Application performance measurement and reporting
US-9311211-B2 · Apr 12, 2016 · US
US10278039B1 · US · B1
| Field | Value |
|---|---|
| Publication number | US-10278039-B1 |
| Application number | US-201816201955-A |
| Country | US |
| Kind code | B1 |
| Filing date | Nov 27, 2018 |
| Priority date | Nov 27, 2017 |
| Publication date | Apr 30, 2019 |
| Grant date | Apr 30, 2019 |
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A method for monitoring vehicle motion using a mobile device associated with a user including collecting activity data at a mobile device associated with the user; determining a user activity state based on the activity data; determining motion data collection parameters associated with an operating state of the mobile device, based on the user activity state; and collecting motion data at the mobile device based on the motion data collection parameters.
Opening claim text (preview).
What is claimed is: 1. A method for monitoring vehicle motion using a mobile device associated with a user, comprising: at an application executing on the mobile device in an idle state, detecting that the user has breached a first geofence using at least one of a motion sensor and a location sensor of the mobile device, wherein the first geofence defines a first origin and a first radius, wherein the application is operable between the idle state, an in-detection state, and a drive state; transitioning the application into the in-detection state in response to detecting that the user has breached the first geofence; determining, at the application in the in-detection state, that the user is in a driving activity state based on a user activity model; transitioning the application into the drive state at a time point in response to determining that the user is in the driving activity state, wherein the drive state comprises: collecting a movement dataset using at least one of the motion sensor and the location sensor, determining a second origin and a second radius based on the movement dataset, and regenerating the first geofence at the second origin and with the second radius; analyzing the movement dataset to generate a set of vehicle movement characteristics; and transmitting the set of vehicle movement characteristics to a database and storing the set of vehicle movement characteristics in association with the user and the time point. 2. The method of claim 1 , wherein the motion sensor comprises an accelerometer, and wherein the location sensor comprises a GPS sensor. 3. The method of claim 1 , further comprising collecting supplementary data at an auxiliary sensor of the mobile device, and determining that the user is in the driving activity state based on the user activity model in combination with the supplementary data. 4. The method of claim 3 , wherein the supplementary data comprises vehicular audio data, and wherein the auxiliary sensor comprises a microphone that is inactive in the idle state of the application, active in the in-detection state of the application, and inactive in the driving state of the application. 5. The method of claim 1 , wherein determining the second radius based on the movement dataset comprises calculating the second radius based on a vehicle speed extracted from the movement dataset. 6. A method for monitoring vehicle motion using a mobile device associated with a user, comprising: receiving a trigger at an application executing on the mobile device in an idle state, wherein the application is operable between the idle state, an in-detection state, and a drive state; in response to the trigger, operating the application in the in-detection state, comprising: collecting activity data using at least one of a motion sensor and a location sensor of the mobile device, determining a user activity state based on the activity data according to a user activity model, based on the user activity state, operating the application in one of the idle state and the drive state; in response to operating the application in the drive state: collecting a movement dataset using at least one of the motion sensor and the location sensor, and analyzing the movement dataset to generate a set of vehicle movement characteristics; and in response to operating the application in the idle state: resetting a trigger condition, wherein satisfaction of the trigger condition generates the trigger. 7. The method of claim 6 , wherein determining the user activity state according to the user activity model comprises: accumulating a series of activity updates extracted from the movement dataset; determining that the user activity state is a driving state based on a comparison between the series of activity updates and a predetermined pattern of activity updates associated with driving; and wherein operating the application in one of the idle state and the drive state comprises operating the application in the drive state. 8. The method of claim 6 , wherein the user activity model comprises a tree-based model defining a set of linked nodes, wherein each node of the linked nodes comprises a binary test condition, and wherein determining the user activity state comprises evaluating the binary test condition of each node along a first path traversing the set of linked nodes. 9. The method of claim 8 , wherein evaluating the binary test condition of each node along the first path is associated with a corresponding resource cost, and wherein the corresponding resource cost of each node is greater than a resource cost of a preceding node along the first path. 10. The method of claim 9 , wherein the corresponding resource cost of each node comprises an energy requirement associated with evaluating the binary test condition using a processor of the mobile device. 11. The method of claim 6 , wherein receiving the trigger comprises detecting that the user has breached a first geofence defining a first origin and a first radius, and wherein resetting the trigger condition comprises regenerating the first geofence with a second origin coincident with a location of the mobile device and a second radius. 12. The method of claim 6 , wherein determining the user activity state according to the user activity model comprises determining that the user activity state is a non-driving state and wherein operating the application in one of the idle state and the drive state comprises operating the application in the idle state. 13. The method of claim 12 , wherein determining that the user activity state is a non-driving state comprises determining that a short-range radio of the mobile device is unconnected to a vehicle computing system. 14. The method of claim 6 , wherein operating the application in the in-detection state further comprises: collecting supplementary data at an auxiliary sensor of the mobile device, and determining that the user is in the driving activity state based on the user activity model in combination with the supplementary data. 15. The method of claim 14 , wherein the supplementary data comprises vehicular audio data, and wherein the auxiliary sensor comprises a microphone that is inactive in the idle state of the application, active in the in-detection state of the application, and inactive in the driving state of the application. 16. The method of claim 6 , wherein operating the application in the drive state further comprises: detecting a cessation condition, and transitioning the application into the idle state in response to detecting the cessation condition, wherein the cessation condition comprises a speed value extracted from the movement dataset falling below a threshold value continuously over a predetermined time period. 17. The method of claim 6 , wherein operating the application in the drive state further comprises: determining a geofence defining an origin at an instantaneous location of the mobile device and a radius, wherein the radius is proportional to an instantaneous speed of the mobile device; detecting a breach of the geofence and an elapsed time between determining the geofence and detecting the breach; in response to the elapsed time exceeding a threshold time, transitioning the application into the idle state; and in response to the elapsed time falling below threshold time, regenerating the geofence wherein the origin is defined at a new instantaneous location of the mobile device at the elapsed time. 18. The method of claim 6 , wherein collecting the movement dataset comprises
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