Aeris Communications, Inc.

Jins George

Subramanian Balakrishnan

Hector Aquiles Rodriguez

Anupam Bagchi

Hyungho Kim

Steve Dotolo

Vikas Kumar

Leif Ronnie Pettersson

The present invention relates generally to network communications, and more particularly to machine-to-machine (M2M) data communications. The present invention provides for a method, apparatus and computer program product for detecting anomalous performance event across a machine-to-machine (M2M) network and thereafter alerting users of performance issues in real-time or near real-time.

Methodology for intelligent pattern detection and anomaly detection in machine to machine communication network

In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.

Learning locations of interest using IoT devices

The present invention relates generally to a process for determining optimized communication rate plans for one or more communications devices across one or more communications networks. The present invention provides for assigning one or more devices operative on a communications network with one or more rate plans to an optimal rate plan. The present invention, in one or more embodiments provides of the one or more communication devices with a cost-effective rate plan solution based on usage profile of the one or more devices and available cost-savings programs available to the one or more devices.

Method and system for automated assignment of devices to an optimal rate plan

A computer implemented method, system and computer readable medium for use in machine to machine (M2M) or internet of Things (IoT) network including network devices are disclosed. The computer implemented method comprises providing a guaranteed price plan to a customer for a predetermined time period and learning about network device usage over the predetermined time period to provide a learned price plan. The computer implemented method also comprises determining a new price plan based on the learned price plan. Finally, the computer implemented method comprises utilizing the new price plan with customer's network devices.

Adaptive M2M billing

A computer-implemented method and system for managing IoT device lifecycle for IoT devices using cellular or wireless networks are disclosed. The method includes receiving network usage information for one or more devices operating on a cellular network; storing network usage information for the one or more devices operating on a cellular network; analyzing the stored data network information for the one or more devices as historical network usage information for each of the one or more devices operating on a cellular network; determining which of the one or more devices have not used cellular network for a pre-determined period of time, where the pre-determined period of time is learned based on the historical network usage information, or provided as one or more static threshold parameters; and recommending lifecycle changes for each of the one or more devices that have not used cellular network for the pre-determined period of time.

Intelligent mechanism to identify and manage cost of IoT device lifecycle

In one example embodiment, a computer-implemented method and system for issuing alerts are disclosed. The method includes receiving device information for one or more mobile devices; sorting the received device information based on pre-determined criteria; evaluating the sorted device information to determine if the device information satisfies a specified condition; and issuing alert based on the determination. The system for issuing alerts includes a storage database, wherein the storage database receives device information for one or more mobile devices; an analytics engine, wherein the analytics engine sorts the received device information based on pre-determined criteria; a rules engine, wherein the rules engine evaluates the sorted device information to determine if the device information satisfies a specified condition and issues alerts based on the determination.

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Freemont, CA, United States of America

Jins George