U.S. patent application number 14/979698 was filed with the patent office on 2016-04-21 for systems and methods for identifying an activity of a user based on a chronological order of detected movements of a computing device.
The applicant listed for this patent is Modena Enterprises, LLC. Invention is credited to Scott Curtis, Greg Evans, Juan Pons.
Application Number | 20160109253 14/979698 |
Document ID | / |
Family ID | 46127172 |
Filed Date | 2016-04-21 |
United States Patent
Application |
20160109253 |
Kind Code |
A1 |
Curtis; Scott ; et
al. |
April 21, 2016 |
Systems And Methods For Identifying An Activity Of A User Based On
A Chronological Order Of Detected Movements Of A Computing
Device
Abstract
Disclosed herein are methods and systems for identifying an
activity of a user based on a chronological order of detected
movements of a computing device. According to embodiments of the
present disclosure, the method may include detecting movements of a
computing device. The method also includes determining a
chronological order of the detected movements. Further, the method
includes identifying an activity of a user of the computing device
based on the detected movements and chronological order. The method
also can include determining a geographic location associated with
at least one of the movements. Further, the method includes
presenting identification of the activity.
Inventors: |
Curtis; Scott; (Durham,
NC) ; Evans; Greg; (Raleigh, NC) ; Pons;
Juan; (Bangor, ME) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Modena Enterprises, LLC |
Wilmington |
DE |
US |
|
|
Family ID: |
46127172 |
Appl. No.: |
14/979698 |
Filed: |
December 28, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12974594 |
Dec 21, 2010 |
9222798 |
|
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14979698 |
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61289120 |
Dec 22, 2009 |
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Current U.S.
Class: |
702/5 ;
702/150 |
Current CPC
Class: |
G01C 21/20 20130101;
G01C 21/3697 20130101; G01S 19/43 20130101; G01C 21/3617 20130101;
G01C 21/3484 20130101 |
International
Class: |
G01C 21/36 20060101
G01C021/36; G01S 19/43 20060101 G01S019/43; G01C 21/34 20060101
G01C021/34 |
Claims
1. A method comprising: at a server device comprising a processor
and memory: receiving from a mobile device an activity inference
rule comprising at least one predetermined transition movement
criteria and at least one predetermined action associated with the
inferred activity of the mobile computing device; determining a
chronological set of movement information based on movement
information received from at least one of an accelerometer and a
magnometer of the mobile computing device; determining a transition
movement based on the chronological set of movement information;
determining whether the transition movement meets the at least one
predetermined transition movement criteria of the activity
inference rule; and in response to determining the transition
movement meets the at least one predetermined transition movement
criteria of the activity inference rule: inferring an activity of
the user of the mobile computing device based on the determined
transition movement; and applying the at least one predetermined
action of the activity inference rule.
2. The method of claim 1 further comprising determining a
geographic location associated with the identified activity
3. The method of claim 2 wherein determining a geographic location
associated with the identified activity comprises determining a
geographic location associated with the identified activity based
on the determined transition movement; and
4. The method of claim 3, wherein determining a geographic location
associated with the inferred activity based on the determined
transition movement comprises: determining current coordinates of
the mobile computing device; and storing the coordinates with the
inferred activity.
5. The method of claim 2, wherein the predetermined action
comprises communicating identification of the inferred activity and
the geographic location to another computing device for
presentation to a user of the other computing device.
6. The method of claim 5, wherein the user of the other computing
device is specified in a contact list of the mobile computing
device.
7. The method of claim 6, further comprising receiving an
indication that the inferred activity and the geographic location
are associated with a contact entry of the contact list.
8. The method of claim 2, comprising determining whether the
geographic location associated with the inferred activity is
confirmed.
9. The method of claim 8, wherein determining whether the
geographic location associated with the inferred activity is
confirmed comprises: accessing a geographic information system to
obtain information associated with the geographic location;
analyzing the information with respect to the inferred activity;
and verifying association of the geographic location with the
inferred activity based on the analysis.
10. The method of claim 1, wherein determining the chronological
set of movement information comprises receiving movement
information received from a satellite navigation system.
11. The method of claim 1, wherein determining the chronological
set of movement information detecting comprises: communicating with
a remote device; and determining that the mobile computing device
is moving with respect to the remote device based on the
communication.
12. The method of claim 1, wherein inferring an activity of the
user based on the determined transition movement comprises:
inferring at least one of the user parking an automobile and an
accident involving an automobile.
13. The method of claim 1, wherein inferring an activity of the
user based on the determined transition movement comprises
inferring movement behavior of the user.
14. The method of claim 1, wherein inferring an activity of the
user based on the determined transition movement comprises
inferring a type of transportation used.
15. The method of claim 1, further comprising analyzing the
chronological set of movement information to determine at least one
of a speed, a velocity, a position, and a route of the mobile
computing device, and wherein inferring an activity comprises
inferring the activity of the user of the mobile computing device
based on the at least one of a speed, a velocity, a position, and a
route of the mobile computing device.
16. The method of claim 1, wherein applying the at least one
predetermined action of the activity inference rule comprises
controlling an application to implement a predetermined
command.
17. The method of claim 1, wherein applying the at least one
user-selected predetermined action of the activity inference rule
comprises: determining a geographic location of the mobile
computing device; and associating the geographic location with the
inferred activity.
18. The method of claim 1, wherein applying the at least one
predetermined action of the activity inference rule comprises
presenting identification of the inferred activity to the user of
the mobile computing device.
19. A method comprising: at a server device comprising a processor
and memory: receiving activity inference rules for inferring
activities of a user of a mobile computing device, each of the
activity inference rules comprising at least one predetermined
transition movement criteria associated with each of the inferred
activities of the mobile computing device; determining a
chronological set of movement information based on movement
information received from at least one of an accelerometer and a
magnometer of the mobile computing device; determining transition
movements based on the chronological set of movement information;
inferring activities of the user of a mobile computing device based
on the determination the transition movements meet the
predetermined transition movement criteria of the activity
inference rules; determining a chronological order and geographic
locations of the inferred activities; deducing another activity of
the user based on the inferred activities and the chronological
order and geographic locations of the inferred activities; and
presenting identification of the deduced activity.
20. The method of claim 19, wherein determining a chronological set
of movement information comprises using a satellite navigation
system.
21. The method of claim 19, wherein determining a chronological
order of the inferred activities comprises determining a time
associated with an occurrence of each inferred activity.
22. The method of claim 19, wherein deducing another activity of
the user comprises: determining confidence levels for the inferred
activities of a user of the mobile computing device; and
determining the deduced activity based on the confidence levels for
the inferred activities.
23. The method of claim 19, further comprising presenting the
chronological order in association with the inferred activities and
the deduced activity.
24. The method of claim 19, further comprising presenting the
geographic locations in association with the inferred activities
and the deduced activity.
25. The method of claim 19, wherein presenting identification of
the deduced activity comprises displaying identification of the
deduced activity.
26. A system comprising: a server device configured to: receive
activity inference rules for inferring activities of a user of a
mobile computing device, each of the activity inference rules
comprising at least one predetermined transition movement criteria
associated with each of the inferred activities of the mobile
computing device; determine a chronological set of movement
information based on movement information received from at least
one of an accelerometer and a magnometer of the mobile computing
device; determine transition movements based on the chronological
set of movement information; infer activities of the user of the
mobile computing device based the determination the transition
movements meet the predetermined transition movement criteria of
the activity inference rules; determine a chronological order of
the inferred activities; deduce another activity of the user based
on the inferred activities and the chronological order; and enable
presentation of identification of the deduced activity.
27. The system of claim 26 wherein determining a chronological
order of the inferred activities further comprises determining
geographic locations of the inferred activities and deducing
another activity of the user is based on the inferred activities
and the chronological order and geographic locations of the
inferred activities
28. A non-transitory computer-readable medium having stored thereon
computer executable instructions for performing the following
steps: receiving an activity inference rule for inferring an
activity of the user of a mobile computing device, the activity
inference rule comprising at least one predetermined transition
movement criteria and at least one predetermined action associated
with the inferred activity of the mobile computing device;
determining a chronological set of movement information based on
movement information received from at least one of an accelerometer
and a magnometer of the mobile computing device; determining a
transition movement based on the chronological set of movement
information; determining whether the transition movement meets the
at least one predetermined transition movement criteria of the
activity inference rule; and in response to determining the
transition movement meets the at least one user selected
predetermined transition movement criteria of the activity
inference rule: inferring an activity of the user of the mobile
computing device based on the determined transition movement; and
applying the at least one user selected predetermined action of the
activity inference rule.
29. A non-transitory computer-readable medium having stored thereon
computer executable instructions for performing the following
steps: receiving activity inference rules for inferring activities
of a user of a mobile computing device, each of the activity
inference rules comprising at least one predetermined transition
movement criteria associated with each of the inferred activities
of the mobile computing device; determining a chronological set of
movement information based on movement information received from at
least one of an accelerometer and a magnometer of the mobile
computing device; determining transition movements based on the
chronological set of movement information; inferring activities of
the user of a mobile computing device based on the rule
determination the transition movements meet the user-selected
predetermined transition movement criteria of the activity
inference rules; determining a chronological order of the inferred
activities; deducing another activity of the user based on the
inferred activities and the chronological order of the inferred
activities; and enabling presentation of identification of the
deduced activity.
30. The computer-readable medium of claim 29 wherein determining a
chronological order of the inferred activities further comprises
determining geographic locations of the inferred activities and
deducing another activity of the user is based on the inferred
activities and the chronological order and geographic locations of
the inferred activities
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/974,594, which was filed Dec. 21, 2010, and
titled "Systems and Methods for Identifying an Activity of A User
Based on A Chronological Order of Detected Movements of A Computing
Device", which claims the benefit of provisional patent application
Ser. No. 61/289,120, filed Dec. 22, 2009 and titled "Automatic
Generation Of Activity Or Transition Breadcrumbs From Detected User
Movements", the disclosures of which are hereby incorporated by
reference in their entireties.
TECHNICAL FIELD
[0002] The present disclosure relates to systems and methods for
identifying an activity of a user.
BACKGROUND
[0003] Many mobile devices, such as smart phones, have been enabled
with functionality for determining a location of the device and for
indicating the location to a user of the device. For example, a
global positioning system (GPS) receiver can reside on a mobile
device for determining a GPS position fix of the device. Using the
GPS position fix, the mobile device can plot the device's location
on a map rendered on a display as the device moves from place to
place. These functions and features are useful for guiding users to
a desired destination, such as when a user is driving or
walking.
[0004] Another location-based feature of mobile devices allows
users to generate a "breadcrumb" at a current location of the
mobile device. The breadcrumb information can be stored on the
mobile device and can be later accessed for use in guiding the user
back to the location where the breadcrumb was generated. Such a
feature can allow a user to find the user's parked car, other
objects, or places of interest to where the user would like to
return. To be useful, the user must remember to control the device
to generate the breadcrumb when at the location of interest. If the
user does not remember to control the device to generate the
breadcrumb, the user will not be able to later use this feature for
finding the location of interest. Accordingly, it is desirable to
provide systems and methods for automatically marking a location of
interest.
SUMMARY
[0005] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used to limit the scope of the claimed
subject matter.
[0006] Disclosed herein are methods and systems for identifying an
activity of a user based on a chronological order of detected
movements of a computing device. According to embodiments of the
present disclosure, the method may include detecting movements of a
computing device. The method also includes determining a
chronological order of the detected movements. Further, the method
includes identifying an activity of a user of the computing device
based on the detected movements and chronological order. The method
also includes determining a geographic location associated with at
least one of the movements. Further, the method includes presenting
identification of the activity and the geographic location.
[0007] According to other embodiments of the present disclosure, a
method includes detecting movements of a computing device. The
method may also include determining a chronological order of the
detected movements. Further, the method includes identifying an
activity of a user of the computing device based on the detected
movements and the chronological order. The method includes applying
a predetermined action in response to identifying the activity.
[0008] According to other embodiments of the present disclosure, a
method includes receiving movement information and associated
activity information from a plurality of computing devices. The
method also includes determining a confidence level for associating
a movement of a computing device with an activity of a user based
on the movement information and associated activity information.
Further, the method includes communicating the confidence level to
another computing device.
[0009] According to other embodiments of the present disclosure, a
method includes identifying activities of a user of a computing
device. The method also includes determining at least one of a
chronological order and geographic locations of the identified
activities. Further, the method may include deducing another
activity based on the identified activities and the at least one of
the chronological order and geographic locations. The method may
also include presenting identification of the deduced activity.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The foregoing summary, as well as the following detailed
description of preferred embodiments, is better understood when
read in conjunction with the appended drawings. For the purposes of
illustration, there is shown in the drawings exemplary embodiments;
however, the present disclosure is not limited to the specific
methods and instrumentalities disclosed. In the drawings:
[0011] FIG. 1 is a schematic diagram of a system for detecting one
or more movements of a computing device, determining a
chronological order of the detected movement(s), identifying an
activity of a user of the computing device based on the detected
movement and the chronological order, determining a geographic
location associated with one or more of the movements, and
presenting identification of the activity and the geographic
location according to embodiments of the present disclosure;
[0012] FIG. 2 is a flow chart of an exemplary method for applying a
predetermined action based on detected movements of a computing
device and a chronological order of the detected movement according
to embodiments of the present disclosure;
[0013] FIG. 3 depicts a road map showing movements of a computing
device that may be detected and used for determining that an
automobile is being parked in accordance with embodiments of the
present disclosure;
[0014] FIG. 4 is a flow chart of another exemplary method for
applying a predetermined action based on detected movement of a
computing device according to embodiments of the present
disclosure;
[0015] FIG. 5 is a schematic diagram of a system 500 for receiving
movement information and associated activity information from
computing devices, determining a confidence level for associating
computing device movement with an activity of a user based on the
movement information and associated activity information, and
communicating the confidence level to another computing device
according to embodiments of the present disclosure;
[0016] FIG. 6 is a flow chart of an exemplary method for receiving
movement information and associated activity information from
computing devices, determining a confidence level for associating
computing device movement with an activity of a user based on the
movement information and associated activity information, and
communicating the confidence level to another computing device
according to embodiments of the present disclosure;
[0017] FIG. 7 is a message flow diagram of an exemplary operation
of the system of FIG. 1 according to embodiments of the present
disclosure;
[0018] FIG. 8 is a block diagram of the mobile device shown in FIG.
1 according to embodiments of the present disclosure;
[0019] FIG. 9 is a flow chart of an exemplary method for presenting
identification of a deduced activity according to embodiments of
the present disclosure; and
[0020] FIG. 10 is a display screen showing display of identified
activities of a computing device user and a deduced activity
according to embodiments of the present disclosure.
DETAILED DESCRIPTION
[0021] The present disclosure is described with specificity to meet
statutory requirements. However, the description itself is not
intended to limit the scope of this patent. Rather, the inventors
have contemplated that the claimed subject matter might also be
embodied in other ways, to include different steps or elements
similar to the ones described in this document, in conjunction with
other present or future technologies. Moreover, although the term
"step" may be used herein to connote different aspects of methods
employed, the term should not be interpreted as implying any
particular order among or between various steps herein disclosed
unless and except when the order of individual steps is explicitly
described.
[0022] Embodiments of the present disclosure enable a computing
device to detect movements of a computing device, to determine a
chronological order of the detected movements, to identify an
activity of a user of the computing device based on the detected
movements and the chronological order, to determine a geographic
location associated with one or more of the movements, and to
present identification of the activity and the geographic location.
For example, the computing device may automatically mark its
location in response to predefined or predetermined movements
detected by the computing device. A GPS receiver, accelerometer,
and/or magnetometer residing on or in communication with the
computing device can detect movements, and the computing device may
interpret the movement as being associated with an activity of the
user, such as, but not limited to, the user exiting or leaving a
parked automobile. In response to determining that the user engaged
in the activity, the computing device may automatically determine
its current geographic location or may have previously determined
the geographic location associated with one or more of the detected
movements, and automatically mark or flag the location(s) in a
mapping application. Subsequently, the user may interact with the
computing device to display a map indicating the flagged geographic
location(s) for use in guiding the user back to the location(s).
Further, other embodiments enable a user to implement other
features of the present disclosure in a computing device as will be
described in further detail herein.
[0023] Other embodiments of the present disclosure enable a server,
or any other suitable computing device, to receive movement
information and associated activity information from a plurality of
computing devices. For example, the computing devices may each
detect movements and identify activities based on the detected
movements. The server may determine a confidence level for
associating computing device movement with an activity of a user
based on the movement information and associated activity
information. For example, the server may apply statistics for
determining whether a specified movement should be correlated to an
activity. A confidence level for correlating a detected movement to
an activity may be communicated to other computing devices. Such
confidence level information may be used by the computing device
for identifying an activity of a user based on a movement and for
applying a predetermined action in response to identifying the
activity.
[0024] As referred to herein, the term "computing device" should be
broadly construed. It can include any type of mobile device, for
example, a smart phone, a cell phone, a pager, a personal digital
assistant (PDA, e.g., with GPRS NIC), a mobile computer with a
smart phone client, or the like. A computing device can also
include any type of conventional computer, for example, a desktop
computer, a laptop computer, a netbook computer, a notebook
computer, or the like. A typical mobile device is a wireless data
access-enabled device (e.g., an iPHONE.RTM. smart phone, a
BLACKBERRY.RTM. smart phone, a NEXUS ONE.TM. smart phone, an
iPAD.TM. device, or the like) that is capable of sending and
receiving data in a wireless manner using protocols like the
Internet Protocol, or IP, and the wireless application protocol, or
WAP. This allows users to access information via wireless devices,
such as smart phones, mobile phones, pagers, two-way radios,
communicators, and the like. Wireless data access is supported by
many wireless networks, including, but not limited to, CDPD, CDMA,
GSM, PDC, PHS, TDMA, FLEX, ReFLEX, iDEN, TETRA, DECT, DataTAC,
Mobitex, EDGE and other 2G, 3G, 4G and LTE technologies, and it
operates with many handheld device operating systems, such as
PalmOS, EPOC, Windows CE, FLEXOS, OS/9, JavaOS, iOS and Android.
Typically, these devices use graphical displays and can access the
Internet (or other communications network) on so-called mini- or
micro-browsers (which are web browsers with small file sizes that
can accommodate the reduced memory constraints of wireless
networks), on other client applications accessed via the graphical
displays, on client applications that do not utilize a graphical
display, or the like. In a representative embodiment, the mobile
device is a cellular telephone or smart phone that operates over
GPRS (General Packet Radio Services), which is a data technology
for GSM networks. In addition to a conventional voice
communication, a given mobile device can communicate with another
such device via many different types of message transfer
techniques, including SMS (short message service), enhanced SMS
(EMS), multi-media message (MMS), email WAP, paging, or other known
or later-developed wireless data formats. Although many of the
examples provided herein are implemented on a mobile device, the
examples may similarly be implemented on any suitable computing
device.
[0025] As referred to herein, an "interface" is generally a system
by which users interact with a computing device. An interface can
include an input for allowing users to manipulate a computing
device, and can include an output for allowing the system to
present information and/or data, indicate the effects of the user's
manipulation, etc. An example of an interface on a computing device
(e.g., a mobile device) includes a graphical user interface (GUI)
that allows users to interact with programs in more ways than
typing. A GUI typically can offer display objects, and visual
indicators, as opposed to text-based interfaces, typed command
labels or text navigation to represent information and actions
available to a user. For example, an interface can be a display
window or display object, which is selectable by a user of a mobile
device for interaction. The display object can be displayed on a
display screen of a mobile device and can be selected by, and
interacted with by, a user using the interface. In an example, the
display of the mobile device can be a touch screen, which can
display the display icon. The user can depress the area of the
display screen at which the display icon is displayed for selecting
the display icon. In another example, the user can use any other
suitable interface of a mobile device, such as a keypad, to select
the display icon or display object. For example, the user can use a
track ball or arrow keys for moving a cursor to highlight and
select the display object.
[0026] Operating environments in which embodiments of the present
disclosure may be implemented are also well-known. In a
representative embodiment, a computing device, such as a mobile
device, is connectable (for example, via WAP) to a transmission
functionality that varies depending on implementation. Thus, for
example, where the operating environment is a wide area wireless
network (e.g., a 2.5G network, a 3G network, or a 4G network), the
transmission functionality comprises one or more components such as
a mobile switching center (MSC) (an enhanced ISDN switch that is
responsible for call handling of mobile subscribers), a visitor
location register (VLR) (an intelligent database that stores on a
temporary basis data required to handle calls set up or received by
mobile devices registered with the VLR), a home location register
(HLR) (an intelligent database responsible for management of each
subscriber's records), one or more base stations (which provide
radio coverage with a cell), a base station controller (BSC) (a
switch that acts as a local concentrator of traffic and provides
local switching to effect handover between base stations), and a
packet control unit (PCU) (a device that separates data traffic
coming from a mobile device). The HLR also controls certain
services associated with incoming calls. Of course, the present
disclosure may be implemented in other and next-generation mobile
networks and devices as well. The mobile device is the physical
equipment used by the end user, typically a subscriber to the
wireless network. Typically, a mobile device is a 2.5G-compliant
device or 3G-compliant device or a 4G-compliant device that
includes a subscriber identity module (SIM), which is a smart card
that carries subscriber-specific information, mobile equipment
(e.g., radio and associated signal processing devices), a user
interface (or a man-machine interface (MMI)), and one or more
interfaces to external devices (e.g., computers, PDAs, and the
like). The mobile device may also include a memory or data
store.
[0027] The illustrative embodiments of the present disclosure are
generally implemented within mobile devices that provide map
display guidance information and that include equipment that
indicate movement of the mobile device. However, as will be
appreciated, the present disclosure is applicable to any computing
device that is not fixed in position. Such devices may be
preprogrammed with map data in a memory, or the map data may be
placed into the memory while the device is in operation. For
example, map data may be supplied through a communications link
from a network, such as the Internet. In an implementation where a
cellular telephone comprises the present disclosure, the map
information may be received in the form of data packets from the
Internet, via the cellular protocol and then stored into the memory
of the device.
[0028] As referred to herein, a "GPS receiver" may be any equipment
or component capable of determining a geographic location. In an
example, the GPS receiver may receive satellite signals for use in
determining the geographic location. Further, the GPS receiver may
output a signal indicating the geographic location, such as
coordinates of the geographic location.
[0029] As referred to herein, an "accelerometer" may be any
equipment or component capable of measuring acceleration. This
acceleration may be proper acceleration, the acceleration
experienced relative to freefall. For example, an accelerometer may
be a single- or multi-axis accelerometer configured to detect
magnitude and direction of the acceleration as a vector quantity,
and can be used to sense orientation, acceleration, vibration
shock, or falling. A micromachined accelerometer may be provided in
a computing device, and may output a signal indicating the
acceleration measurements.
[0030] As referred to herein, a "magnetometer" may be any equipment
or component capable of measuring the strength and/or direction of
a magnetic field. A magnetometer may be provided on a computing
device, such as a mobile device, for indicating direction, for
example. The magnetometer may output a signal indicating a
direction.
Applying a Predetermined Action in Response to Identifying an
Activity of a User of a Computing Device
[0031] The presently disclosed subject matter is now described in
more detail. For example, FIG. 1 is a schematic diagram of a system
100 for detecting one or more movements of a computing device,
determining a chronological order of the detected movement(s),
identifying an activity of a user of the computing device based on
the detected movement and the chronological order, determining a
geographic location associated with one or more of the movements,
and presenting identification of the activity and the geographic
location according to embodiments of the present disclosure.
Further, the system 100 may apply a predetermined action in
response to identifying the activity according to embodiments of
the present disclosure. Referring to FIG. 1, the system 100
includes a mobile device 102, which may be any type of computing
device capable of detecting movement. The mobile device 102
comprises a number of functional components. This representation of
the mobile device 102 is meant to be for convenience of
illustration and description, and it should not be taken to limit
the scope of the present disclosure as one or more of the functions
may be combined. Typically, these components are implemented in
software (as a set of process-executable computer instructions,
associated data structures, and the like). One or more of the
functions may be combined or otherwise implemented in any suitable
manner (e.g., in hardware, in firmware, in combined hardware and
software, or the like). The mobile device 102 may include a
graphics rendering engine for displaying information to the end
user in the usual manner. The mobile device 102 is
Internet-accessible and can interact with a web server 104 using
known Internet protocols such as HTTP, HTTPS, and the like. The web
server 104 is shown as a single device but this is not a
requirement; one or more programs, processes, or other code may
comprise the server and be executed on one or more machines (in one
or more networked locations).
[0032] The mobile device 102 may communicate with various
components of the system 100 using suitable network components,
such as, but not limited to, a base station 106, an MSC 108, PSTN
110, and various other network components, which are not shown
herein for ease of illustration. A wireless input/output (I/O)
component 112 or any other suitable communication interface may be
used for communicating data to other devices and for receiving
communication data from other devices via a network as will be
understood to those of skill in the art. The mobile device 102 may
include an antenna 114 for wirelessly sending and receiving
communications to the base station 106 or any other suitable
communications unit. Geographic location information and any other
information described herein in accordance with embodiments of the
present disclosure may be stored in a data store 116, which can be
any suitable type of memory. The user may use an interface 118 of
the mobile device 102 for interacting with the mobile device
102.
[0033] The operation of the system can be described by the
following example. As shown in FIG. 1, the mobile device 102
includes various functional components and an associated data store
to facilitate the operation. The operation of the disclosed methods
may be implemented using system components other than as shown in
FIG. 1.
[0034] In this example system, the mobile device 102 includes a
movement detection module 120 configured to detect movements of the
mobile device 102. The mobile device 102 also includes a movement
response manager 122 configured to determine a chronological order
of the detected movements, to identify an activity of a user of the
mobile device 102 based on detected movements and the chronological
order, to determine a geographic location associated with one or
more of the movements, and to present identification of the
activity and the geographic location in accordance with embodiments
of the present disclosure. Further, the movement response manager
122 may be configured to apply a predetermined action in response
to identifying the activity in accordance with embodiments of the
present disclosure. The movement detection module 120 and the
movement response manager 122 may be implemented by hardware, by
firmware, by combined hardware and software, or the like.
Particularly, for example, the movement detection module 120 may
include a satellite navigation system (e.g., a GPS receiver), one
or more accelerometers, one or more magnetometers, suitable
hardware, suitable firmware, suitable software, or combinations
thereof configured to detect movements of the mobile device 102.
The movement detection module 120 may communicate one or more
signals to the movement response manager 122 for indicating the
detected movements. The movement response manager 122 may include,
for example, hardware, firmware, software, or combinations thereof
configured to receive the signals indicating the detected
movements, to determine a chronological order of the detected
movements, to identify an activity of a user of the mobile device
102 based on detected movements and the chronological order, to
determine a geographic location associated with one or more of the
movements, and to present identification of the activity and the
geographic location in accordance with embodiments of the present
disclosure. Further, the movement response manager 122 may include,
for example, hardware, firmware, software, or combinations thereof
to apply a predetermined action in response to identifying the
activity. The movement detection module 120 and the movement
response manager 122 may also be configured to implement other
functions described herein in accordance with embodiments of the
present disclosure.
[0035] The movement detection module 120 may include one or more of
a GPS receiver 124, accelerometer 126, and magnetometer 128 for
detecting movements of the mobile device 102. For example, the GPS
receiver 124 may be configured to output a GPS position fix. The
movement detection module 120 may read and process GPS position
fixes and may convert two, or more, GPS position fixes into a
heading fix. The movement detection module 120 may determine
whether the mobile device 102 has moved based on the GPS position
fix information. Particularly, for example, if the position of the
mobile device 102 changes over time, the movement detection module
120 may determine that the mobile device 102 has moved.
[0036] The accelerometer 126 and magnetometer 128 may be configured
to output one or more signals indicating an acceleration or
movement of the mobile device 102. The movement detection module
120 may read and process the signals, and may determine whether the
mobile device 102 has moved based on the signals. In response to
the movement detection module 120 detecting that the mobile device
102 has moved, the movement detection module 120 may indicate the
movement to the movement response manager 122.
[0037] As mentioned herein above, one or more signals containing
information indicating movements of the mobile device 102 may be
communicated to the movement response manager 122. Movement
information may indicate, but is not limited to, a speed of the
mobile device, a velocity of the mobile device, a position of the
mobile device, a route traveled by the mobile device, a direction
of motion of the mobile device, an acceleration of the mobile
device, a change in direction of the mobile device, combinations
thereof, and the like. The movement information may be used by the
movement response manager 122 for identifying or inferring an
activity of the user of the mobile device 102. For example, signals
generated by the GPS receiver 124, the accelerometer 126, and the
magnetometer 128 may be monitored for identifying characteristic
movement patterns that are indicative of transition moments or user
activities, such as the parking of an automobile or an accident
involving an automobile (e.g., a wreck). The movement response
manager 122 may perform a set of statistical analyses and
comparisons against a wide range of rule-based inferences from the
output of the GPS receiver 124, the accelerometer 126, and/or the
magnetometer 128. The rule-based inferences can be applied to
identify the occurrence of the characteristic movement patterns or
user activities. In response to detection of characteristic
movement patterns or user activities, a predetermined action may be
applied in accordance with embodiments of the present disclosure as
described herein.
[0038] FIG. 2 illustrates a flow chart of an exemplary method for
applying a predetermined action based on detected movements of a
computing device and a chronological order of the detected movement
according to embodiments of the present disclosure. In addition,
the exemplary method includes presenting identification of the
activity and the geographic location. In this example, reference is
made to the mobile device 102 shown in FIG. 1 for purposes of
illustration; however, reference to the mobile device 102 should
not be construed as limiting, and it should be appreciated that the
method may be implemented by any suitable computing device.
[0039] Referring to FIG. 2, the method includes detecting movements
of a computing device (step 200). A satellite navigation system, an
accelerometer, a magnetometer, and/or any other suitable movement
detection system or device may be used for detecting movements of
the computing device. The mobile device 102, for example, may
detect one or more predetermined movements or characteristic
movement patterns that may be indicative of one or more user
activities. In addition, two or more of the detected movements may
be detected in a predetermined order, and the ordering may be
indicative of one or more user activities.
[0040] In an example of the mobile device 102 operating to detect
movements, the mobile device 102 may use one or combinations of the
GPS receiver 124, the accelerometer 126, the magnetometer 128 for
detecting movements of the mobile device 102. One or combinations
of the GPS receiver 124, the accelerometer 126, and the
magnetometer 128 may output signals indicative of movements of the
mobile device 102. For example, the GPS receiver 124 may generate
signals indicative of the current position of the mobile device
102, and the movement detection module 120 may determine that the
mobile device 102 is moving if the position output by the GPS
receiver 124 changes over time. In another example, the
accelerometer 126 and/or magnetometer 128 may generate signals
indicative of movements of the mobile device 102. Movement
information may be stored in the data store 116 in any suitable
format.
[0041] The method of FIG. 2 includes identifying a chronological
order of the detected movements (step 202). For example, as
movements are detected, the movement response manager 122 may
determine an order in which the movements were detected.
Identifications and the ordering of the movements may be stored in
the data store 116. Further, an indication of a time in which the
movements were detected may be determined. For example, time stamp
information may be associated with each detected movement.
Information indicating an order of the associated movements may be
stored in the data store 116 in any suitable manner, such as by
suitably associating time stamp information with corresponding
movements.
[0042] FIG. 3 depicts a road map 300 illustrating movements of the
mobile device 102 that may be detected and used for determining
that a user's activity is parking an automobile in accordance with
embodiments of the present disclosure. In this example, reference
is made to the mobile device 102 shown in FIG. 1 for purposes of
illustration; however, reference to the mobile device 102 should
not be construed as limiting, and it should be appreciated that the
method may be implemented by any suitable computing device.
Referring to FIG. 3, the automobile may be driven by the user and
may carry the user's mobile device 102 and travel along the route
designated 302. As the mobile device 102 moves along the route 302,
which is a highway in this example, the GPS receiver 124 may detect
the speed and direction of travel of the mobile device 102. As the
automobile continues along the routes 304, 306, and 308, the
combination of the GPS receiver 124, accelerometer 126, and
magnetometer 128 of the mobile device 102 may automatically detect
the speed, acceleration, and direction of travel of the mobile
device 102. As will be described in further detail below, this data
may be stored in the data store 116 and may be used by the movement
response manager 122 for determining that the user is parking an
automobile. An ordering of the detected movements may also be
determined and stored in the data store 116.
[0043] Returning to FIG. 2, the method includes identifying an
activity of a user of the computing device based on the detected
movements and the chronological order (step 204). The detected
movement may be one of a plurality of predetermined movements
occurring in a predetermined order. These movements may be
indicative of transition moments or user activities. In an example,
the movement response manager 122 may automatically analyze the
movement information and may determine that the movements are
indicative of transition moments or user activities, such as the
parking of an automobile. The ordering of the detected movements
may be used for identifying the activity. For example, the ordering
of the above described detection of movements relating to parking
an automobile may be used for determining that the movements relate
to the parking activity.
[0044] Returning to and continuing the example of FIG. 3, the
movement response manager 122 may retrieve the movement information
from the data store 116. Further, using the movement information,
the movement response manager 122 may determine that the user is a
driver or passenger in an automobile exhibiting highway behavior at
route 302, exhibiting exiting behavior at route 304, exhibiting
city driving behavior at route 306, and parking behavior at route
308. The movement response manager 122 may identify these behaviors
based on high speed motion (e.g., 65 mph speed) at route 302,
slower speed (e.g., 45 mph speed) and changing direction (e.g., a
near circular tum) at route 304, start-and-stop movement at route
306, and circling a short distance at low speed (e.g., circling a
block at low speed) at route 308. The movement response manager 122
may determine that this combination of behaviors occurring in this
order may be indicative of an automobile parking behavior. In this
way, the movement response manager 122 can recognize these detected
movements in a predetermined order, and identify an activity
associated with the movements occurring in this chronological order
as being the automobile parking behavior.
[0045] Other movements of the mobile device 102 in addition to the
aforementioned movements may also be detected and used for
identifying the automobile parking behavior. For example, near
route 308 shown in FIG. 3, the GPS receiver 124 and the
accelerometer 126 may detect backwards and forwards movement, which
relates to backwards and forwards driving, and the movement may be
identified as parallel parking of the automobile. Further, for
example, the accelerometer 126 may detect a jostling motion, which
relates to the user exiting the automobile, and the movement may be
identified as characteristic behavior for exiting an automobile. In
another example, the combination of the GPS receiver 124,
accelerometer 126, and magnetometer 128 may detect a walking
movement (e.g., about a 3 mph speed with jostling), and the
movement may be identified as a walking behavior. These behaviors
in combination with the other behaviors identified in the example
of FIG. 3 may be used by the movement response manager 122 for
identifying the automobile parking behavior. Such behaviors may
serve to confirm identification of the automobile parking
behavior.
[0046] Returning to FIG. 2, the method includes applying a
predetermined action in response to identifying the activity (step
206). For example, the movement response manager 122 may
automatically apply a predetermined action in response to
identifying an activity. One or more predetermined actions may be
specified by a user or otherwise specified for implementation in
response to identifying a specified activity. An exemplary action
applied in response to detection of characteristic movement
patterns or user activities includes, but is not limited to,
flagging a geographic location where the one or more of the
characteristic movement patterns or user activities occurred. The
geographic location may be flagged by storing data identifying the
geographic location, which may be coordinates generated by the GPS
receiver 124 when the characteristic movement patterns or user
activities are detected. Data identifying a geographic location may
include, but is not limited to, coordinates defining a geographic
location, a land area, an address, combinations thereof, and the
like.
[0047] Continuing the example of FIG. 3, the automobile parking
behavior may trigger implementation of flagging a geographic
location. In this example, the current geographic location of the
mobile device 102 associated with the detected backwards and
forwards movement, which is identified as parallel parking of the
automobile, may be flagged and stored in the data store 116 for
later access. The information may later be retrieved when the user
interacts with the mobile device 102 for aid with finding his or
her parked automobile. Later, the user may interact with the mobile
device 102 to retrieve the geographic location information from
storage and to display a representation of the geographic location
on a displayed map. The mobile device 102 may also provide
directions for guiding the user to the geographic location. In this
manner, the user may be guided back to his or her parked
automobile.
[0048] Returning to FIG. 2, the method includes determining a
geographic location associated with one or more of the detected
movements (step 208). Continuing the aforementioned example of
parking an automobile, the geographic location of the last movement
associated with the activity may be determined. For example, the
movement response manager 122 may detect the movements and their
order. When the last movement occurs, the movement response manager
122 may record its current geographic location.
[0049] The method of FIG. 2 may also include presenting
identification of the activity and the geographic location (step
210). For example, the movement response manager 122 may control
the interface 118 to present identification of the activity and the
geographic location to a user. In an example, the mobile device 102
may display a map indicating the geographic location. An icon may
specify each geographic location on the map. Further, an icon,
text, or other suitable indicia may be displayed for indicating the
an activity. Continuing the aforementioned example of parking an
automobile, a map and an icon representing the geographic location
of the parked automobile on the map may be displayed.
[0050] In accordance with embodiments of the present disclosure,
identified activities and their chronological order may be
presented. For example, the movement response manager 122 may
determine a time of at least one movement associated with an
activity. The time may be associated with the activity and stored
in the data store 116. Such times may be associated with multiple
activities. The movement response manager 122 may chronologically
order the activities based on the associated times. The movement
response manager 122 may control the interface 118 to present
identifications of multiple activities and a chronological order of
the activities. For example, the activities and their chronological
order may be presented on a display. In an example, this
information may be presented in table format or any other suitable
format. Text identifying the activities and numbering indicating
the chronological order may be displayed. Further, for example, a
time associated with each of the activities may be presented for
indicating a chronological order of the activities.
[0051] In other embodiments of the present disclosure, the mobile
device 102 may crosscheck the geographic location determined to be
the location of a parked automobile with data obtained from a
geographic information system. This crosscheck may be implemented
to confirm that the mobile device 102 has correctly identified a
geographic location as being a location where automobiles can be
parked. For example, referring to FIG. 1, the mobile device 102 may
access a web server 104 via the Internet for obtaining information
associated with coordinates associated with an identified activity,
e.g., the coordinates determined to be the geographic location
where the automobile was parked. For example, the mobile device 102
may communicate with the web server 104 for requesting information
related to the coordinates. Exemplary geographic information
systems include, but are not limited to, GOOGLE MAPS.TM. or
MAPQUEST.TM. mapping services. The web server 104 may store
information associated with several coordinates such as, but not
limited to, information indicating the coordinates of city parking.
The web server 104 may communicate this information to the mobile
device 102 in response to receiving the request. Upon receipt of
the information, the movement response manager 122 may analyze the
information with respect to the identified activity (e.g., parking
of the automobile), and verify association of the detected movement
with the identified activity based on the analysis. In this way,
for example, the movement response manager 122 may confirm that the
geographic location determined to be the location of a parked
automobile has been correctly identified. In response to
confirmation, the movement response manager 122 may proceed with
applying a predetermined action, such as, for example, flagging a
geographic location as described herein.
[0052] According to embodiments of the present disclosure, a user
of the mobile device 102 may use an application (often referred to
as an "app") residing on the mobile device 102 to interact with the
movement response manager 122 via the interface 118 for sharing
geographic location information with another computing device. The
application may reside on the mobile device 102 and may be a part
of the movement response manager 122. The user may, for example,
input commands into the interface 118 for selecting an icon to
initialize the application. The application may have been
downloaded from a web server and installed on the mobile device 102
in any suitable manner. The application may be downloaded to
another machine (such as the mobile phone user's PC) and then
transferred to the mobile device over a medium, such as a Bluetooth
connection. In an example, the application can enable the mobile
device 102 with one or more of the features according to
embodiments of the present disclosure. Further, the geographic
location information generated by this application may be
automatically shared with another application such as, for example,
an application providing information on locating a parked
automobile or other object.
[0053] After initialization of the application, the movement
response manager 122 may control the user interface 118 to present
one or more prompts for a user to input commands. For example, a
user may be presented with one or more icons, buttons, text, maps,
audio, and/or other graphical information for aiding the user with
identifying a contact in an address book, or other suitable contact
list, residing on the mobile device 102. The address book and its
contact entries may be stored in, for example, the data store 116
shown in FIG. 1. A contact entry may include, but is not limited
to, a name, physical address, telephone numbers, email addresses,
the like, and combinations thereof for an entity, such as, but not
limited to, a person and a business. The user may use the interface
118 to select icons, enter text or other types of data, interact
with a graphical map, and the like for selecting one or more
contacts to be associated with a predetermined action. For example,
the selected contacts may be associated with a flagged geographic
location. This may be a geographic location that has been flagged
in response to identification of an activity of a user in
accordance with embodiments of the present disclosure. In this
embodiment, the mobile device 102 may communicate the coordinates
of the flagged geographic location to another mobile device, such
as mobile device 130. In this way, other users may be made aware of
the flagged geographic location. The information about the flagged
geographic location may be made available to the other mobile
device either automatically in response to identification of the
activity of a user, or in response to manual selection of the
contact for sharing the information.
[0054] As an example, two or more users may be designated as
"friends" in their respective contact lists. Contacts designated as
friends may share a flagged geographic location with each other.
For example, the flagged geographic location information generated
by one user may be automatically communicated with a designated
friend, such as a contact associated with mobile device 120. On
receipt of the information, the contact entry associated with the
sender may be suitably marked to indicate that the contact entry is
associated with flagged geographic information. The recipient may
interact with the contact entry for displaying a representation of
the geographic location on a displayed map.
[0055] In another example, the computing devices of users
designated as friends may share current geographic location
information with one another. For example, the GPS receiver 124 of
the mobile device 102 may output coordinates for indicating a
current geographic location of the mobile device 102. The movement
response manager 122 may be configured to automatically share this
geographic location information with contacts designated as
friends. Alternatively, the movement response manager 122 may share
with other devices information associated with any other identified
activity in accordance with embodiments of the present
disclosure.
[0056] In an example of sharing geographic location information, a
user may share geographic location information indicating the
location of his or her parked automobile with another user. The
other user may be a friend or family member and identified as being
a friend in a contact list. When the user parks his or her
automobile, the user's mobile device may recognize that the
movements are associated with parking an automobile, and in
response, may flag the geographic location. In addition, the user's
mobile device may share the flagged geographic location information
(e.g., coordinates of the geographic location) with the contact
designated as a friend. In an example, the user may verbally tell
the friend that he or she may borrow the parked automobile. The
friend may access his or her contact list for displaying
information about the location of the parked automobile.
[0057] In another example of sharing geographic location
information, a user may be riding or driving an automobile when the
automobile is involved in an accident, such as a wreck. During the
accident, the automobile may come to a sudden stop or quick
deceleration. This movement can be detected by a mobile device
being carried by the automobile in accordance with embodiments of
the present disclosure. The mobile device may identify the movement
as being associated with an automobile accident. In response to
identifying the activity, the mobile device may automatically flag
the geographic location where the activity was detected, and notify
specified contacts, or friends, of the event and the flagged
geographic location. In this way, specified contacts can be
immediately notified of automobile accidents involving the mobile
device user.
[0058] In another example of sharing geographic location
information, the information may be shared among selected other
users of a social networking service. Such services are available
on the Internet. By use of a social networking service (e.g.,
FACEBOOK.RTM. social networking service provided by Facebook, Inc.
of Cambridge, Mass.), users can add other users as friends and send
them messages, and update their personal profiles to notify other
users about themselves, such as the geographic location information
generated in response to detected movements according to
embodiments of the present disclosure. Such information can be
automatically made available to the selected social networking
users in response to generation of the information. In this way,
computing device users can receive geographic location information
of others.
[0059] In another example of sharing geographic location
information, a user can utilize any suitable service that stores
user's geographic location information. For example, FIRE EAGLE.TM.
(available from Yahoo! Inc. of Santa Clara, Calif.) is a service
that acts as a store for user's geographic location information. A
user can authorize other services and applications to update or
access this information via the Fire Eagle API, allowing a user to
share his or her geographic location information and then use it on
any Fire Eagle enabled-website. In this way, mobile device users
can receive geographic location information of others.
[0060] In accordance with embodiments of the present disclosure, a
computing device may determine movement based on proximity of the
computing device to another device. For example, FIG. 4 illustrates
a flow chart of another exemplary method for applying a
predetermined action based on detected movement of a computing
device according to embodiments of the present disclosure. In this
example, reference is made to the mobile device 102 shown in FIG. 1
for purposes of illustration; however, reference to the mobile
device 102 should not be construed as limiting, and it should be
appreciated that the method may be implemented by any suitable
computing device.
[0061] Referring to FIG. 4, the method includes communicating with
a remote device (step 400). For example, the mobile device 102 may
be sufficiently close to a radio frequency identification (RFID)
component or chip 132 such that wireless communication is
established, as depicted in FIG. 1. The mobile device 102 may
recognize that the RFID component 132 is in close proximity in
response to receiving signals from the RFID component 132. In an
example, the RFID component 132 may be a component of an automobile
(e.g., placed in a steering column of an automobile) or otherwise
carried by the automobile.
[0062] At step 402 of FIG. 4, movement of a computing device with
respect to the remote device can be detected based on the
communication. For example, the mobile device 102 may determine
that it has moved if it is no longer receiving signals from the
RFID component 132. For example, if the mobile device 102 moves
further than 10 feet from the RFID component 132, the mobile device
102 may no longer receive signals from the RFID component 132, and
in response, the mobile device 102 may determine that it has moved
from a parked automobile. In another example, if the strength of
the signal decreases beyond a predetermined threshold, the mobile
device 102 may determine that it has moved from a parked
automobile.
[0063] At step 404 of FIG. 4, a geographic location of the detected
movement may be flagged. For example, the geographic location may
be flagged in a mapping application residing on the mobile device
102. Subsequently, the user may interact with the computing device
to display a map indicating the flagged geographic location for
guiding the user back to the location. For example, the mobile
device user may interact with the mobile device for displaying the
flagged geographic location on a displayed map. In this way, for
example, the user can use the map for guidance in returning to his
or her parked automobile.
[0064] As described herein above, a set of statistical analyses and
comparisons against rule-based inferences may be performed for
determining whether to perform a predetermined action based on
detected movement. For example, the movement response manager 122
shown in FIG. 1 may be configured to perform these statistical
analyses and comparisons based on output of the GPS receiver 124,
the accelerometer 126, and the magnetometer 128. The rule-based
inferences can be applied to identify the occurrence of the
characteristic movement patterns or user activities. In response to
detection of characteristic movement patterns or user activities, a
predetermined action may be applied.
[0065] In an example, a user may enter or otherwise select one or
more movement inference rules for identifying an activity of a user
of a computing device based on detected movement. The rules may be
included with an application, and selection of the rules may
require interacting with the application for enabling the rules. A
rule may specify one or more steps or functions that are applied in
response to the computing device detecting a movement. If the
detected movement meets specified criteria, a predetermined action
may be applied in accordance with embodiments of the present
disclosure. For example, the criteria may specify that the detected
movement must meet predetermined criteria for motion such as, but
not limited to, a predetermined speed of the mobile device, a
predetermined velocity of the mobile device, a predetermined
position of the mobile device, a predetermined route traveled by
the mobile device, a predetermined direction of motion of the
mobile device, a predetermined acceleration of the mobile device, a
predetermined change in direction of the mobile device, and
combinations thereof. If the detected movement does not meet the
criteria, no action will be applied.
[0066] In accordance with other embodiments of the present
disclosure, a mobile device may detect movements and identify the
movements as being associated with automobile driving activity. In
response to identifying the movements as being associated with
driving activity, the mobile device may automatically prevent the
device from initiating communications to other devices and/or
accepting communications from other devices. For example, text
messaging may be prevented. Other communications may be allowed,
such as voice communications.
[0067] In accordance with other embodiments of the present
disclosure, a mobile device may detect movements and identify the
movements as being associated with automobile driving activity. In
response to identifying the movements as being associated with
driving activity, the mobile device may automatically report to
another specified device of the driving activity.
[0068] In accordance with other embodiments of the present
disclosure, a mobile device may automatically display a map in
response to detecting predetermined movements. For example, a map
application may automatically be launched on detection of the
movements. Further, certain detected movements may cause the map to
be presented at different zoom levels. For example, if the device
detects slow movement, it may be inferred that the user is walking
and, in response, displays the map zoomed in. In contrast, if the
device detects faster movement, it may be inferred that the user is
driving and, in response, displays the map further zoomed out. This
control of the zoom function of the map application may be
automatically controlled in response to the movement detection.
Indicating a Confidence Level for Associating Computing Device
Movement with an Activity of a User Based on Movement and Activity
Information
[0069] As discussed above, other embodiments of the present
disclosure enable a server, or any other suitable computing device,
to receive movement information and associated activity information
from a plurality of computing devices. For example, the computing
devices may each detect movements, identify activities based on the
detected movements, and communicate the movement and activity
information to the server. The server may determine a confidence
level for associating computing device movement with an activity of
a user based on the movement information and associated activity
information. For example, the server may apply statistics for
determining whether a specified movement should be correlated to an
activity. The server may determine a confidence level for
correlating a detected movement to an activity. The server may also
communicate the confidence level to other computing devices. Such
confidence level information may be used by the other computing
devices for identifying an activity of a user based on a movement
and for applying a predetermined action in response to identifying
the activity.
[0070] For example, FIG. 5 is a schematic diagram of a system 500
for receiving movement information and associated activity
information from computing devices, determining a confidence level
for associating computing device movement with an activity of a
user based on the movement information and associated activity
information, and communicating the confidence level to another
computing device according to embodiments of the present
disclosure. Referring to FIG. 5, the system 500 includes a server
502 and mobile devices 102, which may each be any type of computing
device capable of detecting movement and communications with a
server via a network. One or more of the mobile devices 102 may
include some or all of the functions of the mobile devices
described herein. The functions of receiving movement information
and associated activity information from computing devices,
determining a confidence level for associating computing device
movement with an activity of a user based on the movement
information and associated activity information, and communicating
the confidence level to another computing device and others in
accordance with the present disclosure may be implemented by a
computing device movement and activity analyzer 504 residing on the
server 502. The server 502 may also include a data store 506 for
storing data needed for these and other functions. The server 502
may also include an I/O module 508 for communicating the PSTN 110
or another suitable network.
[0071] FIG. 6 illustrates a flow chart of an exemplary method for
receiving movement information and associated activity information
from computing devices, determining a confidence level for
associating computing device movement with an activity of a user
based on the movement information and associated activity
information, and communicating the confidence level to another
computing device according to embodiments of the present
disclosure. In this example, reference is made to the server 502
shown in FIG. 5 for purposes of illustration; however, reference to
the server 502 should not be construed as limiting, and it should
be appreciated that the method may be implemented by any suitable
computing device.
[0072] Referring to FIG. 6, the method includes receiving movement
information and associated activity information from computing
devices (step 600). For example, the mobile devices 102 may each
detect their movements and identify activities of their respective
users in accordance with embodiments of the present disclosure. All
or a portion of such movement and activity information may be
communicated from the mobile devices 102 to the server 502. This
information may be communicated to the server 502 at set intervals
or in response to a request by the server 502.
[0073] The method of FIG. 6 includes determining a confidence level
for associating computing device movement with an activity of a
user based on the movement information and associated activity
information (step 602). For example, in response to receipt of some
or all of the communicated movement and activity information, the
analyzer 504 of the server 502 may apply statistical analyses to
the information for determining a confidence level for correlating
a detected movement or movements to an activity. For example, if
movements of computing devices are commonly associated with a
particular activity, a high level of confidence can be assigned
between the particular movements and activity. In contrast, if such
computing device movements are not commonly associated with a
particular activity, a low level of confidence may be assigned
between the particular movements and activity. The confidence
information may be stored in a suitable data store of the server
502.
[0074] The method of FIG. 6 includes communicating the confidence
level to another computing device (step 604). For example, the
analyzer 504 of the server 502 may communicate the confidence level
information to another computing device. The computing device may
be one of the computing devices shown in FIG. 5 or another of the
computing devices. The confidence level information may include the
activity and movement(s) associated with the confidence level. Such
confidence level information may be used by the recipient device
for identifying an activity of a user based on a movement and for
applying a predetermined action in response to identifying the
activity.
[0075] In response to receiving the confidence level information,
the recipient computing device may automatically apply an action in
response to determining that one or more of its detected movements
correspond to the movement(s) identified in the confidence level
information. For example, a high confidence level may be assigned
to certain movements as being related to the activity of parking an
automobile. If the recipient computing device detects similar
movements, the device may determine that the movements correspond
to the activity of parking an automobile.
[0076] In accordance with one or more embodiments of the present
disclosure, the server 502 may receive from the mobile devices 102
identifications of actions applied by the mobile devices. The
identified actions may be those that are associated with the
activities identified in the activity information. The determined
confidence level may be used for determining how much of a
correlation there is between detected movement(s) and an action
actually applied by the computing devices. Such information may be
communicated to other computing devices and used by the recipients
for determining whether to apply a predetermined action based on
detected movements.
[0077] In one or more embodiments, a server may compile a listing
of activities and associated movements at a geographic location.
For each listed activity, the server may assign a confidence level
that computing device movement(s) should be associated with the
activity at the geographic location. For example, the server 502
may receive movement information and identification of a geographic
location from each of a plurality of mobile devices. In this
example, the movement information may correspond to the parking of
an automobile. The server 502 may communicate the movement
information, the geographic location information, and confidence
level information, and identify the activity of parking of an
automobile to one or all of the mobile devices 102. When a
recipient device detects corresponding movements at the geographic
location, the device may determine that there is a high level of
confidence that the user is parking an automobile, and in response,
the device may automatically mark the geographic location with a
breadcrumb. Applications may launch in response to a confidence
level being above a predetermined threshold.
[0078] In accordance with other embodiments, multiple activities
and actions can be associated with a geographic location.
Particular identified computing device movements can be associated
with the activities and actions. For example, a particular
geographic location may be identified as being at or near a parking
lot for a grocery store. If the mobile device detects certain
movements at the geographic location, there may be a high level of
confidence that the user is parking his or her automobile. As a
result, a breadcrumb may be automatically associated with the
geographic location. In addition, it may be assumed that the user
is going to shop at the nearby grocery store. In response to
determining or inferring that the user is going to shop, the mobile
device may automatically launch an application for presenting a
grocery list that the user has generated.
[0079] FIG. 7 illustrates a message flow diagram of an exemplary
operation of the system of FIG. 5 according to embodiments of the
present disclosure. Referring to FIG. 7, multiple mobile devices
102 may detect movements and identify activities of their
respective users based on the detected movements in accordance with
embodiments of the present disclosure. The activities may also be
identified based on a chronological order of the detected movements
as described in the examples provided herein. All or a portion of
the movement activity may be communicated by the mobile devices 102
to the server 502 (step 700). In accordance with embodiments of the
present disclosure, the server 502 may determine a confidence level
for associating a computing device movement with an activity of a
user based on the received movement information and associated
activity information. The confidence level information, including
the activity and movement(s) associated with the confidence level,
may be communicated from the server 502 to another mobile device
102 (step 702). Such confidence level information may be used by
the recipient device for identifying an activity of a user based on
a movement and for applying a predetermined action in response to
identifying the activity.
[0080] FIG. 8 is a block diagram of the mobile device 102 shown in
FIG. 1 according to embodiments of the present disclosure.
Referring to FIG. 8, the mobile device 102 may include a control
system 800 having associated memory 802. In this example, the
movement response manager 122 is implemented in software and stored
in the memory 802. However, the present disclosure is not limited
thereto. The movement response manager 122 may be implemented in
software, hardware, or a combination thereof. Further, in this
example, the movement detection module 120 is implemented in
hardware and software, which is stored in memory 802. The movement
detection module 120 may include a GPS receiver, accelerometer, a
magnetometer, and/or any other suitable equipment for detecting
movement. In addition, the mobile device 102 may include one or
more digital storage devices 804 such as, for example, one or more
hard disk drives. The mobile device 102 may also include a
communication interface 806 communicatively coupled to the base
station 106 shown in FIG. 1. The mobile device 102 may also include
the interface 105, which may include components such as, for
example, a display, one or more user input devices, or the
like.
Presenting Identification of a Deduced Activity
[0081] In accordance with embodiments of the present disclosure,
various activities of a user of a computing device may be
identified and used for deducing another activity of the user. For
example, movements of a computing device may be detected using
techniques as described herein. Using the detected movements, the
computing device may infer one or more activities of the user such
as, for example, detecting an activity of leaving a parking space
at work and detecting an activity of parking an automobile at home.
In this example, these activities may be used by the computing
device for deducing that the user is driving home from work. In
accordance with embodiments of the present disclosure, the
computing device may determine a confidence level that the activity
is the user's actual activity. Using the confidence levels
associated with a collection of identified activities, the
computing device can determine a confidence level that the deduced
activity is the user's actual activity. The activities may be
presented, such as by display, to the user in association with
their chronological order and any associated geographic
locations.
[0082] FIG. 9 illustrates a flow chart of an exemplary method for
presenting identification of a deduced activity according to
embodiments of the present disclosure. In this example, reference
is made to the mobile device 102 shown in FIG. 1 for purposes of
illustration; however, reference to the mobile device 102 should
not be construed as limiting, and it should be appreciated that the
method may be implemented by any suitable computing device. In
addition, reference is made to the display shown in FIG. 10 for
purposes of illustration only, and the example of FIG. 10 should
not be construed as limiting as any suitable technique may be used
for presenting activity identifications, chronological ordering,
and/or geographic location information to a computing device
user.
[0083] Referring to FIG. 9, the method includes identifying
activities of a user of a computing device (step 900). For example,
detected movements of a computing device or other information may
be used for identifying an activity of a user. Referring to FIG. 1,
for example, an activity analyzer 134 may receive data
corresponding to detected movements and identify an activity of the
user, such as leaving a parking space at work and detecting an
activity of parking an automobile at home in accordance with
embodiments of the present disclosure.
[0084] The method of FIG. 9 may also include determining at least
one of a chronological order and geographic locations of the
identified activities (step 902). For example, in response to
detecting or identifying an activity of a user, the activity
analyzer 134 may time stamp the activity and store identification
of the activity and its associated time stamp information in the
data store 116. In this way, a chronological order of multiple
activities may be determined. In another example, as activities are
identified, a chronological ordering of the activities may be
generated based only on the order in which the activities were
identified. Further, for example, the geographic location
associated with each activity may be determined in response to
identifying or otherwise detecting the activity.
[0085] The method of FIG. 9 may also include deducing another
activity based on the identified activities and at least one of the
chronological order and geographic locations (step 904). For
example, in response to identifying the activities of leaving a
parking space at work and detecting an activity of parking an
automobile at home, the activity analyzer 134 may deduce or infer
that the user has driven home.
[0086] Further, for example, the activity analyzer 134 may use
chronological information associated with identified activities for
deducing that another activity has occurred. Continuing with the
aforementioned example, if stored information indicates that the
activity of leaving a parking space at work occurred before the
activity of parking the automobile at home, the activity analyzer
134 may determine that the user is driving home. Conversely, for
example, if stored information indicates that the activity of
leaving a parking space at home occurred before the activity of
parking the automobile at work, the activity analyzer 134 may
determine that the user is driving to work.
[0087] Other information, such as geographic location, may be used
for deducing an activity. For example, continuing the
aforementioned example, if it is determined that the automobile
stops at home and had been parked at work, the activity analyzer
134 may determine that the user has driven home from work.
Additional geographic location information may also be used for
deducing the activity. For example, if the activity analyzer 134
receives information indicating movement along a road in a
direction towards the home of the user, further confirmation is
provided that the user has driven home.
[0088] The method of FIG. 9 may also include presenting
identification of the deduced activity (step 906). For example, in
response to deducing the other activity, the activity analyzer 134
may control the interface 118 to present the deduced activity to
the user, such as by use of a display. Referring to FIG. 10 for
example, the figure illustrates a display screen 1000 showing
display of identified activities of a computing device user and a
deduced activity according to embodiments of the present
disclosure. For example, based on previously occurring detected
activities, the activity analyzer 134 may determine that the user
has driven home from work and display text indicating such activity
in text box 1002. These activities may include: leaving work
parking (indicated by text box 1004), driving home (indicated by
text box 1006), and parking at home (indicated by text box 1008).
Text indicating identification of these activities may be displayed
in the text boxes 1004, 1006, and 1008.
[0089] Further, text boxes 1002, 1004, 1006, and 1008 may indicate
a chronological order of the activities and their associated
geographic locations. For example, each text box indicates a time
when the activity was identified or detected. Further, the text
boxes are shown in association with a road map for specifying where
the activities were identified or detected.
[0090] Text boxes 1002, 1004, 1006, and 1008 may each indicate a
confidence level associated with the activity in accordance with
embodiments of the present disclosure. In this example, the
confidence levels are indicated on a scale from 1 to 10, where 1 is
the lowest confidence level and 10 is the highest confidence level.
Text box 1004 indicates that there is a relatively high confidence
of 9 that the user is leaving work parking at 5:31 PM. Text box
1006 indicates that there is a relatively high confidence of 7 that
the user is driving home at 5:40 PM. The highest confidence of 10
is assigned to the activity of text box 1008, which indicates that
the user is parking at home based on a comparison of the current
location of the device to a known location of the home of the user.
Text box 1002 is also assigned the highest confidence of 10, which
is based on the confidence levels of the activities associated with
text boxes 1004, 1006, and 1008.
[0091] The various techniques described herein may be implemented
with hardware or software or, where appropriate, with a combination
of both. For example, the mobile device 102 shown in FIG. 1 may
include suitable hardware, software, or combinations thereof
configured to implement the various techniques described herein.
The methods and apparatus of the disclosed embodiments, or certain
aspects or portions thereof, may take the form of program code
(i.e., instructions) embodied in tangible media, such as floppy
diskettes, CD-ROMs, hard drives, or any other machine-readable
storage medium, wherein, when the program code is loaded into and
executed by a machine, such as a computer, the machine becomes an
apparatus for practicing the presently disclosed subject matter. In
the case of program code execution on programmable computers, the
computer will generally include a processor, a storage medium
readable by the processor (including volatile and non-volatile
memory and/or storage elements), at least one input device and at
least one output device. One or more programs are preferably
implemented in a high level procedural or object oriented
programming language to communicate with a computer system.
However, the program(s) can be implemented in assembly or machine
language, if desired. In any case, the language may be a compiled
or interpreted language, and combined with hardware
implementations.
[0092] The described methods and apparatus may also be embodied in
the form of program code that is transmitted over some transmission
medium, such as over electrical wiring or cabling, through fiber
optics, or via any other form of transmission, wherein, when the
program code is received and loaded into and executed by a machine,
such as an EPROM, a gate array, a programmable logic device (PLD),
a client computer, a video recorder or the like, the machine
becomes an apparatus for practicing the presently disclosed subject
matter. When implemented on a general-purpose processor, the
program code combines with the processor to provide a unique
apparatus that operates to perform the processing of the presently
disclosed subject matter.
[0093] While the embodiments have been described in connection with
the preferred embodiments of the various figures, it is to be
understood that other similar embodiments may be used or
modifications and additions may be made to the described embodiment
for performing the same function without deviating therefrom.
Therefore, the disclosed embodiments should not be limited to any
single embodiment, but rather should be construed in breadth and
scope in accordance with the appended claims.
* * * * *