U.S. patent application number 16/369521 was filed with the patent office on 2020-10-01 for method and system for predictive modeling of signage location and pricing.
The applicant listed for this patent is ADP, LLC. Invention is credited to Ramsay Cole, Debashis Ghosh, Kurt Newman.
Application Number | 20200311774 16/369521 |
Document ID | / |
Family ID | 1000003988222 |
Filed Date | 2020-10-01 |
United States Patent
Application |
20200311774 |
Kind Code |
A1 |
Cole; Ramsay ; et
al. |
October 1, 2020 |
METHOD AND SYSTEM FOR PREDICTIVE MODELING OF SIGNAGE LOCATION AND
PRICING
Abstract
A method, computer system, and computer program product that
aggregates sample data regarding a plurality of factors associated
with income and geographic location; performs iterative analysis on
the sample data using machine learning to construct a predictive
model; populates, using the predictive model, a database with
predicted values of encountered income for a selected set of
predefined signage locations; converts the predicted values of
encountered income in the database into percentages of observed
values of encountered income for signage locations within the
selected set over a specified time period to create indices of
encountered income; and rank orders the signage locations within
the selected set according to their indices of encountered
income.
Inventors: |
Cole; Ramsay; (Brooklyn,
NY) ; Newman; Kurt; (Columbus, GA) ; Ghosh;
Debashis; (Charlotte, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ADP, LLC |
Roseland |
NJ |
US |
|
|
Family ID: |
1000003988222 |
Appl. No.: |
16/369521 |
Filed: |
March 29, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0269 20130101;
G06Q 30/0273 20130101; G06N 20/00 20190101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06N 20/00 20060101 G06N020/00 |
Claims
1. A computer-implemented method for predictive modeling, the
method comprising: aggregating, by one or more processors, sample
data regarding a plurality of factors associated with income and
geographic location; performing, by one or more processors,
iterative analysis on the sample data using machine learning to
construct a predictive model; populating, by one or more processors
using the predictive model, a database with predicted values of an
encountered income for a selected set of predefined signage
locations; converting, by one or more processors, the predicted
values of an encountered income in the database into percentages of
observed values of encountered income for signage locations within
the selected set over a specified time period to create indices of
encountered income; and rank ordering, by one or more processors,
the signage locations within the selected set according to their
indices of encountered income.
2. The method according to claim 1, further comprising: comparing,
by one or more processors, the rank ordering of encountered income
for the selected set of predefined signage locations to observed
encountered income for the predefined signage locations over a
second specified time period; aggregating, by one or more
processors, updated sample data over the second specified time
period; and updating, by one or more processors, the predictive
model using machine learning incorporating the updated sample data
for the second specified time period.
3. The method according to claim 1, wherein categories of the
sample data applied to the machine learning to construct the
predictive model include at least one of: salary; total payroll
deductions; tax filing status; region of residence; home values
within said region; rental costs within said region; job type;
shift; work location; traffic data; road construction/closures;
employer growth trends; industry/sector growth trends; regional
employment; or industry/sector diversity.
4. The method according to claim 1, wherein the machine learning
uses supervised learning to construct the predictive model.
5. The method according to claim 1, wherein the machine learning
uses unsupervised learning to construct the predictive model.
6. The method according to claim 1, wherein the machine learning
uses reinforcement learning to construct the predictive model.
7. A machine learning predictive modeling system, comprising: a
computer system; and one or more processors running on the computer
system, wherein the one or more processors aggregate sample data
regarding a plurality of factors associated with income and
geographic location; perform iterative analysis on the sample data
using machine learning to construct a predictive model; populate,
using the predictive model, a database with predicted values of
encountered income for a selected set of predefined signage
locations; convert the predicted values of encountered income in
the database into percentages of observed values of encountered
income for signage locations within the selected set over a
specified time period to create indices of encountered income; and
rank order the signage locations within the selected set according
to their indices of encountered income.
8. The machine learning predictive modeling system according to
claim 7, wherein the one or more processors running on the computer
system compare the rank ordering of encountered income for the
selected set of predefined signage locations to observed
encountered income for said signage locations over a second
specified time period; aggregate updated sample data over the
second specified time period; and update the predictive model using
machine learning incorporating the updated sample data for the
second specified time period.
9. The machine learning predictive modeling system according to
claim 7, wherein the one or more processors comprise aggregated
graphical processor units (GPU).
10. The machine learning predictive modeling system according to
claim 7, wherein the machine learning uses supervised learning to
construct the predictive model.
11. The machine learning predictive modeling system according to
claim 7, wherein the machine learning uses unsupervised learning to
construct the predictive model.
12. The machine learning predictive modeling system according to
claim 7, wherein the machine learning uses reinforcement learning
to construct the predictive model.
13. A computer program product for machine learning predictive
modeling, the computer program product comprising: a persistent
computer-readable storage media; first program code, stored on the
computer-readable storage media, for aggregating sample data
regarding a plurality of factors associated with income and
geographic location; second program code, stored on the
computer-readable storage media, for performing iterative analysis
on the sample data using machine learning to construct a predictive
model; third program code, stored on the computer-readable storage
media, for populating, using the predictive model, a database with
predicted values of encountered income for a selected set of
predefined signage locations; fourth program code, stored on the
computer-readable storage media, for converting the predicted
values of encountered income in the database into percentages of
observed values of encountered income for signage locations within
the selected set over a specified time period to create indices of
encountered income; and fifth program code, stored on the
computer-readable storage media, for rank ordering the signage
locations within the selected set according to their indices of
encountered income.
14. The computer program product according to claim 13, further
comprising: sixth program code, stored on the computer-readable
storage media, for comparing the rank ordering of encountered
income for the selected set of pre-defined signage locations to
observed encountered income for said signage locations over a
second specified time period; seventh program code, stored on the
computer-readable storage media, for aggregating updated sample
data over the second specified time period; and eighth program
code, stored on the computer-readable storage media, for updating
the predictive model using machine learning incorporating the
updated sample data for the second specified time period.
15. The computer program product according to claim 13, wherein
categories of the sample data applied to the machine learning to
construct the predictive model include at least one of: salary;
total payroll deductions; tax filing status; region of residence;
home values within said region; rental costs within said region;
job type; shift; work location; traffic data; road
construction/closures; employer growth trends; industry/sector
growth trends; regional employment; or industry/sector
diversity.
16. The computer program product according to claim 13, wherein the
machine learning uses supervised learning to construct the
predictive model.
17. The computer program product according to claim 13, wherein the
machine learning uses unsupervised learning to construct the
predictive model.
18. The computer program product according to claim 13, wherein the
machine learning uses reinforcement learning to construct the
predictive model.
19. A computer-implemented method for predictive modeling, the
method comprising: aggregating, by one or more processors, sample
data regarding a plurality of factors associated with income and
geographic location; performing, by one or more processors,
iterative analysis on the sample data using machine learning to
construct a predictive model; populating, by one or more processors
using the predictive model, a database with predicted values of an
encountered income for a selected set of time periods; converting,
by one or more processors, the predicted values of an encountered
income in the database into percentages of observed values of
encountered income for time periods within the selected set at a
predefined signage location to create indices of encountered
income; and rank ordering, by one or more processors, the time
periods within the selected set according to their indices of
encountered income.
20. The method according to claim 19, further comprising:
comparing, by one or more processors, the rank ordering of
encountered income for the selected set of time periods to observed
encountered income for the time periods from a second sample
period; aggregating, by one or more processors, updated sample data
from a second sample period; and updating, by one or more
processors, the predictive model using machine learning
incorporating the updated sample data.
21. The method according to claim 19, wherein categories of the
sample data applied to the machine learning to construct the
predictive model include at least one of: salary; total payroll
deductions; tax filing status; region of residence; home values
within said region; rental costs within said region; job type;
shift; work location; traffic data; road construction/closures;
employer growth trends; industry/sector growth trends; regional
employment; or industry/sector diversity.
22. The method according to claim 19, wherein the machine learning
uses supervised learning to construct the predictive model.
23. The method according to claim 19, wherein the machine learning
uses unsupervised learning to construct the predictive model.
24. The method according to claim 19, wherein the machine learning
uses reinforcement learning to construct the predictive model.
25. A machine learning predictive modeling system, comprising: a
computer system; and one or more processors running on the computer
system, wherein the one or more processors aggregate sample data
regarding a plurality of factors associated with income and
geographic location; perform iterative analysis on the sample data
using machine learning to construct a predictive model; populate,
using the predictive model, a database with predicted values of
encountered income for a selected set of time periods; convert the
predicted values of encountered income in the database into
percentages of observed values of encountered income for time
periods within the selected set at a signage location to create
indices of encountered income; and rank order the time periods
within the selected set according to their indices of encountered
income.
26. The machine learning predictive modeling system according to
claim 25, wherein the one or more processors running on the
computer system compare the rank ordering of encountered income for
the selected set of time periods to observed encountered income for
said time periods from a second sample period; aggregate updated
sample data from a second sample period; and update the predictive
model using machine learning incorporating the updated sample
data.
27. The machine learning predictive modeling system according to
claim 25, wherein the one or more processors comprise aggregated
graphical processor units (GPU).
28. The machine learning predictive modeling system according to
claim 25, wherein the machine learning uses supervised learning to
construct the predictive model.
29. The machine learning predictive modeling system according to
claim 25, wherein the machine learning uses unsupervised learning
to construct the predictive model.
30. The machine learning predictive modeling system according to
claim 25, wherein the machine learning uses reinforcement learning
to construct the predictive model.
31. A computer program product for machine learning predictive
modeling, the computer program product comprising: a persistent
computer-readable storage media; first program code, stored on the
computer-readable storage media, for aggregating sample data
regarding a plurality of factors associated with income and
geographic location; second program code, stored on the
computer-readable storage media, for performing iterative analysis
on the sample data using machine learning to construct a predictive
model; third program code, stored on the computer-readable storage
media, for populating, using the predictive model, a database with
predicted values of encountered income for a selected set of time
periods; fourth program code, stored on the computer-readable
storage media, for converting the predicted values of encountered
income in the database into percentages of observed values of
encountered income for time periods within the selected set at a
signage location to create indices of encountered income; and fifth
program code, stored on the computer-readable storage media, for
rank ordering the time periods within the selected set according to
their indices of encountered income.
32. The computer program product according to claim 31, further
comprising: sixth program code, stored on the computer-readable
storage media, for comparing the rank ordering of encountered
income for the selected set of time periods to observed encountered
income for said time periods from a second sample period; seventh
program code, stored on the computer-readable storage media, for
aggregating updated sample data from a second sample period; and
eighth program code, stored on the computer-readable storage media,
for updating the predictive model using machine learning
incorporating the updated sample data.
33. The computer program product according to claim 31, wherein
categories of the sample data applied to the machine learning to
construct the predictive model include at least one of: salary;
total payroll deductions; tax filing status; region of residence;
home values within said region; rental costs within said region;
job type; shift; work location; traffic data; road
construction/closures; employer growth trends; industry/sector
growth trends; regional employment; or industry/sector
diversity.
34. The computer program product according to claim 31, wherein the
machine learning uses supervised learning to construct the
predictive model.
35. The computer program product according to claim 31, wherein the
machine learning uses unsupervised learning to construct the
predictive model.
36. The computer program product according to claim 31, wherein the
machine learning uses reinforcement learning to construct the
predictive model.
Description
BACKGROUND INFORMATION
1. Field
[0001] The present disclosure relates generally to an improved
computer system and, in particular, to a method and apparatus for
machine learning predictive modeling. Still more particularly, the
present disclosure relates to a method and apparatus for predicting
income based on geography.
2. Background
[0002] Ideally, physical signage space is positioned where
consumers in a target demographic will encounter the signage.
However, determining where consumers in that target demographic are
with a high degree of probability is exceedingly difficult.
[0003] Often consumers may be targeted based on having
discretionary money to spend. However, simply looking at a static
snapshot of so-called "high rent" areas provides a very simplistic
model of approximately likely discretionary income.
[0004] Furthermore, once physical signage is present, owners of
digital signs will rent advertising based on location and time of
day. Owners of a digital sign desire information about a quantity
of consumers encountering the signage during different times of the
day to know how to market and price the signage. Further, owners of
a digital sign also desire information about a quantity of
consumers of a target demographic, such as a desired amount of
discretionary money, and to know how to market and price the
signage. The challenge is determining the signage locations that
have the most disposable income as well as the times for each
signage location that have the most disposable income.
[0005] Therefore, it would be desirable to have a method and system
that provides predictive modeling and indices that predict an
encountered income for a selected set of either predefined signage
locations or time periods.
SUMMARY
[0006] An embodiment of the present disclosure provides a
computer-implemented method for predictive modeling. The computer
system aggregates sample data regarding a plurality of factors
associated with income and geographic location and performs
iterative analysis on the sample data using machine learning to
construct a predictive model. The computer system then populates,
using the predictive model, a database with predicted values of
encountered income for a selected set of predefined signage
locations. The computer system converts the predicted values of
encountered income in the database into percentages of observed
values of encountered income for signage locations within the
selected set over a specified time period to create indices of
encountered income. The computer system then rank orders the
signage locations within the selected set according to their
indices of encountered income.
[0007] Another embodiment of the present disclosure provides a
machine learning predictive modeling system comprising a computer
system and one or more processors running on the computer system.
The one or more processors aggregate sample data regarding a
plurality of factors associated with income and geographic
location; perform iterative analysis on the sample data using
machine learning to construct a predictive model; populate, using
the predictive model, a database with predicted values of
encountered income for a selected set of predefined signage
locations; convert the predicted values of encountered income in
the database into percentages of observed values of encountered
income for signage locations within the selected set over a
specified time period to create indices of encountered income; and
rank order the signage locations within the selected set according
to their indices of encountered income.
[0008] Yet another embodiment of the present disclosure provides a
computer program product for machine learning predictive modeling
comprising a persistent computer-readable storage media; first
program code, stored on the computer-readable storage media, for
aggregating sample data regarding a plurality of factors associated
with income and geographic location; second program code, stored on
the computer-readable storage media, for performing iterative
analysis on the sample data using machine learning to construct a
predictive model; third program code, stored on the
computer-readable storage media, for populating, using the
predictive model, a database with predicted values of encountered
income for a selected set of predefined signage locations; fourth
program code, stored on the computer-readable storage media, for
converting the predicted values of encountered income in the
database into percentages of observed values of encountered income
for signage locations within the selected set over a specified time
period to create indices of encountered income; and fifth program
code, stored on the computer-readable storage media, for rank
ordering the signage locations within the selected set according to
their indices of encountered income.
[0009] Still another embodiment of the present disclosure provides
a computer-implemented method for predictive modeling. The computer
system aggregates, by one or more processors, sample data regarding
a plurality of factors associated with income and geographic
location and performs iterative analysis on the sample data using
machine learning to construct a predictive model. The computer
system then populates, by one or more processors using the
predictive model, a database with predicted values of an
encountered income for a selected set of time periods. The computer
system converts, by one or more processors, the predicted values of
an encountered income in the database into percentages of observed
values of encountered income for time periods within the selected
set at a predefined signage location to create indices of
encountered income. The computer system then rank orders, by one or
more processors, the time periods within the selected set according
to their indices of encountered income.
[0010] Yet another embodiment of the present disclosure provides a
machine learning predictive modeling system comprising a computer
system and one or more processors running on the computer system.
The one or more processors aggregate sample data regarding a
plurality of factors associated with income and geographic
location; perform iterative analysis on the sample data using
machine learning to construct a predictive model; populate, using
the predictive model, a database with predicted values of
encountered income for a selected set of time periods; convert the
predicted values of encountered income in the database into
percentages of observed values of encountered income for time
periods within the selected set at a predefined signage location to
create indices of encountered income; and rank order the time
periods within the selected set according to their indices of
encountered income.
[0011] Another embodiment of the present disclosure provides a
computer program product for machine learning predictive modeling
comprising a persistent computer-readable storage media; first
program code, stored on the computer-readable storage media, for
aggregating sample data regarding a plurality of factors associated
with income and geographic location; second program code, stored on
the computer-readable storage media, for performing iterative
analysis on the sample data using machine learning to construct a
predictive model; third program code, stored on the
computer-readable storage media, for populating, using the
predictive model, a database with predicted values of encountered
income for a selected set of time periods; fourth program code,
stored on the computer-readable storage media, for converting the
predicted values of encountered income in the database into
percentages of observed values of encountered income for time
periods within the selected set at a predefined signage location to
create indices of encountered income; and fifth program code,
stored on the computer-readable storage media, for rank ordering
the time periods within the selected set according to their indices
of encountered income.
[0012] The features and functions can be achieved independently in
various embodiments of the present disclosure or may be combined in
yet other embodiments in which further details can be seen with
reference to the following description and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The novel features believed characteristic of the
illustrative embodiments are set forth in the appended claims. The
illustrative embodiments, however, as well as a preferred mode of
use, further objectives and features thereof, will best be
understood by reference to the following detailed description of an
illustrative embodiment of the present disclosure when read in
conjunction with the accompanying drawings, wherein:
[0014] FIG. 1 is a pictorial representation of a network of data
processing systems in which illustrative embodiments may be
implemented;
[0015] FIG. 2 is an illustration of a block diagram of a computer
system for predictive modeling in accordance with an illustrative
embodiment;
[0016] FIG. 3 is an illustration of a database for access by a
predictive modeling application in accordance with an illustrative
embodiment;
[0017] FIG. 4 is an illustration of a flowchart of a process for
calculating factors used in predictive modeling in accordance with
an illustrative embodiment;
[0018] FIG. 5 is an illustration of a flowchart of a process for
predictive modeling and indexing in accordance with an illustrative
embodiment;
[0019] FIG. 6 is an example table for use with a dataset in machine
learning in accordance with an illustrative embodiment;
[0020] FIG. 7 is an illustration of a flowchart of a method for
predictive modeling of encountered income based on signage location
in accordance with an illustrative embodiment;
[0021] FIG. 8 is an illustration of a flowchart of a method for
predictive modeling of encountered income based on time period in
accordance with an illustrative embodiment; and
[0022] FIG. 9 is an illustration of a block diagram of a data
processing system in accordance with an illustrative
embodiment.
DETAILED DESCRIPTION
[0023] The illustrative embodiments recognize and take into account
one or more different considerations. For example, the illustrative
embodiments recognize and take into account that several factors
affect income distribution across different geographic regions.
[0024] The illustrative embodiments also recognize and take into
account that different types of products are targeted to different
types of markets, requiring identification of the most lucrative
target markets. The illustrative embodiments further recognize and
take into account that average disposable income can vary between
regions with similar geographic and non-geographic
characteristics.
[0025] The illustrative embodiments recognize and take into account
that when a company considers a sign location, the company would
like to know the size of their target demographic. The illustrative
embodiments recognize and take into account that the company would
like to understand trends over time, within potential target
locations as well as current pay and trending pay changes. The
illustrative embodiments recognize and take into account that it
would be desirable to compare areas based on a point in time as
well as trends over time to identify the best possible location for
establishing new signage locations.
[0026] The illustrative embodiments recognize and take into account
that digital signage pricing varies based on location and time of
day. Digital signage companies would like to know what areas
different demographics will be spending time at and/or passing by.
The illustrative embodiments recognize and take into account that
decisions about pricing by location and time of day are desirably
calculated using the best possible information about the areas of
travel of individuals. The illustrative embodiments create indices
to compare multiple time periods for digital signage based on data
including at least one of home addresses, work addresses, commute
paths, job codes, and pay ranges by geography.
[0027] The illustrative embodiments recognize and take into account
that it would be desirable to create indices to compare multiple
geographic signage areas based on industries, job codes, and pay
ranges. The illustrative embodiments recognize and take into
account that it would be desirable to compare digital signage areas
based on a point in time as well as trends over time to help
identify fixed pricing for that particular location and as well as
how pricing may change over time.
[0028] Thus, a method and apparatus that would allow for accurately
analyzing consumer exposure to signage at designated locations or
signage at designated times would fill a long-felt need in the
field of marketing. Further, a method and apparatus that would
allow for predicting encountered income for signage at designated
locations or signage at designated times would fill a long-felt
need in the field of marketing. The illustrative embodiments create
indices to compare multiple signage locations based on industries,
job codes, and pay ranges. Some of the illustrative embodiments
create indices to compare multiple time periods for a digital sign
based on industries, job codes, and pay ranges.
[0029] As used herein, the phrase "at least one of," when used with
a list of items, means different combinations of one or more of the
listed items may be used and only one of each item in the list may
be needed. In other words, "at least one of" means any combination
of items and number of items may be used from the list, but not all
of the items in the list are required. The item may be a particular
object, thing, or a category.
[0030] For example, without limitation, "at least one of item A,
item B, or item C" may include item A, item A and item B, or item
B. This example also may include item A, item B, and item C or item
B and item C. Of course, any combinations of these items may be
present. In some illustrative examples, "at least one of" may be,
for example, without limitation, two of item A, one of item B, and
ten of item C; four of item B and seven of item C; or other
suitable combinations.
[0031] With reference now to the figures and, in particular, with
reference to FIG. 1, an illustration of a diagram of a data
processing environment is depicted in accordance with an
illustrative embodiment. It should be appreciated that FIG. 1 is
only provided as an illustration of one implementation and is not
intended to imply any limitation with regard to the environments in
which the different embodiments may be implemented. Many
modifications to the depicted environments may be made.
[0032] The computer-readable program instructions may also be
loaded onto a computer, a programmable data processing apparatus,
or other device to cause a series of operational steps to be
performed on the computer, a programmable apparatus, or other
device to produce a computer implemented process, such that the
instructions which execute on the computer, the programmable
apparatus, or the other device implement the functions and/or acts
specified in the flowchart and/or block diagram block or
blocks.
[0033] FIG. 1 depicts a pictorial representation of a network of
data processing systems in which illustrative embodiments may be
implemented. Network data processing system 100 is a network of
computers in which the illustrative embodiments may be implemented.
Network data processing system 100 contains network 102, which is a
medium used to provide communications links between various devices
and computers connected together within network data processing
system 100. Network 102 may include connections, such as wire,
wireless communication links, or fiber optic cables.
[0034] In the depicted example, server computer 104 and server
computer 106 connect to network 102 along with storage unit 108. In
addition, client computers include client computer 110, client
computer 112, and client computer 114. Client computer 110, client
computer 112, and client computer 114 connect to network 102. These
connections can be wireless or wired connections depending on the
implementation. Client computer 110, client computer 112, and
client computer 114 may be, for example, personal computers or
network computers. In the depicted example, server computer 104
provides information, such as boot files, operating system images,
and applications to client computer 110, client computer 112, and
client computer 114. Client computer 110, client computer 112, and
client computer 114 are clients to server computer 104 in this
example. Network data processing system 100 may include additional
server computers, client computers, and other devices not
shown.
[0035] Program code located in network data processing system 100
may be stored on a computer-recordable storage medium and
downloaded to a data processing system or other device for use. For
example, the program code may be stored on a computer-recordable
storage medium on server computer 104 and downloaded to client
computer 110 over network 102 for use on client computer 110.
[0036] In the depicted example, network data processing system 100
is the Internet with network 102 representing a worldwide
collection of networks and gateways that use the Transmission
Control Protocol/Internet Protocol (TCP/IP) suite of protocols to
communicate with one another. At the heart of the Internet is a
backbone of high-speed data communication lines between major nodes
or host computers consisting of thousands of commercial,
governmental, educational, and other computer systems that route
data and messages. Of course, network data processing system 100
also may be implemented as a number of different types of networks,
such as, for example, an intranet, a local area network (LAN), or a
wide area network (WAN). FIG. 1 is intended as an example, and not
as an architectural limitation for the different illustrative
embodiments.
[0037] As used herein, "a number of," when used with reference to
items, means one or more items. For example, "a number of different
types of networks" is one or more different types of networks.
[0038] The illustration of network data processing system 100 is
not meant to limit the manner in which other illustrative
embodiments can be implemented. For example, other client computers
may be used in addition to or in place of client computer 110,
client computer 112, and client computer 114 as depicted in FIG. 1.
For example, client computer 110, client computer 112, and client
computer 114 may include a tablet computer, a laptop computer, a
bus with a vehicle computer, and other suitable types of
clients.
[0039] In the illustrative examples, the hardware may take the form
of a circuit system, an integrated circuit, an application-specific
integrated circuit (ASIC), a programmable logic device, or some
other suitable type of hardware configured to perform a number of
operations. With a programmable logic device, the device may be
configured to perform the number of operations. The device may be
reconfigured at a later time or may be permanently configured to
perform the number of operations. Programmable logic devices
include, for example, a programmable logic array, programmable
array logic, a field programmable logic array, a field programmable
gate array, and other suitable hardware devices. Additionally, the
processes may be implemented in organic components integrated with
inorganic components and may be comprised entirely of organic
components, excluding a human being. For example, the processes may
be implemented as circuits in organic semiconductors.
[0040] Turning to FIG. 2, a block diagram of a computer system for
predictive modeling is depicted in accordance with an illustrative
embodiment. Computer system 200 is connected to internal databases
260, external databases 270, and devices 280. Internal databases
260 comprise payroll 262, tax forms 264, employer information 266,
and employee place of residence 268. External databases 270
comprise regional employment databases 272, regional traffic
databases 273, employer industry/sector databases 274, regional
road construction databases 275, and regional housing cost
databases 276. Devices 280 comprise non-mobile devices 282 and
mobile devices 284.
[0041] Computer system 200 comprises processing unit 216, machine
intelligence 218, and indexing program 230. Machine intelligence
218 comprises machine learning 220 and predictive algorithms
222.
[0042] Machine intelligence 218 can be implemented using one or
more systems such as an artificial intelligence system, a neural
network, a Bayesian network, an expert system, a fuzzy logic
system, a genetic algorithm, or other suitable types of systems.
Machine learning 220 and predictive algorithms 222 may make
computer system 200 a special purpose computer for dynamic
predictive modelling of encountered income according to at least
one of signage location or a time period.
[0043] In an embodiment, processing unit 216 comprises one or more
conventional general purpose central processing units (CPUs). In an
alternate embodiment, processing unit 216 comprises one or more
graphical processing units (GPUs). Though originally designed to
accelerate the creation of images with millions of pixels whose
frames need to be continually recalculated to display output in
less than a second, GPUs are particularly well suited to machine
learning. Their specialized parallel processing architecture allows
them to perform many more floating point operations per second than
a CPU, on the order of 1000.times. more. GPUs can be clustered
together to run neural networks comprising hundreds of millions of
connection nodes.
[0044] Indexing program 230 comprises information gathering 252,
selecting 232, modeling 234, comparing 236, indexing 238, ranking
240, and displaying 242. Information gathering 252 comprises
internal 254 and external 256. Internal 254 is configured to gather
data from internal databases 260. External 256 is configured to
gather data from external databases 270.
[0045] Thus, processing unit 216, machine intelligence 218, and
indexing program 230 transform a computer system into a special
purpose computer system as compared to currently available general
computer systems that do not have a means to perform machine
learning predictive modeling. Currently used general computer
systems do not have a means to accurately predict encountered
income according to signage location. Currently used general
computer systems do not have a means to accurately predict
encountered income according to time period.
[0046] Turning to FIG. 3, a block diagram of a database is depicted
in accordance with an illustrative embodiment. Database 300
comprises connections 310, financial data 320, personal data 330,
and employment data 340. Connections 310 comprise internet 312,
wireless 314, and others 316. Connections 310 may provide
connectivity with internal databases 260, external databases 270,
and devices 280 shown in FIG. 2. Internet 312 and wireless 314 as
well as others 316 in connections 310 in FIG. 3 may connect with
internal databases 260, external databases 270, and devices 280,
shown in FIG. 2, through a network such as network 102 in FIG. 1.
Others 316 may comprise any additional available means of
connection other than internet 312 and wireless 314 such as a hard
wired connection or a landline.
[0047] In an illustrative embodiment, financial data 320 comprises
employee financial data, including employee salary 322 and
withholdings 324. Information regarding employee salaries is
maintained in salary 322. Information about the number and amount
of deductions is maintained in withholdings 324.
[0048] Personal data 330 comprises employee personal information
and employee personal data, including residence 332 and marital
status 334. Information regarding the specific geographic region of
employee residence is maintained in residence 332. The more
specific and smaller the predefined region in questions (e.g.,
zip/postal code, state, multistate region, etc.), the more accurate
the predictive model. Information about employee marital status is
maintained in marital status 334. Marital status 334 can be
extrapolated from tax filing status and/or from insurance and
benefits forms.
[0049] Employment data 340 comprises shift 341, work location 342,
job type 343, and industry/sector 344. Information regarding the
employee's office location (e.g., zip/postal code, state,
multistate region, etc.) is maintained in work location 342.
Information regarding the employee's shift, such as days worked, a
start time, an end time, a length of shift, a rotation schedule, or
a shift number (e.g., first shift, second shift, etc.) is
maintained in shift 341. Information regarding the employee's
position (e.g. job title, job code, assigned tasks, etc.) is
maintained in job type 343. Information about the employer's
industry/sector is maintained in industry/sector 344. A sector
identifies a high-level group of related businesses. It can be
thought of as a generic type of business. For example, the North
American Industry Classification System (NAICS) uses a six digit
code to identify an industry. The first two digits of that code
identify the sector in which the industry belongs.
[0050] Regional data 350 comprises information about general
economic trends and roadway information within a predefined
geographic region (e.g., zip/postal code, state, multistate region,
etc.). Information regarding unemployment in the region is
maintained in unemployment rate 352. Information regarding the
types of industries/sectors within the region is maintained in
industries/sectors 354. Information regarding home prices in the
region is maintained in home values 356. Information regarding
housing rental costs and rates for the region is maintained in
rental costs 358.
[0051] Information regarding traffic is maintained in traffic 360.
Traffic 360 includes traffic information for any desired period of
time. Traffic 360 may include at least one of current traffic
conditions or anticipated traffic conditions based on historical
data. Traffic 360 includes traffic information based on any desired
time intervals (e.g. 5 min, 15 min, 1 hour, etc.).
[0052] Information regarding road construction and road closures is
maintained in road construction/closures 362. Road
construction/closures 362 includes information describing
implementation of planned construction (e.g. locations, lengths,
number of lanes, dates, and times of construction). In some
illustrative examples, road construction/closures 362 includes
information describing unplanned lane closures (e.g. lane closures
due to automobile accidents, weather, or other unplanned
events).
[0053] The illustrations of the different components in FIGS. 2-3
are not meant to imply physical or architectural limitations to the
manner in which an illustrative embodiment may be implemented.
Other components in addition to or in place of the ones illustrated
may be used. Some components may be unnecessary. Also, the blocks
are presented to illustrate some functional components. One or more
of these blocks may be combined, divided, or combined and divided
into different blocks when implemented in an illustrative
embodiment.
[0054] For example, internal databases 260 in FIG. 2 may have
additional information other than payroll 262, tax forms 264,
employer 266, and residence 268, all of which are in FIG. 2. In
another illustrative example, database 300 in FIG. 3 may not have
data for traffic 360 or road construction/closures 362.
[0055] Turning to FIG. 4, an illustration of a flowchart for
calculating factors used in predictive modeling is depicted in
accordance with an illustrative embodiment. This process can be
implemented in software, hardware, or a combination of the two.
When software is used, the software comprises program code that can
be loaded from a storage device and run by a processor unit in a
computer system such as computer system 200 in FIG. 2. Computer
system 200 may reside in a network data processing system such as
network data processing system 100 in FIG. 1. For example, computer
system 200 may reside on one or more of server computer 104, server
computer 106, client computer 110, client computer 112, and client
computer 114 connected by network 102 in FIG. 1. Moreover, the
process can be implemented by data processing system 900 in FIG. 9
and a processing unit such as processor unit 904 in FIG. 9.
[0056] It should be emphasized that the specific sequence of steps
in the illustrative embodiment shown in FIG. 4 is chosen merely for
convenience. The factors shown in FIG. 4 can be calculated
independently in other orders or may be calculated in parallel by
separate processors or processor threads, depending on the specific
architecture of the computer system used. In the illustrative
embodiment, the factors are calculated using the information
maintained in database 300 shown in FIG. 3.
[0057] Process 400 begins by calculating employee salary (step
402). Next, process 400 calculates a total amount of payroll
deductions (step 404). These deductions can include
retirement/savings, insurance deductions for family members, and
similar items. Taking such withholdings into account gives a more
accurate picture of employees' actual available funds for purchases
and spending habits than simply nominal salary.
[0058] Next, process 400 determines tax filing status (step 406).
The tax filing status (i.e. single or joint) can indicate the
presence (or lack thereof) of more than one income within a
household.
[0059] Process 400 then determines a geographic region of residence
(step 408). The size of the predefined region can vary in size
(e.g., postal/zip code, city, state, multistate region, etc.). The
smaller the region, the more precise the predictive model.
[0060] In some illustrative examples, after the geographic region
is determined, home value trends within that region are determined
(step 410). Step 410 may be optional. Home value can be both a
measure of wealth as well as a measure of living costs. Generally,
as home values increase, the income of the owners increases as
well. However, some people buy the most expensive house lenders
will allow, pushing the limits of their available cash flow.
Furthermore, the wealth effect of home value can reverse in an
economic downturn marked by falling home values. Therefore,
calculating trends over specified time periods produces more
accurate predictive models than looking at a snapshot of housing
costs and home values at a given point in time.
[0061] In some illustrative examples, process 400 also calculates
rental cost trends in the region (step 412). Step 412 may be
optional. Like home values, rental costs are representative of
living expenses and overall income and lifestyle. Again,
calculating trends produces more accurate predictive modeling of
how income is trending in specific regions than a snapshot of
rental costs at any given time.
[0062] Process 400 determines the geographic region of employment
(step 413). The size of the predefined region can vary in size
(e.g., postal/zip code, city, state, multistate region, etc.). The
smaller the region, the more precise the predictive model. In some
illustrative examples, the region of residence and the region of
employment are the same. In some illustrative examples, the region
of residence and the region of employment are different.
[0063] In some illustrative examples, process 400 calculates growth
trends for the employee's employer over a specified time period
(step 414). Step 414 may be optional. This also points to the
probable future income of an employee beyond a snapshot of current
salary. Is the employer hiring, downsizing, and/or automating? In
addition, if a particular employer accounts for a significant
percentage of employment in the predefined region in question
(e.g., "factory town"), growth trends for that employer might have
a disproportionate effect on the predictive model for that
region.
[0064] In some illustrative examples, process 400 calculates growth
trends over a specified time period for the industry/sector in
which the employee is employed (step 416). Step 416 may be
optional. This measure helps capture non-local economic factors
that might impact the local regional economy but might not be
properly accounted for in the predictive model if only local data
were used.
[0065] In some illustrative examples, employment trends for the
selected region are calculated for a specified time period (step
418). Step 418 may be optional. Again, trends provide better
predictive modeling than a momentary snapshot. For example, a
region (e.g., zip code, city) might have a relatively high average
income, but if unemployment in the area is on the rise, a
predictive model that relied on that momentary current income would
not be very accurate going forward.
[0066] In some illustrative examples, process 400 calculates the
diversity of industries/sectors within the selected region (step
420). Step 420 may be optional. This can include both the number of
different industries/sectors in the region but also the percentages
of employment for which they account. The diversity of
industries/sectors of employment affects the potential upside or
vulnerability of a region to trends in a particular
industry/sector. Taken together with the other factors above, this
measure can help the predictive model account for the interplay
between local and non-local economic factors on the regional
economy.
[0067] Process 400 calculates commutes based on the determined
region of residence from step 408 and region of employment from
step 413 (step 422). Commutes are paths between the region of
residence and the region of employment. Commutes are paths which
individuals may travel between their place of employment or their
place of residence. There will often be multiple viable routes or
commutes between a place of residence and a place of employment. A
commute may be selected based on a desire to avoid construction,
avoid toll roads, arrive in a shortest period of time, avoid
traffic lights, or using any other desired criteria. In some
illustrative examples, commutes are calculated taking into account
at least one of traffic or road construction/closure data.
[0068] In some illustrative examples, process 400 determines
traffic (step 424). Step 424 may be optional. In some illustrative
examples, process 400 determines traffic based on historical
trends. In some illustrative examples, process 400 determines
traffic based on current traffic reports.
[0069] In some illustrative examples, process 400 determines road
closures/construction (step 426). Step 426 may be optional.
[0070] In some illustrative examples, process 400 determines a
shift that is worked (step 428). In some illustrative examples, the
shift worked influences the most desirable commute. For example,
traffic patterns are different at different times of the day. A
night shift worker may traverse a different path between two points
than a day shift worker. By determining a shift worked, a window of
time that a consumer is traveling in selected regions or areas
within the commute can be determined.
[0071] The method of the present disclosure utilizes machine
learning and predictive algorithms such as those provided by
machine intelligence 218 in FIG. 2. Machine learning is a branch of
artificial intelligence (AI) that enables computers to detect
patterns and improve performance without direct programming
commands. Rather than relying on direct input commands to complete
a task, machine learning relies on input data. The data is fed into
the machine, a predictive algorithm is selected, parameters for the
data are configured, and the machine is instructed to find patterns
in the input data through trial and error. The data model formed
from analyzing the data is then used to predict future values.
[0072] Turning to FIG. 5, an illustration of a flowchart of a
process for predictive modeling and indexing is depicted in
accordance with an illustrative embodiment. Process 500 can be
implemented in software, hardware, or a combination of the two.
When software is used, the software comprises program code that can
be loaded from a storage device and run by a processor unit in a
computer system such as computer system 200 in FIG. 2. Computer
system 200 may reside in a network data processing system such as
network data processing system 100 in FIG. 1. For example, computer
system 200 may reside on one or more of server computer 104, server
computer 106, client computer 110, client computer 112, and client
computer 114 connected by network 102 in FIG. 1. Moreover, the
process can be implemented by data processing system 900 in FIG. 9
and a processing unit such as processor unit 904 in FIG. 9.
[0073] Process 500 begins by aggregating sample data regarding a
plurality of factors associated with income, employment data, and
geographic location (step 502). Process 500 aggregates the regional
income, employment data associated with the factors determined in
the process flow in FIG. 4 in step 502. Referring to FIG. 6, an
example table for use with a dataset in machine learning is
depicted in accordance with an illustrative embodiment. The dataset
used to form predictions is defined and labeled in a table such as
table 600. Each column is known as a vector, and the data within
each column is a feature, also known as a variable, dimension, or
attribute. Each row represents a single observation of a given
feature and is referred to as a case or value. The y values
represent the output and are typically expressed in the final
column as shown. For ease of illustration, the example shown in
FIG. 6 is a simple 2-D table, but it should be noted that multiples
vectors (forming matrices) are typically used to represent large
datasets. Referring back to FIG. 4, each category of data
determined in the process flow could be represented by a separate
vector (column) in a tabular dataset depending on how the data is
aggregated.
[0074] After the dataset is aggregated, process 500 scrubs the
dataset (step 504). Very large datasets, sometimes referred to as
Big Data, often contain noise and complicated data structures.
Bordering on the order of petabytes, such datasets comprise a
variety, volume, and velocity (rate of change) that defies
conventional processing and is impossible for a human to process
without advanced machine assistance. Scrubbing refers to the
process of refining the dataset before using it to build a
predictive model and includes modifying and/or removing incomplete
data or data with little predictive value. It can also entail
converting text-based data into numerical values (one-hot encoding)
or convert numerical values into a category.
[0075] Iterative analysis is performed on the sample data using
machine learning to construct a predictive model. Preparation for
and performance of the iterative analysis is performed in steps
506-512.
[0076] After the dataset has been scrubbed, process 500 divides the
data into training data and test data to be used for building and
testing the predictive model (step 506). To produce optimal
results, the same data that is used to test the model should not be
the same data used for training. The data is divided by rows, with
70-80% used for training and 20-30% used for testing. Randomizing
the selection of the rows avoids bias in the model.
[0077] Process 500 then performs iterative analysis on the training
data by applying predictive algorithms to construct a predictive
model (step 508). There are three main categories of machine
learning: supervised, unsupervised, and reinforcement. Supervised
machine learning comprises providing the machine with test data and
the correct output value of the data. Referring back to table 600
in FIG. 6, during supervised learning, the values for the y column
(output) are provided along with the training data (labeled
dataset) for the model building process in step 508. The algorithm,
through trial and error, deciphers the patterns that exist between
the input training data and the known output values to create a
model that can reproduce the same underlying rules with new data.
Examples of supervised learning algorithms include regression
analysis, decisions trees, k-nearest neighbors, neural networks,
and support vector machines.
[0078] If unsupervised learning is used, not all of the variables
and data patterns are labeled, forcing the machine to discover
hidden patterns and create labels on its own through the use of
unsupervised learning algorithms. Unsupervised learning has the
advantage of discovering patterns in the data no one previously
knew existed. Examples of algorithms used in unsupervised machine
learning include k-means clustering (k-NN), association analysis,
and descending clustering.
[0079] After the model is constructed, the test data is fed into
the model to test its accuracy (step 510). In an embodiment, the
model is tested using mean absolute error, which examines each
prediction in the model and provides an average error score for
each prediction. If the error rate between the training and test
dataset is below a predetermined threshold, the model has learned
the dataset's pattern and passed the test.
[0080] If the model fails the test, the hyperparameters of the
model are changed and/or the training and test data are
re-randomized, and the iterative analysis of the training data is
repeated (step 512). Hyperparameters are the settings of the
algorithm that control how fast the model learns patterns and which
patterns to identify and analyze. Once a model has passed the test
stage, it is ready for application.
[0081] Whereas supervised and unsupervised learning reach an
endpoint after a predictive model is constructed and passes the
test in step 510, reinforcement learning continuously improves its
model using feedback from application to new empirical data.
Algorithms such as Q-learning are used to train the predictive
model through continuous learning using measurable performance
criteria (discussed in more detail below).
[0082] After the model is constructed and tested for accuracy,
process 500 uses the model to calculate predicted encountered
income for a desired set of variables, such as a selected set of
predefined signage locations or a selected set of time periods
(step 514). When the selected set is of predefined signage
locations, the signage locations include at least one of current
signage locations or potential signage locations. In some
illustrative examples, the signage locations included in the set
might have similar characteristics other than geographic proximity,
such as, for example, population size and/or density,
industry/sector distributions, urban, rural, technology companies,
heavy industry, agriculture, etc.
[0083] The predicted encountered income for the set is then
converted into a percentage of observed values of encountered
income of the individual signage locations or the individual time
periods in the set to form an index (step 516). The index is
calculated by dividing the observed value by the predicted value
and then multiplying by 100. A percentage greater than 100%
identifies a signage location that has greater encountered income
than most signage locations within the set. A percentage less than
100% identifies signage locations that have lower encountered
income than most signage locations within the set.
[0084] After the indices have been calculated, they are used to
rank order the items of the set, either signage locations or time
periods (step 518). Rank order allows comparison of encountered
income between signage locations that have similar characteristics,
whichever characteristics those happen to be as determined by the
modeler. The characteristics may be chosen according to the type of
product being marketed. The signage locations with those
characteristics that have the highest indices are likely to be the
most lucrative target markets. When the set comprises time periods,
rank order allows comparison of encountered income between time
periods for a signage location.
[0085] If reinforcement learning is used with the predictive
modeling, the encountered income rankings are compared to the
actual observed encountered incomes over a subsequent time period
(e.g., month, quarter, year, etc.) (step 520). The actual
encountered income levels for the set variables in question might
not conform as expected to the relative index rankings.
Furthermore, the sample data used to construct the predictive model
might become outdated. Updated regional income and employment data
is collected after the subsequent time period and fed back into the
machine learning to update and modify the predictive model (step
522).
[0086] Turning to FIG. 7, an illustration of a flowchart of a
method for predictive modeling of encountered income based on
signage location is depicted in accordance with an illustrative
embodiment. Method 700 can be implemented in software, hardware, or
a combination of the two. When software is used, the software
comprises program code that can be loaded from a storage device and
run by a processor unit in a computer system such as computer
system 200 in FIG. 2. Moreover, method 700 can be implemented by
data processing system 900 in FIG. 9 and a processing unit such as
processor unit 904 in FIG. 9.
[0087] Method 700 aggregates, by one or more processors, sample
data regarding a plurality of factors associated with income and
geographic location (operation 702). The plurality of factors
associated with income and geographic location includes at least
one of salary, total payroll deductions, tax filing status, region
of residence, home values within said region, rental costs within
said region, job type, shift, work location, traffic data, road
construction/closures, employer growth trends, industry/sector
growth trends, regional employment, or industry/sector diversity.
In some illustrative examples, the plurality of factors associated
with income and geographic location are used to calculate
additional elements such as commutes, disposable income, or any
other desirable element in predicting values of an encountered
income.
[0088] Method 700 performs, by one or more processors, iterative
analysis on the sample data using machine learning to construct a
predictive model (operation 704). In some illustrative examples,
the machine learning uses supervised learning to construct the
predictive model. In some illustrative examples, the machine
learning uses unsupervised learning to construct the predictive
model. In some illustrative examples, the machine learning uses
reinforcement learning to construct the predictive model.
[0089] Method 700 populates, by one or more processors using the
predictive model, a database with predicted values of an
encountered income for a selected set of predefined signage
locations (operation 706). An encountered income is a variable
indicative of a value of visibility. An encountered income takes
into account a quantity of consumers encountering the sign as well
as the income of the consumers encountering the sign. For example,
in some illustrative examples, an encountered income is an average
income of consumers encountering the sign in a time period. In
other illustrative examples, an encountered income is a cumulative
calculated income of consumers encountering the sign in a time
period. In yet another illustrative example, an encountered income
is a median income of consumers encountering the sign in a time
period. In yet another illustrative example, an encountered income
is a quantity of consumers having a minimum desired income that
encounter the sign in a time period. In an illustrative example, an
encountered income is a percentage of consumers encountering the
sign that have a minimum desired income in a time period. In other
illustrative examples, an encountered income is a weighted value
taking into account the incomes and quantities of consumers
encountering the sign in a time period. The selected set of
predefined signage locations includes at least one of locations
where signs are currently located or potential locations for future
signage.
[0090] Method 700 converts, by one or more processors, the
predicted values of an encountered income in the database into
percentages of observed values of encountered income for signage
locations within the selected set over a specified time period to
create indices of encountered income (operation 708). Method 700
rank orders, by one or more processors, the signage locations
within the selected set according to their indices of encountered
income (operation 710). Afterwards, method 700 terminates.
[0091] In some illustrative examples, method 700 compares, by one
or more processors, the rank ordering of encountered income for the
selected set of predefined signage locations to observed
encountered income for the predefined signage locations over a
second specified time period (operation 712). In these illustrative
examples, method 700 aggregates, by one or more processors, updated
sample data over the second specified time period (operation 714).
In these illustrative examples, method 700 updates, by one or more
processors, the predictive model using machine learning
incorporating the updated sample data for the second specified time
period (operation 716).
[0092] Turning to FIG. 8, an illustration of a flowchart of a
method for predictive modeling of encountered income based on a
time period is depicted in accordance with an illustrative
embodiment. Method 800 can be implemented in software, hardware, or
a combination of the two. When software is used, the software
comprises program code that can be loaded from a storage device and
run by a processor unit in a computer system such as computer
system 200 in FIG. 2. Moreover, method 800 can be implemented by
data processing system 900 in FIG. 9 and a processing unit such as
processor unit 904 in FIG. 9.
[0093] Method 800 aggregates, by one or more processors, sample
data regarding a plurality of factors associated with income and
geographic location (operation 802). The plurality of factors
associated with income and geographic location includes at least
one of salary, total payroll deductions, tax filing status, region
of residence, home values within said region, rental costs within
said region, job type, shift, work location, traffic data, road
construction/closures, employer growth trends, industry/sector
growth trends, regional employment, or industry/sector diversity.
In some illustrative examples, the plurality of factors associated
with income and geographic location are used to calculate
additional elements such as commutes, disposable income, or any
other desirable element in predicting values of an encountered
income.
[0094] Method 800 performs, by one or more processors, iterative
analysis on the sample data using machine learning to construct a
predictive model (operation 804). In some illustrative examples,
the machine learning uses supervised learning to construct the
predictive model. In some illustrative examples, the machine
learning uses unsupervised learning to construct the predictive
model. In some illustrative examples, the machine learning uses
reinforcement learning to construct the predictive model.
[0095] Method 800 populates, by one or more processors using the
predictive model, a database with predicted values of an
encountered income for a selected set of time periods (operation
806). Method 800 converts, by one or more processors, the predicted
values of an encountered income in the database into percentages of
observed values of encountered income for time periods within the
selected set at a predefined signage location to create indices of
encountered income (operation 808). Method 800 rank orders, by one
or more processors, the time periods within the selected set
according to their indices of encountered income (operation 810).
Afterwards, method 800 terminates.
[0096] In some illustrative examples, method 800 compares, by one
or more processors, the rank ordering of encountered income for the
selected set of time periods to observed encountered income for the
time periods from a second sample period (operation 812). In some
illustrative examples, method 800 aggregates, by one or more
processors, updated sample data from a second sample period
(operation 814). In some illustrative examples, method 800 updates,
by one or more processors, the predictive model using machine
learning incorporating the updated sample data (operation 816).
[0097] The illustrative embodiments thus produce the technical
effect of constructing accurate, complex predictive models from
large datasets and do so in a timely manner in the face of rapidly
changing empirical data. The illustrative embodiments produce the
technical effect of constructing accurate, complex predictive
models that predict values of encountered income for at least one
of signage locations or time periods for a set of signage
locations. The predictive models take into account at least one of
a region of residence, a work location, or a salary. The predictive
models also take into account commutes based on at least one of a
shortest path, predicted traffic, current traffic, road
construction/closures, or a work shift.
[0098] The flowcharts and block diagrams in the different depicted
embodiments illustrate the architecture, functionality, and
operation of some possible implementations of apparatuses and
methods in an illustrative embodiment. In this regard, each block
in the flowcharts or block diagrams may represent at least one of a
module, a segment, a function, or a portion of an operation or
step. For example, one or more of the blocks may be implemented as
program code.
[0099] In some alternative implementations of an illustrative
embodiment, the function or functions noted in the blocks may occur
out of the order noted in the figures. For example, in some cases,
two blocks shown in succession may be performed substantially
concurrently, or the blocks may sometimes be performed in the
reverse order, depending upon the functionality involved. Also,
other blocks may be added, in addition to the illustrated blocks,
in a flowchart or block diagram.
[0100] Turning now to FIG. 9, an illustration of a block diagram of
a data processing system is depicted in accordance with an
illustrative embodiment. Data processing system 900 may be used to
implement one or more of server computer 104 in FIG. 1, server
computer 106 in FIG. 1, client devices 109 in FIG. 1, or computer
system 200 in FIG. 2. In this illustrative example, data processing
system 900 includes communications framework 902, which provides
communications between processor unit 904, memory 906, persistent
storage 908, communications unit 910, input/output unit 912, and
display 914. In this example, communications framework 902 may take
the form of a bus system.
[0101] Processor unit 904 serves to execute instructions for
software that may be loaded into memory 906. Processor unit 904 may
be a number of processors, a multi-processor core, or some other
type of processor, depending on the particular implementation. In
an embodiment, processor unit 904 comprises one or more
conventional general purpose central processing units (CPUs). In an
alternate embodiment, processor unit 904 comprises one or more
graphical processing units (CPUs).
[0102] Memory 906 and persistent storage 908 are examples of
storage devices 916. A storage device is any piece of hardware that
is capable of storing information, such as, for example, without
limitation, at least one of data, program code in functional form,
or other suitable information either on a temporary basis, a
permanent basis, or both on a temporary basis and a permanent
basis. Storage devices 916 may also be referred to as
computer-readable storage devices in these illustrative examples.
Memory 906, in these examples, may be, for example, a random access
memory or any other suitable volatile or non-volatile storage
device. Persistent storage 908 may take various forms, depending on
the particular implementation.
[0103] For example, persistent storage 908 may contain one or more
components or devices. For example, persistent storage 908 may be a
hard drive, a flash memory, a rewritable optical disk, a rewritable
magnetic tape, or some combination of the above. The media used by
persistent storage 908 also may be removable. For example, a
removable hard drive may be used for persistent storage 908.
[0104] Communications unit 910, in these illustrative examples,
provides for communications with other data processing systems or
devices. In these illustrative examples, communications unit 910 is
a network interface card.
[0105] Input/output unit 912 allows for input and output of data
with other devices that may be connected to data processing system
900. For example, input/output unit 912 may provide a connection
for user input through at least one of a keyboard, a mouse, or some
other suitable input device. Further, input/output unit 912 may
send output to a printer. Display 914 provides a mechanism to
display information to a user.
[0106] Instructions for at least one of the operating system,
applications, or programs may be located in storage devices 916,
which are in communication with processor unit 904 through
communications framework 902. The processes of the different
embodiments may be performed by processor unit 904 using
computer-implemented instructions, which may be located in a
memory, such as memory 906.
[0107] These instructions are referred to as program code,
computer-usable program code, or computer-readable program code
that may be read and executed by a processor in processor unit 904.
The program code in the different embodiments may be embodied on
different physical or computer-readable storage media, such as
memory 906 or persistent storage 908.
[0108] Program code 918 is located in a functional form on
computer-readable media 920 that is selectively removable and may
be loaded onto or transferred to data processing system 900 for
execution by processor unit 904. Program code 918 and
computer-readable media 920 form computer program product 922 in
these illustrative examples. In one example, computer-readable
media 920 may be computer-readable storage media 924 or
computer-readable signal media 926.
[0109] In these illustrative examples, computer-readable storage
media 924 is a physical or tangible storage device used to store
program code 918 rather than a medium that propagates or transmits
program code 918. Alternatively, program code 918 may be
transferred to data processing system 900 using computer-readable
signal media 926.
[0110] Computer-readable signal media 926 may be, for example, a
propagated data signal containing program code 918. For example,
computer-readable signal media 926 may be at least one of an
electromagnetic signal, an optical signal, or any other suitable
type of signal. These signals may be transmitted over at least one
of communications links, such as wireless communications links,
optical fiber cable, coaxial cable, a wire, or any other suitable
type of communications link.
[0111] The different components illustrated for data processing
system 900 are not meant to provide architectural limitations to
the manner in which different embodiments may be implemented. The
different illustrative embodiments may be implemented in a data
processing system including components in addition to or in place
of those illustrated for data processing system 900. Other
components shown in FIG. 9 can be varied from the illustrative
examples shown. The different embodiments may be implemented using
any hardware device or system capable of running program code
918.
[0112] Thus, the illustrative examples provide a method, computer
system, and computer program product that can be used in decision
making regarding placement and pricing for signage. More
specifically, the illustrative examples provide a method, computer
system, and computer program product that predictively model the
encountered income for a signage location for a designated time
period.
[0113] In one illustrative example, one or more technical solutions
are present that overcome a technical problem with the
conventionally subjective analysis utilized to project future
success of signage placement. In one illustrative example, one or
more technical solutions are present that overcome a technical
problem with the conventionally subjective analysis utilized in
setting pricing for time periods for digital signs. As a result,
one or more technical solutions may provide a technical effect of
at least one of increasing speed, reducing cost, or reducing errors
in sign placement and advertisement pricing. One or more technical
solutions are presented in which predictive modeling is utilized to
objectively analyze locations and/or time periods for signage using
an encountered income.
[0114] The description of the different illustrative embodiments
has been presented for purposes of illustration and description and
is not intended to be exhaustive or limited to the embodiments in
the form disclosed. The different illustrative examples describe
components that perform actions or operations. In an illustrative
embodiment, a component may be configured to perform the action or
operation described. For example, the component may have a
configuration or design for a structure that provides the component
an ability to perform the action or operation that is described in
the illustrative examples as being performed by the component.
[0115] Many modifications and variations will be apparent to those
of ordinary skill in the art. Further, different illustrative
embodiments may provide different features as compared to other
desirable embodiments. The embodiment or embodiments selected are
chosen and described in order to best explain the principles of the
embodiments, the practical application, and to enable others of
ordinary skill in the art to understand the disclosure for various
embodiments with various modifications as are suited to the
particular use contemplated.
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