U.S. patent application number 16/730560 was filed with the patent office on 2020-04-30 for user description based on contexts of location and time.
This patent application is currently assigned to Verve Wireless, Inc.. The applicant listed for this patent is Verve Wireless, Inc.. Invention is credited to David ROSENBERG.
Application Number | 20200137526 16/730560 |
Document ID | / |
Family ID | 49292682 |
Filed Date | 2020-04-30 |
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United States Patent
Application |
20200137526 |
Kind Code |
A1 |
ROSENBERG; David |
April 30, 2020 |
USER DESCRIPTION BASED ON CONTEXTS OF LOCATION AND TIME
Abstract
A description of a user is estimated based on the context of a
user's past and present locations. A disclosed data-processing
system continually receives data points for each user that
represent spatial and/or temporal events. These events represent,
for example, presence of a person at a specific geographic location
such as a geographic area or point of interest (POI). The
data-processing system evaluates the received data points in
relation to one or more of the geographic locations, yielding
results that are also based on the demographic characteristics of
each visited location and the commercial characteristics of each
visited location. The data-processing system evaluates the data
points also to determine patterns exhibited in each user's activity
or inactivity, and patterns exhibited in the distance traveled and
the type of travel. The data-processing system bases the user
descriptions on the results of these evaluations.
Inventors: |
ROSENBERG; David; (New York,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Verve Wireless, Inc. |
Carlsbad |
CA |
US |
|
|
Assignee: |
Verve Wireless, Inc.
Carlsbad
CA
|
Family ID: |
49292682 |
Appl. No.: |
16/730560 |
Filed: |
December 30, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14496234 |
Sep 25, 2014 |
10524093 |
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16730560 |
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13488608 |
Jun 5, 2012 |
8849312 |
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14496234 |
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61622131 |
Apr 10, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/22 20130101;
H04W 4/21 20180201; G06F 2221/2111 20130101; G06F 21/316 20130101;
H04L 67/18 20130101; H04W 4/029 20180201 |
International
Class: |
H04W 4/029 20060101
H04W004/029; H04L 29/08 20060101 H04L029/08; H04W 4/21 20060101
H04W004/21 |
Claims
1. A method comprising: receiving, by a data-processing system, a
plurality of data points D that correspond to a first user, wherein
each of the data points in the plurality represents at least one of
i) a spatial event and ii) a temporal event, and wherein the
plurality comprises at least i) a first data point that occurs at a
first time t.sub.1, d(t.sub.1), and ii) a second data point that
occurs at a second time t.sub.2, d(t.sub.1); determining a
difference in time between a first time that is represented by the
first data point d(t.sub.1) and a second time that is represented
by the second data point d(t.sub.2); evaluating i) a first
characteristic c.sub.1 for a first geographic location l.sub.a,
yielding a first value c.sub.1 (l.sub.1), and ii) the first data
point d(t.sub.1) in relation to the first geographic location
l.sub.1, yielding a first result that is based on the first value
c.sub.1 (l.sub.1); and estimating a description of the first user,
wherein the description is based on i) the determined difference in
time and ii) the first result.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent. Application No. 61/622,131, filed on 10 Apr. 2012, entitled
"User Description Based on Location Context" (attorney docket no.
375-005us), which is incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The present invention relates to sensor analytics in
general, and, more particularly, to describing a user based on one
or more contexts of location and time.
BACKGROUND OF THE INVENTION
[0003] Global positioning system (GPS) and other position
determining systems are enabled in a wide variety of devices,
including mobile phones, taxis, personal navigation devices, and
automobiles. The proliferation of such enabled devices has resulted
in an enormous amount of historic and real-time data being
generated. The type of data generated typically consists of a
latitude, a longitude, a unique identifier and, in some cases,
metadata.
[0004] The assessed location, or "geolocation," provided by the
position determining systems can be used to deliver location-based
services to a user. For example, location-based media (LBM) systems
deliver multimedia directly to the user of a mobile device
dependent upon the device's assessed location. Media sequences can
be delivered to, or triggered within, portable wireless devices
that are location-enabled (location-aware) and have the capacity to
display audiovisual content. Such content is managed and organized
externally to the device. The mobile device then downloads
formatted content with location-coordinated triggers applied to
each media sequence. As the location-aware device enters a
geographic area or region, the media sequence assigned to that area
is triggered. The assigned media can be designed to be of optimal
relevance to the user in the context of the user's immediate
surroundings.
[0005] FIG. 1 depicts geographic view 100 in the prior art and
illustrates an example of the delivery of a location-based service.
View 100 comprises geographic areas 102, 104, 106, and 108. Area
102 corresponds to the area occupied by a city's train station.
Areas 104 through 108 correspond to various areas surrounding the
train station. Persons 112, 114, 116, and 118 are examples of
different people presently within the train station area, who are
carrying mobile devices. The locations of persons 112 through 118
are known through position determining equipment (PDE) that
determines the geolocation of the mobile device carded by each
person.
[0006] By knowing the geolocation coordinates of each person 112
through 118 and by knowing the boundary coordinates of areas 102
through 108, a system of the prior art is able to infer that the
four people are presently at the train station. As a result,
location-based services can be provided to all of the people
depicted, within the context of the people being presently at the
train station.
SUMMARY OF THE INVENTION
[0007] The present geolocation of a person can be helpful in
understanding the person. Such static information, however, does
not provide a unique description of that person. For example,
although four people might be presently at the same train station,
it is impossible to determine solely from their current geolocation
that i) the first person is a student who is going to class, ii)
the second is a commuter coming from work, iii) the third is a
transit employee working at the train station, and iv) the fourth
is a mother who is returning home from a day at the museum with her
children. Consequently, it night be inappropriate to deliver the
same location-based services to all four people while only
accounting for theft present assessed location. Using another
example, although household census demographics are helpful in
understanding the people who are living in specific: geographic
areas, such static information similarly may not provide a
sufficiently unique description of each user.
[0008] The present invention enables a description of a user to be
estimated based on the context of the user's past and present
locations, and as a function of time. The context of a user
location can comprise, for example, i) what the location is near (a
park, a store, etc.), ii) what the population is in the location's
vicinity (by income, by race, etc.), or iii) what action the user
is taking at the particular moment that corresponds to a particular
position determination (texting, tweeting, talking to another
party, etc.). This is in contrast to at least some techniques in
the prior art, which only account for the context of the present
location and at the present moment in time, as is the case of the
four people in the aforementioned example.
[0009] In accordance with an illustrative embodiment of the present
invention, a data-processing system continually receives data
points that represent spatial events or temporal events, or both.
These events represent, for example, presence of a person, thing,
or object at each visited location, as well as the times that the
visits occurred. Such locations include, while not being limited
to, a geographic area that is based on a census tract or a
geographic point of interest (POI) such as a place of business.
[0010] The data-processing system evaluates each received data
point by comparing its represented geolocation against one or more
geographic areas or geographic. POIs, in order to determine whether
the data point falls within the area or is near the POI, as
appropriate. One or more characteristics specific to the area or
POI are also considered; these characteristics include, while not
being limited to, demographic and commercial categories and
subcategories that can be provided from an external database. The
data-processing system also evaluates the data points with respect
to one another, in order to determine activity or inactivity
patterns exhibited by the user and to determine patterns exhibited
in the distance traveled and the type of travel. The
data-processing system estimates a user description for that user,
based on compiling the results of one or more of the evaluations of
the data points and characteristics.
[0011] The methods and system of the various embodiments described
herein are advantageous for a variety of reasons. The demographic
contexts of the various places that a user visits, coupled with the
time of day visited, impart information about a user's exposure to
people and places. Similarly, the commercial contexts of the
various places that a user visits impart their qualities to the
user, and the commercial exposure defines the user's lifestyle. The
context of various activity and inactivity patterns exhibited by a
user over time, as well as the amount of activity, also impart
useful information about a user. Lastly, the context of amount of
distance traveled and the travel patterns exhibited by a user over
time impart useful information as well.
[0012] The information acquired about a user, which information
comprises various contexts (demographic, commercial, user
inactivity, distance traveled, etc.), becomes part of the user's
description. Each user description can be made unique in relation
to other users' descriptions, given a sufficient number of data
points being evaluated and characteristics being considered as part
of the estimated description. By estimating a unique description
for each user, the system disclosed herein is able to distinguish,
for example, i) the aforementioned student who is going to class,
from ii) the commuter coming from work, from iii) the transit
employee working at the train station, from iv) the mother who is
returning home. In doing so, and by accounting for more than just
the present assessed location of each user, the disclosed system is
able to provide location-based, time-based, and other event-based
services that are customizable to each user, based on each user's
unique description. For example, based on the user descriptions
produced by the disclosed system, a media delivery service is able
to deliver i) a first customized advertisement to a first user and
ii) a second customized advertisement concurrently to a second user
who is standing next to the first user.
[0013] An illustrative embodiment of the present invention
comprises: receiving, by a data-processing system, a plurality of
data points D that correspond to a first user, wherein each of the
data points in the plurality represents at least one of i) a
spatial event and ii) a temporal event, and wherein the plurality
comprises at least i) a first data point that occurs at a first
time t.sub.1, d(t.sub.1), and ii) a second data point that occurs
at a second time t.sub.2, d(t.sub.2); evaluating i) a first
characteristic c.sub.1 for a first geographic area a.sub.1 and for
a second geographic area a.sub.2, yielding a first value
c.sub.1(a.sub.1) and a second value c.sub.1(a.sub.2), respectively,
ii) the first data point d(t.sub.1) in relation to the first
geographic area a.sub.1, yielding a first result that is based on
the first value c.sub.1(a.sub.1), and iii) the second data point
d(t.sub.2) in relation to the second geographic area a.sub.2,
yielding a second result that is based on the second value
c.sub.1(a.sub.2); and estimating a description of the first user,
wherein the description is based on the first result and the second
result,
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 depicts geographic view 100 in the prior art.
[0015] FIG. 2 depicts a block diagram of the salient components of
sensor analytics system 200, in accordance with an illustrative
embodiment of the present invention.
[0016] FIG. 3 depicts a block diagram comprising the salient
elements of data-processing system 210, in accordance with an
illustrative embodiment of the present invention.
[0017] FIG. 4 depicts a flowchart of the salient tasks performed by
data-processing system 210, in accordance with an illustrative
embodiment of the present invention.
[0018] FIG. 5 depicts a flowchart of the salient subtasks of task
405.
[0019] FIG. 6 depicts a flowchart of the salient subtasks of task
410.
[0020] FIG. 7 depicts a flowchart of the salient subtasks of task
605.
[0021] FIG. 8 depicts an example of describing users in terms of
their demographic exposure.
[0022] FIG. 9 depicts an example of describing users in terms of
their commercial exposure.
[0023] FIG. 10 depicts occurrences of events that are attributable
to a particular user, across a timeline.
[0024] FIG. 11 depicts a flowchart of the salient subtasks of task
615.
DETAILED DESCRIPTION
[0025] The following terms are defined for use in this
Specification, including the appended claims: [0026] The term
"location," and its inflected forms, is defined as a
zero-dimensional point, a one-dimensional line, a two-dimensional
area, or a three-dimensional volume. For example, a location
l.sub.i can be a geographic point of interest p.sub.i or a
geographic area a.sub.i. [0027] The term "spatial-temporal (S-T)
event," or "event," and its inflected forms, is defined as any
activity or occurrence that can be identified by the location
and/or time at which it occurs. For example and without limitation,
a spatial-temporal event can represent the arrival or departure of
a person(s), animal(s), or product(s) to and/or from a specific
geographic location such as, but not limited to, a place of
employment, a transit terminal, a food store, a landmark, a
shopping center, a hospital, a residence, a street, town, city,
state, country, or any location determined by a global positioning
system (GPS)-enabled device or assessed by other position
determining equipment.
[0028] The term "spatial-temporal (S-T) data point," or "data
point," and its inflected forms, is defined as data or other
information that identifies a specific event, user, or device at a
specific location and/or time. For example and without limitation,
a spatial-temporal data point can include: a time stamp along with
a corresponding geographic location, such as, the time at a
latitude and longitude; a time stamp along with an indicium of a
specific event at a fixed geographic location, such as the time of
a special or sale at a store or entertainment venue; measurement
uncertainty information, such as the accuracy of the position
determination; the occurrence of an event or action at a particular
time and location, such as a taxi being full in the warehouse
district at 2:00 am, texting occurring, tweeting occurring, etc,;
details about a user communication, such as a Short Message Service
(SMS) text having been sent; or other supplemental information.
Data points originate from various data sources that include, while
not being limited to, a location enabled device such as a cellular
telephone, a GPS enabled device, a networked device, a WiFi enabled
device, a radio-frequency identification (RFID)-enabled device, and
an automated teller machine (ATM) machine. [0029] The term "unique
identifier," and its inflected forms, is defined as any information
that identifies a particular person, device, object, event or place
at a particular time, occurrence or location. A unique identifier
can be that of a location enabled device such as a cellular
telephone, a GPS enabled device, a networked device, a WiFi enabled
device, a radio-frequency identification (RFID)-enabled device, an
automated teller machine (ATM) machine, or any other device that
identifies a spatial-temporal data point. A unique identifier can
also include a place or event that identifies a spatial-temporal
data point associated with that place or event. [0030] The term
"individualized data point," and its inflected forms, is defined as
a spatial-temporal data point that is identified with a unique
identifier. An identifier is considered unique if it is the only
one of its kind within a defined address space, such as that of a
telecommunications system or network.
[0031] FIG. 2 depicts a block diagram of the salient components of
sensor analytics system 200, in accordance with an illustrative
embodiment of the present invention, FIG. 2 depicts data-processing
system 210; display 212; data store 214; and telecommunications
network 220. Also depicted are: wireless telecommunications
terminal 222; personal computer 224; personal digital assistant
(PDA) 225; position determining equipment (PDE) 228; and data store
230. The components depicted in FIG. 2 are interconnected as
shown.
[0032] As those who are skilled in the art will appreciate, after
reading this disclosure, sensor analytics system 200 can comprise
additional components that also provide sources and repositories of
data, in some embodiments. Furthermore, in addition to the
components depicted in FIG. 2, sensor analytics system 200 can also
be connected to external components that provide additional sources
and repositories of data, in some embodiments.
[0033] Data-processing system 210 is a computer that comprises
non-transitory memory, processing component(s), and communication
component(s), as described in more detail in FIG. 2.
Data-processing system 210 executes and coordinates the salient
tasks of sensor analytics system 200 according to an illustrative
embodiment of the present invention. For example, data-processing
system 210 receives, via network 220, spatial and/or temporal data
from one or more of the data sources, as described in detail below.
Data-processing system 210 then analyzes the received data as
described below and with respect to the tasks depicted in FIGS. 4
through 11. System 210 is able to send the results of the analysis
to user devices (e.g., terminal 222, computer 224, PDE 226, etc.)
for presentation, export the results to display 212 separate from
the user devices, and/or store the results in data store 214 or
230. Although depicted as a component that is separate from system
210, data store 214 can alternatively be located in data-processing
system 210's memory, in some embodiments.
[0034] Display 212 is an image display device. Display 212 receives
video signals conveying analysis results from data-processing
system 210 and displays the signals in a manner that is visible to
a user, in well-known fashion.
[0035] Data store 214 is an electronic data storage device. Data
store 214 comprises non-transitory memory (e.g., a hard disk, etc.)
that is used by sensor analytics system 200 to store, archive, and
retrieve information, in well-known fashion. For example, data
store 214 receives signals conveying video and/or analysis data
from data-processing system 210 and archives the data. Data store
214 can also transmit supplemental information data to
data-processing system 210 in response to a retrieval request, in
some embodiments. Data store 214 can also transmit archived data to
data-processing system 210 in response to a retrieval request, in
some embodiments.
[0036] Telecommunications network 220 comprises a collection of
links and nodes that enable telecommunication between devices, in
well-known fashion. Telecommunications network 220 provides sensor
analytics system 200 with connectivity to other systems that enable
sensor analytics system 200 to retrieve data and also to transmit,
store, and archive data as needed. in some embodiments,
telecommunications network 220 is the Public Switched Telephone
Network (PSTN); in some embodiments, network 220 is the Internet;
in some embodiments, network 220 is a private data network. It will
be clear to those with ordinary skill in the art, after reading the
present disclosure, that in some embodiments network 220 can
comprise one or more of the above-mentioned networks and/or other
telecommunications networks, without limitation. Furthermore, it
will be dear to those will ordinary skill in the art, after reading
this disclosure, that telecommunications network 220 can comprise
elements that are capable of wired and/or wireless communication,
without limitation.
[0037] The user devices of sensor analytics system 200 include, but
are not limited to, electronic devices such as wireless
telecommunications terminal 212, personal computer 224, and
personal digital assistant 226. Terminal 212. can be, for example
and without limitation: a mobile, a cell phone, a smart phone, a
cordless phone, and so on. Personal computer 224 can be, for
example and without limitation: a desktop computer, a notebook
computer, a tablet computer, and so on. The user devices can
include one or more program applications that are designed to
interact with data-processing system 210 in order to facilitate
presentation of data to a user, for example and without
limitation.
[0038] As those who are skilled in the art will appreciate, one or
more of the user devices can be global positioning system
(GPS)-enabled or are at least capable of providing an indication of
a spatial and/or temporal event occurring at the user device.
[0039] Position determining equipment (PDE) 228 identifies the
location of mobile devices, in well-known fashion. As those who are
skilled in the art will appreciate, after reading this disclosure,
PDE 228 is capable of determining the location of one or more of
the other user devices depicted and of providing the location, with
or without a timestamp to data-processing system 210. In doing so,
PDE 228 is also capable of providing an indication of a spatial
and/or temporal event occurring at a measured user device.
[0040] Data store 230 is capable of providing data related to
spatial and/or temporal events. The data provided by data store 230
may have originated from other sources of data, such as terminal
222, computer 224, PDA 226, or PDE 228, In some embodiments, data
store 230 is analogous to, and performs the same functions as, data
store 214 described above.
[0041] The data points provided to data-processing system 210 from
the aforementioned devices can include information relating to
and/or identifying one or more particular events, users, or devices
at a certain location and/or time. In accordance with an
illustrative embodiment of the present invention, the event can
correspond to a spatial-temporal event. In some embodiments, the
event can correspond to one or more environmental changes, such as
a change in weather or temperature. In some other embodiments, the
event may correspond to a user activity, such as placing a phone
call or purchasing an item either in person or through a network
connected device. The event may correspond to public events or
entertainment such as speeches, games, movies, dance shows,
musicals, or sales promotions. In some embodiments, the event may
correspond to a change in patterns, such as the onset of a traffic
jam. In some other embodiments, the event may correspond to an
electronic device based activity, such as the startup of computer
program application or login activity. Other electronic
device-based activity may be identified as well.
[0042] In some embodiments, the data points received by
data-processing system 210 can include data provided from a
wireless network-based communication device such as terminal 222.
Such data may include, but is not limited to, i) the location of a
particular cell phone within a cellular network at a particular
time and/or ii) the GPS location and time data. Alternatively, or
in addition, the data may include user information, such as a user
identifier (ID) or an account ID associated with a particular
device. The data originating at a communication device can be
passed directly from the device or indirectly through another
device such as PDE 228 or data store 230. In some embodiments, the
data received by data-processing system 210 can be provided by a
passive location-based service such as an ATM, which gives the
location and/or time of a unique user. This also can include
RFID-enabled devices such as RFID tags used for toll collection
services, proximity cards, product and inventory tracking, and
animal tracking and identification. Moreover, the data can include
information that relates to the user device from which it is being
provided, such as whether the device is a cell phone, laptop,
personal digital assistant or GPS-enabled device.
[0043] The data points may be provided to data-processing system
210 in real-time as an event or activity occurs. For example, an
RFID-enabled system may pass location and time data in real-time to
data-processing system 210 when the RFID-enabled system is
triggered by an RFID tag, such as those included in automobiles or
proximity cards. Alternatively, or in addition, data may be
provided from a data provider or data aggregator. The data provider
or data collector can collect the data points over a specified
period prior to sending them to data-processing system 210. For
example, PDE 228 or data store 230 may store, over a specified time
period, data that represents the occurrence of one or more
particular events that occur on a computing platform, such as
operating system startup, user login, or an application specific
activity. The stored data then may be provided to data-processing
system 210 periodically or sporadically according to a
predetermined schedule or at user-specified times.
[0044] In some embodiments, the data provided to data-processing
system 210 can include demographic and/or commercial information.
Such information can be of a general nature or can be specifically
associated with the locations and/or times of one or more events
and/or activities.
[0045] In some embodiments, data-processing system 210, in order to
perform some of its functions, also communicates, coordinates, and
electronically interacts (wired or wirelessly as appropriate) with
systems outside of sensor analytics system 200.
[0046] It will be clear to those skilled in the art, after reading
the present disclosure, that the system illustrated in FIG. 2 can
be embodied in different variations that are consistent with the
present invention. For example, some embodiments comprise several
displays such as display 212 for a plurality of users. For example,
in some embodiments, data store 214 and/or data store 230 each
comprise a plurality of data stores or a plurality of data storage
technologies (e.g., a cloud-based storage system, etc.). For
example, in some embodiments, not all depicted components are
on-site. For example, in some embodiments, the depicted components
are interconnected indirectly (e.g., through servers, gateways,
switches, networks, the Internet, etc.). In any event, it will be
clear to those skilled in the art, after reading the present
disclosure, how to make and use sensor analytics system 200.
[0047] FIG. 3 depicts a block diagram comprising the salient
elements of data-processing system 210, in accordance with an
illustrative embodiment of the present invention. Data-processing
system 210 comprises: processor 301; memory 302; transceiver 303;
and communication paths to display 212, data store 214, and
telecommunications network 220, interconnected as shown.
[0048] Processor 301 is a processing device such as a
microprocessor that, in conjunction with the other components in
data-processing system 210, is capable of executing the software
and processing the data according to the tasks described herein.
Processor 301 processes data points and other data received via
transceiver 303. After processing, it transmits video signals to
display 212 based on the processing results. Processor 301 is well
known in the art.
[0049] Memory 302 is non-transitory memory that stores program code
and data sufficient to enable the execution of software and data
processing according to the tasks recited herein. Memory 302 is
well known in the art.
[0050] Transceiver 303 is a component that enables data-processing
system 210 to communicate electronically, whether in a wired or
wireless configuration, with other components internal and external
to sensor analytics system 200, including receiving data from
telecommunications network 220, such as data originating at the
individual devices connected to network 220; and transmitting to
and from data store 214 and external systems via telecommunications
network 220. Transceiver 303 is well known in the art.
[0051] It will be dear to those skilled in the art, after reading
the present disclosure, that data-processing system 210 can be
embodied in a different configuration than that depicted, as a
multi-processor platform, as a server (e.g., application server,
etc.), as a sub-component of a larger computing platform, or in
some other computing environment--all within the scope of the
present invention. It will be clear to those skilled in the art,
after reading the present disclosure, how to make and use
data-processing system 210.
[0052] FIGS. 4 through 11 depict flowcharts and related examples of
the salient tasks performed by data-processing system 210, in
accordance with an illustrative embodiment of the present
invention. The operations performed by system 210 are depicted in
the drawings in a particular order. It will, however, be clear to
those skilled in the art after reading this disclosure that such
operations can be performed in a different order than that depicted
or can be performed in a non-sequential order. For example, in
certain circumstances, multitasking and parallel processing may be
advantageous. Some or all of the depicted tasks can be combined,
performed in a different order, performed by different devices.
Some of the depicted tasks can be omitted.
[0053] Moreover, the separation of various components in the
embodiments described herein should not be understood as requiring
such separation in all embodiments. Furthermore, it will be clear
to those skilled in the art, after reading this disclosure, that
the described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0054] For pedagogical purposes, the tasks depicted in the
flowcharts herein are presented from the perspective of applying to
a single user. It will, however, be clear to those skilled in the
art, after reading this disclosure, that the performed operations
can be applied to multiple users, either concurrently and/or
sequentially. Furthermore, the depicted tasks can be repeated,
either periodically and/or sporadically, for example in order to
update the information that is processed for one or more users.
[0055] FIG. 4 depicts a flowchart of the salient tasks performed by
data-processing system 210, as shown in FIG. 3, in accordance with
an illustrative embodiment of the present invention.
[0056] At task 405, data-processing system 210 imports
spatial-temporal (S-T) data points. The salient subtasks of task
405 are described below and with respect to FIG. 5,
[0057] At task 410, system 210 estimates a description for a user,
based on one or more data points, or based on one or more
characteristics, or both. Estimating such a description for each
user, for example and without limitation, allows users to be
compared (e.g., to each other, to a database, etc.) and classified.
The salient subtasks of task 410 are described below and with
respect to FIG. 5.
[0058] At task 415, system 210 exports the results of the estimate
from task 410 to one or more other devices. The other devices can
be, for example and without limitation, display 212, data store
214, terminal 222, computer 224, PDA 226, PDE 228, and/or data
store 230. in exporting the results, system 210 transmits signals
that convey the results in well-known fashion.
[0059] In accordance with the illustrative embodiment, the
disclosed system is able to provide location-based, time-based, and
other event-based services that are customizable to each user,
based on each user's description estimate. For example, based on a
user description specific to a particular user, the disclosed
system may present an advertisement customized to that user. In
addition or in the alternative, the disclosed system may present an
advertisement customized to a particular user, based on one or more
differences between that user's description and one or more other
user descriptions. The disclosed system may present the
advertisement, for example, by transmitting a signal that conveys
the advertisement either directly to the user's device (e.g.,
wireless terminal, personal computer, etc,) or to another
data-processing system.
[0060] FIG. 5 depicts a flowchart of the salient subtasks of task
405, in accordance with an illustrative embodiment of the present
invention.
[0061] At task 505, data-processing system 210 receives one or more
spatial-temporal data points originating from a user device (i.e.,
data point set D consisting of data points d(t.sub.i) occurring at
different points in time t.sub.i), In some embodiments, system 210
receives additional data used for processing the data points. In
importing the data, system 210 receives signals that convey the
data in well-known fashion.
[0062] At task 510, system 210 associates a unique identifier with
each raw data point that is received, to obtain one or more
individualized data points. Based on the information already
contained within the data point, for example and without
limitation, the unique identifier may correspond to a time stamp
specifying a time the event occurred, a location stamp specifying a
location of where the event occurred, a user stamp identifying the
particular user to which the event corresponds, or a device stamp
identifying a particular device from which the event data is
received.
[0063] At task 515, system 210 detects location data that exhibits
problems and corrects the data. The types of problems detected and
corrected by system 210 include, but are not limited to: missing
location data; duplicate records; observation gaps in time; forced
snapping to discrete locations (e.g., roads, cell towers, arcs at a
fixed distance from a cell tower, etc.); out-of-range location
points; bursty behavior; incorrect or truncated data; and/or
"superhuman" travel (e.g., travel faster than an airplane,
etc.).
[0064] FIG. 6 depicts a flowchart of the salient subtasks of task
410, in accordance with an illustrative embodiment of the present
invention.
[0065] At task 605, data-processing system 210 evaluates one or
more data points, in which a datum or data (e.g., latitude,
longitude, timestamp, accuracy, metadata, identifier, etc.) that
are represented by each data point, or information derived from
these data (e.g., geolocation, time of day, date, etc,), are
evaluated against one or more of:
[0066] i. a geographic area or areas,
[0067] ii. a geographic point of interest or points of interest,
and
[0068] iii. other data points.
Moreover, system 210 evaluates one or more characteristics or other
information against the areas and/or points of interest, in order
to evaluate the data points further. For pedagogical purposes, the
results of the evaluations are referred to as "sufficient
statistics."
[0069] The salient subtasks of task 605 for generating the
sufficient statistics are described in detail below and with
respect to FIG. 7, along with the aforementioned characteristics
and relationships in both time and space between data points. In
accordance with an illustrative embodiment, each statistic
generated is a precursor to a corresponding user attribute that
constitutes a user description. As such, the statistics can be
regarded as intermediate data between the received data points and
the user attributes that constitute the estimated user
descriptions.
[0070] At task 610, system 210 combines the sufficient statistics
across time. In some embodiments, the time period across which the
combining is to take place is configurable (e.g., one week, four
weeks, etc.). In accordance with an illustrative embodiment, count
data for a given user and a given spatial event (e.g., stadium,
park, airport, etc.) is summed across the time instances (e.g.,
each day in a week, etc.) that make up a time period (e.g., one
week, etc.). In some alternative embodiments, the combining task
can comprise a function other than a straight summing of the
counts, as those who are skilled in the art will appreciate.
[0071] At this point in the processing, the data is still preserved
at a user level, but is now summarized across time. For example and
without limitation, more recent statistics may be weighted more
heavily than older statistics. In generating the statistics, system
210 reduces the quantity of the data to a tractable amount while
still preserving enough information to generate user descriptions,
as described below. As those who are skilled in the art will
appreciate, after reading this disclosure, various sufficient
statistics can be summarized across time to varying degrees with
respect to one another, or not summarized across time at all.
[0072] At task 615, system 210 generates user description
estimates, in part by normalizing and/or shrinking data that makes
up each sufficient statistic. The salient subtasks of task 615 are
described later and with respect to FIG. 11.
[0073] In a general sense, the process of going from the raw data
being made available to system 210 (prior to task 605) to
generating a user description (in task 615), including the user
description's individual user attribute estimates, uses estimation
theory techniques. Some of the actions performed by system 210 and
associated with tasks 605, 610, and 615 make up one such estimation
technique. However, as those who are skilled in the art will
appreciate, after reading this specification, various estimation
techniques can be used in place of, or to modify, some or all of
the processing associated with tasks 605, 610, and 615 as described
herein. For example, although there are other ways of system 210 to
perform the normalization and shrinkage associated with task 615,
the system of the illustrative embodiment can perform an entirely
different set of tasks than normalization and shrinkage, in order
to generate the user attribute estimates.
[0074] The user attribute estimates of the illustrative embodiment
are "point estimates," in that system 210 generates a single number
for each user attribute. In some embodiments, however, system 210
generates an "interval estimate," in that a specific confidence
interval expressed in terms of a percentage (e.g., 95%, etc.) is
given for the user attribute estimated, thereby allowing the
uncertainty of the estimate to be reflected.
[0075] FIG. 7 depicts a flowchart of the salient subtasks of task
605 for evaluating the data points of a user, in accordance with an
illustrative embodiment of the present invention. As described
earlier, system 210 performs these subtasks in order to generate
sufficient statistics that system 210 uses, in turn, to generate
user descriptions.
[0076] In accordance with the illustrative embodiment, system 210
evaluates a data point by comparing the data that it represents to
some compared-to property. For example and without limitation, the
datum being evaluated can be a latitude/longitude geolocation. The
compared-to property can be the location of an area or point of
interest. The comparison itself can involve determining whether the
latitude/longitude of the data point is contained within the area
or whether it is near the point of interest. As those who are
skilled in the art will appreciate, a compared-to geographic area
can be represented in software by a polygon that is defined by
numeric coordinates that are stored in a memory. Based on the
outcome of the comparison (e.g., the compared condition being
true), system 210 then increments the corresponding event count
that is used for tracking.
[0077] System 210 also evaluates the relationship of one or more
data points with respect to one another. For example and without
limitation, system 210 determines the user's usage gaps, which are
based on the time differences between two or more data points of a
user, and determines the user's distance traveled, which is a
cumulative calculation of a user's distance traveled based on the
geolocation information in the user's data points.
[0078] For each evaluation of a data point, in some embodiments,
system 210 also evaluates one or more characteristics (i.e.,
{c.sub.1, . . . , c.sub.H} in characteristic set C, wherein H is a
positive integer) or other information against the areas and/or
points of interest. Specific examples of such evaluations are
described below. System 210, in some embodiments, also evaluates
the data point for the time of day and/or the calendar time (e.g.,
day, week, etc.) at which the corresponding event occurred and
maintains event counts based on the time of day and/or the calendar
time.
[0079] More specific examples and embodiments of the present
invention are now discussed. At task 705, system 210 evaluates one
or more data points of a user based on the user's demographic
exposure--that is, the user's exposure to people. The demographic
contexts of the various places that a user visits, coupled with the
time of day visited (or not), impart information about a user's
exposure to people and places.
[0080] In accordance with the illustrative embodiment, the
demographic characteristics are used to measure a user's exposure
to each of multiple demographic categories, as are enumerated
below, and according to different day parts (e.g., weekday day,
weekday night, weekend day, and weekend night, etc.) and/or
divisions of the week (e.g., 168 "weekhours," etc.). For each user,
system 210 uses the demographic characteristics of the geographic
areas (e.g., census blocks or tracts, etc.) that a user visits to
determine a composite (e.g., an average, etc.) exposure description
for that user on weekday days, weekday nights, and so on. For
example, a first characteristic c.sub.1 such as "gender" can have
different values when assessed for a second geographic area a.sub.2
and a third geographic area a.sub.3 represented as c.sub.1(a.sub.2)
and c.sub.1(a.sub.3), respectively). In this example, the value for
c.sub.1(a.sub.2) might be "40% male population," and the value for
c.sub.1(a.sub.3) might be "52% male population." System 210
ascertains the value of the particular characteristic for the
particular area, and increments a count to the exposure to the
characteristic.
[0081] In order to describe a user in terms of their demographic
exposure, system 210 measures one or more characteristics in
various demographic categories, including and without limitation:
[0082] i. census age group (e.g., age 30-34, age 35-39, etc.);
[0083] ii. gender (male, female); [0084] iii. income (e.g.,
$75K-<$100K, $100K-<$125K, etc.); [0085] iv. race (e.g.,
White, Black, Asian, Hispanic, etc.); [0086] v. educational
enrollment (e.g., High School, College, Not Enrolled, etc.); [0087]
vi. marital status (e.g., single, married with children, etc.); and
[0088] vii. educational attainment--highest grade attained (e.g.,
High School, College, etc). In some embodiments, demographic
exposure can also be measured in terms of, while not being limited
to: other Census Bureau data, religion, ethnicity, national or
regional origin, employment, occupation, vocation, career, hobby
interest, sexual orientation, consumer preferences, consumer
habits, or organizational memberships and participation.
[0089] An example of describing users in terms of their demographic
exposure, specifically with respect to the demographic category of
"race," is depicted in FIG. 8. Over the period of interest, user
801 is seen moving along path 811 through two geographic areas in
area 800: census tract 821 and, presently, census tract 822, Census
tract 821 has a demographic profile by category group of 10% White,
10% Black, and 80% Hispanic. Census tract 822 has a demographic
profile of 70% White, 5% Black, 5% Asian, and 20% Hispanic.
Assuming an equal number of pings (i.e., spatial-temporal events)
in each visited census tract, on average user 801 has a demographic
exposure of 40% White, 7.5% Black, 2.5% Asian, and 50% Hispanic. In
contrast, more pings occurring in tract 821 than in tract 822 would
weight the exposure assessment more toward the demographic profile
associated with tract 821, and vice-versa, in some embodiments. In
describing user 801, system 210 updates the corresponding user
attributes (e.g., Demographic Exposure to Category Group 1, Weekday
Day; Demographic Exposure to Category Group 2, Weekday Evening,
etc.) for the user, in accordance with the illustrative
embodiment.
[0090] Meanwhile, over the period of interest, user 802 is seen
moving along path 812 through two other geographic areas in area
800: presently in census tract 823 and, earlier, in census tract
824, Census tract 823 has a demographic profile by category group
of 10% White, 30% Black, 40% Asian, and 20% Hispanic. Census tract
824 has a demographic profile of 80% White, 5% Asian, and 15%
Hispanic. Assuming an equal number of pings in each visited census
tract, on average user 802 has a demographic exposure of 45% White,
15% Black, 22.5% Asian, and 17.5% Hispanic. In describing user 802,
system 210 updates the corresponding user attributes (e.g.,
Demographic Exposure to Category Group 1, Weekday Day; Demographic
Exposure to Category Group 2, Weekday Evening, etc.) for the user,
in accordance with the illustrative embodiment.
[0091] In a variation of the above example, system 210 combines
demographic exposures in a different way, namely by hour-groups of
pings instead of by a straight number of pings. First, system 210
groups pings into groups by hour. Once grouped, every ping in an
hour group of size G will get weight 1/G in the average. In other
words, three pings in first hour will each get weight one-third,
and two pings in the second hour will each get weight one-half. The
resulting effect is that if there are two pings occurring in tract
821 in first hour and one ping in track 822 in the second hour, the
same result would be obtained as if there were nine pings in tract
821 in first hour and three pings in tract 822 in second hour.
[0092] At task 710, system 210 evaluates one or more data points of
a user based on the user's commercial exposure--that is, the user's
exposure to business and commercial areas. In accordance with the
illustrative embodiment, commercial exposure measures each user's
exposure to various categories of businesses over time. The
interpretation of commercial exposure is similar to that of
demographic exposure, in that the commercial context of the places
that a user visits, coupled with the time of day visited (or not),
impart their qualities to the user.
[0093] In accordance with the illustrative embodiment, system 210
measures commercial exposure to each of numerous North American
Industry Classification System (NAICS) commercial categories, as
are known in the art. System 210 measures exposure to a particular
geographic point of interest, or "POI" as is known in the art, when
a user's location is in proximity to the POI, System 210 then
ascertains the specific value for the characteristic c.sub.i of
interest (e.g., "automobile dealer", "bed store," etc.) for the
particular POI p.sub.i, and increments a count to the exposure to
the category value. In some embodiments, system 210 presents
commercial exposure-based characteristics as a percentage "rate"
such that the exposures are between 0 and 100. If there are
multiple points of interest nearby, within the accuracy of the
position determination or meeting other proximity-determining
criteria, system 210 considers all exposures as valid.
[0094] System 210 measures commercial exposure distinctly in the
following commercial categories and without limitation:
[0095] i. Commercial exposure to each of J NAICS codes;
[0096] ii, Commercial exposure to each of K restaurant types;
[0097] iii. Commercial exposure to each of L cuisine types; and
[0098] iv. Commercial exposure to each of M top chains (e.g.,
Circle K, Starbucks, etc.),
wherein J, K, L, and M are positive integers. System 210 tracks
commercial exposure to NAICS codes such as, for example and without
limitation: automobile dealers, new only or new and used;
all-terrain vehicle (ATV) dealers; bed stores, retail; quick-lube
shops; undercoating shops, automotive; and wind and water erosion
control agencies, government.
[0099] An example of describing users in terms of their commercial
exposure, specifically with respect NAICS category, is depicted in
FIG. 9. Over the period of interest, users 901 through 905 can be
seen throughout area 900. Some users (i.e., users 901, 902, and
905) have moved during the period of interest (along paths 911,
912, and 915, respectively) while some users have not (i.e., users
903 and 904). Also depicted are the locations of businesses 921
through 929. For illustrative purposes, each business location can
be considered as having an associated area of proximity (i.e.,
proximity areas 931 through 939, respectively), which is defined to
be the area within which the user is considered to be at the
PCI.
[0100] As those who are skilled in the art will appreciate after
reading this specification, a user can be considered to be at or
near a POI based on a variety of criteria. For example and without
limitation, the area of proximity can be related to the uncertainty
(e.g., accuracy) of the position determination. The area of
proximity can be configured (engineered) ahead of time as a
specified distance to the POI. The area of proximity can be made
uniform or non-uniform, in shape and/or in area, as shown in the
drawing. In some embodiments, the user's device can be considered
to be at or near one or more POIs when its position is within a
specified distance (e.g., 50 meters, etc.) of each POI that the
data point is evaluated against. In some other embodiments, system
210 can find the P closest points and determine the POI or POIs
based on a weighting of the distances.
[0101] As can be seen in FIG. 9, each user's present location and
path is characterized as having been exposed to one or more
businesses, as defined by their areas of proximity. More
specifically, each user can be described as having been exposed to
specific NAICS categories (i.e., corresponding to the business
locations depicted), where the user's description can be further
shaped based on the relative number of pings associated with each
NAICS category that the user was exposed to. In describing each
user, system 210 updates the corresponding user attributes (e.g.
Exposure Rate to Business Category 1, Exposure Rate to Business
Category 2, etc.) for the user, in accordance with the illustrative
embodiment of the present invention.
[0102] As with the concept of demographic exposure described
earlier and with respect to FIG. 8, system 210 can apply
normalization by hour-group also to commercial exposure, in some
embodiments. In other words, instead of incrementing ping counts by
one for every ping exposed, system 210 can increment by 1/G,
wherein G is the number of pings in that hour-group.
[0103] Furthermore, system 210 can use an additional level of
normalization in the case of commercial exposure. In particular,
system 210 can optionally normalize at the ping level. For example,
if a single ping is exposed to 10 places (e.g., 10 businesses
adjacent to one another, etc.), then instead of incrementing the
counters associated with each of those 10 places by one, system 210
increments each counter by one-tenth. This has the effect of
spreading the weight equally among the places in situations where
it is unclear which specific place out of the ten the user actually
visited. In another variation, system 210 accounts for both
hour-group and the 10 adjacent places by normalizing using the
factor 1/(10*G). In yet another variation, system 210 can spread
out the weight from a single ping in other ways, such as in
proportion to the distance between the location and the POI, or in
proportion to the overall popularity of that POI based on data from
another source.
[0104] At task 715, data-processing system 210 evaluates one or
more data points of a user based on amount and pattern of activity,
as well as inactivity. The context of various activity and
inactivity patterns, as well as the amount of activity, exhibited
by a user over time impart useful information about a user.
[0105] In accordance with the illustrative embodiment, system 210
tracks a user's activity. In accordance with an illustrative
embodiment, each activity-related event count measures the number
of user S-T events in a particular weekhour, and reports a relative
weekhour activity based on the total activity across all weekhours.
For each user, system 210 tracks one event count per each weekhour
in a week (i.e., weekhour 1 count through weekhour 168 count). In
some other embodiments, system 210 can track user activity based on
some other partitioning across time.
[0106] Additionally, system 210 tracks specific types of user
events or actions occurring, which are derivable from the
event-related information that is available in the received data
points and which have corresponding time and location information.
Such events or actions that can be tracked include, while not being
limited to, a taxi cab being lull in the warehouse district at 2:00
am, texting occurring, tweeting occurring, and so on. As those who
are skilled in the art will appreciate, after reading this
disclosure, system 210 can track other types of events or
actions.
[0107] In accordance with the illustrative embodiment,
data-processing system 210 also tracks a user's inactivity (in
contrast to "activity"), which is inferred from usage gaps in the
data points received for a user. Such gaps are continuous time
periods when a user is completely inactive, from a spatial-temporal
event perspective. A usage gap is defined as the difference of time
(e.g., in seconds, etc.) between two consecutive events, manifested
as data points d(t.sub.i) and d(t.sub.i+1) representing the user.
System 210 measures the length of each gap and characterizes the
gap into one of N buckets, wherein N is a positive integer. There
is one usage characteristic per usage gap "bin," corresponding to
the count of gaps observed for that bin length. As those who are
skilled in the art will appreciate, after reading this disclosure,
the value of N can be selected in order to provide a good balance
between sufficient resolution and having too many individual
characteristics to track.
[0108] For example and as depicted in FIG. 10, occurrences of
events that are attributable to a particular user are shown as
pings 1001 through 1006 across timeline 1000. Gaps in time exist
between consecutive pings. In accordance with the illustrative
embodiment, data-processing system 210 measures the length of each
gap (i.e., determines the time difference between consecutive
pings), characterizes the gap into one of the N buckets, and
updates the corresponding count and/or percentage rate. Different
gap patterns and/or ping patterns, such as those depicted in FIG.
10, can be used in support of estimating a description of a
user.
[0109] At task 720, system 210 evaluates one or more data points of
a user based on the user's distance traveled and travel patterns.
The context of amount of distance traveled and the travel patterns
exhibited by a user over time impart useful information about a
user.
[0110] In accordance with the illustrative embodiment, system 210
tracks a user's distance traveled, by measuring the sum of distance
traveled between consecutive S-T events per unit time (e.g., per
week, etc.). System 210 calculates distance-related event counts,
on the bases of daytime, evening, and nighttime hours. In addition,
system 210 calculates the median, standard deviation, and mean
distance between consecutive S-T events per hour, on the bases of
daytime, evening, and nighttime hours.
[0111] System 210 also tracks a user's travel patterns. In
accordance with the illustrative embodiment, system 210 counts the
number of places visited. System 210 also determines the entropy of
travel, where entropy is a statistical measure of randomness. High
entropy is associated with a uniform number of visits to different
places. Low entropy is associated with a high number of visits to a
small number of places, with low visits to other places.
[0112] System 210 also tracks the number of S-T event occurrences
having valid location data used in the calculation of each
distance-traveled count and travel count.
[0113] FIG. 11 depicts a flowchart of the salient subtasks of task
615 for generating a user description estimate, in accordance with
an illustrative embodiment of the present invention. As discussed
above and with respect to FIG. 6, in some embodiments system 210
uses estimation theory techniques that are different from those
described below.
[0114] At task 1105, system 210 normalizes the statistics. For
example, the normalization allows for the comparison of subscribers
with different spatial-temporal (S-T) event counts or observation
periods, or both, and enables more accurate comparison across users
with different counts of location observations. In accordance with
an illustrative embodiment, system 210 normalizes at least some of
the statistics by converting event "counts" to "rates" (e,g.,
occurrences as a function of time, percentage out of total
occurrences, etc.). In some alternative embodiments, another
technique can be used to normalize the statistics. In still some
other embodiments, normalization can be disabled.
[0115] At task 1110, system 210 statistically shrinks the
normalized statistics. Shrinking, as is known in the art, is
performed to address certain users with sparse data, with the
effect that users with little information will in effect look like
the average user. In accordance with an illustrative embodiment,
system 210 performs a weighted average, based on the number of
observations for the user, of the user's initial-characteristic
value and the overall average-characteristic value. In some
alternative embodiments, another technique can be used to shrink
the statistics. In still some other embodiments, shrinking can be
disabled.
[0116] At task 1115, system 210 creates one or more user
description estimates from the normalized and shrunk statistics
derived from the received data points. In accordance with the
illustrative embodiment of the present invention, a user's
description comprises one or more of the possibly normalized and/or
shrunk evaluation results (e.g., event counts, etc.) described
above and with respect to FIGS. 7 through 10, in any combination of
said results. In some embodiments, a user description comprises
user attributes that are represented as numbers describing behavior
of the user. For example and without limitation, a user description
can be represented as a collection of counts and other numeric
information (e.g., rates, etc.) that describe a user, in which each
count or rate, such as the counts or rates described above,
represents a different user attribute. Being composed of a
collection of counts or rates, in some embodiments, the user
description can be represented in a spreadsheet (e.g., in a
particular row or column corresponding to a particular user, etc.)
or stored in a database. As those who are skilled in the art will
appreciate, after reading this specification, other representations
of the individual data that compose a user description are possible
and other representations of the user description as a whole are
possible.
[0117] Embodiments of the subject matter and the functional
operations described in this specification can be implemented in
digital electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Embodiments of the subject matter described in this
specification can be implemented as one or more computer program
products--that is, one or more modules of computer program
instructions encoded on a computer-readable medium for execution
by, or to control the operation of, a data-processing system. The
computer-readable medium can be a machine-readable storage device,
a machine-readable storage substrate, a memory device, or a
combination of one or more of them. The term "data-processing
system" encompasses all apparatus, devices, and machines for
processing data, including by way of example a programmable
processor, a computer, or multiple processors or computers. The
data-processing system can include, in addition to hardware, code
that creates an execution environment for the computer program in
question, such as code that constitutes processor firmware, a
protocol stack, a database management system, an operating system,
or a combination of one or more of them.
[0118] It is to be understood that the disclosure teaches just one
example of the illustrative embodiment and that many variations of
the invention can easily be devised by those skilled in the art
after reading this disclosure and that the scope of the present
invention is to be determined by the following claims.
* * * * *