A destination recommending apparatus includes: a navigation device configured to collect driving pattern information of a user; and a controller configured to calculate a visit probability of a destination at a current location or the visit probability of the destination at a current time, based on the driving pattern information. The controller is configured to predict the destination based on the visit probability.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises, at each of one or more time steps: generating a respective action score for each action in a set of possible actions, wherein the set of possible actions comprises: (i) a plurality of atomistic actions, and (ii) one or more optimization actions, wherein each optimization action is associated with a respective objective function that measures performance of the agent on a corresponding auxiliary task; selecting an action from the set of possible actions in accordance with the action scores, wherein the selected action is an optimization action; in response to selecting the optimization action, performing a numerical optimization to identify a sequence of one or more atomistic actions that are predicted to optimize the objective function.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Systems and methods for creating indicators to quantify and index financial market liquidity risk that is market-wide among a broad set of securities or asset classes or portfolio specific relative to an individual investor's portfolio holdings. A liquidity risk index can be created as a counterpart to any well-known market index, such as the Dow Jones Industrial Average® or the S&P 500® index. The present disclosure relates to risk management in financial markets, and in particular to systems and methods for quantifying and indexing liquidity risk such that these indices can serve as underlying assets for futures, options, or other financial instruments that investors would use to hedge against the liquidity risk.

A system for preconditioning a power source of an electric aircraft is presented. The apparatus includes a power source of an electric aircraft, a computing device, and a user device. The computing device is configured to receive a flight plan, determine a predicted power usage model as a function of the flight plan, and initiate a power source modification on the electric aircraft as a function of the predicted power usage model. The user device is configured to display a flight performance infographic as a function of the predicted power usage model.

A method may include obtaining power production and fuel consumption data of a first piece of rig equipment through a flow meter, where the rig equipment includes a plurality of pieces of equipment. The method further includes feeding the power production and fuel consumption data of the first piece of rig equipment into a real-time monitoring system of the rig via the flow meter. The method further includes determining an energy efficiency, based on real-time performance, of the first piece of rig equipment using a consumption efficiency model. The method further includes comparing the energy efficiency of the first piece of rig equipment against continuously updated historical data of the first piece of rig equipment by a real-time database monitoring system. The method further includes identifying deficiencies of the first piece of rig equipment in real-time and determining maintenance or replacement of the first piece of rig equipment.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.