A configuration method for configuring an electronic device is described. The configuration method comprises: receiving at least one status parameter that is associated with at least one of an input parameter of the electronic device, an output parameter of the electronic device, a target output parameter of the electronic device, and a state of the electronic device; processing at least one status parameter by a first artificial intelligence module within a predefined time interval, thereby generating at least one first control parameter within the predefined time interval, wherein the at least one first control parameter is a rough estimate of an optimal control parameter for the electronic device; and processing at least one status parameter by a second artificial intelligence module, thereby generating at least one second control parameter wherein the at least one second control parameter is a more precise estimate of the optimal control parameter for the electronic device compared to the at least one first control parameter. Moreover, a configuration system is described.

A smart space may be provided by a hub and an artificial intelligence server in communication with the hub. The hub may receive data from at least one smart object in the smart space. The artificial intelligence server may generate clusters of the data received from each of the at least one smart objects. The server may perform processing comprising using a cluster to detect an anomaly in the smart object, identify the smart object, classify the smart object, determine a user behavior, determine a user mood, determine an energy consumption pattern, or create an automated action, or a combination thereof.

A smart space may be provided by a hub and an artificial intelligence server in communication with the hub. The hub may receive data from at least one smart object in the smart space. The artificial intelligence server may generate clusters of the data received from each of the at least one smart objects. The server may perform processing comprising using a cluster to detect an anomaly in the smart object, identify the smart object, classify the smart object, determine a user behavior, determine a user mood, determine an energy consumption pattern, or create an automated action, or a combination thereof.

Methods and systems for extracting and processing data using optical character recognition in real-time environments. For example, the methods and systems provide novel techniques during extracting data using OCR and for a mechanism to process that data. These methods and systems are particularly relevant in real-time environments as the methods and system limit the need for manual review.

A first context update indicative of a present state associated with an application is received. A first contextual situation, among a plurality of contextual situations associated with a plurality of conditions, that corresponds to the first context update is identified. Identifying the first contextual situation includes implementing a decision tree to minimize a number of the plurality of conditions to be evaluated, identifying the first contextual situation using the decision tree, and determining one or more actions to be performed corresponding with the first contextual situation.

Privacy-preserving federated learning apparatuses, systems, computer program products, and methods are provided that generate an updated global model based on a set of client models while maintaining privacy regarding the data values embodying each client model and the updated global model. In this regard, masked client models are utilized, which cryptographically obfuscate data values embodying the client model while still enabling combination, or “aggregation,” of the masked client models to generate a masked updated global model. The masked updated global model similarly includes obfuscated data values embodying the updated global model, but may be unmasked to reveal the true values of the updated global model for use. Some embodiments utilize specific steps for communication between environments, systems, devices, and/or the like, to ensure the masked models can only be unmasked by intended entities.

Disclosed are various embodiments for dynamically determining media consumption of a user. A user may perform at least one of a plurality of consumption indication events for a media item. The consumption indication events may include submitting a rating of the media item, submitting a review of the media item, indicating a present consumption of the media item, indicating a past consumption of the media item, etc. It may be determined that the user has consumed the media item in response to determining that the user has performed at least one of the consumption indication events for the media item.

Provided are an automata processing method and apparatus capable of transforming a regular expression pattern into a specific type of nondeterministic finite automata (NFA), selectively applying a matching algorithm to the nondeterministic finite automata according to whether to include an extended grammar to minimize the use of temporal and spatial resources, and preventing regular expression denial of service (ReDoS).

Systems, methods, and computer program methods for modifying a configuration of a document management system are described. In some implementation document data are received as machine learning inputs, where the document data represent one or more documents. Then, a pattern is recognized in the one or more documents using machine learning. Based on the recognized pattern, a configuration of a document management system is modified.

Remote health services may be provided, for example, in real time, based on values of health metrics monitored remotely, and in some embodiments, continuously, for a recipient. The system may include a mobile health device configured to continually monitor or intermittently detect values of one or more health metrics. A mobile health device may be locally coupled to a recipient, and may include a health sensor that detects values of one or more health metrics of the recipient. Servers may be remotely coupled to the mobile health device and provide one or more health services based at least in part on health metric values detected by the health device. Providing the services may include determining health communication characteristics, and sending health communications from one or more servers to the mobile health device according to determined health communication characteristics.