Location prediction for power conservation in a mobile device

Abstract

Architecture that facilitates power conservation in mobile devices such as cell phones using prediction. The architecture is an algorithmic-based solution that transforms infrequently-captured geolocation data of an entity into a continuous probable location approximation. Given the location history and additional data about the recent location of the mobile device, the current location of the device can be estimated with some probability. Additionally, given the location history and additional data about the recent location of the device, the probability of the device actually being at a given point on a map is computed.

Description

Background technique [0001] As location becomes an ever-increasing factor in many personal services (e.g., geodefense, automated check-in, location-based advertising, real-time social location sharing, and more), the technology behind the collection, distribution, and action of location updates continues to suffer from technological limit. One of the most obvious limitations is reduced battery life. [0002] In the context of a cell phone, continuous position sampling currently requires active microprocessor state on the phone. This in turn prevents the phone from entering a deep sleep state and prevents advanced power saving options from being engaged. This is true even when using Wi-Fi hotspots or cell tower ID triangulation instead of GPS. [0003] Also, many scenarios require communicating the user's location to a web service (eg, group location sharing, find my phone, dynamically interrogating geodefenses, etc.). In these cases, even a fundamental hardware-based power...

AI Technical Summary

Problems solved by technology

These connections using either cellular or Wi-Fi technologies in turn cause another considerable power drain on the mobile device

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