Abstract: A semantic search system integrates with an AI platform to provide advanced search capabilities by leveraging automatically generated ontologies and knowledge graphs. The system employs natural language processing, machine learning, and large language models to create, update, and align ontologies from diverse data sources. It supports context-aware query interpretation, personalized results, and complex reasoning by incorporating user context, feedback, and domain knowledge. The system optimizes search performance and efficiency through indexing techniques, distributed computing, and continuous learning. With a modular architecture and scalable infrastructure, the semantic search system enables users to retrieve relevant, meaningful, and context-specific information from vast amounts of structured and unstructured data.

Abstract: A system and methods for creating artificial intelligence models that accurately represent target entities while enabling controlled knowledge evolution. The system initializes a base language model using entity-specific corpora, creates a distillate model through fine-tuning and reinforcement learning, and extracts symbolic rules implemented in a formal logic framework. The system creates temporal snapshots, beginning with a baseline state (T=0), and implements controlled exposure therapy by generating subsequent snapshots (T=n), measuring divergence between them, and updating symbolic rules accordingly. This approach preserves core characteristics of the target entity while simulating how they might evolve when exposed to new information. The system validates outputs through accuracy verification relative to the target entity, ensuring authenticity while enabling exploration of developmental progression beyond historical endpoints.

Abstract: A system and method for artificial intelligence agents capable of strategic information management in social and professional contexts is disclosed. The system and method combines context-aware decision-making with game theory principles to enable appropriate information disclosure, ranging from simple omission to strategic misdirection. An ethics enforcer ensures communications remain within role-appropriate boundaries, while integrated humor capabilities maintain social grace. A comprehensive audit system provides transparent tracking of all decisions and actions. The invention enables AI agents to effectively represent users across various scenarios—from social interactions to business negotiations—while maintaining ethical boundaries and professional responsibilities, continuously learning and adapting through user feedback and outcome analysis.

Abstract: The present invention discloses an advanced materials design platform that integrates novel geometric shapes, particularly small volume bodies of constant width (SVBOCW), into enhanced finite element analysis (FEA) for improved material modeling and design. The system generates and manipulates, and implements these novel shapes, incorporating them into multi-physics simulations with adaptive mesh refinement optimized for complex geometries and high-fidelity numerical accuracy. It enables seamless multi-scale modeling from atomic to macroscopic levels, leveraging the unique properties of SVBOCW to improve accuracy in quantum confinement effects and other nanoscale phenomena. The platform employs artificial intelligence, including reinforcement learning, to optimize material designs incorporating these novel geometries. A knowledge graph framework facilitates reasoning about material properties based on geometric structures.

Abstract: An AI-enhanced computing system and method optimizes power consumption in data centers through intelligent cooling control. The system integrates real-time thermal monitoring, workload analysis, and physics-based simulations to dynamically optimize cooling parameters across multiple scales, from individual chips to facility-level cooling infrastructure. Using a combination of artificial intelligence processing and multi-scale physics modeling, the system predicts thermal loads, determines optimal cooling strategies, and generates control signals to maintain component temperatures within operational limits while minimizing overall power consumption. The system adapts to changing conditions by balancing computational workload distribution with cooling system operation, enabling efficient thermal management across various cooling technologies including air, liquid, and immersion cooling.

Abstract: An artificial intelligence enhanced system for designing and optimizing environmentally resilient semiconductor devices integrates wave-based thermal modeling with multi-physics simulation and neuro-symbolic computing capabilities. The system enables comprehensive consideration of environmental effects throughout the semiconductor design process, incorporating traditional diffusive heat transfer models and wave-like heat transfer phenomena alongside other environmental stressors such as radiation exposure, mechanical strain, and chemical corrosion. A federated data-centric graph architecture manages and interconnects knowledge across multiple physics domains and environmental scenarios, while a physics model integration layer coordinates multi-scale simulations incorporating quantum-level effects and system-level behaviors. The system employs advanced uncertainty quantification and multi-objective optimization techniques to ensure robust designs for extreme environments.

Abstract: The present invention discloses an advanced materials design platform that integrates field theory techniques for multi-scale modeling and simulation of materials. The platform bridges quantum and classical physics descriptions through sophisticated computational methods, enabling seamless transitions across different scales while maintaining physical accuracy. By combining field theory approaches with hybrid quantum-classical computing, machine learning, and knowledge graph technologies, the system optimizes material designs across multiple physical domains. The platform enhances traditional simulation methods with quantum effects and field theory insights, enabling more accurate predictions of material properties and behavior. This comprehensive approach accelerates the discovery and development of novel materials for advanced technological applications while ensuring practical feasibility through real-time experimental validation and manufacturing considerations.

Abstract: A sports betting and fantasy gaming platform that incorporates AI-enabled fraud detection across multiple sports and competition levels. The platform integrates real-time game telemetry, social media data, betting patterns, and performance metrics to detect potential fraud through sophisticated pattern analysis. Sport-specific AI models, including generative adversarial networks and temporal convolutional networks, analyze player movements, team dynamics, and betting behaviors to identify anomalous patterns. The system enables event-oriented trading of fantasy assets and betting positions while maintaining regulatory compliance across jurisdictions. Advanced features include real-time integration with gaming platforms, collaborative team ownership structures, and automated hedging strategies. The platform’s modular design allows for customization based on sport type, regional gaming laws, and competition levels, from youth sports to professional leagues.

Abstract: Disclosed embodiments provide a system and method for animal-to-human translation. Disclosed embodiments can accept multimodal non-human animal communication data as input, such as vocalizations, gestures, brainwaves, and/or biometric indicators, and apply a machine-learning enabled debate-based oversight approach for determining a likely translation outcome. Disclosed embodiments perform a debate-based oversight process to obtain a decision on one or more meanings for received non-human animal communication data. The one or more meanings are associated with a human interpretation. A cross-species operation is performed based on the human interpretation. The cross-species operation can include rendering and/or presenting a translation on an output device such as an electronic display and/or audio speaker. The cross-species operation can include issuing a robot control command based on the human interpretation.

Abstract: A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for flexible and contextualized multi-agent AI and human collaboration at scale. The system provides a universal multi-modal key-value subsystem for sharing partial computations across agents, implements a hybrid greedy/non-greedy placement strategy for dynamic memory management, orchestrates dynamic computational workflows and tensor workflows using hierarchical tensor-fragment scheduling, enables cross-agent orchestration with policy-based privacy preservation, and incorporates quantum-resistant secure memory enclaves. The architecture supports continuous learning without catastrophic forgetting, compositional reasoning across modalities, and secure task execution in distributed environments.

Abstract: A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a multi-layer key-value subsystem for sharing computations across GPU partitions, applying hybrid placement strategies for dynamic memory management. It unifies physical GPU sub-allocation and virtual GPU time-slicing through a common abstraction layer while maintaining isolation through policy-based multi-tenancy. A predictive resource orchestration system forecasts computational needs, while speculative data scheduling proactively manages memory access patterns. The architecture includes risk-based scheduling for uncertain workloads, test-time compute scaling, and federated learning for edge collaboration. This framework delivers computational efficiency, security, and adaptive intelligence in high-dimensional environments while supporting incremental adoption through modular interfaces.

Abstract: A computer system implements a unified framework integrating an adaptive elastic funnel (AEF), convergent intelligence fabric (CIF), and context-aware quantum-enhanced optimization layer (CQOL) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid greedy/non-greedy placement strategies, and employs quantum-inspired optimization techniques for resource allocation. CQOL utilizes quadratic unconstrained binary optimization (QUBO) formulations and quantum-inspired annealing to efficiently manage tensor fragment placement across distributed resources. The architecture incorporates quantum-resistant secure memory enclaves, enables policy-based privacy preservation, supports continuous learning without catastrophic forgetting, and ensures secure task execution in distributed environments.

Abstract: A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

Abstract: A semantic search system integrates with an AI platform to provide advanced search capabilities by leveraging automatically generated ontologies and knowledge graphs. The system employs natural language processing, machine learning, and large language models to create, update, and align ontologies from diverse data sources. It supports context-aware query interpretation, personalized results, and complex reasoning by incorporating user context, feedback, and domain knowledge. The system optimizes search performance and efficiency through indexing techniques, distributed computing, and continuous learning. With a modular architecture and scalable infrastructure, the semantic search system enables users to retrieve relevant, meaningful, and context-specific information from vast amounts of structured and unstructured data.

Abstract: The Generative AI Content Verification Exchange systematically registers and stores content generated by AI and real people alike. Upon submission, the system categorizes content into distinct groups, then deconstructs it into multiple segments using various methods. Each segment is assigned a unique hash value, termed a “part identifier,” ensuring individualized identification. This registration process, combining grouping, segmentation, and hashing, enhances content traceability and retrieval. The resulting database not only organizes generated content by groups but also allows for efficient and secure referencing of specific content segments. A content similarity score may be generated by comparing hash values against a large corpus of registered content. The similarity score is indicative of the likelihood, or not, of an input content being in part registered content.

Abstract: A system and methods for detecting and mitigating authentication object forgery and manipulation attacks against services is provided, comprising a policy manager configured to observe a new authentication object generated by an identity provider, and retrieve the new authentication object; and a hashing engine configured to create a security cookie for each valid authentication session; wherein subsequent access requests accompanied by authentication objects are validated by checking for a valid security cookie.

Abstract: The proposed AI drug discovery platform represents a new approach to pharmaceutical research and development, integrating cutting-edge artificial intelligence and machine learning technologies across the entire drug discovery pipeline. This comprehensive system leverages multi-modal data integration, quantum-classical hybrid computing, environmental factor analysis, and digital twin simulations to address the complexities of drug discovery and development. By combining advanced predictive modeling, generative design, and virtual clinical trial capabilities, the platform aims to significantly accelerate the identification and optimization of novel therapeutic compounds while improving safety and efficacy predictions. The system's modular architecture incorporates state-of-the-art techniques in protein design, biomarker discovery, and personalized medicine, enabling a more holistic and precise approach to drug development.

Abstract: A coherent, intelligent, packet-switched memory fabric enables predictive, cache-coherent access across distributed compute, accelerator, and memory resources using a Memory-Fabric Transaction Layer Protocol (MF-TLP). MF-TLP defines routable packet formats for read, write, vectorized, atomic, reduction, collective, and predictive-prefetch transactions executed by memory-centric network interface controllers (MC-NICs). Each MC-NIC performs packet parsing, address translation, coherence management, and near-memory arithmetic or tensor operations while coordinating with MF-TLP-aware switches providing hierarchical directory control, multi-path routing, and in-network aggregation. Vectorized and multimodal packets encode multiple addresses or tensor offsets to reduce scatter/gather overhead, and programmable caching and quality-of-service modules manage tiered memory and tenant fairness.

Abstract: An AI-powered music registry, collaboration, and workflow management platform that addresses the challenges faced by music industry participants in the digital age. The system comprises a segmentation and hashing subsystem for musical pieces, segments, and isolated elements, enabling the evaluation of uniqueness and the consideration of individual creators' contributions. An artificial intelligence (AI) and machine learning (ML) subsystem is employed for extracting and isolating individual instruments, vocals, and performer contributions, while a component-level tracking module enables enhanced crediting and royalty distribution.

Abstract: A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow/process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.