U.S. patent application number 13/905678 was filed with the patent office on 2013-10-03 for providing digital content based on expected user behavior.
The applicant listed for this patent is Sumit Agarwal, Dipchand Nishar, Andrew E. Rubin. Invention is credited to Sumit Agarwal, Dipchand Nishar, Andrew E. Rubin.
Application Number | 20130262631 13/905678 |
Document ID | / |
Family ID | 41582032 |
Filed Date | 2013-10-03 |
United States Patent
Application |
20130262631 |
Kind Code |
A1 |
Agarwal; Sumit ; et
al. |
October 3, 2013 |
Providing Digital Content Based On Expected User Behavior
Abstract
In a computing system, information regarding a plurality of
events that use a computing device is obtained, and a
time-dependant increase in activity for each of at least some of
the events is identified. An observed interest by a user in an
event is correlated with an identified increase in activity for the
event. Information about the activity at a time related to the
event is provided for review by the user.
Inventors: |
Agarwal; Sumit; (Wahington,
DC) ; Nishar; Dipchand; (Los Altos, CA) ;
Rubin; Andrew E.; (Portola Valley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Agarwal; Sumit
Nishar; Dipchand
Rubin; Andrew E. |
Wahington
Los Altos
Portola Valley |
DC
CA
CA |
US
US
US |
|
|
Family ID: |
41582032 |
Appl. No.: |
13/905678 |
Filed: |
May 30, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13616699 |
Sep 14, 2012 |
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13905678 |
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12277432 |
Nov 25, 2008 |
8271413 |
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13616699 |
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Current U.S.
Class: |
709/217 |
Current CPC
Class: |
G06F 16/9535 20190101;
G06N 5/02 20130101; G06F 16/9537 20190101; H04L 67/32 20130101 |
Class at
Publication: |
709/217 |
International
Class: |
H04L 29/08 20060101
H04L029/08 |
Claims
1. A computer-implemented method, comprising: receiving, at a
computer system, information that expresses interest by a user in
one or more events; determining whether each of a plurality of
different event categories have had a triggering occurrence that
indicates presence of content for distribution to the user, the
triggering occurrence being independent of input provided by the
user; identifying a first category of the plurality of different
event categories determined to have had a triggering occurrence and
obtaining the content for the first category for distribution to
the user; formatting the content for delivery to the user in a
first format that is based on the first category that had a
triggering occurrence; and providing the content in the first
format and for review by the user.
2. The computer-implemented method of claim 1, further comprising:
identifying a second category of the plurality of different event
categories determined to have had a triggering occurrence and
obtaining the content for the second category for distribution to
the user; formatting the content for delivery to the user in a
second format that is based on the second category that had a
triggering occurrence; and providing the content in the second
format and for review by the user, wherein the first category is
different than the second category, and wherein the first format is
different than the second format.
3. The computer-implemented method of claim 1, wherein the content
is obtained and provided, for review by the user, in the first
format at a time related to an event in the first category.
4. The computer-implemented method of claim 1, further comprising
correlating the information that expresses the interest by the user
in one or more events with a triggering occurrence in one or more
event categories.
5. The computer-implemented method of claim 1, wherein formatting
the content for delivery to the user comprises determining whether
an event characterized by the first category is included in
calendar information stored for the user.
6. The computer-implemented method of claim 1, wherein the
triggering occurrence comprises receiving GPS location-based data
indicating a physical location for the user and the formatted
content comprises a document detailing an upcoming event near the
location.
7. The computer-implemented method of claim 1, wherein the
information comprises search requests related to the one or more
events and the formatted content comprises promotional information
for an entity in a geographic vicinity of the non-user specific
activity.
8. A computer-readable medium having instructions encoded thereon,
which, when executed by a processor, cause the processor to perform
operations comprising: receiving, at a computer system, information
that expresses interest by a user in one or more events;
determining whether each of a plurality of different event
categories have had a triggering occurrence that indicates presence
of content for distribution to the user, the triggering occurrence
being independent of input provided by the user; identifying a
first category of the plurality of different event categories
determined to have had a triggering occurrence and obtaining the
content for the first category for distribution to the user;
formatting the content for delivery to the user in a first format
that is based on the first category that had a triggering
occurrence; and providing the content in the first format and for
review by the user.
9. The computer-readable medium of claim 8, further comprising:
identifying a second category of the plurality of different event
categories determined to have had a triggering occurrence and
obtaining the content for the second category for distribution to
the user; formatting the content for delivery to the user in a
second format that is based on the second category that had a
triggering occurrence; and providing the content in the second
format and for review by the user, wherein the first category is
different than the second category, and wherein the first format is
different than the second format.
10. The computer-readable medium of claim 8, wherein the content is
obtained and provided, for review by the user, in the first format
at a time related to an event in the first category.
11. The computer-readable medium of claim 8, further comprising
correlating the information that expresses the interest by the user
in one or more events with a triggering occurrence in one or more
event categories.
12. The computer-readable medium of claim 8, wherein formatting the
content for delivery to the user comprises determining whether an
event characterized by the first category is included in calendar
information stored for the user.
13. The computer-readable medium of claim 8, wherein the triggering
occurrence comprises receiving GPS location-based data indicating a
physical location for the user and the formatted content comprises
a document detailing an upcoming event near the location.
14. The computer-readable medium of claim 8, wherein the
information comprises search requests related to the one or more
events and the formatted content comprises promotional information
for an entity in a geographic vicinity of the non-user specific
activity.
15. A system comprising: one or more computers and one or more
storage devices storing instructions that are operable, when
executed by the one or more computers, to cause the one or more
computers to perform operations comprising: receiving, at a
computer system, information that expresses an interest by a user
in one or more events; determining whether each of a plurality of
different event categories have had a triggering occurrence that
indicates presence of content for distribution to the user, the
triggering occurrence being independent of input provided by the
user; identifying a first category of the plurality of different
event categories determined to have had a triggering occurrence and
obtaining the content for the first category for distribution to
the user; formatting the content for delivery to the user in a
first format that is based on the first category that had a
triggering occurrence; and providing the content in the first
format and for review by the user.
16. The system of claim 15, wherein the operations further
comprise: identifying a second category of the plurality of
different event categories determined to have had a triggering
occurrence and obtaining the content for the second category for
distribution to the user; formatting the content for delivery to
the user in a second format that is based on the second category
that had a triggering occurrence; and providing the content in the
second format and for review by the user, wherein the first
category is different than the second category, and wherein the
first format is different than the second format.
17. The system of claim 15, wherein the content is obtained and
provided, for review by the user, in the first format at a time
related to an event in the first category.
18. The system of claim 15, wherein formatting the content for
delivery to the user comprises determining whether an event
characterized by the first category is included in calendar
information stored for the user.
19. The system of claim 15, wherein the triggering occurrence
comprises receiving GPS location-based data indicating a physical
location for the user and the formatted content comprises a
document detailing an upcoming event near the location.
20. The system of claim 15, wherein the information comprises
search requests related to the one or more events and the formatted
content comprises promotional information for an entity in a
geographic vicinity of the non-user specific activity.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation application of, and
claims priority to, pending U.S. patent application Ser. No.
12/277,432, filed on Nov. 25, 2008, entitled "Providing Digital
Content based on Expected User Behavior". The disclosure of the
foregoing application is incorporated herein by reference in its
entirety.
TECHNICAL FIELD
[0002] This disclosure relates to providing digital content based
on predictive determinations that are made in response to
observations of user behavior.
BACKGROUND
[0003] As the speed of computing devices has increased, so have the
demands that users of the devices place on them. Also, advancements
in semiconductor design have led to smaller devices that consume
less power, making it more practical for users to carry such
computing devices with them throughout the day. For example, users
of mobile computing devices, such as cellular phones, portable
email devices, smartphones, and personal digital assistants, may
demand that a single portable device be capable of performing
multiple functions, and may expect to use the device for various
tasks as they go about their daily lives.
[0004] Mobile devices may be used to request information such as
stock quotes, movie listings, or driving directions, and may
receive the information from one or more external servers, to which
the devices may be communicably connected over a network. The
external servers may provide the information to the mobile devices,
which may then present the information to the mobile device users.
Other presented information can include search results in response
to a search request from the user.
[0005] In one example, a single mobile device may be used for
placing or receiving phone calls, checking emails, viewing video
data, listening to audio data, text messaging, and browsing the
Internet. In some examples, the mobile device can also be used to
perform a business transaction, such as purchasing a sandwich in a
local restaurant. For example, a user of the mobile device may dial
a specific number to initiate a monetary transfer from a user
account to a restaurant account.
SUMMARY
[0006] Methods and systems are described for providing digital
content based on predictive determinations that are made in
response to observations of user behavior.
[0007] In a first general aspect, a computer-implemented method
includes obtaining information regarding a plurality of events that
use a computing device, and identifying a time-dependant increase
in activity for each of at least some of the events. The method
also includes correlating an observed interest by a user in an
event with an identified increase in activity for the event. The
method further includes providing, for review by the user,
information about the activity at a time related to the event.
[0008] In various implementations, the events may be selected from
a group consisting of a television program, a movie, a shopping
transaction, and a musical song. Identifying the time-dependant
increase in activity for each of the events may include analyzing
search requests related to the event. Identifying the one or more
time-dependant increases in activity for each of the events may
include identifying a location, or identifying a sales transaction.
The observed interest by the user may include a received user
profile, or an inferred user profile. The one or more
time-dependant increases in activity for the event may relate to
timing of a transaction. Correlating an observed user interest may
include supplementing information about the time-dependent increase
in activity with data from an user identifying an explicit user
preference for information about the activity. The method may
further include correlating the observed interest by the user in
the event with information reflecting occurrences of first
non-user-specific events having an event type, and providing the
information about the activity upon the occurrence of a
non-user-specific event having the event type. The method may
further include identifying a lack of correlation between the
observed interest of the user and second non-user-specific events,
and identifying the information based on a combination of the
correlation and the lack of correlation.
[0009] In a second general aspect, a recordable media having
recorded and stored thereon instructions that when executed on a
computing device perform actions, including obtaining information
regarding a plurality of events that use a computing device, and
identifying a time-dependant increase in activity for each of at
least some of the events. The performed actions also include
correlating an observed interest by a user in an event with an
identified increase in activity for the event, and providing, for
review by the user, information about the activity at a time
related to the event.
[0010] In various implementations, the events may be selected from
a group consisting of a television program, a movie, a shopping
transaction, and a musical song. Identifying the time-dependant
increase in activity for each of the events may include analyzing
search requests related to the event. Identifying the one or more
time-dependant increases in activity for each of the events may
include identifying a location associated with each of the
events.
[0011] In a third general aspect, a computer-implemented method
includes obtaining information regarding a plurality of
user-specific events corresponding to a computing device, and
correlating the user-specific events with one or more
non-user-specific events. The method also includes identifying an
interest by a user of the device with one or more of the
non-user-specific events using the information regarding the
plurality of user-specific events, and providing, for review by the
user, information about a future non-user-specific activity using
the identified interest.
[0012] In various implementations, the information may include
promotional information for the non-user-specific activity, or
promotional information for an entity in a geographic vicinity of
the non-user-specific activity.
[0013] In a fourth general aspect, a computer-implemented system
includes a first interface arranged to gather information regarding
a plurality of events corresponding to a mobile computing device.
The system also includes an observation module to identify a
time-dependant increase in activity for each of at least some of
the events, and a prediction module to correlate an observed
interest by a user in an event with an identified increase in
activity for the event. The system further includes a second
interface arranged to provide, for review by the user, information
about the activity at a time related to the event.
[0014] In various implementations, the events may be selected from
a group consisting of a television program, a movie, a shopping
transaction, and a musical song. Identifying the time-dependant
increase in activity for each of the events may include analyzing
search requests related to the event. Identifying the one or more
time-dependant increases in activity for each of the events may
include identifying a location associated with each of the
events.
[0015] The details of one or more implementations are set forth in
the accompanying drawings and the description below. Other
features, objects, and advantages will be apparent from the
description and drawings, and from the claims.
DESCRIPTION OF DRAWINGS
[0016] FIG. 1 is a schematic diagram showing maps of exemplary
time-related routes.
[0017] FIGS. 2A-2B are timing diagrams showing examples of events
and their occurrences with respect to time.
[0018] FIG. 3 shows examples of user information provided based on
time-related user data.
[0019] FIG. 4 is an exemplary set diagram that includes two
information sets and their intersection.
[0020] FIG. 5 is a schematic diagram of a system that can be used
to present time-related user information.
[0021] FIG. 6 is a schematic diagram of a wireless communication
handset for generating requests and receiving user information.
[0022] FIG. 7 is a flow chart of an exemplary method that can be
performed by a system for presenting time-related information.
[0023] FIG. 8 shows example operations between a client and a
server to provide time-related target information.
[0024] FIG. 9 shows an example of a generic computer device and a
generic mobile computer device.
[0025] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION
[0026] Users of computing devices, such as a computer connected to
the Internet, a mobile device (e.g., cellular phone, smartphone, or
personal digital assistant (PDA) communicably connected to a
network), a positioning device (e.g., global positioning system
(GPS) device, which may be part of a mobile device), or an
interactive video delivery system may tend to perform some
activities in regular or semi-regular ways. To various degrees, the
users may tend to live fairly structured lives, where they may
perform some recurring or semi-recurring actions, or may engage in
recurring or semi-recurring activities or events. These actions,
activities, or events may tend to recur over various time periods
(e.g., within a minute, within an hour, over several hours, during
the course of a day, over a few days, a week, a month, a year, or
over multiple years, etc.). For example, a user may generally drive
from home to work each weekday and may return home from work in the
afternoon. Though occasionally the user may depart from this
routine, such as by carpooling, taking a vacation or sick day,
riding the bus, etc., in general, the user may typically follow the
daily routine of driving to and from work. In other examples, a
user may tend to golf, cycle, or go boating each weekend, attend a
club meeting the first Tuesday of each month, or wash the car
almost every Saturday morning.
[0027] Some activities may recur at particular intervals, such as
each weekend or on one or more particular days of the week or
month, but may additionally be correlated to another time interval,
such as a season. For example, a golfer may tend to play golf at a
local golf course (or one of several local courses, e.g.) each
weekend (or many or most weekends) during the spring, summer, and
fall seasons, but may tend to practice instead at an indoor driving
range on weekends during the winter. In another example, a user may
engage in activities that recur on an annual basis, bi-annual
basis, or some other long-term recurring schedule. For example, the
user may travel to visit family during Thanksgiving each year. In
another example, a group of six couples may enjoy a tradition of
getting together once every six months for dinner, drinks, and
fellowship, and may alternate hosting the event such that each
couple hosts an event once every three years. The systems, devices
and methods described herein discuss using these tendencies to
anticipate future actions and provide information, such as digital
content delivered to the user's mobile computing device, that may
be useful to the user.
[0028] While users are performing these activities, or at some time
before or after performing the activities, the users may interact
with their mobile or other computing devices. When these
interactions occur, a system in communication with the mobile or
other computing device may gather or otherwise obtain information
related to the activities or events. In some cases, such
information may be obtained based on a communication initiated by
the user of the mobile device, such as a request for information, a
selection, a command, an initiation of a transaction, and the like.
Also, the system can obtain information not specifically related to
a communication with the user (e.g., time of day, day of the week,
season, calendar information, regional information, worldwide,
national or local news or event information, alerts, warnings, and
the like). This information may be obtained, for example, from a
digital storage location within or outside the system, including
information stored on a computing system communicably coupled to a
communication network, such as a LAN, WAN, or the computers and
networks that comprise the Internet.
[0029] The system may store the information, and may associate the
information with a timestamp or other indication of when the
interaction occurred or when the information was obtained. Such
indications can be used to determine patterns of activity and can
be referenced to assess whether information is current or dated.
The system may use the collected information to identify a
time-dependent increase in activity for at least some of the
events. In some cases, increases in activity may indicate that the
user is becoming more inclined to partake in the event or similar
events, and may portend an increasing likelihood that the user may
similarly partake in the future. In some cases, increases in
activity may indicate a generalized increase in popularity of a
particular action, event, or phenomenon, independent of specific
user indications. The system may then correlate an observed
interest by the user in an event with an identified increase in
activity for the event, and may provide information about the
activity to the user for review. In this fashion, a user may
conveniently receive information that may be of use to the user
without having to specifically or generally request such
information.
[0030] In various implementations, the systems, devices, and
methods described here can be used to learn user tendencies by
observing external signals, and can be used to predict future user
actions. In some implementations, appropriate digital content may
be provided based on the user tendencies or predicted user actions.
External signals that can be observed include user requests or
communications made using a computing device (e.g., signals
transmitted from the user's device or devices), and temporal or
time-related information, including time-related information
associated with actions of the user (e.g., the time that the user
requested information using a mobile computing device) or
time-related information independent of actions of the user (e.g.,
the dates and times that the television program "Law & Order"
is broadcast, a basketball team's published schedule, the schedule
of nights that a local dance bar will feature line dancing,
etc.).
[0031] By correlating the external signals and the time-related
information, for example, the systems, devices and methods
described here can determine how the user may act in the future
based on observed user activity, and can provide digital content or
information to the user based on the determination. In some
implementations, observed increases in activity may be used in the
determination. Such increases in activity may be specific or
general. For example, the system may observe that the user is
interested in the television show "Law & Order" by observing
external signals, such as search requests initiated from the user's
mobile device that pertain to the show. Requests a user might make
may include requests for episode information, character biography
information, episode teasers, merchandise availability, ratings
information, program scheduling information, episode or trailer
download requests, ringtone or screen saver requests, and so
on.
[0032] The systems, methods, and apparatuses described herein can
be flexible, in that they may accommodate variations of user
activity or preferences at various times, and may adapt to a user's
steady, increasing or decreasing interest or engagement in
particular activities or events (or inquiries or communications
regarding the activities or events, e.g.). The determinations of
variations of user preferences may be developed over time, as
patterns of regular, semi-regular, or occasional user behavior are
observed. In various implementations, such determinations may be
made based on the user's normal day-to-day interactions with their
mobile or computing devices. That is, the system may not rely on
having received explicit user indications of preferences (or
preferred activities, events, causes, associations,
characteristics, habits, tendencies, settings and the like) as can
typically be submitted or entered under a "preferences" view in
some user interfaces of some program applications. Rather, the
systems, devices, and methods can make determinations of user
preferences and predict user actions, activities, events, needs,
etc., based on information gathered in the course of the user's
normal interactions, and optionally also based on external signals
unrelated or only tangentially related to specific user
interactions with the system, for example. In some implementations,
such information may be supplemented by explicit user preferences,
as described above, which may be used to further refine predictive
determinations for providing appropriate content for a user in some
implementations.
[0033] The system may present a wide variety of information to the
user based on the determinations described. Examples can include
advertisements, promotion information, news, event information,
recommendations, reviews, directions, and the like. In this
fashion, the user can be benefited by receiving information of
interest at a relevant time, which may reduce or minimize later
search efforts that the user might otherwise perform. As such, user
resources, such as time and expended effort, for example, may be
conserved, and user quality-of-life may be improved.
[0034] The information may be presented to the user at the time the
determination is made, or may be stored for later presentation at
an appropriate time, according to various implementations. The
information may be in the form of graphical or textual data,
audible data, video data, or combinations of the above. In some
cases, the user may be alerted that content is available by various
alert indications, including an audible alert (e.g., a beep,
ringtone, recorded message, audio clip, etc.), a tactile alert
(e.g., vibrating or pulsing of a housing of the device), or visual
alert (e.g., light indicator or message screen indicator, etc.).
Combinations of the above, and others, may also be used.
[0035] FIG. 1 is a schematic diagram showing maps 102a, 102b, 102c,
102d of exemplary time-related routes 103a, 103b, 103c. For
example, each of the routes 103a, 103b, 103c may represent
geographical information related to a user at a specific time or
over a period of time (e.g., 8 a.m. on Monday, 5 p.m. on Wednesday,
the period from 8:00 a.m. to 8:15 a.m. on Thursday, etc.), and may
indicate a path traversed by the user over the period of time.
Similarly, the routes 103 may provide an indication of actions that
a user may regularly or semi-regularly perform, including providing
information about actions that a user may be likely to perform in
the future. Each of the maps 102a-d shows an area near a home
(indicated by an "H" in each of the maps 102) of a user. For
simplicity, the exemplary maps 102 in FIG. 1 depict a relatively
confined area, but maps of any appropriate scope and/or granularity
may be used in various implementations.
[0036] The first route 103a is an exemplary path that the user may
traverse in traveling to work (indicated by a "W" in map 102a). The
second route 103b shows an exemplary path that the user may follow
to travel to a baseball stadium (indicated by an "S" in map 102b).
The third route 102c shows an exemplary path that the user may
follow to get to a movie theater. The fourth map 102d identifies a
grocery store and a gas station near the user's home, without
showing a path. Because no associated route is shown in the fourth
map 102d, the map 102d may represent the user remaining at
home.
[0037] In the depicted examples, the maps 102a-d and/or routes
103a-c may be associated with a timeline 104.
[0038] The timeline 104 is a graph that presents time on a
horizontal axis, where one week is shown in FIG. 1. The routes 103
can be correlated to the timeline 104, and can be represented in
the timeline by events 106a-e, 108a-c, 110, depicted illustratively
as circles in FIG. 1. Representative lines are shown linking a
particular map 102 and associated route 103 with events in one or
more timeline periods (e.g., days of the week) of the timeline 104.
For example, events 106a-e represent the user commuting from home
to work each weekday (Monday, 106a; Tuesday 106b; Wednesday 106c;
Thursday 106d, Friday 106e) during the week, as represented by
route 103a in map 102a. In similar fashion, events 108a, 108b, and
108c represent the user attending baseball games at the stadium on
Monday, Tuesday, and Wednesday, respectively, and event 110
represents the user viewing a movie at the movie theater on Friday.
Event position within a particular day in the timeline 104 may also
be representative of time-related information. For example, it can
be seen that event 106a occurred before event 108a on Monday
because event 106a is shown to the left of event 108a in the
timeline 104. While not shown in FIG. 1, each event may be
associated with a timestamp or time period indication that
identifies the time that the corresponding action occurred. Each of
the events 106a-e, 108a-c, and 110 may represent a sample of user
information obtained during a week represented by the timeline 104.
In various implementations, timelines of any appropriate length of
time may be used, and for a given user the system may maintain one
or multiple timelines.
[0039] Time and route information may be generalized to increase
the number of matches between and among events. For example, a day
may be split into several periods, such as morning, mid-day,
afternoon, and evening, so that events that occur during any such
period will be considered to be common events, even if they differ
somewhat in time. In a like manner, the locations of events may be
classified primarily by their endpoints, so that limited deviations
in paths between the endpoints will not prevent the events from
being classified together. For example, if a user takes an
alternative route for part of their commute one day, the event for
that commute may be treated as being common with other commutes
that used slightly different routes.
[0040] In some implementations, an information-providing system can
obtain information related to the maps 102 or routes 103 and can
use the information to determine or predict future user activity.
In one example, the information-providing system can obtain
information pertinent to the user and related to the routes 103 by
associating the routes with the user. For example, the
information-providing system can receive data from a GPS device
that detects the paths in the routes 103a-c. In another example, an
external computing system, such as a server hosting various
applications (including, e.g., an application that provides driving
directions or mapping capability), can determine the routes 103a-c
by observing queries and results related to driving. In another
example, the information-providing system can receive data from a
payment processing service provider, which can be used to determine
where and when the user stopped to purchase products. For example,
the system may determine a location of a coffee shop where the user
stopped to purchase coffee, or a location of a gas station where
the user purchased gasoline. This information can be used to
determine routes or predict user activity. In other examples,
calendar information, such as appointments, meetings, schedules,
etc., may be used to determine the routes 103a-c. In some
implementations, a route may be determined based on one or more
information requests pertaining to establishments along the route.
Combinations of the above may also be possible.
[0041] A user may also be allowed to opt in or opt out of certain
information collection. For example, the user may be allowed to
determine when information will be collected, and their computing
device may alert them when information is being collected. Also,
the user may be permitted to set the type of information so that,
for example, information on the user's search activity or locations
is collected, but information about purchasing activities is not.
Also, to help maintain privacy for the user, appropriate mechanisms
may be used to ensure that certain information is handled only on
the user's device, and not by a separate server, or that
information is adequately anonymized or otherwise shielded (e.g.,
by hashing information where the actual value of the information is
not key).
[0042] Using the obtained information, a system can generate the
events 106a-e, 108a-c, and 110 by associating the routes 103 to the
timeline 104. In this example, the route 103a is associated with
weekday morning events 106a-e. The route 103b is associated with
the Monday, Tuesday, and Wednesday evening events 108a-c, and the
route 103c is associated with the Friday evening event 110. In some
implementations, the system can receive information that is related
to a time. For example, the payment processing service provider can
provide a location where the user purchased a product as well as a
time when the user purchased the product. By associating the
purchase events with the time, the system can generate an event in
the timeline 104. In some implementations, the system can associate
received data with a reception time. For example, the GPS device
can transmit user travel data to the system in, for example, near
real time. The information-providing system can generate a travel
event in the timeline 104 that corresponds to the time when the
travel data is received.
[0043] Based on the time-related information (for example, events
in the timeline 104), the information-providing system can observe
patterns of the user's behavior associated with time, and can use
the observed information to make predictions of future user
behavior and provide content accordingly. In one example, the
events 106a-d may be related to purchasing of coffee in a first
coffee shop (labeled "Coffee Shop 1" in the map 102a), such as if
the user makes a habit of stopping at Coffee Shop 1 to purchase an
espresso each morning before arriving at work. In some
implementations, the system can correlate the events and the times
of the events to observe a user behavior pattern. For example, one
user behavior pattern may include purchasing coffee on weekday
mornings, as described above. In various implementations, events
may be sub-divided to include additional information. For example,
the user may follow the route 103a depicted in map 102a each
weekday on the way to work, but may only stop at Coffee Shop 1 on
Monday, Tuesday and Friday, for example. In this case, events 106a,
106b, and 106e, corresponding to those days when the user stopped
for coffee, may include a sub-event indicative of the coffee stop,
while events 106c and 106d may not.
[0044] Using observed or determined patterns, the system can
determine appropriate information for presentation to the user at a
relevant time. For example, the system may use the observed
information that the user enjoys a morning coffee before work, and
may present a coupon from Coffee Shop 1, or from another coffee
shop (e.g., "Coffee Shop 2") located along the route 103a usually
traveled by the user to work on a weekday morning. In an example,
the system may present the information to the user shortly before
the user typically departs from home for work, for example. The
user may appreciate this information, because it may be tailored to
a preference of the user (e.g., the user's enjoyment of coffee on
weekday mornings), and because it may be delivered or presented in
a time-opportunistic fashion (i.e., near the time when the user
often purchases coffee). As such, the user may save money or time
with little or no additional effort expended in obtaining the
promotion information.
[0045] In some implementations, the system can use information
indicative of a first user pattern and information from an external
signal to present the user with information at a time outside of a
time associated with the pattern. For example, suppose the user in
the example above is driving about town on a Saturday morning at
9:00 A.M. The system may use the observed information pertaining to
the user's weekday coffee purchases (that is, associated with a
pattern of activity occurring on weekday mornings), and may use the
external signal that the user is presently driving in the morning
(albeit on a Saturday) to present content associated with a coffee
shop, such as a coupon for a free bagel with the purchase of a
medium or large mocha. One or more additional external signals,
such as the user's present location, calendar information, mobile
device communications or requests, etc., may further be used to
tailor content for presentation to the user, including for example
presenting a coupon for a nearby coffee shop.
[0046] In certain implementations, the information-providing system
can also present time-related user information based on a
combination of global information and an observed pattern. As an
illustrative example, the system can observe that the user traveled
to the stadium the evenings of Monday, Tuesday, and Wednesday based
on events 108a-c. In one example, the system can determine that
baseball games are held in the stadium at a time near the
occurrence of the events 108a-c by accessing global information,
such as the baseball team's schedule, which may be stored in a
server and available on a web page, for example. For example, the
system may determine that the team plays home games at the stadium
and may access the team's schedule from a team or league web site.
In various implementations, the system can request that the user
upload a game schedule for the baseball team, or send a query to a
search engine to search for results relating to the team and its
schedule, or access the baseball team's website to obtain the
schedule. The system can use the user-related information
(traveling to the stadium on Monday, Tuesday and Wednesday),
combined with the global information or external signal (that a
baseball game was played at the stadium each of those nights), to
determine that the user likely attended baseball games at the
stadium each of Monday, Tuesday, and Wednesday nights.
[0047] Such a determination might indicate any of several meanings
For example, it may indicate that the user is a full or partial
season ticket holder for the baseball team, and that the user may
be likely to attend many future games at the stadium. Such an
indication may be bolstered if the user continues to attend
baseball games at the stadium on a regular or semi-regular basis,
or if external signals are observed that indicate an interest in
the local baseball team (e.g., using one's mobile device to check
league standings or read articles on the team). Alternatively, if
each of the games that the user attended were against a common
opponent, it may instead indicate that the user is a fan of the
opponent, rather than the local team, and may portend that the user
is likely to attend future games at the stadium when the opponent
visits, but may be less indicative that the user will attend games
when other opponents come to town. Such an indication may be
bolstered if the user never or rarely attends baseball games
involving other opponents, attends future games involving the same
opponent, or exhibits observable behavior indicating an interest in
the opponent baseball team.
[0048] By combining the global information (e.g., the baseball team
schedule) and the events 108a-c, the system can observe a pattern
that indicates that the user may be likely to attend many or all of
the upcoming baseball games scheduled at the stadium. In some
examples, the system may use the pattern to predict that the user
will attend the next-scheduled baseball game, which may be
determined from the team's schedule. Based on the prediction, the
system can present relevant time-related user information for user
review at an appropriate time. For example, the system may retrieve
a date and time of the next baseball game by accessing the global
information. Then, the system can, for example, present dining
information for a restaurant located near the stadium, or along or
near a route that the user may be likely to travel from home to the
stadium. Such information may be presented, for example, two hours
prior to the baseball game, so that the user may have an
opportunity to receive the information and use it accordingly. For
example, with reference to map 102b, the system may present a
coupon or selected menu item information for restaurant 112, which
is along the observed route 103b that the user has previously taken
to the stadium. As another example, the system may present
promotional information during the game for a bar or tavern near
the stadium (not shown in FIG. 1) that is a popular after-game
meeting place.
[0049] In certain implementations, the information-providing system
can also determine user preferences based on detected events. For
example, the system may observe that the event 110, visiting a
movie theater on Friday evening, occurred even though a baseball
game was concurrently being played in the stadium. This may
indicate, for example, that the user prefers seeing a movie on
Friday nights to attending a baseball game, even if the user may
have previously purchased tickets for the baseball game (e.g., if
the user is a season ticket holder). In certain implementations,
the system can determine that the user prefers to go to the movie
theater rather than watch baseball on Friday nights. This may
distinguish Friday evenings, for example, because the user selected
baseball over movies during the other days (e.g., Mon., Tues.,
Wed., as described above) where both choices were available. Based
on the user preference, the system may provide movie-related
information instead of baseball-related information on Friday
evenings when both activities are scheduled, even though another
user-observed pattern (associated with events 108) may have
indicated that the user attends many or most baseball games played
at the stadium. For example, the system can present movie schedule
information, movie trailers, advance-ticket-purchase information,
2-for-1 buttered popcorn promotions, and the like, at 6:00 P.M. on
Friday evening, in anticipation that the user will again choose to
attend a movie on Friday evening over a baseball game. Similarly,
the system may present information on events similar to movies
(e.g., a musical play) that may be occurring in a nearby venue
(e.g., in a student-run theatre near the movie theater) for user
review on Friday evening. In some cases, the system may present
information related to the baseball game and information related to
movies on Friday evening, given the user tendencies described
above.
[0050] In certain implementations, the information-providing system
can also present user information based on a global user profile.
As an illustrative example, the system can observe that a
significant portion of users tend to shop for groceries and refill
their vehicles with gas during weekends. In some implementations,
the system may present the user with information related to grocery
stores or gas stations (e.g., advertisements, coupons, promotions,
locations or directions) near the user's home, as shown in the map
102d, during weekend periods. Such global information can be
combined with observed user tendencies in various implementations,
or may be used independently to predict user behavior and provide
appropriate content accordingly.
[0051] By determining time-related information to be presented to
the user, the system can anticipate information to be transmitted
to the user's presentation device. In some implementations, the
system can transmit the information at a time that improves the
user's experience. In one implementation, the system can store the
information in the user's device sometime prior to the determined
presentation time. For example, the system can generate relevant
information (e.g., information on starting lineups for the day's
baseball game, happy hour specials at nearby pubs, movies showing
at the movie theater on Friday evening, etc.) and store the
information locally on the user's device prior to the event. The
system may pre-fetch information that the system expects the user
to be interested in, and may store the information locally on the
user's device. Alternatively, the system can transmit the
information for presentation on the user's device at the determined
presentation time.
[0052] FIGS. 2A-2B are timing diagrams 200, 250 showing examples of
events and their occurrences with respect to time. Similar to the
timeline 104 in FIG. 1, the timing diagrams 200, 250 present time
on the horizontal axes. As shown in FIG. 2A, the timing diagram 200
shows user-specific events 202, 204, 206 during a week. Each
occurrence of the events 202, 204, 206 is represented by a spike in
the timing diagram 200 at a particular point in time along the
diagram 200. In various implementations, events may be represented
by database objects and each occurrence of an event may be
represented by an instance of the database object. In the depicted
example, event 202 is related to a purchasing event at a coffee
shop. As shown, the timing diagram 200 indicates that the user made
purchases each weekday morning at the coffee shop, in this example.
Event 204 is related to attendance at a baseball game, and event
206 is related to viewing a movie in a movie theatre.
[0053] As described above, the system may observe indications of
user behavior over any appropriate period of time (minutes, hours,
days, weeks, months, years, etc.). In some examples, an
information-providing system can observe user events on a daily
basis. From the timing diagram 200, the system can determine user
patterns for the events 202, 204, 206. Similar to the system as
described in FIG. 1, the information-providing system can obtain
information related to a user's activity. For example, the system
can receive user purchasing information and determine that the user
tends to purchase coffee at the coffee shop every morning during
the week. In another example, the system can receive GPS or
location-based data (or ticket-purchase information, merchandise or
concession purchase information, parking ticket purchase
information, search requests, communications, and the like) that
indicates that the user tends to attend baseball games on Monday,
Tuesday, and Wednesday evenings that coincide with games scheduled
on those days. In another example, the system can determine from
search queries that the user often visits the movie theater on
Fridays. In some implementations, the system can also observe user
events in other time periods. For example, the system may observe
the user events over a period of weeks to determine that the user
goes to the movie theatre every Friday evening, or may observe user
activity over a period corresponding to the baseball team's season
(e.g., April through September) to determine activity patterns
related to interest in baseball.
[0054] As shown in FIG. 2B, the timing diagram 250 shows two events
252, 254 (indicated by event A 252 and event B 254, respectively)
that are not user-specific events. For example, event A 252 may
correspond to global event involving a worldwide stock market
scare, where several stock markets around the world may
substantially decline in value concurrently or in succession. Event
B 254 may be a regional event, such as the Independence Day holiday
in the United States. These and other non-user-specific events may
be used to provide users with appropriate time-related information,
and may be used independently or in conjunction with observed user
behavior information or other external signal information. In some
implementations, the information-providing system may store the
events 252, 254 in a data store. By combining the events 252, 254
with users' profiles determined based on observed behavior or
preferences, the system can present time-related information to
users, as will be described more fully below.
[0055] FIG. 3 shows examples of user information provided based on
time-related user data. A timing diagram 300 includes nodes 302,
304, 306, 308, 310, 312. Each of the nodes 302, 304, 306, 308, 310,
312 represents a presentation event of user information. In the
depicted example, the nodes 302 may represent presentations of
coupons for a coffee shop. The node 304 may represent, for example,
a presentation of an advertisement for a restaurant near the
baseball stadium or near the movie theater. The node 306 may
represent presentation of information related to the stock market.
The node 308, 310, 312 may represent presentation of information
related to Independence Day. Information may be presented in
various manners. For example, the information may be displayed on
an output device, such as a display device, of the user's mobile
computing device. The information may be of any suitable form,
including visual, audible, tactile, etc.
[0056] Based on the user-specific events 202, 204, 206 and the
global events 252, 254, the information-providing system can
generate user information (e.g., advertisements, promotional
material, news articles, recommendations, and the like) and present
the user information to the user at a relevant time. In this
example, the system can observe the user's activities on the first
two days of the week (Mon., Tues.) and determine a user behavior
pattern. In various implementations, the system can use determined
patterns to forecast user activity for future periods. In this
case, the system can use determined patterns to predict user
activity for the rest of the week. For example, the system can
predict that the user tends to purchase coffee on weekday mornings
based on the instances of event 202 on Monday and Tuesday morning
(see FIG. 2A), so the system may present coupons for the same or a
competing coffee shop each of the remaining weekday mornings (the
nodes 302).
[0057] In another example, the system may observe that the user may
be a season ticket holder for the local profession baseball team.
The system can retrieve a schedule of the baseball games and
present restaurant information (e.g., dinner menu/lunch menu) at a
time before or after each of the baseball games. As shown,
information for the restaurants near the stadium is presented based
on the baseball game schedule. For example, the information can be
presented on a Friday evening when a Friday night game is
scheduled. Similarly, the information may be presented at noon on
Saturday to coincide with a Saturday afternoon game.
[0058] In some examples, the system can detect that the presented
information was not useful for the user. In some implementations,
the system can assess usefulness of presented information by
detecting whether the user reviews the information, uses the
information, or performs a predicted activity. For example, the
user may not go to the baseball game on Friday evening as
predicted, and instead may go to a movie theater. In some examples,
the system can detect that the node 304 presented on Friday evening
may not be useful because the user did not review the information
or use the information (e.g., if the user did not use a presented
coupon). In some cases, the user may provide direct feedback
concerning presented information, such as whether particular
information or classes of information was useful or not.
Assessments of presented information's effectiveness or usefulness
can be used to better tailor future information presentation to the
user. Also, such information can be used to update predicted user
behavior patterns or expectations. In certain implementations, the
system can update the user's behavior pattern based on the detected
information. For example, the system can update the user's behavior
pattern to reflect a user preference for movies over baseball on a
Friday evenings.
[0059] In some implementations, the system can identify increases
or decreases in user activity related to events. Using the increase
or decrease in activity, the system can correlate user interests in
each of the activities. In some examples, the system can present
more related information based on an increasing frequency of an
activity. For example, suppose that the baseball team is having an
excellent season, and that as a result the user begins forgoing
Friday night movies to attend the games. The system may note the
change in behavior, and may alter the presentation of information
accordingly. For example, the system may observe indications of the
user attending one or more Friday night games and not attending
Friday night movies. The system may also observe an increase in
external signal activity involving the baseball team. These and
other indications may be used to determine the change in user
behavior patterns, and predictive assessments may be
correspondingly adjusted.
[0060] Conversely, if the team begins to struggle, the user may
elect against attending some games, even those not falling on
Fridays. Suppose further that the user begins to show an increased
interest in golf, and plays a series of rounds after work over a
period of days or weeks. In some examples, the system can correlate
the increased interest in golf with the decreased interest in
baseball, and present more golf-related information (e.g.,
discounts on golf equipment, coupons for reduced greens fees, etc.)
while presenting less baseball-related (or stadium-related)
information. For example, the system can decrease presentation of
restaurant information near the stadium, and replace it with
presented information related to the user's substitute activity,
golf in this example.
[0061] Referring again to FIG. 3, the system can also present the
node 306 in response to non-user-specific events. In one example,
the information-providing system can present information in
response to a global event, such as a global stock market
correction. For example, the system can present information about
investments, such as safer financial products or particular
securities that may now be attractively priced, broker information,
and the like, during the market correction. In some
implementations, the system can also identify other local,
national, or global events, such as a local weather alert, a
natural disaster (e.g., earthquake, tornado, tsunami, etc.), or a
political warning (e.g., riots in country X, presidential
assassination in country Y). Based on the event information, the
information-providing system can provide timely information to the
users. For example, in conjunction with the first winter storm
warning of the season, the system may present information
pertaining to snow tires that may be appropriate for the user's
vehicle. The information may include sale information for tires
available at a tire dealer along a route that the user is expected
to traverse, or which may be near the user's present location, or
which may be near the user's home or workplace, for example.
[0062] In some implementations, the system can provide information
based on a user profile. For example, the user profile may include
one or more locations, occupations, interests, etc., and may be
determined by observing the user's normal day-to-day activities,
such as by observing the user's daily interactions with a mobile
device.
[0063] The information-providing system can also provide
information based on information gathered from multiple users. For
example, nodes 308, 310, 312 may be generated based on time-related
preferences of more than one user. The information-providing system
may observe a spike in user preferences for events related to the
event 254 (the Independence Day holiday). In an implementation,
because of a general increase in activity among other users with
respect to a certain activity or event, the system may present
information to the user concerning the activity or event because
the system may predict that the user may be similarly interested in
such information.
[0064] Based on the observation, the system may present
advertisements for fireworks on July 3 (the node 308), fireworks
displays information or concert information on July 4 (the node
310), and car dealer extended sale information on July 5 (the node
312). Based on user profiles, the system may present different
information to the users. For example, the system may only present
fireworks show information for shows occurring within 20 miles of a
user's home. As another example, the system may not present any
July 4 Independence Day information if the user is a Russian
citizen living in Moscow, as such a user would likely have no
interest in such information.
[0065] Information can be presented in anticipation of an expected
action or in response to an observed action. For example, suppose
that a baseball game is scheduled for a Thursday evening in June,
and that the system has determined that the user is likely to
attend the game at the stadium. Suppose that the weather forecast
calls for a dangerous thunderstorm for that evening. The system may
present a weather warning to the user prior to game time, which may
include a suggestion to pack an umbrella if planning to attend the
game. Later, if the storm materializes and intensifies to include a
tornado warning during the game, the system may present information
that describes tornado shelter locations within the stadium, for
example. In one example, such a message may only be presented if
the system determines that the user is in attendance at the game.
The message may consider the user's seating location in the stadium
and may provide walking directions from the user's seating location
to the shelter area.
[0066] Preexisting connections between event types may be used to
provide such information to a user. For example, events at an
outdoor venue may be assigned a connection with weather, where the
location of the venue is a parameter for the connection. As a
result, when a user is associated with an event at the venue, the
various events that are associated with the venue may be checked,
such as by checking whether for the geography around the venue
(e.g., centered around an address determined to be associated with
the venue).
[0067] FIG. 4 is an exemplary set diagram 400 that includes two
information sets 402, 404 and their intersection 406. The
information sets 402, 404 may include information eligible for
presentation to a user (e.g., product information, service
information, location information, promotional information, etc.).
Based on the intersection 406 of the sets 402, 404, an
information-providing system can select information to be presented
to the user. The sets 402, 404 may represent information
corresponding to two different events or to other such items.
[0068] In some implementations, the set 402 includes time-related
information. The information-providing system can gather
information by, for example, accessing external servers on the
Internet or accessing a database. For example, the set 402 can
include time-related activities related to a current time. In one
example, if the current time is near Independence Day, then the
system may gather information related to events occurring during
Independence Day in the information set 402.
[0069] As an illustrative example, the set 402 may include
time-related information related to a time period from July 3 to
July 5. For example, the set 402 can include information on events
such as parades, fireworks displays, baseball games, festivals,
etc., and promotions (e.g., information on retailers having sales
corresponding to the holiday period) that are occurring between
July 3 and July 5. In an example, the set 402 can include coupons
for a fireworks retailer offering discounts of 33% on July 3, 50%
on July 4, and 70% on July 5.
[0070] The information set 404 includes user-specific information,
such as the user's location of residence (e.g., Minneapolis,
Minn.), user behavior patterns (e.g., user's tendency to get lunch
at the burger joint two blocks from the user's workplace), user
interests (e.g., the local professional baseball team, golf,
boating, the stock market, World War II history books and videos),
and other user-specific information. In some implementations, the
set 404 can include information related to a user profile. For
example, the information-providing system can gather information,
such as user's interests, user's work information, user's
behaviors, user's residential location, etc., from the user
profile. Again, the information provided in such a manner may be
controlled by, and limited by, the user.
[0071] In some implementations, the user profile can be constructed
by receiving profile information from the user and/or observing
user behaviors. In one example, the information-providing system
can receive a user profile uploaded from a user indicating that the
user lives in Minneapolis, Minn. In one example, the
information-providing system can determine that the user is living
in Minneapolis, Minn. by observing multiple search queries of
restaurants in the Minneapolis area over a period of time, and/or
observing other user behavior that indicates that the user resides
in a particular area. In one example, the information provider
system can determine that the user is located in Minneapolis based
on GPS information.
[0072] In one example, the information-providing system can
determine a user profile based on observed user behaviors. For
example, the information-providing system can determine a user's
preferences related to a particular event, such as Independence
Day, based on the user's past behavior in relation to the event or
to a similar event. For example, the set 404 can include
information relating to the user's behavior for previous
Independence Day holidays. As an example, the set 404 can include
information relating to past activities of the user, such as
watching fireworks on Independence Day after attending a baseball
game, and purchasing fireworks on July 5 to get the best
bargains.
[0073] In some implementations, the information-providing system
can include information that is common to both sets 402, 404,
symbolized in FIG. 4 by intersection region 406. Accordingly, the
intersection region 406 may include information that is related to
user preferences and to a time period of interest. In one example,
the intersection 406 may include fireworks events after baseball
games during July 3 to July 5 near Minneapolis, Minn., because the
user attended a firework display following a baseball game on
Independence Day the year before. The intersection 406 may also
include fireworks coupons on July 5 for stores near Minneapolis
because the user purchased fireworks on July 5 last year.
[0074] Information can be presented to the user based on the
intersection 406. In some examples, the system can present, at the
beginning of a baseball game during the Independence Day weekend,
information concerning fireworks events following the baseball
game. As another example, the system can present the fireworks
coupon information each morning on July 3, 4 and 5 to notify the
user of the fireworks discounts that day or in the near future.
[0075] FIG. 5 is a schematic diagram of an exemplary system 500
that can be used to present time-related user information. In one
example, the system 500 can present user information based on
observed user behaviors and predicted future user actions. In some
examples, the system 500 can perform some or all functions
described above with respect to FIGS. 1-4.
[0076] The system 500 includes an information-providing system
(IPS) 502, a network 504, a traffic service module 506, and a
database 508. The IPS 502 can communicate with the traffic service
module 506 and the database 508. For example, the IPS 502 can
request and receive traffic information (e.g., road closure
information and traffic congestion information) from the traffic
service module 506. For example, the IPS 502 communicates with the
database 508 to retrieve information for a user. In one
implementation, the database 508 can include user-related
information, such as a schedule of the user's favorite baseball
team, a list of local events near the user's location, and/or
promotional information associated with the user's favorite stores,
among others. In various examples, the databases 508 may be
connected to the IPS 502 by a high bandwidth LAN or WAN. In some
examples, the database 508 can be connected to the IPS 502 through
the network 504.
[0077] Through the network 504, the IPS 502 is connected to one or
more external servers 510, a mobile device 512, and a computing
device 514. The external sever 510 can include, for example, a
mapping server that provide routing information, a business
directory server that provide business information (e.g., store
information, restaurant information, etc.), and/or web servers that
provide information about news items, current events, or various
companies and organizations (e.g., the baseball team website that
provides game schedule information). In some implementations, the
IPS 502 can retrieve information from the external server 510 based
on a user preference (e.g., a baseball game schedule based on
user's interest in watching baseball). In some implementations, the
external server 510 can also provide information to be stored in
the IPS 502. For example, the external server 510 may provide
business information, such as location information, operating
hours, features, contact information, and/or promotional
information for a business, to the IPS 502. In some examples, the
IPS 502 may present the received business information to a user
based on the user's preferences.
[0078] The mobile device 512 and the computing device 514 may be
used by users of the system 500. In the depicted example, the
mobile device 512 is shown as a cellular telephone handset for
communicating with a user. For example, the mobile device 512 can
communicate, via a cellular telephone network, with the IPS 502
using a Wireless Application Protocol (WAP) standard or other
appropriate communication protocols. Other examples of the mobile
device may be possible. For example, the mobile device 512 can be a
personal digital assistant, a personal computer, or a voice-driven
communication device. The mobile device 512 may include appropriate
input and output structures, such as, for example, a display screen
which may have a touch-sensitive surface, data entry keys,
clickable data entry wheels, speakers, and a microphone, including
for voice recognition.
[0079] In some implementations, the IPS 502 can gather information
from and present information to a user using the mobile device 512.
For example, the mobile device 512 can receive target information
from the IPS 502. By displaying the target information, for
example, the user of the mobile device 512 can review the target
information. In some implementations, the mobile device 512 can
present the target information in other formats. For example, the
mobile device 512 can present the target information by playing
audio data received from the IPS 502. In an example where the IPS
502 provides a coupon to the mobile device 512, the user may redeem
the coupon in various ways. For example, the user may display the
coupon to an attendant, who may honor the coupon. The user may use
the mobile device 512 to send a communication to a computing device
at the place of business associated with the coupon for redemption.
Also, the display of the coupon (e.g., on a display screen of the
device 512) may include a machine-readable representation that may
be scanned for redemption of the coupon, to list just a few
examples.
[0080] The computing device 514 may be a desktop computer, a set
top box, or other device that can be connected to the network 504.
In some implementations, a user of the computing device 514 may
transmit queries (e.g., web search request, driving direction
request, and/or dinning information request) to the IPS 502. For
example, the computing device 514 may be connected to the Internet.
The IPS 502 can observe user behaviors based on the received
queries, and can determine user interest in various activities,
events, or subject matter based on user requests or interaction
with the system. In some examples, the IPS 502 can present
time-related user information to the computing device 514. For
example, the IPS 502 can present coupons for various coffee shops
to the computing device 514. The user may print the coupons for
later redemption, according to some implementations.
[0081] The system 500 also includes a vehicle navigation system
516, a satellite 518, and a base station 520. The navigation system
516 communicates through a satellite network via the satellite 518
and the base station 520. As shown in FIG. 5, the base station 520
is connected to the network 504 to communicate with the IPS 502.
The user can use the vehicle navigation system 516 to obtain route
information (e.g., driving directions). For example, a user of the
vehicle navigation system 516 can transmit a request to the IPS 502
for route information. Upon receiving the request, the IPS 502 can
provide the requested route information to the user, perhaps after
first communicating with the database 508 or external server 510.
In some implementations, the navigation system 516 can be used to
gather location information (e.g., through a GPS system) and to
display maps and other data. In some examples, the IPS 502 can also
query the vehicle navigation system 516 for a current location of
the vehicle navigation system 516. After receiving the current
location, the IPS 502 can, for example, present target information
(e.g., restaurants, gas prices, auto repair shop, etc., in the
area) related to the current location of the vehicle navigation
system 516. In some implementations, the mobile device 512 can
include a GPS system, and can implement the features described
above.
[0082] As described, the IPS 502 can receive requests from the
devices 512, 514, 516. In an implementation shown in FIG. 5, the
IPS 502 includes an interface 522 to receive requests from the
devices 512, 514, 516. For example, the interface 522 can be a
network interface, such as a network card. In some examples, the
interface 522 is configured to transmit and receive data via the
network 504. The interface 522 is coupled to the database 508 and
the traffic service 506. For example, the IPS 502 can retrieve
information from the database 508 and the traffic service 506 via
the interface 522. In some examples, the IPS 502 can also access
the external server 510 to retrieve information via the interface
522 and the network 504.
[0083] In the depicted example, the IPS 502 includes a request
processor 524 and a response formatter 526 to process user requests
and generate responses to the user requests, respectively. The
request processor 524 processes requests received by the interface
522. For example, the request processor 526 can parse a received
request and format the request from a network message format (e.g.,
an HTML format or a text format) to a format usable by the IPS
502.
[0084] After formatting the received request, the IPS 502 obtains
information required by the received request. In some examples, the
IPS 502 may access the database 508, the traffic service module
506, and/or the external servers 510 to obtain the required
information. For example, the IPS 502 may access the traffic
service module 506 if the user requests traffic-related
information. The IPS 502 also includes a mapping data repository
530. For example, the IPS 502 can access the mapping data
repository 530 to obtain route information and other information
related to a location or a destination of the user.
[0085] In the depicted example, the IPS 502 includes a data bus 528
that interconnects various components of the IPS 502. Through the
data bus 528, the IPS 502 can transmit the obtained information to
the response formatter 526. For example, the response formatter 526
can receive information from the mapping data repository 530 via
the data bus 528. After receiving the information, the response
formatter 526 formats the information in a format that can be used
by the requesting device.
[0086] Based on the user requests and/or other types of received
user communications or interactions, or other received user events,
the IPS 502 can determine user preferences and generate a user
profile related to the user preferences. In the depicted example,
the IPS 502 includes an observation module 532 to determine the
user preferences. For example, the observation module 532
determines the user preferences based on, for example, search
requests received from the user, shopping transactions originated
by the user, route information used by the user, selections made by
the user, information requests, or other user activities. For
example, the user events can be received from the devices 512, 514,
516.
[0087] In one example, the observation module 532 can determine a
user preference by observing an increased frequency of user
activity related to one or more events, activities, or subject
matter categories. For example, the observation module 532 can
determine a user preference for a TV program if the user frequently
searches information (e.g., show time, characters, episodes
description, and/or spoilers) related to the TV program. The
observation module 532 can also detect increases in user activity,
and can use the detected increases to refine the user profile or
preferences.
[0088] After determining the user information, the IPS 502 stores
the determined user preferences in a user data repository 534. In
some implementations, the user data repository 534 can also store
user profile data (e.g., address, occupation, interests, and other
personal information) received from the user.
[0089] Based on information stored in the user data repository 534,
the IPS 502 presents user related information to the user. In the
depicted example, the IPS 502 includes a user related information
generator (URIG) 536 to generate the user-related information. In
some implementations, the URIG 536 can select information to be
presented based on the user data in the user data repository 534.
For example, the URIG 536 can present information (e.g.,
soundtrack, spoiler, or show time information) related to a
particular TV program that the user is interested in.
[0090] In various implementations, the URIG 536 can generate user
related information based on one or more associations common to the
received user events. For example, the observation module 532 can
collect a number of user events related to downloading of songs. In
one example, the observation module 532 can generate associations
with the downloaded songs based on associated categories, such as
an artist or collection of artists associated with the songs,
genres of the songs (e.g., rock, country, blues, classical, or
hip-hop), and/or time periods when the songs were released (e.g.,
1970s, 1980s, 1990s, etc.). In some examples, the observation
module 532 can identify one or more user preferences by associating
the downloaded songs and the associated categories. For example,
the observation module 532 may determine that the user is
interested in rock music from the 1980s. In some implementations,
the determined user interest is stored in the user data repository
534. In some examples, the URIG 536 can present, based on the user
interest in rock music from the 1980s, upcoming concert touring
information for Bon Jovi, U2, or Guns N'Roses, rock bands that
enjoyed popularity in the 1980s.
[0091] The URIG 536 can also use the prediction module 540 to
generate time-related user information. For example, the prediction
module 540 can generate predictive events by combining user related
information and time-related information. In one example, the
prediction module 540 can correlate search requests related to a TV
program with the time the search requests were received. For
example, suppose that a user submits a search request regarding
episode information for "American Idol," and that the request is
submitted shortly after the program finishes airing on a particular
night. The next time the program is aired, the user may again
submit a similar search request. In some examples, based on a
relationship between the search request and the time of receiving
the search request, the prediction module 540 can generate
time-related information by predicting a next time when the user
will request similar information. For example, the prediction
module 540 can generate and store, in the user data repository 534
for example, a prediction that the user may request "American
Idol"-related information the next time that the program is aired,
or shortly thereafter.
[0092] Using the prediction generated by the prediction module 540,
in one implementation, the URIG 536 can select and present
time-related user information based on a user preference. The URIG
536 can select to present information such as a purchase offer for
the TV program soundtrack before, during, or after the program
airs, because, for example, the prediction may indicate that the
user will likely view the TV program, and may be interested in
receiving such a promotional offer at a time proximate the viewing
of the show. In the example described in FIG. 1, the prediction
module 540 may determine that the user will purchase a coffee each
weekday morning. Based on the received prediction, the URIG 536 can
retrieve information related to coffee shops near the travel route
of the user from the database 508 and/or the external servers 510,
and can present such information to the user at a relevant time,
such as shortly before the user departs from home each weekday.
[0093] In some implementations, the URIG 536 can also select
information from an ad content repository 550. For example, the ad
content repository 550 includes ad content that can be presented to
the user based on user interests. For example, the ad content
repository 550 can include promotion information (e.g., coupons)
for coffee shops, given the user's observed interest in morning
coffee. In one example, the URIG 536 may select coupons from a
coffee shop near a user's route to work on weekday mornings, or
links for purchasing a soundtrack compact disc of a TV program
within thirty minutes after the program finishes airing on
television.
[0094] FIG. 6 is a schematic diagram of a communication system 600.
For example, the communication system 600 may be implemented in the
mobile device 512 of FIG. 5. The system 600 receives and transmits
information wirelessly using a transceiver 602, with the received
signals being passed to a signal processor 604. The signal
processor 604 can include digital signal processing (DSP) circuitry
for processing the received signals. Normal voice communication can
be routed to or from audio processor 606.
[0095] As shown, the audio processor 606 communicates with a user
interface 608. The user interface 108 handles communications
between the user and the system 600, such as voice, visual, and
data entry communication. Visual presentation of information may be
provided via a display screen 610. General data entry, apart from
the entered voice data, may occur through a keypad 612. In the
depicted example, the keypad 612 is arranged as a standard 12-key
telephone keypad. In other examples, the keypad 612 can be a touch
screen or a keypad having other layout, such as a standard "qwerty"
keyboard layout. The system 600 also includes appropriate control
keys 614 for performing control functions. In some examples, the
keypad 612 and the control keys 614 may include contact
push-buttons, joysticks, portions of touch-sensitive panels, or
other appropriate input devices. Although the communications are
shown for clarity as occurring through a single user interface 608,
multiple interfaces may be used, and may be combined with other
components.
[0096] The system 600 includes a number of computer applications
616, such as games, applications to assist in dialing numbers, and
applications to permit web browsing, including the entry of data as
part of the web browsing. The applications 616 may be stored in
ROM, Flash memory, RAM, MRAM, or other memory device accessible by
the system 600. The system 600 includes a dialing module 618 to
provide standard dialing functionalities for the system 600. After
receiving entered dialing digits or voice dialing instructions
through the interface 608, the dialing module 618 may, for example,
transmit dial signals through the transceiver 602 via a
communication interface 620.
[0097] In some implementations, the applications 616 can include a
voice recognition application. For example, the voice recognition
application is configured to convert voice signals (e.g., the
user's voice communication) received from the user interface 608
into digital data. In some examples, the IPS 502 can use the
digital data to determine a user preference.
[0098] A data entry module 622 may operated on data received via
the transceiver 602. For example, the received data may be search
results requested by a user of the system 600. In another example,
the received data may be target information presented by the IPS
502. The data entry module 622 can pass the entered data to one of
the applications 616. In some implementations, the application 616
can display the target information on the display 610. In some
implementations, the application 616 can display an icon to
represent that target information has been received and is
available for display. For example, the user may select one of the
control keys 614 to display the target information.
[0099] In some implementations, the system 600 uses an information
module 624 to manage the target information. For example, the
information module 624 can determine a time to present the target
information. In some implementations, the information module 624
may periodically request target information to be downloaded to the
system 600. In various implementations, the received data or
information from the system 502 of FIG. 5 may include display
commands that instruct when to deliver the content (e.g., by
displaying on display 610) to the user. In some implementations,
the information module 624 may itself determine when to present the
information, and may do so in response to a user-initiated
interaction with the mobile device.
[0100] In some examples, the downloaded target information may be
stored in a data store 626. The data store 626 may be a dedicated
memory device, or may be one or more blocks of memory in a shared
memory space. For example, the data store 626 may be, for example,
RAM, Flash memory, ROM, MRAM, or other appropriate memory
technologies.
[0101] In certain implementations, the downloaded target
information may include presentation instructions, such as a time
for presenting the information. In some implementations, the
information module 624 is configured to present the target
information based on presentation instructions specified with the
target information. For example, the system 600 may receive target
information with instructions that the information be displayed at
a particular time (e.g., 7:32 p.m. on May 8, 2008), or in response
to a particular user action (e.g., when the user indicates an
interest in particular subject matter, observable based on the
user's interaction with the mobile device). The information module
624 can store the target information in the data store 626. At the
appropriate time, the information module 624 retrieves the stored
target information and presents the target information to the
user.
[0102] FIG. 7 is a flow chart of an exemplary method 700 that can
be performed by a system for presenting time-related information.
At step 702, information regarding events is obtained. Such
information may be based on user interaction with a mobile device.
Search requests, information requests, communications, selections,
and the event, activity, or subject matter they pertain to, are
examples of information that may be obtained. At step 704, a time
dependent increase in activity for an event may be identified. Such
an increase in activity may take several forms. For example, the
increase may include observation of a predetermined number of
events within a predetermined period of time. As another example,
the increase may include an increase in activity of a predetermined
percentage over a period of time as compared to the percentage of
activity over a previous period of time. The increase in activity
may be based on actions of other users. For example, the system may
notice an increase in movie-related-information-seeking activity in
the month leading up to the Oscars award show.
[0103] If a user has not indicated interest in the event at step
706, the process returns to step 704. If, however, the user has
indicated an interest in the event at step 706, the observed
interest is correlated with an identified increase in activity for
the event at step 708. Whether or not a user has demonstrated an
interest in the event may be determined by examining stored user
behavior data, for example, which may include indications of
interest the user has expressed via interactions with the user's
mobile device, for example. Indications may also be determined
using location-based information, as another example. A user's
interest level may be scored, where the score indicates a level of
interest. For example, if the user has indicated interest in an
event on multiple occasions, the user may have greater interest in
the event than if the user has indicated an interest only once.
Similarly, if the user has indicated interest over a period of
time, perhaps in a regular or semi-regular fashion, this may be
indicative of a higher level of user interest in the event.
Increases in user activity can also be used in the correlation.
Increases can include observing a predetermined number of user
indications over a period of time, including a percentage-increase
over a period of time as compared to an earlier period, or
extension of a pattern of behavior, such as observing an indication
of interest on a third consecutive day (extending the pattern from
the observed behavior on the first two consecutive days, e.g.).
[0104] At step 710, information is provided to the user, as by
providing the information for review at the user's mobile device,
and the method 700 ends. For example, target information can be
provided related to a current user interest and the event. The
information may be provided at a time that is predicted to
correspond to a time that the user may be especially interested in
receiving the information, which time may be determined using the
prediction techniques disclosed herein. For example, patterns of
user behavior, whether regular or semi-regular, may be observed and
predictions of future user behavior may be determined. In some
cases, the predictions can also be based on external signals, such
as information retrieved from storage locations or from other
computing devices, where the information does not involve past
behavior of the user.
[0105] FIG. 8 shows example operations 800 between a client and a
server to provide time-related target information. At step 802, a
client device, such as a mobile phone, smartphone, or PDA,
transmits user event information to a server. User event
information, such as transaction information, information requests,
location information, communication information, and the like may
be transmitted in this fashion, such as over a communication
network. At step 804, the server receives the user event
information. After receiving the user event information, the server
can identify a user preference related to the user event at step
806. For example, the user preference for buying coffee on weekday
mornings based on frequency information (e.g., a purchasing
pattern) and time information (e.g., purchasing time) may be
identified.
[0106] After identifying the user preferences related to the user
event, the server updates user data based on the identified user
preferences at step 808. Such information can be stored in a
digital storage location, for example, and can supplement
information that comprises a profile for the user, which may be
developed over time by the server for use in making predictions of
future user behavior. At step 810, the server determines whether
target information is to be transmitted to the client. For example,
a prediction module may determine a time related to a user
preference. As an example, the prediction module can determine that
the user may attend the local baseball team's next-scheduled home
game at the stadium based on a pattern of observed user activity.
The prediction module may obtain the team's schedule, determine the
next home game, and determine an appropriate time for information
delivery. In some implementations, the determined time associated
with a predicted event can be scheduled and stored in a data
repository. A scheduling program may then manage delivery schedules
so that the time-dependent information may be delivered at
appropriate times.
[0107] If no information stored is related to the user preference
of the current time, then the step 810 is repeated. If some
information stored is related to the user preference of the current
time, then the server generates time-related target information at
step 812. After generating the time-related target information, the
generated target information is transmitted to the client at step
814. Such information can be transmitted, for example, over a
communication network.
[0108] The client device may receive the information at step 816.
The client can provide the information for user review at step 820.
In some cases, the client may provide the information for review
upon receiving the information. In other cases, the client will
provide the information for review at a scheduled time, often some
time following receipt of the information. In yet other cases, the
client can provide the information in response to an action by the
user, as opposed to at a particular time independent of user action
following receipt of the information. In this case, the client may
receive the information and wait for a particular response from the
user (e.g., selecting a key on the device), before presenting the
information to the user. In some cases, the user response may
indicate that the user is not interested in the information, in
which case the information may not be presented. As examples of the
scenarios just described, the user can accept (e.g., select to view
the presented information) or reject to review the information. In
some implementations, if the user rejects the information, the
server can update the user data to adjust an indication of user
interest of the presented information. In some examples, the
adjustment can decrease a probability of presenting the information
or similar information to the user in the future.
[0109] Similarly, user response to presented information may be
tracked and used to update user profile information. These
adjustments may guide future predictive determinations and
information delivery. For example, if the user redeems a presented
coupon, such system may be more likely to present similar coupons
in the future. In various implementations, the operations described
above can be combined or separated in various manners, more or
fewer operations may be executed, and operations may occur at
either the client or the server, e.g.
[0110] In any of the implementations described above, the period
for observing user activity or global activity may be a definite
period or an indefinite period. For example, the system may be
programmed to capture data for one week and then stop capturing
data. Alternatively, data may be gathered until a recurring or
semi-recurring pattern begins to show in the data. In such a case,
a minimum collection period may be established, such as one week,
one month, etc., so that the system does not stop gathering data
prematurely if by coincidence events are observed and thought to
constitute a pattern. Maximum periods may similarly be
established.
[0111] The presence or absence of commonality may be determined at
various levels of granularity. For example, if several instances of
an event have been observed, a point may be considered "common" if
it is common to a particular percentage of the instances. As an
example, if the user stops for coffee each morning Monday through
Friday during a first week, but skips stopping for coffee on Friday
of the second week, the system may yet predict that the user will
stop for coffee on each future workday. In this fashion, the
incidence of the skipped day may be considered an aberration, not
worthy of altering the predictive schedule developed based on the
observed user activity. On the other hand, if the user begins
repeatedly skipping the morning coffee stop, the system may
determine that the user's behavior activity is changing, and may
adjust predictive determinations and information delivery
accordingly.
[0112] Nested or overlapping patterns may also be observed. For
example, on top of the pattern of stopping for coffee each weekday,
a pattern of purchasing a particular delicacy on particular days
may be determined based on transaction records. As another example,
the user may typically purchase a newspaper each Friday to get an
early look at happenings during the upcoming weekend, or may
purchase a paper on Monday mornings during the football season to
check the statistics of his fantasy football team. The papers may
be purchased, e.g., at the coffee shop or from a street-side news
vendor or distribution box near the coffee shop. If the user
decides to go on a health kick and swear off caffeine, including
abstaining from coffee, such newspaper purchases may be expected to
similarly diminish in frequency of occurrence.
[0113] FIG. 9 shows an example of a generic computer device 900 and
a generic mobile computer device 950. In various implementations,
the devices may be used to implement the systems and methods
described in this document. Computing device 900 is intended to
represent various forms of digital computers, such as laptops,
desktops, workstations, personal digital assistants, servers, blade
servers, mainframes, and other appropriate computers. Computing
device 950 is intended to represent various forms of mobile
devices, such as personal digital assistants, cellular telephones,
smartphones, and other similar computing devices. The components
shown here, their connections and relationships, and their
functions, are meant to be exemplary only, and are not meant to
limit implementations of the systems, methods, devices or
techniques described and/or claimed in this document.
[0114] Computing device 900 includes a processor 902, memory 904, a
storage device 906, a high-speed interface 908 connecting to memory
904 and high-speed expansion ports 910, and a low speed interface
912 connecting to a low speed bus or port 914 and storage device
906. Each of the components 902, 904, 906, 908, 910, and 912 are
interconnected using various busses, and may be mounted on a common
motherboard or in other manners as appropriate. The processor 902
can process instructions for execution within the computing device
900, including instructions stored in the memory 904 or on the
storage device 906 to display graphical information for a GUI on an
external input/output device, such as display 916 coupled to high
speed interface 908. In other implementations, multiple processors
and/or multiple busses may be used, as appropriate, along with
multiple memories and types of memory. Also, multiple computing
devices 900 may be connected, with each device providing portions
of the necessary operations (e.g., as a server bank, a group of
blade servers, or a multi-processor system).
[0115] The memory 904 stores information within the computing
device 900. In one implementation, the memory 904 is a
computer-readable medium. In one implementation, the memory 904 is
a volatile memory unit or units. In another implementation, the
memory 904 is a non-volatile memory unit or units.
[0116] The storage device 906 is capable of providing mass storage
for the computing device 900. In one implementation, the storage
device 906 is a computer-readable medium. In various
implementations, the storage device 906 may be a floppy disk
device, a hard disk device, an optical disk device, a tape device,
a flash memory or other similar solid-state memory device, or an
array of devices, including devices in a storage area network or
other configurations. In one implementation, a computer program
product is tangibly embodied in an information carrier. In an
implementation, the computer program product may be stored on a
computer-readable medium, including those discussed above. The
computer program product contains instructions that, when executed,
perform one or more methods, such as those described above. The
information carrier is a computer- or machine-readable medium, such
as the memory 904, the storage device 906, memory on processor 902,
or a propagated signal.
[0117] The high speed controller 908 manages bandwidth-intensive
operations for the computing device 900, while the low speed
controller 912 manages lower-bandwidth-intensive operations. Such
allocation of duties is exemplary only. In one implementation, the
high-speed controller 908 is coupled to memory 904, display 916
(e.g., through a graphics processor or accelerator), and to
high-speed expansion ports 910, which may accept various expansion
cards (not shown). In the implementation, low-speed controller 912
is coupled to storage device 906 and low-speed expansion port 914.
The low-speed expansion port, which may include various
communication ports (e.g., USB, Bluetooth, Ethernet, wireless
Ethernet) may be coupled to one or more input/output devices, such
as a keyboard, a pointing device, a scanner, or a networking device
such as a switch or router, e.g., through a network adapter.
[0118] The computing device 900 may be implemented in a number of
different forms, as shown in the figure. For example, it may be
implemented as a standard server 920, or multiple times in a group
of such servers. It may also be implemented as part of a rack
server system 924. In addition, it may be implemented in a personal
computer such as a laptop computer 922. Alternatively, components
from computing device 900 may be combined with other components in
a mobile device (not shown), such as device 950. Each of such
devices may contain one or more of computing device 900, 950, and
an entire system may be made up of multiple computing devices 900,
950 communicating with each other.
[0119] Computing device 950 includes a processor 952, memory 964,
an input/output device such as a display 954, a communication
interface 966, and a transceiver 968, among other components. The
device 950 may also be provided with a storage device, such as a
microdrive or other device, to provide additional storage. Each of
the components 952, 964, 954, 966, and 968 are interconnected using
various busses, and several of the components may be mounted on a
common motherboard or in other manners as appropriate.
[0120] The processor 952 can process instructions for execution
within the computing device 950, including instructions stored in
the memory 964. The processor may also include separate analog and
digital processors. The processor may provide, for example, for
coordination of the other components of the device 950, such as
control of user interfaces, applications run by device 950, and
wireless communication by device 950.
[0121] Processor 952 may communicate with a user through control
interface 958 and display interface 956 coupled to the display 954.
The display 954 may be, for example, a TFT LCD display or an OLED
display, or other appropriate display technology. The display
interface 956 may comprise appropriate circuitry for driving the
display 954 to present graphical and other information to a user.
The control interface 958 may receive commands from a user and
convert them for submission to the processor 952. In addition, an
external interface 962 may be provided in communication with
processor 952, so as to enable near-area communication of device
950 with other devices. External interface 962 may provide, for
example, for wired communication (e.g., via a docking procedure) or
for wireless communication (e.g., via Bluetooth or other such
technologies).
[0122] The memory 964 stores information within the computing
device 950. In one implementation, the memory 964 is a
computer-readable medium. In one implementation, the memory 964 is
a volatile memory unit or units. In another implementation, the
memory 964 is a non-volatile memory unit or units. Expansion memory
974 may also be provided and connected to device 950 through
expansion interface 972, which may include, for example, a SIMM
card interface. Such expansion memory 974 may provide extra storage
space for device 950, or may also store applications or other
information for device 950. Specifically, expansion memory 974 may
include instructions to carry out or supplement the processes
described above, and may include secure information also. Thus, for
example, expansion memory 974 may be provided as a security module
for device 950, and may be programmed with instructions that permit
secure use of device 950. In addition, secure applications may be
provided via the SIMM cards, along with additional information,
such as placing identifying information on the SIMM card in a
non-hackable manner.
[0123] The memory may include, for example, flash memory and/or
MRAM memory, as discussed below. In one implementation, a computer
program product is tangibly embodied in an information carrier. The
computer program product contains instructions that, when executed,
perform one or more methods, such as those described above. The
information carrier is a computer- or machine-readable medium, such
as the memory 964, expansion memory 974, memory on processor 952,
or a propagated signal.
[0124] Device 950 may communicate wirelessly through communication
interface 966, which may include digital signal processing
circuitry as appropriate. Communication interface 966 may provide
for communications under various modes or protocols, such as GSM
voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA,
CDMA2000, or GPRS, among others. Such communication may occur, for
example, through radio-frequency transceiver 968. In addition,
short-range communication may occur, such as using a Bluetooth,
WiFi, or other such transceiver (not shown). In addition, GPS
receiver module 970 may provide additional wireless data to device
950, which may be used as appropriate by applications running on
device 950.
[0125] Device 950 may also communicate audibly using audio codec
960, which may receive spoken information from a user and convert
it to usable digital information. Audio codec 960 may likewise
generate audible sound for a user, such as through a speaker, e.g.,
in a handset of device 950. Such sound may include sound from voice
telephone calls, may include recorded sound (e.g., voice messages,
music files, etc.) and may also include sound generated by
applications operating on device 950.
[0126] The computing device 950 may be implemented in a number of
different forms, as shown in the figure. For example, it may be
implemented as a cellular telephone 980. It may also be implemented
as part of a smartphone 982, personal digital assistant, or other
similar mobile devices.
[0127] Various implementations of the systems and techniques
described here can be realized in digital electronic circuitry,
integrated circuitry, specially designed ASICs (application
specific integrated circuits), computer hardware, firmware,
software, and/or combinations thereof. These various
implementations can include implementation in one or more computer
programs that are executable and/or interpretable on a programmable
system including at least one programmable processor, which may be
special or general purpose, coupled to receive data and
instructions from, and to transmit data and instructions to, a
storage system, at least one input device, and at least one output
device.
[0128] These computer programs (also known as programs, software,
software applications or code) include machine instructions for a
programmable processor, and can be implemented in a high-level
procedural and/or object-oriented programming language, and/or in
assembly/machine language. As used herein, the terms
"machine-readable medium" or "computer-readable medium" refer to
any computer program product, apparatus and/or device (e.g.,
magnetic discs, optical disks, memory, programmable logic devices
(PLDs)) used to provide machine instructions and/or data to a
programmable processor, including a machine-readable medium that
receives machine instructions as a machine-readable signal. The
term "machine-readable signal" refers to any signal used to provide
machine instructions and/or data to a programmable processor.
[0129] To provide for interaction with a user, the systems, devices
and techniques described here can be implemented on a computer
having a display device (e.g., a CRT (cathode ray tube) or LCD
(liquid crystal display) monitor) for displaying information to the
user and a keyboard and a pointing device (e.g., a mouse or a
trackball), by which the user can provide input to the computer.
Other kinds of devices can be used to provide for interaction with
a user as well; for example, feedback provided to the user can be
any form of sensory feedback (e.g., visual feedback, auditory
feedback, or tactile feedback), and input from the user can be
received in any form, including acoustic, speech, or tactile
input.
[0130] The systems, devices and techniques described here can be
implemented in a computing system that includes a back-end
component (e.g., a data server), or that includes a middleware
component (e.g., an application server), or that includes a
front-end component (e.g., a client computer having a graphical
user interface or a web browser through which a user can interact
with an implementation of the systems and techniques described
here), or any combination of such back-end, middleware, or
front-end components. The components of the system can be
interconnected by any form or medium of digital data communication
(e.g., a communication network). Examples of communication networks
include a local area network ("LAN"), a wide area network ("WAN"),
and the Internet.
[0131] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other.
[0132] A number of implementations have been described.
Nevertheless, it will be understood that various modifications may
be made without departing from the spirit and scope of the systems,
devices, methods and techniques described here. For example,
various forms of the flows shown above may be used, with steps
re-ordered, added, or removed. It will be appreciated that any
appropriate time interval may be used to make the determinations
described above, and that the determinations may be made using any
appropriate number of data points within the time interval.
Accordingly, other implementations are within the scope of the
following claims.
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