Abstract: The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.

Abstract: The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.

Abstract: A multivariate sensing device is to be coupled to a containment asset designed to contain a pressurized fluid, the multivariate sensing device comprising: sensors to provide sensor data, circuitry to implement a local ML engine operable in accordance with a first ML model to detect leaks of the pressurized fluid from the containment asset based on the sensor data; a wireless interface to couple the multivariate sensing device to a cloud ML engine operable in accordance with a second ML model to detect leaks of the pressurize fluid with greater accuracy, the multivariate sensing device to transmit a first message via the wireless interface to the cloud-based management service in response to a detected leak, the cloud ML engine to determine if the sensor data indicates a leak and, if not, then the cloud-based management service is to transmit a message to the multivariate sensing device for additional training.

Abstract: The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.

Abstract: A growing system is described where plants are grown in containers and the containers are stored in stacks. Above the stacks runs a grid network of tracks on which load handling devices run. The load handling devices take containers from the stacks and deposit then at alternative locations in the stacks or deposit them at stations where goods may be picked out. The containers may be provided with one or more of the following services: power, power control, heating, lighting, cooling, sensing means, data logging means, growing means, water and nutrients.

Abstract: A growing system is described where plants are grown in containers and the containers are stored in stacks. Above the stacks runs a grid network of tracks on which load handling devices run. The load handling devices take containers from the stacks and deposit them at alternative locations in the stacks or deposit them at stations where goods may be picked out. The containers may be provided with one or more of the following services: power, power control, heating, lighting, cooling, sensing means, data logging means, growing means, water and nutrients.

Abstract: The invention is generally directed to systems and methods of monitoring or predicting a service event for an industrial asset using an artificial intelligence of things (AIoT) system including an AIoT device, AIoT cloud, and a self-learning AI classification and analytics engine. The device may include one or more sensors and an inference engine for reducing power consumption and detecting anomalies at the edge and sending data associated with anomalies to a signal processor for classification and AI-driven automatic configuration. Classification may be based on narrow-band analysis and/or machine learning models. If an anomaly is detected power may be provided to a communication module to send sensor data to the signal processor for classification and/or further processing. Classifications or determinations made by the signal processor or detected through a work-order system may be used to automatically retrain the inference model on the edge, so that the system is self-learning.

Abstract: A growing system is described where plants are grown in containers and the containers are stored in stacks. Above the stacks runs a grid network of tracks on which load handling devices run. The load handling devices take containers from the stacks and deposit then at alternative locations in the stacks or deposit them at stations where goods may be picked out. The containers may be provided with one or more of the following services: power, power control, heating, lighting, cooling, sensing means, data logging means, growing means, water and nutrients.

Abstract: Systems and methods for controlling access to content are disclosed. Content can be consumed by a device. Access to the content is controlled by duration. A device is provided with a token that allows the user to consume content via a subscription basis.

Abstract: Systems and methods are provided for distributing revenue derived from content consumption to publishers of the content and to a content aggregator.