Abstract: Flexible electronic devices may be provided. A flexible electronic device may include a flexible display, a flexible housing and one or more flexible internal components configured to allow the flexible electronic device to be deformed. Flexible displays may include flexible display layers, flexible touch-sensitive layers, and flexible display cover layers. The flexible housing may be a multi-stable flexible housing having one or more stable positions. The flexible housing may include a configurable support structure that, when engaged, provides a rigid support structure for the flexible housing. The flexible internal components may include flexible batteries, flexible printed circuits or other flexible components. A flexible battery may include flexible and rigid portions or may include a lubricious separator layer that provides flexibility for the flexible battery.

Abstract: Disclosed are hierarchical buoyant materials comprising porous components having multiple length scales that provide reduced density, higher strength, and improved buoyancy. The hierarchical buoyant materials include components having at least two length scales including a first porous material having elements characterized by a first linear length scale and a second porous material having elements characterized by a second linear length scale. The first porous material includes hollow microspheres, and the second porous material provides low effective density via geometrical arrangements. The second linear length scale is substantially different than the first linear length scale in terms of size, e.g., diameter, length, or distance.

Abstract: A system and method is described for labelling data for trigger identification. The system and method comprising receiving data, transforming the data, extracting content from the data, processing data content through a classification machine learning model to receive a label, and trigger a secondary system based on the label. The system and method may further include maintaining a database of labeled data.

Abstract: There is disclosed a system and method for forecasting the positions of marine vessels. In an aspect, the present system is adapted to execute a forecasting algorithm to forecast the positions of one or a great many marine vessel(s) based on one or more position reporting systems including coastal and satellite AIS (S-AIS) signals or LRIT received from the vessel. The forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible positions based on previous paths taken by vessels from that location, and heading in substantially the same direction. Thus, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. Other types of information may also be taken into account, such as ship-specific data, nearby weather, ocean currents, the time of year, and other spatial variables specific to that bin.

Abstract: A system for reducing the time to train a machine learning program for predicting a subsequent event. The system includes a computer configured to implement instructions to receive training data and time data indicative of training events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, training event characteristics, or both. The system implements instructions associating training event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and training event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed training data.

Abstract: A system includes a computer to implement a front-end input condensation program and a back-end machine learning program. Steps of the front-end program include receive input data and time data indicative of previous events associated with users; determine interface channels associated with modes of interface with the users and/or previous event characteristics; associate previous event data with time windows; generate user window values for the combinations of users and time windows indicating the interface channels and previous event characteristics of data within the respective time windows; and form condensed input data without raw input data having a low association with respect to preceding the subsequent event. Steps of the back-end program include receive the condensed input data and use the condensed data to generate an inference related to the subsequent event such that a time required by the machine learning algorithm to generate the inference is reduced.

Abstract: A system for reducing input data size for use in an artificial intelligence (AI) engine for predicting a subsequent event. The system includes a computer configured to implement instructions to receive input data and time data indicative of previous events associated with users. The instructions configure the system to determine interface channels associated with modes of interface with the users, previous event characteristics, or both. The system implements instructions associating previous event data with time windows. The instructions configure the system to generate user window values for the combinations of users and time windows. The user window values indicate the interface channels and previous event characteristics of data within the respective time windows. Implementing the instructions configures the system to form a first portion of the raw input data having an association value below a threshold with respect to preceding the subsequent event and generate condensed input data.

Abstract: A system and method is described for training a machine learning model to label data for trigger identification. The system and method comprising receiving data from a database, extracting content from the data, transforming the data, clustering the data, labeling the data, creating a training dataset and a test data set, and training a classification machine learning model to label data accordingly to identify a trigger.

Abstract: A system and method is described for labelling data for trigger identification. The system and method comprising receiving data, transforming the data, extracting content from the data, processing data content through a classification machine learning model to receive a label, and trigger a secondary system based on the label. The system and method may further include maintaining a database of labeled data.

Abstract: A system for employing artificial intelligence to initiate a concurrent reaction according to predicted behavior is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed, causes the at least one processor to receive labeled interaction data for a group of users, receive user information for the group of users, correlate the labeled interaction data with the user information, apply a first algorithm to the correlated data to segment the users, based on the user’s segment apply a second algorithm to make a prediction about the user’s future behavior, and based on the predicted behavior trigger a concurrent communication to the user. Additionally, the system may update a user dashboard.

Abstract: There is disclosed a system and method for forecasting the positions of marine vessels. In an aspect, the present system is adapted to execute a forecasting algorithm to forecast the positions of one or a great many marine vessel(s) based on one or more position reporting systems including coastal and satellite AIS (S-AIS) signals or LRIT received from the vessel. The forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible positions based on previous paths taken by vessels from that location, and heading in substantially the same direction. Thus, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. Other types of information may also be taken into account, such as ship-specific data, nearby weather, ocean currents, the time of year, and other spatial variables specific to that bin.

Abstract: An air handler, comprising: a heating and cooling coil to exchange heat with input air to generate conditioned air; an air diffuser to diffuse the conditioned air to generate diffused air; and an air blower to draw the input air into the air handler via an air inlet, pass the input air over the heating and cooling coil, pass the conditioned air through the diffuser, and expel the diffused air through an air outlet as output air, wherein the air diffuser contains one or more baffles to create two or more air passages that operate to control the expansion and reduction of kinetic energy of the conditioned air to generate the diffused air, a first direction represents a direction of a shortest line between the air blower and the outlet, the air diffuser passes air in a second direction displaced from the first direction by a deflection angle.

Abstract: Computer-implemented systems and methods are provided for managing user account data and award redemptions. In one embodiment, a system is provided that includes a web server configured to operate an online platform that enables a user to activate an award enhancement option for an award. The system also includes a data integration server communicatively coupled to the server. The data integration server may be configured to receive, from the web server, first identity data representing an identity of the user and activation data identifying the award enhancement option activated by the user, and store, in a memory device, the activation data with account data of the user.

Abstract: An air handler, comprising: a heating and cooling coil to exchange heat with input air to generate conditioned air; an air diffuser to diffuse the conditioned air to generate diffused air; and an air blower to draw the input air into the air handler via an air inlet, pass the input air over the heating and cooling coil, pass the conditioned air through the diffuser, and expel the diffused air through an air outlet as output air, wherein the air diffuser contains one or more baffles to create two or more air passages that operate to control the expansion and reduction of kinetic energy of the conditioned air to generate the diffused air, a first direction represents a direction of a shortest line between the air blower and the outlet, the air diffuser passes air in a second direction displaced from the first direction by a deflection angle.

Abstract: A computer-implemented method is provided for cutscene management. The method is implemented with at least one processor and includes receiving, from a game server, game state data representing a state of an electronically displayed game, the game being hosted by a plurality of gameplay client devices that are configured to transmit the game state data to the game server. The method also includes determining an event associated with the game based on the game state data, determining at least one cutscene based on the event, the at least one cutscene including a visual representation of the event, and inserting the at least one cutscene into a cutscene queue, the cutscene queue including at least one cutscene associated with the game and a sequence for displaying the at least one cutscene from the cutscene queue.

Abstract: Computer-implemented systems and methods are provided for tracking user activity and managing quests. By way of example, a method may include receiving, from a website server, first identity data representing an identity of a user and data identifying a quest activated by the user, the quest comprising one or more activities to be performed at a physical location, and receiving, from at least one reporting device, second identity data representing the identity of the user at the physical location, and activity tracking data in response to the user performing an activity associated with the quest at the physical location. The method may also include identifying the user and the quest activated by the user based on the first identity data and data identifying the quest, further identifying the user and activity data related to the quest based on the second identity data and activity tracking data, and monitoring a progress of the user.

Abstract: The present invention relates to a shipping container for mitigating external fire risks. The shipping container is configured for shipping lithium ion batteries with a cargo cavity proximate and interior to a second inner flame-retardant corrugated layer. The second inner flame-retardant corrugated layer is proximate and interior to an inner thermal paste panel. The inner thermal paste panel is proximate and interior to a dead air space panel. The dead air space panel is proximate and interior to a first inner flame-retardant corrugated layer. The first inner flame-retardant corrugated layer is proximate and interior to an outer thermal paste panel. The outer thermal paste panel is proximate and interior to a flame-retardant corrugated carton. The thermal paste panels preferably comprise aluminum flashing containing a mesh matrix for holding flowable thermal paste. The shipping container is lidded by a fitted cap above an air cap above a vacuum plenum cap.

Abstract: A vessel tracking system and method are provided. The system comprises a processor, and a memory comprising a sequence of instructions which when executed by the processor configure the processor to perform the method. The method comprises obtaining characteristics data of a vessel, obtaining destination identifiers for historical destinations of vessels having similar characteristics data, determining a percentage of likelihood for each destination identifier, and reporting the destination identifiers and corresponding percentages of likelihood.

Abstract: Computer-implemented systems and methods for secondary game creation and management include a plurality of gaming machines, and a scheduler configured to initiate a secondary game in response to a request from one or more players or a schedule defined by an administrator, the secondary game including one or more games executed on the plurality of gaming machines. A controller is also provided that is configured to validate each player to participate in the secondary game on respective ones of the plurality of gaming machines and enable secondary gameplay by the validated players on the plurality of gaming machines. Further, a data analyzer is provided that is configured to receive primary game player data and secondary game player data from the plurality of gaming machines and process the primary game player data and secondary game player data to determine at least one of a progression, position, or score of a player of a secondary game.

Abstract: There is disclosed a system and method for forecasting the positions of marine vessels. In an aspect, the present system is adapted to execute a forecasting algorithm to forecast the positions of one or a great many marine vessel(s) based on one or more position reporting systems including coastal and satellite AIS (S-AIS) signals or LRIT received from the vessel. The forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible positions based on previous paths taken by vessels from that location, and heading in substantially the same direction. Thus, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. Other types of information may also be taken into account, such as ship-specific data, nearby weather, ocean currents, the time of year, and other spatial variables specific to that bin.