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: A system and method for hierarchical, clustered reinforcement learning is disclosed. A plurality of subject objects may be obtained, and a plurality of clusters of the subject objects may be determined. Clustered reinforcement learning may be performed on each cluster, including training a respective cluster agent for the each cluster. A first cluster of the plurality of clusters may be selected for revision based on selection criteria. After selection of the selected first cluster, individual reinforcement learning may be performed on each individual subject object included in the selected first cluster, including training a respective individual agent for the each individual subject object. An action may be controlled based on a result of the hierarchical, clustered reinforcement learning.

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: A device may receive user data identifying vitals of users when receiving treatments and dosages of the treatments, and may process the user data, with a divergence model, to determine divergence data identifying divergences between the users. The device may process the divergence data, with a clustering model, to group the users into clusters of users, and may train a first neural network model, with the user data, to generate a trained first neural network model. The device may train a second neural network model, with the user data, to generate a trained second neural network model, and may generate a treatment model based on the trained first and second neural network models. The device may process new user data identifying a new user, with the treatment model, to determine a recommended treatment for the new user, and may perform one or more actions based on the recommended treatment.

Abstract: A system may receive, from one or more data sources, one or more de-identified data sets that include de-identified personal data. The system may receive a request for a feature set of the one or more de-identified data sets, wherein the feature set includes a set of quasi-identifiers included in the de-identified personal data. The system may calculate a re-identification risk score for the set of quasi-identifiers. The system may selectively output, based on the re-identification risk score, one of: actual data, from the one or more de-identified data sets, of the feature set if the re-identification risk score satisfies a condition, or synthetic data, generated by the device from the one or more de-identified data sets, for the feature set, or a combination of the synthetic data and the actual data for the feature set, if the re-identification risk score does not satisfy the condition.

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 system and method for hierarchical, clustered reinforcement learning is disclosed. A plurality of subject objects may be obtained, and a plurality of clusters of the subject objects may be determined. Clustered reinforcement learning may be performed on each cluster, including training a respective cluster agent for the each cluster. A first cluster of the plurality of clusters may be selected for revision based on selection criteria. After selection of the selected first cluster, individual reinforcement learning may be performed on each individual subject object included in the selected first cluster, including training a respective individual agent for the each individual subject object. An action may be controlled based on a result of the hierarchical, clustered reinforcement learning.

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.