Abstract: A system and method for computer-implemented adaptive elastic funnel system that efficiently processes complex scenarios through intelligent resource allocation. The system transforms input data into standardized vector representations and uses tensor network compression to reduce computational complexity from exponential to linear. An adaptive funnel mechanism dynamically controls exploration depth based on scenario criticality, focusing resources where they deliver the most value. The system evaluates scenarios through interpretable differentiable logic structures that maintain transparency while enabling sophisticated decision-making. Based on these evaluations, the system determines appropriate actions, securely delegates tasks to specialized agents using cryptographically signed authorization tokens, and optimizes computational resource allocation across the system.

Abstract: A system and method for artificial intelligence enhanced mobile energy and data and network control, planning and optimization. The present invention relates to a system and method for optimizing energy and data and network use in mobile platforms, such as facilities, vehicles, tools, and devices. The system leverages AI, data analytics, and context-aware techniques to collect, process, and analyze data from various sources, including sensors, weather data, and spatial and temporal data with locality aware computing, transport, storage and networking across assets that may be owned or operated by multiple stakeholders. By considering a variety of factors, the system generates optimized recommendations for data storage, compute, transmission, device settings, fleet management, and physical and virtual route planning and logic locality planning. The invention offers benefits, including improved energy efficiency, enhanced data and network management, increased operational efficiency, and cost savings.

Abstract: Disclosed embodiments provide a system and method for enabling real-time bilateral neural communication between humans and non-human animals using a combination of non-invasive neural interfaces with optional implantable neural enhancements. Advanced multi-modal pattern recognition algorithms are utilized for real-time neural signal processing, incorporating surface-level sensors and/or enhanced neural monitoring capabilities. These signals are processed through a sophisticated translation unit that converts neural patterns into meaningful communications, while simultaneously providing feedback mechanisms that may deliver precise haptic, auditory, or neural stimulation patterns for reinforcement and response.

Abstract: The advanced materials design platform implements a novel quantum-classical hybrid computation system that integrates quantum and classical computing resources for materials simulation, design, and engineering applications. The system generates parallel computational results using quantum and classical processors while maintaining quantum coherence through sophisticated state-preserving protocols. The platform features a mathematical translation layer, which enables accurate conversion between quantum and classical state representations. The platform implements real-time error detection and correction mechanisms, continuously monitoring quantum coherence and classical consistency while optimizing computation parameters and orchestration of tasks to achieve design goals. Resource management strategies dynamically allocate quantum and classical computing resources based on coherence requirements and computational complexity and cumulatively accumulated knowledge.

Abstract: A system and method for enhancing computer numerical control (CNC) operations through integrated spatial and material processing capabilities. The system combines real-time spatial capture using multiple scanning modalities with material property assessment and dynamic process optimization. The architecture comprises multi-modal scanning systems, real-time spatial processing, material characterization capabilities, and adaptive control optimization. The system implements comprehensive spatial mapping through coordinated scanning devices, processes captured data through parallel computing pipelines, and maintains dynamic spatial and material models for process optimization. Advanced algorithms enable real-time adaptation of manufacturing parameters based on detected material properties and spatial conditions.

Abstract: Adaptive semiconductor process control integrates multi-modal sensor data, predictive modeling, and real-time optimization to dynamically manage semiconductor manufacturing processes. The system includes distributed thermal sensors collecting diverse process data, a processor that maintains a state model using particle-based estimation techniques, a knowledge graph for representing process relationships and economic factors, and a controller that adaptively adjusts manufacturing equipment parameters. The processor generates topology-aware features from the state model by computing persistent homology at multiple scales and performing feature matching with confidence scoring. An upper confidence tree (UCT) optimization algorithm with super-exponential regret bounds dynamically adjusts exploration factors based on risk-weighted calculations.

Abstract: A system and method for controlling computer numerical control (CNC) operations using an enhanced neurosymbolic architecture. The system combines symbolic planning and reasoning with neural perception and state estimation, implementing extended Action Notation Modeling Language (ANML) constructs for probabilistic state estimation, perception integration, and human-robot collaboration. The architecture includes a symbolic planner, neurosymbolic bridge component, execution engine, and physical layer interface, enabling advanced capabilities such as real-time adaptation, multimodal sensing integration, and intelligent human-robot collaboration. The system incorporates simulation capabilities through a digital twin interface and hybrid simulation models, supporting continuous improvement through learning from demonstration and online parameter adaptation.

Abstract: A system for dynamic mapping that integrates real-time data from multiple sources into context-aware visualizations. The system creates semantic map overlays that adapt based on user roles and operational needs, while fusing diverse data inputs through confidence-weighted scoring. It processes spatial information through optimized tokenization and implements a multi-tiered memory architecture that distinguishes between temporary and permanent features. The system intelligently promotes or demotes spatial data across memory tiers based on confidence thresholds and operates across different spatial scales with independent update cycles. Data is organized in a multi-tenant knowledge graph with domain-specific rules and temporal awareness. A hybrid edge-cloud architecture enables both local processing and global consistency, while contextual guardrails ensure appropriate data access based on user identity and operational context.

Abstract: A quantum-enhanced semiconductor process control system addresses quantum effects in advanced semiconductor manufacturing through integrated tunneling prediction and physics-informed neural networks with quantum corrections. The system combines a quantum tunneling prediction module using high-resolution edge sensors and coherence detectors, a state model with particle-based quantum estimation, and a physics-informed neural network incorporating both classical and quantum physics layers. An upper confidence tree optimization algorithm with super-exponential regret bounds generates control signals for manufacturing equipment. The dynamic process window adaptation module performs quantum state assessment, potential barrier recalculation, and edge roughness projection in real-time. This quantum-aware approach achieves significant improvements in edge placement accuracy, pattern variation control, and manufacturing yield at sub-5 nm technology nodes where quantum phenomena directly impact fabrication outcomes.

Abstract: Disclosed embodiments provide a system and method for adaptive real-time animal training and welfare management, utilizing non-invasive neural monitoring and cognitive assessment via machine learning. The system includes conductive polymer ink electrodes or specialized wearable headgear for continuous acquisition of neural signals. Advanced algorithms perform frequency-domain analysis to correlate specific neural profiles with cognitive states such as attentiveness and stress. A multi-layered machine learning framework interprets these signals, dynamically adjusting training protocols and welfare assessments. Integrated environmental sensors enable multimodal data fusion, refining contextual accuracy. This invention supports continuous data-driven optimization of training efficacy, animal welfare, and predictive performance analytics throughout the animal’s lifecycle.

Abstract: A system and method for adaptive semiconductor process control integrates multi-modal sensor data, predictive modeling, and real-time optimization to dynamically manage semiconductor manufacturing processes. The system includes sensors collecting diverse process data, a processor that maintains a state model using particle-based estimation techniques, a knowledge graph for representing process relationships and economic factors, and a controller that adaptively adjusts manufacturing equipment. The processor generates topology-aware features from the state model by computing persistent homology at multiple scales and performing feature matching with confidence scoring. An upper confidence tree (UCT) optimization algorithm with super-exponential regret bounds dynamically adjusts exploration factors based on risk-weighted calculations.

Abstract: A system and method for adaptive semiconductor process control integrates multi-modal sensor data, predictive modeling, and real-time optimization to dynamically manage semiconductor manufacturing processes. The system includes sensors collecting diverse process data, a processor that maintains a state model using particle-based estimation techniques, a knowledge graph for representing process relationships and economic factors, and a controller that adaptively adjusts manufacturing equipment. The processor generates topology-aware features from the state model by computing persistent homology at multiple scales and performing feature matching with confidence scoring. An upper confidence tree (UCT) optimization algorithm with super-exponential regret bounds dynamically adjusts exploration factors based on risk-weighted calculations.

Abstract: A system and method for enhancing computer numerical control (CNC) operations through comprehensive environmental adaptation capabilities. The system combines multi-modal environmental sensing with compensation mechanisms, real-time control adaptation, and adaptive control strategies to optimize CNC performance under dynamic operating conditions. The architecture implements distributed sensor arrays for monitoring temperature, electromagnetic, pressure, gravity, and vibration effects, enabling precise characterization of local environmental conditions. Advanced physics-based compensation algorithms integrate with neural network-based prediction to enable real-time adaptation of manufacturing parameters. The system maintains dynamic models of environmental effects on machine behavior, material properties, and process outcomes. Predictive control strategies enable proactive compensation for environmental variations while maintaining manufacturing precision.

Abstract: A dynamic semantic mapping system and method implements a multi-layer memory architecture that processes geospatial data through short-term, mid-term, and long-term memory layers with distinct momentum parameters controlling data retention and adaptation rates. The system detects environmental changes through sensor data analysis and generates ephemeral expansions representing temporary spatial features. A momentum-based validation mechanism requires data consistency across multiple time bins before advancing ephemeral expansions between memory layers. The system maintains stable map references in long-term memory while enabling immediate local map updates through ephemeral expansions in short-term memory. Processing is distributed between edge computing nodes that spawn and manage ephemeral expansions locally and high-performance computing resources that maintain global stable map data.

Abstract: Disclosed embodiments provide a multi-modal orchestration system that enables coordinated adaptive, context-aware task execution between humans, animals, and robotic agents by leveraging advanced neurosymbolic AI, multi-modal sensor fusion, and reinforcement learning techniques. The system integrates SLAM-based environmental mapping, point cloud processing, and dynamic sensor networks to maintain real-time awareness of all agent positions, movement patterns, and behavior states. The system can utilize learned patterns of leadership and followership (derived from natural systems like octopus-fish hunting groups) to optimize collaborative behaviors, while continuously adapting to changing environmental conditions through a sensor fusion pipeline.

Abstract: A phylogenetic-based knowledge curation system implements adaptive information transfer protocols for cross-generational transmission of behavioral patterns in animals. The system comprises a neural signal acquisition subsystem that captures multi-channel electrophysiological data; a phylogenetic analysis framework that quantifies evolutionary communication pattern divergence; and a hierarchical knowledge repository architecture that encodes successful behavioral adaptations with mathematically optimized information density. The disclosed methods implement information-theoretic algorithms to maximize information transfer during knowledge transfer operations while minimizing computational and information complexity in the transmitted behavioral patterns and information to drive significant improvements in knowledge acquisition efficiency and transfer across multiple taxonomic lineages.

Abstract: A system and methods for authentication attack detection with embedded authentication and delegation is provided, comprising an authentication object inspector configured to observe a new authentication object generated by an identity provider, and retrieve the new authentication object, wherein subsequent access requests accompanied by authentication objects are validated by comparing identifiers for each authentication object to previous identifiers. The authentication objects are able to include physical authentication information gathered via a multi-factor authentication kiosk.

Abstract: A semiconductor manufacturing system and method integrating hybrid interface control, topology-aware feature extraction, and advanced adaptive process management to optimize interactions of heterogeneous materials and enhance device performance. The system utilizes neural symbolic reasoning, GPU, FPGA or ASIC acceleration and quantum-informed modeling to precisely define operational targets and predict interfacial behavior, managing quantum mechanical phenomena and stochastic variability effectively. Structural design capabilities enable the optimization of physical geometry parameters and interaction modes between diverse materials. Advanced lithography masks incorporate integrated thermal management solutions, and innovative dual-response resist materials enhance pattern resolution and fidelity.

Abstract: A system and method for adaptive semiconductor process control integrates multi-modal sensor data, predictive modeling, and real-time optimization to dynamically manage semiconductor manufacturing processes. The system includes distributed thermal sensors collecting diverse process data, a processor that maintains a state model using particle-based estimation techniques, a knowledge graph for representing process relationships and economic factors, and a controller that adaptively adjusts manufacturing equipment parameters. The processor generates topology-aware features from the state model by computing persistent homology at multiple scales and performing feature matching with confidence scoring. An upper confidence tree (UCT) optimization algorithm with super-exponential regret bounds dynamically adjusts exploration factors based on risk-weighted calculations.

Abstract: A system and methods for adaptive semantic world modeling implements multi-layered data processing and real-time adaptation capabilities through an ephemeral memory architecture. The system ingests multi-modal data from diverse sources including satellite imagery, IoT sensors, video feeds, and crowdsourced inputs using AI-driven world modeling with hybrid tokenization and semantic compression. A momentum-based memory management system controls data promotion and demotion between short-term, mid-term, and long-term memory layers based on temporal consistency and validation frequency while maintaining data consistency across spatial and temporal scales. Multi-tier guardrails ensure operational compliance through pre-guard, contextual-guard, and post-guard validation components. The system enables immersive AR/VR visualization, natural language interaction, and multi-user collaboration while supporting distributed edge-cloud processing architecture.