Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and using a predictive model that is configured to output an indication of whether at least one failure type from the group of possible failure types is likely to occur at an asset within the given period of time in the future.

Abstract: A platform discovery and deployment automation system is provided. The system discovers and/or monitors the current versions of all applications and services running in an environment. The system also deploys updates or new versions of the applications and services. The monitoring and the deployment are both done through a single interface. Methods and machine readable media are also provided.

Abstract: A platform discovery and deployment automation system is provided. The system discovers and/or monitors the current versions of all applications and services running in an environment. The system also deploys updates or new versions of the applications and services. The monitoring and the deployment are both done through a single interface. Methods and machine readable media are also provided.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.

Abstract: In an environment where a plurality of data platforms are communicatively coupled via a communication network, one platform in the environment may govern whether other platforms in the environment are permitted to participate in the sharing of asset-related information. For example, a first platform may receive, from a second platform (e.g., a master or seed platform) that is communicatively coupled to the first platform via the communication network, criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network. In turn, the first platform may assess the reliability of the platform's stored asset-related data and then apply the criteria to the assessed reliability to determine whether the first platform is permitted to share asset-related data with the one or more other platforms and then operate in accordance with that determination.

Abstract: In an environment where a plurality of data platforms are communicatively coupled via a communication network, two of more of the platforms may be configured to share asset-related information. For example, a first platform may receive asset-related data and determine that a portion of the received data should be pushed to another platform that is communicatively coupled to the first platform via the communication network. Based on that determination, the first platform may prepare a portion of the received data to be transmitted to another platform and then push the portion of the data to another platform over a network connection.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: An on-demand economy operating system is provided. The system matches supply and demand in products and services based on provider-shared supply data and operational system-determined demand data. Quotes, purchase agreements, bills and payments are seamlessly generated and published between participants in the on-demand economy, such as via a blockchain. The system provides continuous analysis and utilizes resulting success metrics to further improve efficiencies of the system. Methods and machine readable media are also provided.

Abstract: An automated interconnection system for creating and utilizing smart policies on a blockchain is provided. An agent generates risk units that are associated with an asset and publishes the risk units to a blockchain. Insurance carriers create quotes covering the published risk units. An agreement is generated between a coverage seller and a policyholder as to a smart policy that covers the risk units. The coverage seller subdivides the risk units into secondary risk units and publishes the secondary risk units to the blockchain. An agreement is generated by the owner of the secondary risk units to a smart contract that covers the secondary risk units. Bids are generated by capital providers for coverage based on properties of the smart policy. Methods and computer readable media are also provided.

Abstract: An automated interconnection system for executing a smart contract in a blockchain is provided. A payout notification is generated either a notification to the smart contract of a behavior event or the smart contract periodically scanning for a payout request. Behavior data is retrieved from the blockchain and a payout validity is determined based on logic of the smart contract. If a payout is valid, a payout amount is calculated and a payout is made from a payout fund. If a payout is not valid, payout funds are returned to the creator of the smart contract if the smart contract does not allow for retrying the payout process, or the payout funds are held pending retrying the payout process. Methods and computer readable media are also provided.

Abstract: An automated interconnection system for recording and transferring ownership of insurance-related assets is provided. Risk units associated with an asset and perils to the asset are created and stored in a shared database. The risk units are tied to smart policies that pertain to risk coverage of the asset. The risk units are priced and incorporated into smart contracts created between participants of the system. Methods and computer readable media are also provided.

Abstract: Disclosed herein are systems, devices, and methods for governing data platforms that are communicatively coupled via a network. According to an example, a first platform may receive, from a second platform (e.g., a master or seed platform), criteria that governs whether the first platform is permitted to share asset-related data with one or more other platforms in the network and assess the reliability of the platform's stored asset-related data. In turn, the first platform may apply the criteria to the assessed reliability to determine whether the first platform is permitted to share asset-related data with the one or more other platforms and then operate in accordance with that determination.

Abstract: Disclosed herein are systems, devices, and methods for sharing asset-related information between data platforms that are communicatively coupled via a network. According to an example, a first platform may receive asset-related data and determine that a portion of the received data should be pushed to another platform. Based on that determination, the first platform may prepare a portion of the received data to be transmitted to another platform and then push the portion of the data to another platform over a network connection.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.

Abstract: Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.

Abstract: Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.