Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing intelligent extraction of information from a document. A computing module receives input data representing an image of a document. The module also receives context data for the document. The context data includes parameters that are descriptive of the document in the image. The module processes the input data and the context data to determine a complexity value that characterizes a level of complexity in identifying information to be extracted from the document. The system selects a machine-learning model to use in extracting information from the document. The model is selected based on the complexity value and from multiple candidate models. The system extracts information from the document using the selected model, including converting a portion of the image of the document that shows typed or handwritten text into a digitized text string.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated failure mode detection for rotating machinery based on vibration analysis. To illustrate, a computing device may receive vibration data from one or more sensors configured to measure vibrations of a rotating machine, such as an engine. The computing device may generate a frequency spectrum based on the vibration data (or receive the frequency spectrum) and compare the frequency spectrum to one or more predetermined frequency models to determine one or more similarity metrics. The one or more predetermined frequency models may each be associated with a respective failure mode of the rotating machine. The computing device may identify a failure mode associated with a predetermined frequency model that corresponds to a similarity metric that satisfies a threshold, and the computing device may output an indication of the identified failure mode.

Abstract: A first device receives, via a blockchain node of the first device, data and constraints associated with a smart contract, where the smart contract is deployed via a blockchain node associated with a second device, the first and second devices are provided in a blockchain network, through their blockchain nodes. The first device provides, via the blockchain node, the data and constraints to an analytics engine of the first device, and performs, via the analytics engine, a data analytics technique on the data and constraints to generate an offer with optimized parameters. The first device provides the offer with the optimized parameters to the smart contract associated with the second device, and receives, via the blockchain node, a confirmation of a transaction associated with the smart contract. The first device causes the offer with the optimized parameters to be implemented based on receiving the confirmation of the transaction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing intelligent extraction of information from a document. A computing module receives input data representing an image of a document. The module also receives context data for the document. The context data comprises parameters that are descriptive of the document in the image. The module processes the input data and the context data to determine a complexity value that characterizes a level of complexity in identifying information to be extracted from the document. The system selects a machine-learning model to use in extracting information from the document. The model is selected based on the complexity value and from multiple candidate models. The system extracts information from the document using the selected model, comprising converting a portion of the image of the document that shows typed or handwritten text into a digitized text string.

Abstract: A first device receives, via a blockchain node of the first device, data and constraints associated with a smart contract, where the smart contract is deployed via a blockchain node associated with a second device, the first and second devices are provided in a blockchain network, through their blockchain nodes. The first device provides, via the blockchain node, the data and constraints to an analytics engine of the first device, and performs, via the analytics engine, a data analytics technique on the data and constraints to generate an offer with optimized parameters. The first device provides the offer with the optimized parameters to the smart contract associated with the second device, and receives, via the blockchain node, a confirmation of a transaction associated with the smart contract. The first device causes the offer with the optimized parameters to be implemented based on receiving the confirmation of the transaction.