Intergraph Corporation

Javier Buzzalino

Julio P Roque

Peng Tang

Gregory Louis Bondy

Srinivas Kodali

Generating wireless coverage maps includes receiving, for each of a plurality of mobile devices communicating wirelessly with a server system, mobile device location information and mobile device communication service information; generating a dead zone volume map based on the mobile device location information and the mobile device communication service information, the dead zone volume map defining a dead zone volume for a wireless communication service; and generating a dead zone geofence boundary volume map based on the dead zone volume map, the dead zone geofence boundary volume map defining a geofence boundary relative to the dead zone volume and further defining a buffer zone between at least a portion of the geofence boundary and at least a portion of the dead zone volume, the buffer zone defining a zone in which the wireless communication service is available for at least one of the mobile devices.

Generating wireless coverage maps

Each of a plurality of mobile devices communicates wirelessly with a server system via a wireless communication service, mobile device location information and mobile device communication service information. Weather data is correlated with the mobile device location information and the mobile device communication service information to characterize the effect of weather on the wireless communication service. A dead zone volume map is generated to predict future weather-related dead zone volume for the wireless communication service.

Predicting future wireless coverage based on weather

Synchronization and resynchronization includes monitoring location of a mobile device relative to a dead zone geofence boundary volume map including a geofence boundary, a dead zone volume, and a buffer zone defining a zone in which the wireless communication service is available for mobile device communication with a server system; determining that the mobile device is approaching or crossing the geofence boundary into the buffer zone toward the dead zone volume; determining data asset(s) that will be needed by the mobile device within the dead zone volume; automatically transmitting the data asset(s) to the mobile device while the mobile device is within the buffer zone; determining that the mobile device has subsequently entered the buffer zone from the dead zone volume; and automatically resynchronizing offline data stored locally by the mobile device while in the dead zone volume with corresponding data stored on the server system.

Synchronization/resynchronization of data assets based on entering a wireless dead zone geofence

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity. In various embodiments, optimized solutions for input problems are determined using a cloud-based multi-domain solver system configured to dynamically allocate computing and processing resources between different solution-determining tasks.

Cloud-based systems for optimized multi-domain processing of input problems using a serverless request management engine native to a server cloud infrastructure

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner using multiple solver types. In one embodiment, an example method involves receiving a problem type of an input problem originating from a client computing entity, mapping the problem type to one or more selected solver types, generating one or more container instances of one or more compute containers with each compute container corresponding to a selected solver type, generating a problem output using the one or more container instances, and providing the problem output to the client computing entity, where the problem output includes an optimized solution to the input problem that can be used to perform one or more prediction-based actions.

Growing community of inventors

Simpsonville, SC, United States of America

Javier Buzzalino