Abstract: A computer system processes dining context data across multiple time scales to generate personalized dining recommendations. The system includes a temporal processing engine that simultaneously analyzes both immediate and historical dining data using multiple time-scale processors while maintaining temporal coherence. A dining context integration engine extracts and organizes features related to individual preferences and group dining dynamics, ensuring state continuity. A fusion engine combines outputs from the temporal processors with contextual representations to generate informed dining recommendations. These components work cooperatively to integrate real-time and historical dining patterns, enhancing dining experiences through adaptive and context-aware recommendations.

Abstract: 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.

Abstract: A system and methods for upsampling of decompressed biological 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 biological data sets.

Abstract: 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.

Abstract: 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.

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

Abstract: The present invention relates to a modular and customizable toy system, referred to as “Mojee,” comprising a base unit stylized as an emoji and configured with a plurality of predefined connection points. The system includes a variety of interchangeable components, such as facial features, accessories, and thematic embellishments, which can be selectively attached to the base unit to create unique and customizable configurations. By enabling users to construct and manipulate facial expressions, the system fosters emotional intelligence and communication skills by helping users recognize, interpret, and express emotions through play. The standardized attachment mechanisms ensure ease of assembly and compatibility across various components. The invention promotes creative expression, imaginative play, and skill development, while its modular design supports adaptability through themed accessory sets and future expansions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for encoding and decoding optical codes with context-aware, multi-level security. Input data is classified into security levels with associated context sensitivity requirements. The system compresses data using public and private codebooks based on security classifications, then generates optical codes incorporating both compressed data and context requirements. When scanned, the system collects environmental contextual data (location, network environment, device security, user behavior), analyzes it against embedded context requirements, and dynamically determines which security levels are accessible in the current environment. Only authorized security levels are decoded using appropriate codebooks based on both user credentials and current contextual factors. This approach enables fine-grained, context-sensitive access control that adapts to changing environments while maintaining the compression benefits and capacity advantages of the multi-level security framework.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system for processing and integrating contextual information across multiple time scales simultaneously analyzes immediate and historical data. The system comprises three main components working together to maintain temporal coherence. A temporal processing engine processes input data at different time scales using multiple processors, each dedicated to a specific time horizon. A context integration engine extracts and organizes contextual features from the input data, generating representations for different types of context while maintaining state information across these context types. A fusion engine combines the outputs from the temporal processors and integrates them with the context representations to generate coherent, unified outputs. The three engines operate cooperatively to ensure temporal consistency while processing context across multiple time scales.