Abstract: Predictive analytics techniques are provided for produce leading indicators of economic activity based on observed pedestrian attributes—e.g., appearance and behavior—and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Abstract: A Hypothesis Generation and Testing System (HGTS) includes a framework for enabling artificial intelligence (AI) systems to autonomously formulate, test, refine, and store hypotheses using structured experimental workflows. HGTS may integrate with, but operates independently from, traditional databases, model repositories, context-aware AI databases and the like to provide a persistent, traceable, and interpretable record of hypothesis lifecycles. The system incorporates a probationary hypothesis database for unverified ideas, a validation engine for controlled experimentation, a confidence scoring and lifecycle management agentic subsystem for hypothesis evaluation, and recursive learning agents that iteratively refine models and experimental methods. Modular agents autonomously propose, test, and document hypotheses using statistical, symbolic, and deep learning techniques. The system ranks outcomes and retains full contextual metadata, enabling reproducible discovery.

Abstract: A context-aware AI database (CAAD) integrated with modular, hierarchically organized and collaborative, goal-driven context agents that perform specific cognitive functions such as sentiment analysis, priority ranking, and feature extraction through standardized APIs. The system generates and manages multiple types of metadata: data-associated metadata (source attributions, embeddings, statistical features, timestamps) and model-associated metadata (model provenance, analysis conditions and parameters, performance scores, methodological details), using agentic AI approaches to improve data accuracy, experimental transparency, traceability, reproducibility, contextual explainability in both training and inference operations. The architecture enables automated context refinement and includes innovative “pointers” and “deep pointers” that link analytical insights directly to source data and sub-features, enabling interpretable and auditable iterative reasoning.

Abstract: Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

Abstract: Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

Abstract: Generative AI systems and methods are developed to provide recommendations regarding the control, optimization, and troubleshooting of industrial equipment and manufacturing systems as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users and/or AI agents (i.e., a form of “agentic AI”) may then subscribe to such information for the use in connection with their own manufacturing systems.

Abstract: Generative AI systems and methods are provided to produce leading indicators of economic activity based on, for example, agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Generative AI systems and methods are developed to provide recommendations regarding the prevention, detection, mitigation, and/or remediation of cybersecurity threats as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users and/or AI agents (i.e., a form of “agentic AI”) may then subscribe to the data for the use in cybersecurity analytics, protection, mitigation, containment, remediation, and/or counterattacks of cybersecurity threats.

Abstract: Generative AI systems and methods are provided to provide recommendations as to whether a particular security associated with a corporate entity and/or its competitors should be purchased, sold, or held, as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Generative AI systems and methods are provided to provide recommendations as to whether a particular security associated with a corporate entity and/or its competitors should be purchased, sold, or held, as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Generative AI systems and methods are developed to provide recommendations regarding product sales, pricing, inventory, orders, manufacturing, distribution, shipping, packaging or other product analytics as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users and/or AI agents (i.e., a form “agentic AI”) may then subscribe to the date for the use in economic forecasting.

Abstract: Generative AI systems and methods are provided to produce leading indicators of economic activity based on, for example, agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Predictive analytics techniques are provided to produce leading indicators of economic activity based on agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage process excursions. An example apparatus includes a digital twin comparer to determine when a product fails to satisfy a tolerance metric of a digital twin, and a fingerprint manager to generate a fingerprint corresponding to a sensor pattern. The example apparatus also includes a node interfacer to determine a number of workstations of a process control system that exhibit the fingerprint, and an excursion statistics calculator to invoke a corrective action for respective ones of the number of workstations, the corrective action based on a threshold count of the number of workstations that exhibit the fingerprint.

Abstract: Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Abstract: Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Abstract: Predictive analytics techniques are used to produce leading indicators of economic activity based on factors determined from a range of available data sources, such as public and/or private transportation data. A fee-based subscription system may be provided for the sharing of leading indicators to users. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode.

Abstract: Orchestration and/or management of services on deployed computing, electronic, appliance and like devices are described. A service orchestration and/or management system (“SOMS”) may be configured to provide for orchestration and/or management of services to one or more deployed devices. The SOMS may obtain information from deployed devices, such as status, capabilities and service metadata. The SOMS may then encapsulate and store the obtained information for later use. The SOMS may also store service information, facilitate creation of services based on stored service definition meta-data, modeling or simulating a service prior to deployment, and deployment of a service to remotely deployed devices followed by activation to begin operation of the service. Other embodiments may be described and claimed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage process excursions. An example apparatus includes a digital twin comparer to determine when a product fails to satisfy a tolerance metric of a digital twin, and a fingerprint manager to generate a fingerprint corresponding to a sensor pattern. The example apparatus also includes a node interfacer to determine a number of workstations of a process control system that exhibit the fingerprint, and an excursion statistics calculator to invoke a corrective action for respective ones of the number of workstations, the corrective action based on a threshold count of the number of workstations that exhibit the fingerprint.

Abstract: Orchestration and/or management of services on deployed computing, electronic, appliance and like devices are described. A service orchestration and/or management system (“SOMS”) may be configured to provide for orchestration and/or management of services to one or more deployed devices. The SOMS may obtain information from deployed devices, such as status, capabilities and service metadata. The SOMS may then encapsulate and store the obtained information for later use. The SOMS may also store service information, facilitate creation of services based on stored service definition meta-data, modeling or simulating a service prior to deployment, and deployment of a service to remotely deployed devices followed by activation to begin operation of the service. Other embodiments may be described and claimed.