In accordance with an embodiment, described herein are systems and methods for auto-completion of ICS flow using artificial intelligence/machine learning. Next actions prediction is a service that assists users in modeling the flows quickly by predicting and suggesting the next set of actions a user might be thinking of adding. The service also assists the user to follow some of the best practices while creating an integration flow.

A computer-enabled system and method that collects and processes various data from mobile devices possessed by on-demand (“ride-hailing”) transportation drivers, along with other possible external data sources, and subsequently recognizes, classifies, stores, and delivers ride-hailing-related activity information of individual or multiple drivers to various consumers of the system, while also iteratively improving the system and method's ride-hailing activity classification accuracy over time.

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

A system, method and computer program product for open innovation including an asset valuation device receiving asset information about tangible or non-tangible assets, and generating a valuation signal, based on the asset information; a self-executing code device receiving the valuation signal, and generating a self-executing code signal, based on the valuation signal; an air router device having both a low band radio channel, and an internet router channel for redundant internet communications, and a malicious code removal device for scrubbing malicious code from data received, receiving the valuation signal, and generating a node voting request signal, based on the valuation signal; and a mesh network having a plurality of node devices receiving the node voting request signal, and generating vote confirmation signals, based on the node voting request signal. Computing devices are connected to the node devices to perform problem solving, smart contract processing, and/or cryptocurrency mining.

To enable provision of appropriate information for a user query even in a case there are multiple information provision modules which are different in answer generation processing. A query sending unit 212 sends a user query to each one of a plurality of information provision module units 220 that are different in the answer generation processing and that each generate an answer candidate for the user query. An output control unit 214 performs control such that the answer candidate acquired from each one of the plurality of information provision module units 220 is displayed on a display unit 300 on a per-agent basis with information on an agent associated with that information provision module unit 220.

Technologies for a distributed Internet of Things (IoT) system are disclosed. Several IoT devices may form a peer-to-peer network without requiring a central server. Information may be stored in a distributed manner in the distributed IoT system, allowing for storing information without transmitting it to a remote server, which may be costly and introduce security or privacy risks. Each IoT device of the distributed IoT system includes a machine learning algorithm that is capable of uncovering patterns in the input of the distributed IoT system, such as a pattern of user inputs in certain situations, and the distributed IoT system may adaptively anticipate a user's intentions.

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

The disclosed technologies can compute inferred taste preferences relating to food products and food providers using objectively verifiable data, including order data, and use these inferred taste preferences to create new functionality in an information search and retrieval system and/or a product ordering system.

The disclosed technologies can compute inferred taste preferences relating to food products and food providers using objectively verifiable data, including order data, and use these inferred taste preferences to create new functionality in an information search and retrieval system and/or a product ordering system.

A sequential data analysis apparatus extracts a pattern of two or more sets of items based on an appearance frequency of each of different sets of items in first sequential data, selects a pattern of two or more sets of items based on an appearance frequency of a sub-pattern formed of a portion of the extracted pattern, creates a related pattern including the same last set of items as and the other sets of items different from the selected characteristic pattern, calculates an evaluation value of the related pattern, creates a prediction model by organizing data of the characteristic pattern and the related pattern, and applies second sequential data to the prediction model to determine a result which the second sequential data is likely to lead to.