously monitored to detect structural inadequacy. When invariants exceed thresholds, a learning event modifies the spectral decomposition through eigen decomposition with warm-start initialization while enforcing mode-specific plasticity bounds that are tighter for low-frequency eigenvectors than high-frequency eigenvectors, thereby preventing catastrophic forgetting. The system operates continuously by alternating between inference using fixed spectral decompositions and learning events that modify spectral decompositions through controlled spectral evolution.
A neuromorphic computing system is disclosed in which inputs are embedded onto a continuous manifold realized by a dynamical substrate of interconnected processing elements that update their states in response to events. The substrate converges to attractor states that encode input-dependent representations, from which geometric properties, including metric tensor components and curvature, are derived from physical characteristics such as spike timing, synaptic weights, and conduction delays. Trajectories on the manifold emerge through evolution of the substrate state and follow geodesic-like paths arising from competitive propagation dynamics. Perturbation-based methods estimate curvature by measuring divergence of nearby trajectories. Parameters of the substrate are adaptively modified via activity-dependent plasticity rules, enabling experience-driven reshaping of the manifold geometry. The substrate may comprise spiking neural networks, memristive arrays, photonic processors, or analog dynamical systems.
A system and method for hierarchical spectral landmark graphs in cognitive manifolds implements cognition through discrete landmark structures that provide scalability, interpretability, and auditability. The system maintains a landmark graph on a cognitive manifold with vertices representing landmark points selected based on geometric properties including curvature and cognitive trajectory density. A spectral basis derived from the landmark graph encodes long-term semantic structure. Spectral continuation updates the basis when geometric invariants indicate structural change, while enforcing differential plasticity constraints protecting foundational low-frequency modes. Reversible edges constructed with forward and reverse displacement vectors enable auditable trajectory replay through cryptographic certificates and manifold journals. The system generates probability estimates by fusing geometric priors from landmark paths, empirical evidence from simulations, and historical evidence from archived cases. Landmark-conditioned naturalization produces interpretable explanations mapping geometric structures to domain-specific semantic labels, enabling transparent, auditable reasoning grounded in verifiable landmark-based evidence.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
32.
ENHANCED SYSTEMS AND METHODS FOR SYNTHETIC APERTURE RADAR IMAGE COMPRESSION WITH CROSS POLARIZATION PREDICTION
A system and method for processing Synthetic Aperture Radar (SAR) data using cross polarization prediction. The system processes complex SAR data from two different polarizations, typically HH and VV, and stores their representations. A specialized reconstruction component then reconstructs data from the first polarization while utilizing the second polarization data as auxiliary information. This cross-polarization approach enhances the reconstruction process, leveraging complementary information from both polarizations to produce improved SAR imagery. The method can employ neural network-based compression and reconstruction techniques, adaptive processing based on SAR data characteristics, and sophisticated analysis of cross-polarization relationships. Advanced implementations may include interpolation between polarizations, extrapolation of polarization effects, and arithmetic operations on processed representations to further enhance the SAR data. This innovation enables the generation of high-quality, information-rich SAR images with potential applications in environmental monitoring, urban planning, agriculture, and disaster response.
A system and method for simultaneous compression and encryption of data. The system analyzes input data to determine its properties and creates a transformation matrix based on these properties. Using this matrix, the input data is transformed into a modified distribution, generating a main data stream of transformed data and a secondary stream of transformation information. The main data stream is compressed, and both streams are combined into a single output. The system implements security measures to protect against various attacks, including side-channel vulnerabilities. By using a dyadic distribution algorithm, the system achieves both compression and encryption in a single pass over the data, offering significant efficiency gains. The system can operate in both lossless and lossy modes, providing flexibility for different application requirements. This approach offers a unique solution for data transmission and storage scenarios where both data reduction and security are critical concerns.
A system and method for self-executing graphs wherein execution semantics are encoded within graph elements themselves rather than imposed by external schedulers. The system comprises a dynamic property graph with vertices and edges that encode execution semantics specifying computational operations and graph-internal triggering conditions. An execution engine evaluates these triggering conditions by monitoring graph state, detects satisfaction through graph-internal evaluation, and initiates bound computational operations. Triggering conditions are expressed in terms of vertex or edge traversal, property changes, geometric properties such as curvature, or topological connectivity patterns. The graph autonomously determines when and which operations execute based on graph-resident execution semantics. In distributed embodiments, multiple local coordinators evaluate triggers within assigned graph regions and coordinate through peer-to-peer messaging without centralized scheduling. Applications include autonomous cognitive systems, adaptive control systems, and scalable distributed computation requiring self-regulating execution without centralized bottlenecks.
A system and method for implementing persistent cognitive computation through geometric representation augmented with holonomy-based experiential memory. The system encodes inputs into a curved latent manifold and maintains bounded sets of holonomy descriptors at each location, enabling two-component cognitive states comprising position and experiential context. Cognition occurs through holonomy-sensitive traversal where paths depend jointly on geometric structure and accumulated path-dependent constraints. Holonomy generators are created during traversal from prediction errors and constraint encounters, composed into consolidated descriptors, and undergo lifecycle management including reinforcement, decay, and irreversible export to residual constraint regions. This architecture escapes location-only representations by distinguishing cognitive states that occupy identical semantic positions but arise through different experiential histories. The system supports counterfactual reasoning through holonomy switching at fixed locations and preserves semantic memory as compressed transport deformation rather than stored trajectories, enabling scalable experiential learning where repeated patterns strengthen constraints while capacity remains bounded.
micro) that preserve tensor structure, temporal causality, and geometric relationships. The compressed representations are embedded in a Lorentzian manifold, where video content is organized as navigable geodesic trajectories. The hierarchy enables continuous multidimensional zoom operations, including fiber bundle expansion, semantic scale-shifting, and projection between scales, while maintaining semantic coherence and geometric consistency. Symbolic anchors, spatiotemporal routing protocols, and correlation-network-based restoration support intelligent navigation and high-fidelity decompression. Synthetic content is generated in context to extend exploration beyond original media boundaries. The architecture enables seamless transitions across spatial, temporal, spectral, and semantic dimensions for applications in immersive media, analysis, and visualization.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
37.
Virtual Management Layer for Type-Aware Routing of Multi-Type Data to Compression and Decompression Subsystems
A distributed system and method uses a virtual management layer to classify incoming heterogeneous data streams by type, attach association flags to related streams, and route each stream to a selected compression or decompression subsystem executing on available computing devices. The virtual layer allocates tasks using live resource conditions to enable parallel processing across devices and per-type optimization (e.g., statistical, codebook, or neural techniques). Outputs returned from disparate devices are collected and deterministically regrouped using the association flags so related streams are preserved regardless of where processing occurred. The architecture supports both compression and decompression workflows, load-balanced scheduling, and scalable operation across multiple devices, improving throughput while maintaining relationships among multi-type data.
An asymmetric compression system and method maintain a first codebook usable to map between data representations and a behavior specification that governs how the first codebook is applied during encoding. The system encodes digital data in accordance with rules, limits, or parameters of the behavior specification while decoding using the first codebook alone, enabling distribution of the codebook for universal decoding with restricted access to the behavior specification for encoding. In various embodiments, multiple rule sets are selectable based on context signals; primary/secondary encoding paths are chosen using mismatch probabilities; multiple codebooks may be selected or shuffled; and multi-stage encoding propagates residual outputs between stages. The behavior specification can enforce constraints (e.g., block sizes, quantizers, transforms), and the system may emit index and residual streams to facilitate reconstruction. Telemetry such as compression ratio or latency may drive optimization that updates behavior parameters.
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 cognitive manifold in a continuous, differentiable, cognitive manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. A mechanism inspired by magnetohydrodynamics is provided for coupling of typed spaces where thoughts on a cognitive manifold are structured as typed entities.
A system and method for implementing a Persistent Cognitive Machine (PCM) that extends beyond the traditional prompt-response paradigm of artificial intelligence are disclosed. A PCM maintains persistent cognitive processes regardless of external interaction, stores and organizes thoughts in a thought cache, retrieves relevant thoughts based on current stimuli, generates new thoughts through reasoning processes, and curates stored thoughts during periods of reduced external interaction. The PCM includes language and reasoning model components, a thought cache, an executive component, and an embedding system. The PCM remains continuously active, remembers previous experiences, learns from these experiences, creates new thought experiences independently, and initiates interactions without waiting for external prompts. The PCM enters sleep-like states during which it curates its thought cache, generalizes experiences, and performs other memory management functions. Applications may include but are not limited to synthetic cognitive colleagues, strategic war gaming platforms, and personal cognitive assistants.
G06F 16/353 - ClusteringClassification into predefined classes
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 3/049 - Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
41.
Executing self-evolving property graphs on GPU hardware
A system and method for executing self-evolving property graphs on GPU hardware for unbounded experiential processing. The system stores a dynamic property graph comprising event and communication vertices in GPU memory. Input streams are projected to graph vertices through specialized operators. Multiple GPU-executable operator kernels transform the graph through geometric operations including diffusion, geodesic computation, and curvature analysis. These operators are captured as a directed acyclic graph that executes repeatedly without external scheduling, with execution frequency adjusted based on a logarithmic relationship with input stream density. A compression mechanism identifies and removes redundant graph elements based on geometric properties, maintaining memory growth proportional to the logarithm of processed inputs. The system enables continuous transformation of experiential inputs into cognitive trajectories while avoiding the linear or quadratic memory scaling that limits conventional architectures. Applications include real-time decision support, pattern recognition, and autonomous system control.
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 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.
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
67.
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 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 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 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]
77.
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.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
87.
Latent geodesic traversal across multi-axis hyperspaces for real-time video reconstruction and augmentation
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.
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
88.
Correlation-Aware Adaptive Codebook System for Multi-Modal Data Compression with Neural Enhancement
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
89.
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
94.
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.
