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. MF-TLP supports extension headers for predictive prefetch, collective coordination, and tenant governance, operating across hierarchical leaf-spine topologies using Ultra-Ethernet Transport, InfiniBand, or CXL fabrics. The system delivers scalable, low-latency, memory-centric orchestration for large-language-model training, multimodal AI, and data-intensive analytics.
Cybersecurity mission planning and analysis uses artificial intelligence systems to make red and blue team exercises more comprehensive and effective by supplementing individual expertise, reducing reliance on intuition, and eliminating gaps in knowledge. In an embodiment, a platform for cyberattack missions planning and analysis by red and blue teams is coordinated by a control center. An incident generator generates cyberattack scenarios and events using data from external databases and an internal attack knowledge manager having a knowledge graph of data about the network under attack in conjunction with one or more machine learning algorithms configured to identify potential network vulnerabilities. Red are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths. Blue teams are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
3.
Federated Distributed Computational Graph Platform for Advanced Robotic Integration in Precision Oncological and Gene Therapies
A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
G16H 20/40 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des thérapies mécaniques, la radiothérapie ou des thérapies invasives, p. ex. la chirurgie, la thérapie laser, la dialyse ou l’acuponcture
G16H 30/40 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le traitement d’images médicales, p. ex. l’édition
G16H 40/63 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement local
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
G16H 80/00 - TIC spécialement adaptées pour faciliter la communication entre les professionnels de la santé ou les patients, p. ex. pour le diagnostic collaboratif, la thérapie collaborative ou la surveillance collaborative de l’état de santé
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 universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
5.
System and method for efficient scene continuity in visual and multimedia using generative artificial intelligence
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.
Detection and mitigation of data source compromises in an adversarial information environment, featuring the ability to scan for, ingest and process, and then use relational, wide column, and graph stores for capturing entity data, their relationships, and actions associated with them. Metadata is gathered and linked to the ingested data, which provides a broader contextual view of the environment leading up to and during an event of interest. Data quality analysis is conducted as data is ingested in order to identify if a data source may be compromised. The results are used to manage the reputation of the contributing data sources.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
G06F 16/951 - IndexationTechniques d’exploration du Web
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
7.
Artificial intelligence-powered music registry, collaboration, and workflow management system
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.
G06Q 20/14 - Architectures de paiement spécialement adaptées aux systèmes de facturation
G06F 16/483 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/683 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/908 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
G06Q 10/101 - Création collaborative, p. ex. développement conjoint de produits ou de services
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
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.
An AI-powered content generation system that creates consistent, coherent, and engaging multi-modal content by integrating multiple specialized AI components. The system analyzes user input, identifies key elements, and maintains continuity throughout the generation process. It incorporates a feedback loop to learn and adapt based on user preferences, enabling personalized content experiences. The modular architecture allows for seamless integration of AI components focusing on text, images, audio, and interactive elements. The system ensures consistency across modalities and over extended periods, while managing rights, licenses, and royalties using blockchain technology. This advanced platform revolutionizes content creation, consumption, and management in the digital age.
G06N 3/0442 - Réseaux récurrents, p. ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p. ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners/designers to create UX/UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites/applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX/UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX/UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.
The provided system and methods describe a Multi-factor Authentication (MFA) kiosk that utilizes various sensors to capture biometric, behavioral, and physiological data for authentication. The kiosk includes a user interface, a set of sensors, and services such as kiosk management, rules configuration, sensor management, and an authentication service. The sensors, both integrated and external, gather diverse data, including facial recognition, fingerprint scans, voice recognition, gait analysis, and more, constructing a physical profile for authentication. The system incorporates a rules service for configuring authentication policies and a sensor management service to optimize sensor performance. Authentication service uses a scoring model, potentially a deep learning algorithm like an autoencoder, to generate an authentication score based on inputs from sensors, rules, and previous attempts. Security measures include encryption, isolation of components, and compliance with data protection regulations. A plurality of MFA kiosks may form an authentication network.
A system and method AI-enabled telematics and actuation for electronic entertainment, simulation, training, and remote operations systems. The system and method disclosed support neuro symbolic reasoning and generative AI enabled experience generation to allow a user or collection of users to experience a wide range of realistic scenarios where the user can pick and choose an experience that best fits their individual or collective preferences. Additionally, the system and method have wide applications to a variety of environments, including but not limited to, racing, sports, military training, vehicle and aircraft operation, and training simulations. The proposed system and method enable realistic, immersive video game, simulation, training, and remote operations environments which are applicable to a wide range of devices, platforms, and mediums for recreational, commercial, industrial, and security uses.
A63F 13/65 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p. ex. les mesures en direct dans les compétitions de course réelles
A system and method for creating complex, immersive, and interactive digital content is disclosed. The system integrates advanced artificial intelligence, multi-modal input processing, cloud-based shared environments, and immersive hardware to generate, optimize, and deliver rich interactive experiences. The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats.
A63F 13/67 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu en s’adaptant à ou par apprentissage des actions de joueurs, p. ex. modification du niveau de compétences ou stockage de séquences de combats réussies en vue de leur réutilisation
A63F 13/285 - Génération de signaux de retour tactiles via le dispositif d’entrée du jeu, p. ex. retour de force
A63F 13/355 - Réalisation d’opérations pour le compte de clients ayant des capacités de traitement restreintes, p. ex. serveurs transformant une scène de jeu qui évolue en flux vidéo codé à transmettre à un téléphone portable ou à un client léger
A63F 13/65 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p. ex. les mesures en direct dans les compétitions de course réelles
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
14.
PERSONAL HEALTH DATABASE PLATFORM WITH SPATIOTEMPORAL MODELING AND SIMULATION
A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
15.
Federated Distributed Computational Graph Platform with Advanced Multi-Expert Integration and Adaptive Uncertainty Quantification for Precision Oncological Therapy
A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
16.
RANDOM ENTITY SELECTION WITH A FUZZY BLOCKLIST CAPABILITY
A system and methods for selecting a random entity with a fuzzy blocklist capability, comprising a range editor which acts on data received by either a plurality of databases or a possible address map; and an advanced random number generator. The range editor allows for breaking data into either desirable or blocked chunks which may be normalized and passed to the advanced random number generator. The advanced random number generator may select a random value from within a randomly selected chunk. Various methods for randomly selecting both a chunk and a value may be used to maximize the efficiency of the method and system.
A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.
A system and method that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified threat or potential security risk.
A system and method for comprehensive data loss prevention and compliance management designed to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring and protection.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
H04L 43/045 - Traitement des données de surveillance capturées, p. ex. pour la génération de fichiers journaux pour la visualisation graphique des données de surveillance
20.
Cybersecurity Analysis and Protection Using Distributed Systems
Cybersecurity reconnaissance, analysis, and scoring uses distributed, cloud or edge-based pools of computing services to provide sufficient scalability for analysis of IT/OT networks using only publicly available characterizations. An in-memory associative array manages a queue of configuration and vulnerability search tasks through at least one public-facing proxy network which uses configurable search nodes to approach the target network with search tools in a desired manner to control certain aspects of the search in order to obtain the desired results, especially when target network behavior adjusts based on counterparty characteristics. A data packet modifier reveals IP addresses of threat actors behind port scans and subsequently block the threat actors.
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
G06F 16/951 - IndexationTechniques d’exploration du Web
H04L 61/4511 - Répertoires de réseauCorrespondance nom-adresse en utilisant des répertoires normalisésRépertoires de réseauCorrespondance nom-adresse en utilisant des protocoles normalisés d'accès aux répertoires en utilisant le système de noms de domaine [DNS]
A dynamic experience curation platform utilizing AI and ML techniques, including neural networks and generative models, to enhance user interactions with content on the Internet. The platform employs an Experience Broker (EB) service to disintermediate user devices from the internet (or other content/information sources), improving security and user experience. It processes content requests, extracts relevant information, filters out unwanted content like ads, transforms, combines, and/or mutates existing content, and generates curated content consistent with user preferences and other constraints. The platform's generative AI process renders content into a user interface consistent with user preferences. It allows for personalized content delivery across devices, including virtual and augmented reality environments. The platform supports a spectrum of personalization, allowing users to fine-tune their content consumption experience. Additionally, it manages user sessions across devices and integrates various databases for storing user profiles, preferences, and other relevant information.
This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.
H04N 19/189 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par le procédé d’adaptation, l’outil d’adaptation ou le type d’adaptation utilisés pour le codage adaptatif
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
H04N 19/164 - Retour d’information en provenance du récepteur ou du canal de transmission
H04N 19/42 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par les détails de mise en œuvre ou le matériel spécialement adapté à la compression ou à la décompression vidéo, p. ex. la mise en œuvre de logiciels spécialisés
A dynamic experience curation platform utilizing Al and ML techniques, including neural networks and generative models, to enhance user interactions with content on the Internet. The platform employs an Experience Broker (EB) service to disintermediate user devices from the internet (or other content/information sources), improving security and user experience. It processes content requests, extracts relevant information, filters out unwanted content like ads. transforms, combines, and/or mutates existing content, and generates curated content consistent with user preferences and other constraints. The platform's generative Al process renders content into a user interface consistent with user preferences. It allows for personalized content delivery across devices, including virtual and augmented reality environments. The platform supports a spectrum of personalization, allowing users to fine-tune their content consumption experience. Additionally, it manages user sessions across devices and integrates various databases for storing user profiles, preferences, and other relevant information.
24.
COMPUTING PLATFORM FOR NEURO-SYMBOLIC ARTIFICIAL INTELLIGENCE APPLICATIONS
A distributed generative artificial intelligence (AI) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative AI models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.
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.
A system and method for AI based user experience curation across multiple scenarios and finite time horizons of interest. The present invention integrates connectionist and symbolic AI techniques to generate coherent, relevant, and personalized content across various domains. The invention bridges the gap between connectionist AI and symbolic AI, enabling more adaptive, immersive, and engaging user experiences while prioritizing security and traceability and contextualization considerations behind recommendations or content generation. The platform furthers dynamic and tailored user interactions with digital systems across individual interactions, preferences, sequences and ongoing engagements across sessions by leveraging the power of analytics, deep learning, and AI to create truly intelligent, dynamic, and responsive user experiences enhanced with optimization and planning faculties.
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
A63F 13/40 - Traitement des signaux de commande d’entrée des dispositifs de jeu vidéo, p. ex. les signaux générés par le joueur ou dérivés de l’environnement
27.
PLATFORM FOR INTEGRATION OF MACHINE LEARNING MODELS UTILIZING MARKETPLACES AND CROWD AND EXPERT JUDGMENT AND KNOWLEDGE CORPORA
A system and method for flexibly incorporating machine learning models into applications using a marketplace platform and distributed computational graph (DCG) architecture. The DCG enables dynamic selection, creation and incorporation of trained models with data sources and marketplaces for data, algorithms, simulation models, ontologies, knowledge corpora, and crowd or expert judgment. Multiple models can be used in series or parallel. An expert judgment marketplace allows human and artificial intelligence (AI) experts to score the accuracy of training data and model outputs. Consumers can select and rank AI agents or experts based on the helpfulness of their judgments. A symbolic knowledge corpora and retrieval augmented generation (RAG) marketplace enables selling access to proprietary datasets as RAGs and knowledge bases. The system includes knowledge corpora and RAG marketplaces with domain-specific components and user experience customization.
Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.
C12G 3/02 - Préparation d'autres boissons alcoolisées par fermentation
C12H 1/22 - Vieillissement ou mûrissage par emmagasinage, p. ex. blondissement de la bière
C12H 3/00 - Procédés de réduction de la teneur en alcool des solutions fermentées ou des boissons alcoolisées pour obtenir des boissons à faible teneur en alcool ou sans alcool
29.
SYSTEM AND METHODS FOR AI-ENHANCED CELLULAR MODELING AND SIMULATION
The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.
A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.
A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI/ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.
A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.
A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.
A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.
A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.
G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
An advanced model management platform for optimizing and securing generative artificial intelligence systems such as large language models (LLMs) and diffusion models. The platform incorporates various techniques to address the limitations of current generative AI systems, such as hallucination, lack of validation, security vulnerabilities, and inadequate model management. The system employs reinforcement learning algorithms for model optimization, retrieval augmented generation (RAG) for hallucination mitigation, domain-specific validation against expert knowledge, model distillation and similarity scoring for security, adversarial training for robustness, and attention mechanism search and model blending for advanced management and neuro symbolic AI routine combinations. By integrating these techniques, the platform significantly improves the performance, reliability, and security of generative AI across a wide range of tasks and domains leveraging the best elements of symbolic and connectionist techniques alongside automated planning and modeling simulation.
A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.
A platform for coordinating networks of specialized AI agents that enables secure collaboration through token-based communication and real-time result streaming. The system features a central orchestration engine managing interactions between domain-specific expert agents, with memory management and optional encryption for secure data handling. The platform uses efficient communication protocols for knowledge compression and faster reasoning, while a standardized agent interface system handles security, privacy, and policy requirements. It scales across distributed computing environments to enable complex collaborative tasks like personalized content creation, materials discovery, and drug development while optimizing resource usage and maintaining data privacy.
A federated distributed computational system enables secure collaboration across institutions for unified biological and multiomics data analysis. It comprises interconnected computational nodes managed by a central federation manager. Each node includes specialized components: a local computational engine for biological data processing, a privacy-preservation system, a knowledge integration component leveraging dynamic knowledge graphs, and a secure communication interface. The federation manager coordinates computational activities while ensuring security, privacy, legality, and contractual adherence. This architecture allows institutions, citizen scientists, and patients to collaborate on complex biological analyses without compromising sensitive data. By enabling shared computational resources and expertise, the system facilitates breakthrough discoveries while maintaining confidentiality. Additionally, it supports pro-rata or contractually defined participation in resultant benefits or knowledge, ensuring equitable collaboration.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
41.
AI AGENT DECISION PLATFORM WITH DEONTIC REASONING AND QUANTUM-INSPIRED TOKEN MANAGEMENT
A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.
A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.
A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.
A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.
G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
G16B 20/00 - TIC spécialement adaptées à la génomique ou protéomique fonctionnelle, p. ex. corrélations génotype-phénotype
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
45.
Convergent Intelligence Fabric for Multi-Domain Orchestration of Distributed Agents with Hierarchical Memory Architecture and Quantum-Resistant Trust Mechanisms
A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.
A distributed generative artificial intelligence (AT) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative Al models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.
A system and method for cybersecurity mission planning and analysis which uses artificial intelligence systems to make red and blue team exercises more comprehensive and effective by supplementing individual expertise, reducing reliance on intuition, and eliminating gaps in knowledge. In an embodiment, a platform for cyberattack missions planning and analysis by red and blue teams is coordinated by a control center. An incident generator generates cyberattack scenarios and events using data from external databases and an internal attack knowledge manager having a knowledge graph of data about the network under attack in conjunction with one or more machine learning algorithms configured to identify potential network vulnerabilities. Red are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths. Blue teams are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths.
48.
Discrete compatibility filtering using genomic data
A system is disclosed for discreetly assessing the compatibility of two or more human genomes across diverse elements, activities, and engagement platforms relevant to potential mating scenarios. The genomic data is subjected to encryption, with the option of employing homomorphic encryption to safeguard user privacy and security. Processing of the data is facilitated through a personal health database processing system, which may be cloud-based or edge-based. The application of homomorphic encryption ensures that the genomic information of individual users remains encrypted during processing, with the outcome limited to the display of progeny compatibility to the respective end users.
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
49.
HOST-LEVEL TICKET FORGERY DETECTION AND EXTENSION TO NETWORK ENDPOINTS
A system and method for detection and prevention of ticket forgery cyberattacks by improving host-level analytics and monitoring and extending the improved host-level analytics and monitoring to endpoints of a network. The methodology described herein comprises the use of a ticket-granting log extension utility which stores every logon session on a network, queries the local ticket cache, and generates additional custom data as a part of an event log stream such as a start time, end time, renew time, and related session data. This comprehensive log extension data can be used to identify certain types of ticket forgery cyberattacks by comparing the user session name with the client name identified in the ticket presented for access to network resources and other means. This host-level ticket forgery detection can be extended to network endpoints for additional security.
50.
AI-DRIVEN DEFENSIVE CYBERSECURITY STRATEGY ANALYSIS AND RECOMMENDATION SYSTEM
A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
The Generative Al Content Verification Exchange systematically registers and stores content generated by Al-enabled or enhanced services. 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. The systematic registration and storage framework enable streamlined management of diverse generative Al-generated content for various applications, such as registration, analysis, search, and verification by diverse counterparties.
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.
The Generative AI Content Verification Exchange processes system obtained data and user input data, creating a set of hash values using a robust hashing algorithm. It then compares these hash values against a registry of content. If an exact match or sufficient similarity is detected, the system indicates that the input content, or component of input content, is likely a copy or generated by an AI tool. This approach enables users to discern whether the content is original and if it is probabilistically likely to have originated from an generative AI process or from humans. It has applications in content authenticity verification, aiding users in identifying content utilization, distribution dynamics, AI-generated content and promoting transparency and trust in online interactions and content.
A system and methods for the creation of domain-specific languages that are both domain-agnostic and language-agnostic for use in a multi-language abstract digital simulation model generation and execution, comprising an onboarding module that creates domain specific models from declarative languages, domain-specific language engine, that uses the declarative domain-specific models to create a domain specific language, a meta-model structuring and creation system, meta-model mapping table, remote server, simulation execution process, computer domain-specific language, and methods for user-creation and editing of meta-models, simulation models, and parametrization of simulation environments, actors, objects, and events in real-time using heuristic searching.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 8/10 - Analyse des exigencesTechniques de spécification
G06F 9/448 - Paradigmes d’exécution, p. ex. implémentation de paradigmes de programmation
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
55.
COLLABORATIVE GENERATIVE ARTIFICIAL INTELLIGENCE CONTENT IDENTIFICATION AND VERIFICATION
The Generative AI Content Verification Exchange systematically registers and stores content generated by AI-enabled or enhanced services. 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. The systematic registration and storage framework enable streamlined management of diverse generative AI-generated content for various applications, such as registration, analysis, search, and verification by diverse counterparties.
A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis driven simulations of alternate candidate business actions has been devised and reduced to practice. This business operating system may be used predict the outcome of enacting candidate business decisions based upon past and current business data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Both single parameter set and multiple parameter set analyses are supported. Risk to value estimates of candidate decisions are also calculated.
Autonomous management of risk transfer is provided using an automated underwriting processor that creates a contract block by compiling the request into a computational graph-based format, links the contract block to the requester, stores the contract block into memory, retrieves a plurality of available underwriting agreements from memory, and creates an offer list by perform computational graph operations on the contract block to determine viable risk-transfer agreements; and presenting the offer list to the requester.
G06F 16/951 - IndexationTechniques d’exploration du Web
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
A system and method for the secure and private demonstration of cloud-based cyber-security tools. Using an advanced sandboxing design patterns, isolated instances of virtual networks allow a potential client to compare their existing cyber defense tools against a set of cloud-based tools. Capitalizing on non-persistent and secure sandboxes allow the invention to demonstrate fully functional and devastating cyber-attacks while guaranteeing strict privacy and security to both existing customers and potential ones. Additionally, instantiating separate sandboxed observed systems in a single multi-tenant infrastructure provide each customer with the ability to rapidly create actual representations of their enterprise environment offering the most realistic and accurate demonstration and comparison between products.
A system and methods for cybersecurity rating using active and passive external reconnaissance, comprising a web crawler that send message prompts to external hosts and receives responses from external hosts, a time-series data store that produces time-series data from the message responses, and a directed computational graph module that probes, scans, and fingerprints devices within a cyber-physical graph and analyzes the results as time-series data to produce a weighted score representing the overall cybersecurity state of an organization.
A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
61.
AI-CONTROLLED SENSOR NETWORK FOR THREAT MAPPING AND CHARACTERIZATION AND RISK ADJUSTED RESPONSE
A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.
G06F 16/909 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
G06F 16/951 - IndexationTechniques d’exploration du Web
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
62.
SYSTEM AND METHODS FOR CREATION AND USE OF META-MODELS IN SIMULATED ENVIRONMENTS
A system and method for generating and applying meta-models in simulated environments, in which an agent simulation is selected, one or more agent goals are received, and agents are created which are individual instances of the agent simulation with each agent having at least one of the agent goals, wherein the agents are used in the execution of an environment simulation which dynamically changes based on the collective behavior of the agents. The agents operate in the environment simulation using meta-models which describe how the agents interact with other agent and how the agents interact within the simulation.
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
H04L 41/0813 - Réglages de configuration caractérisés par les conditions déclenchant un changement de paramètres
H04L 41/147 - Analyse ou conception de réseau pour prédire le comportement du réseau
H04L 41/149 - Analyse ou conception de réseau pour la prédiction de la maintenance
H04L 41/40 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant la virtualisation des fonctions réseau ou ressources, p. ex. entités SDN ou NFV
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
63.
CORRELATING NETWORK EVENT ANOMALIES USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE TO IDENTIFY ATTACK INFORMATION
A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.
A system for probe-based risk analysis for multi-factor authentication having a multi-dimensional time series data server configured to monitor and record a network's traffic data and to serve the traffic data to other modules and a directed computational graph module configured to probe connection destinations for a response, analyze any received responses, and determine a verification score needed before granting access based at least in part on the analysis of the received responses. A plurality of verification methods build up a user's verification score to required level to gain access.
A system and method for AI-driven advertising and content generation that integrates connectionist and symbolic AI techniques to deliver personalized, contextually relevant user experiences. The invention leverages specialized agent networks, knowledge graphs, and retrieval-augmented generation to create dynamic, adaptive content with seamlessly integrated advertisements. It employs a sophisticated ad integration layer and experience broker to ensure relevance and engagement. The system prioritizes security, traceability, and user preferences while optimizing ad placement and content delivery. By bridging connectionist and symbolic AI, the platform enables immersive, tailored interactions across multiple scenarios and time horizons, enhancing user engagement and advertiser value in an AI-driven digital landscape.
A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.
A system and method for providing access and agency to individual entities and people over their data for the purpose of data set validation to facilitate data set and algorithm bias certification and scoring. A first data set is filtered to extract its core information content and to create a certified data set. A certified model is created by training a machine learning algorithm on the certified data set, which certified model is then used to evaluate the bias of subsequent data sets. The data set may be given a value score which represents the overall validity of the data set and its bias characterization. A bias characterization audit can help identify the root causes of bias outcomes from predictive software and algorithms that perform third party tasks and services. The score can be used as a metric to further facilitate market transactions.
A system and method for cybersecurity risk analysis and anomaly detection using active and passive external reconnaissance, that identifies critical network entities within a cyber-physical graph, identifies anomalous events within the network, determines the risk of identified anomalies based on the value of the entities involved, and determines an effectiveness score for the network based on the identified risks.
A system and method for attacker interdiction using user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the dependency graphs and provide deeper insight into application and user security on a given network. Attacks may be detected by analyzing the dependency graphs, and attacker interdiction may be implemented by actively orchestrating network security and IT devices using SOAR workflows.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
G06F 21/56 - Détection ou gestion de programmes malveillants, p. ex. dispositions anti-virus
70.
Host-level ticket forgery detection and extension to network endpoints
A system and method for detection and prevention of ticket forgery cyberattacks by improving host-level analytics and monitoring and extending the improved host-level analytics and monitoring to endpoints of a network. The methodology described herein comprises the use of a ticket-granting log extension utility which stores every logon session on a network, queries the local ticket cache, and generates additional custom data as a part of an event log stream such as a start time, end time, renew time, and related session data. This comprehensive log extension data can be used to identify certain types of ticket forgery cyberattacks by comparing the user session name with the client name identified in the ticket presented for access to network resources and other means. This host-level ticket forgery detection can be extended to network endpoints for additional security.
G06F 11/00 - Détection d'erreursCorrection d'erreursContrôle de fonctionnement
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
71.
SYSTEM AND METHOD FOR CYBER EXPLOITATION PATH ANALYSIS AND RESPONSE USING FEDERATED NETWORKS
A system and method for cyber exploitation path analysis and response using federated networks to minimize network exposure and maximize network resilience, with the ability to simulate complex and large scale network traffic through the use of federated training networks, by gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Lastly, network attack path analysis and automated task planning for minimizing network exposure and maximizing resiliency is performed with machine learning, generative adversarial networks, hierarchical task networks, and Monte Carlo search trees.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
A system and method for user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the executable graph and provide deeper insight into application and user security on a given network.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
73.
SYSTEM AND METHOD FOR TRACK AND TRACE USER AND ENTITY BEHAVIOR ANALYSIS
A system and method for user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the executable graph and provide deeper insight into application and user security on a given network.
A system and method for comprehensive data utilization and tracking comprising an ontological engine which in some embodiments is configured to create and curate various industry-specific ontologies which can be used to provide deeper context to an enterprise's network traffic and data transmission. The system and method further comprise a tagging and tracking engine configured to inspect network packets, apply a first tag associated with an authentication object, apply a second tag associated with an identified ontology, and track the tagged packets as they traverse the enterprise network, generating data utilization tracking information as the packets move through the network. A scoring engine may leverage the data utilization tracking information in combination with user entity and behavior data to compute a risk score associated with data utilization on the enterprise network.
A system and method for attacker interdiction using user-level network trace and tracking which leverages the uniqueness of verified authentication objects as metadata tags on captured network packets to gain insight at the user-level of how a network and various applications interact. The tagged network packets may be tracked, and the resulting data formed into a trace and track dataset to create one or more user-level dependency graphs alongside captured temporal dynamics. The trace and track dataset may be enriched with application trace information and runtime instruction data to improve the dependency graphs and provide deeper insight into application and user security on a given network. Attacks may be detected by analyzing the dependency graphs, and attacker interdiction may be implemented by actively orchestrating network security and IT devices using SOAR workflows.
A system and method for comprehensive data utilization and tracking comprising an ontological engine which in some embodiments is configured to create and curate various industry-specific ontologies which can be used to provide deeper context to an enterprise's network traffic and data transmission. The system and method further comprise a tagging and tracking engine configured to inspect network packets, apply a first tag associated with an authentication object, apply a second tag associated with an identified ontology, and track the tagged packets as they traverse the enterprise network, generating data utilization tracking information as the packets move through the network. A scoring engine may leverage the data utilization tracking information in combination with user entity' and behavior data to compute a risk score associated with data utilization on the enterprise network.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/50 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation
G06F 16/36 - Création d’outils sémantiques, p. ex. ontologie ou thésaurus
H04H 60/33 - Dispositions de contrôle du comportement ou des opinions des utilisateurs
H04W 12/67 - Sécurité dépendant du contexte dépendant du risque, p. ex. choix du niveau de sécurité selon les profils de risque
77.
PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS USING LATERAL MOVEMENT DETECTION AND PREVENTION
A system and method for the privilege assurance of enterprise computer network environments using lateral movement detection and prevention. The system uses local session monitors to monitor logon sessions within a network, generating and verifying event logs and authentication records to ensure the legitimacy of authenticated user sessions and to revoke credentials when an illicit session is detected, halting lateral movement in real-time.
A system and method for providing a cloud identity verification exchange service which ingests a plurality of identity assertion data from various Identity Providers and/or Service Providers and aggregates the ingested plurality of data into a master global authentication ledger. The system and method comprise: a data ingestion engine configured for acquiring, extracting, and loading data into the system as well as providing hashing capabilities; a metadata manager for collecting, organizing, and cataloguing ingested data based on collected metadata; and database for storing the master global authentication ledger. The master ledger acts as a central repository that consolidates authentication objects from various Identity Providers, allowing for centralized authentication management, auditing, reporting, and analysis. It provides a comprehensive view of authentication activities across multiple systems and enables the tracking of user authentication events across different identity providers.
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
79.
A SYSTEM AND METHOD FOR CYBER EXPLOITATION PATH ANALYSIS AND TASK PLAN OPTIMIZATION
A system and method for cyber exploitation path analysis and task plan optimization to minimize network exposure and maximize network resilience. The system and method involve gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Lastly, network attack path analysis and automated task planning for minimizing network exposure and maximizing resiliency is performed with machine learning, generative adversarial networks, hierarchical task networks, and Monte Carlo search trees.
G06F 21/50 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
A system and methods for Kerberos protocol collection, interdiction and decryption for realtime analysis to aid in both operational and security functions in SSO-enabled networks, using agent processes that intercept and decrypt Kerberos traffic to identify compromised credentials and accounts in real-time without exposing sensitive information.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/28 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant un algorithme de chiffrement particulier
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
81.
COLLABORATIVE CLOUD IDENTITY AND CREDENTIAL FORGERY AND ABUSE DEFENSE
A system and method for providing a cloud identity verification exchange service which ingests a plurality of identity assertion data from various Identity Providers and/or Service Providers and aggregates the ingested plurality of data into a master global authentication ledger. The system and method comprise: a data ingestion engine configured for acquiring, extracting, and loading data into the system as well as providing hashing capabilities; a metadata manager for collecting, organizing, and cataloguing ingested data based on collected metadata; and database for storing the master global authentication ledger. The master ledger acts as a central repository that consolidates authentication objects from various Identity Providers, allowing for centralized authentication management, auditing, reporting, and analysis. It provides a comprehensive view of authentication activities across multiple systems and enables the tracking of user authentication events across different identity providers.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/30 - Authentification, c.-à-d. détermination de l’identité ou de l’habilitation des responsables de la sécurité
82.
Correlating network event anomalies using active and passive external reconnaissance to identify attack information
A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.
A system and methods for Kerberos protocol collection, interdiction and decryption for real-time analysis to aid in both operational and security functions in SSO-enabled networks, using agent processes that intercept and decrypt Kerberos traffic to identify compromised credentials and accounts in real-time without exposing sensitive information.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
84.
PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS USING ATTACK PATH DETECTION AND PREDICTION
A system and method for the privilege assurance of enterprise computer network environments using attack path detection and prediction. The system uses local session monitors to monitor logon sessions within a network, track session details, and log session and network host details. Cyber-physical graphs are produced and used to identify paths within the network based on the logged information, and to apply risk weighting to the identified paths and determine likely attack paths an attacker may use.
A system for contextual and risk-based multi-factor authentication having a multi-dimensional time series data server configured to monitor and record a network's traffic data and to serve the traffic data to other modules and a directed computation graph module configured to receive network traffic data from the multi-dimensional time series data server, determine a network traffic baseline from the network traffic data, and determine a verification score needed before granting access based at least in part by the network traffic baseline. A plurality of verification methods build up a user's verification score to required level to gain access.
A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.
A system for contextual and risk-based multi-factor authentication having a multi-dimensional time series data server configured to monitor and record a network's traffic data and to serve the traffic data to other modules and a directed computation graph module configured to receive network traffic data from the multi-dimensional time series data server, determine a network traffic baseline from the network traffic data, and determine a verification score needed before granting access based at least in part by the network traffic baseline. A plurality of verification methods build up a user's verification score to required level to gain access.
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
A system and method for cybersecurity mission planning and analysis which uses artificial intelligence systems to make red and blue team exercises more comprehensive and effective by supplementing individual expertise, reducing reliance on intuition, and eliminating gaps in knowledge. In an embodiment, a platform for cyberattack missions planning and analysis by red and blue teams is coordinated by a control center. An incident generator generates cyberattack scenarios and events using data from external databases and an internal attack knowledge manager having a knowledge graph of data about the network under attack in conjunction with one or more machine learning algorithms configured to identify potential network vulnerabilities. Red are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths. Blue teams are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths.
A system and method for large-scale internet health forecasting and internet noise analysis. The system and method feature the ability7 to scan for, ingest and process, and then use various data stores for capturing entity data, their relationships, and actions associated with them. This data forms the basis for cvber enrichment service databases which can used to provide information responsive to user submitted queries as well as to produce large-scale (e.g., Internet scale) simulation models using statistical models, generative ML models, massively multiplayer online gaming simulation systems, or full discrete event simulation engines or some combination. User submitted queries can be ran against the raw data or against simulations to provide simulation results that can be used improve cy bersecurity for an organization against actual observed behaviors or simulations.
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
G06F 16/248 - Présentation des résultats de requêtes
G06F 16/951 - IndexationTechniques d’exploration du Web
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
90.
DYNAMIC AUTHENTICATION ATTACK DETECTION AND ENFORCEMENT AT NETWORK, APPLICATION, AND HOST LEVEL
A system and method for dynamic authentication attack detection an enforcement at the network, application, and host level that enables zero trust network security principles when combined with stateful authentication object tracking, authentication object manipulation and forgery7 detection, and assessment of authentication and identity7 attack surface. The methodology involves gathering all authentication objects issued by a network, storing the authentication objects in a centralized location for use in stateful deterministic authentication object tracking, scoring the completeness of the authentication risk observations, and intervening in authentication processes when the potential risk is too great.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A system and method for the dynamic authentication revocation using privilege assurance of enterprise computer network environments using lateral movement detection and prevention. The system uses local session monitors to monitor logon sessions within a network, generating and verifying event logs and authentication records to ensure the legitimacy of authenticated user sessions and to revoke credentials when an illicit session is detected and further removing potentially compromised network components from the network, halting lateral movement in real-time.
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A system and method for scoring and enforcing authentication standards that actually enable zero trust network security principles when combined with stateful authentication object tracking, authentication object manipulation and forgery detection, and assessment of authentication and identity attack surface. The methodology involves gathering all authentication objects issued by a network, storing the authentication objects in a centralized location for use in stateful deterministic authentication object tracking, scoring the completeness of the authentication observations, assessing the quality of the authentication observations, and assigning organization-specific penalty functions.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A system and method for analyzing integrated operational technology and information technology systems with sufficient granularity to predict key elements of their composite behavior. The system and method involve creating high-fidelity models of the operational technology and information technology systems using one or more cyber-physical graphs, performing parametric analyses of the models to identify key components, scaling the parametric analyses of the models to analyze the key components at a greater level of granularity, and iteratively improving the models via ongoing search and testing against observed data from the real-world systems.
A system and method for providing time-series geospatial data and a world-scale simulation platform used to generate simulated-world environments by rendering data-dense geographical regions corresponding to heterogenous sourced data and formats for highly scalable parallel simulations, and comprised of a multi-dimensional time-series database used for enabling query support across multiple simulations via individual simulation and entity swimlanes for cyber, physical and cyber-physical entities and regions.
A system and method for the dynamic authentication revocation using privilege assurance of enterprise computer network environments using lateral movement detection and prevention. The system uses local session monitors to monitor logon sessions within a network, generating and verifying event logs and authentication records to ensure the legitimacy of authenticated user sessions and to revoke credentials when an illicit session is detected and further removing potentially compromised network components from the network, halting lateral movement in real-time.
A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
97.
Rapid predictive analysis of very large data sets using the distributed computational graph
A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis driven business decisions and analysis driven simulations of alternate candidate business actions has been devised and reduced to practice. This business operating system may be used to monitor and predictively warn of events that impact the security of business infrastructure and may also be employed to monitor client-facing services supported by both software and hardware to alert in case of reduction or failure and also predict deficiency, service reduction or failure based on current event data.
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
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. The integration of the semantic search system with the AI platform's components, such as knowledge graphs and model blending, enhances the platform's overall reasoning, decision-making, and problem-solving capabilities, empowering users with intelligent and intuitive search experiences across various domains and applications.
A system and method for trigger-based scanning of cyber-physical assets, including a distributed operating system, parameter evaluation engine, at least one cyber-physical asset, at least one crypt-ledger, a network, and a scanner that detects trigger conditions and events and performs scans of cyber-physical assets based on the trigger and any relevant stored scan rules before storing scan results as time-series data.
G06F 16/909 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
G06F 16/951 - IndexationTechniques d’exploration du Web
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
100.
Rapid predictive analysis of very large data sets using the distributed computational graph
A distributed computing cluster includes first, second, and third pluralities of computer systems. A first computer of the first plurality applies a first transformation pipeline to a stream of data to generate a output data, and transmits the output data to a computer of the second plurality, which is distinct from the first plurality. A second computer of the second plurality applies a second transformation pipeline. The second transformation pipeline includes a first storage transformation. A third computer of the third plurality stores a representation of a distributed computational graph (DCG), which includes a representation of a portion of the second transformation pipeline. The third computer processes the representation of the DCG, and determines whether the second transformation pipeline includes a storage transformation. The third computer monitors the second transformation pipeline, and in response, causes a fourth computer of the third plurality to apply a second storage transformation.
