U.S. patent application number 14/499057 was filed with the patent office on 2015-07-02 for automated tool for property assessment, prospecting, and targeted marketing.
The applicant listed for this patent is John Nicholas and Kristin Gross Trust U/A/D April 13, 2010. Invention is credited to John Nicholas Gross.
Application Number | 20150186953 14/499057 |
Document ID | / |
Family ID | 53482292 |
Filed Date | 2015-07-02 |
United States Patent
Application |
20150186953 |
Kind Code |
A1 |
Gross; John Nicholas |
July 2, 2015 |
Automated Tool for Property Assessment, Prospecting, and Targeted
Marketing
Abstract
An advertising delivery assessment system uses building
structure attributes from image data and rates identified features
and associated conditions so that vendors can target appropriate
products and services. The outputs can include customized,
personalized advertising and marketing directed to particular
structures, owners, etc.
Inventors: |
Gross; John Nicholas;
(Berkeley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
John Nicholas and Kristin Gross Trust U/A/D April 13, 2010 |
Berkeley |
CA |
US |
|
|
Family ID: |
53482292 |
Appl. No.: |
14/499057 |
Filed: |
September 26, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61990429 |
May 8, 2014 |
|
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61883609 |
Sep 27, 2013 |
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Current U.S.
Class: |
705/14.58 ;
705/14.72 |
Current CPC
Class: |
G06T 1/0007 20130101;
G06Q 30/0276 20130101; G06Q 30/0643 20130101; G06K 9/00637
20130101; G06T 7/0004 20130101; G06T 7/0008 20130101; G06T 5/50
20130101; G06T 2207/30161 20130101; G06K 2209/21 20130101; G06Q
50/16 20130101; G06T 2207/30184 20130101; G06Q 30/0261 20130101;
G06T 2207/30132 20130101; G06F 16/5838 20190101; G06T 2207/10004
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/16 20060101 G06Q050/16 |
Claims
1. A method of targeting advertisements based on features of
building structures with a computing system comprising: a.
providing a first reference file containing first image data for an
outside portion of a first target building structure ; b.
generating a second reference file using the computing system, said
first reference file and a coding interface, said second reference
file containing: i. a plurality of individual building elements
identified in said first target building structure based on said
first image data; ii. a condition associated with each of said
plurality of individual building elements identified from said
first image data; iii. wherein said first and second reference
files are processed to generate a first target structure
characterization profile that identifies at least a set of
structure features and conditions for said first target building
structure;. c. processing first vendor advertising content with the
computing system identifying at least a first product/service
provided by a first vendor; d. correlating said first advertising
content to said first target structure characterization profile
with the computing system, such that at least one product/service
from said first vendor is matched to said first target building
structure based on said first target structure characterization
profile; e. generating a first advertisement targeted to said first
target building structure based on step (d), which first
advertisement contains personalized advertising consisting of a
combination of at least some of said first image data, said first
target structure characterization profile and said first
advertising content.
2. The method of claim 1 wherein said detected individual building
elements, associated conditions and first rating are stored in a
database of properties.
3. The method of claim 1 wherein a database of building elements
and attributes is compiled and consulted during step (b).
4. The method of claim 1 wherein a size and extent of a condition
or impairment for a feature is also stored as part of said first
target structure characterization profile.
5. The method of claim 1 further including a step: generating a
visual output containing preliminary annotation data for said first
image data identifying automatically identified individual building
elements and associated conditions for said first building
structure generated by an image processing system.
6. The method of claim 1 further including a step: detecting and
classifying other objects in said first image data, including one
or more of a vehicle, an living organism, person, or other personal
property item.
7. The method of claim 1 further including a step: detecting and
classifying other objects in said first image data, including one
or more of trash, debris or other item correlated with an occupancy
of such building structure.
8. The method of claim 1 further including a step: detecting and
classifying an architectural type of said first building
structure.
9. The method of claim 1 further including a step: processing a
query to generate a search report identifying other building
structures similar to said target building structure, including
structures matching said first target structure characterization
profile for one or more specific features.
10. The method of claim 1 further including a step: performing an
online verification test for a first user requesting designation as
an owner of said first target building structure, which
verification test includes measuring a first location of a device
associated with such first user.
11. The method of claim 10 wherein said first location is measured
at different time intervals to confirm a presence of said first
user at said first target building structure.
12. The method of claim 10 wherein said first location is measured
by directing said first user to different locations proximate to
said first target building structure and obtaining different
location signals from said first user at said different
locations.
13. The method of claim 12 wherein at least one location is inside
said first target building structure, and at least another location
is outside said first target building structure.
14. The method of claim 1 wherein the computing system
automatically processes a plurality of building structures across
an entire block, zip code and/or city.
15. The method of claim 14 wherein the computing system
automatically creates logical geographic proximate groups of
building structures having common building elements and conditions
in a designated area.
16. The method of claim 15 wherein said logical groupings and said
designated area can be varied.
17. The method of claim 15 wherein common advertising is
communicated to said logical geographic proximate groups with group
discounts in hard copy or electronic form.
18. The method of claim 1 wherein said second reference file is
created within the coding interface by a human using an electronic
annotation tool that tags said individual building elements
including a type identifier and a condition identifier.
19. The method of claim 18 wherein said second reference file is
created in multiple phases including a first phase in which only
impairments are tagged.
20. The method of claim 1 wherein said personalized advertising
includes a visual image of said first target building structure
annotated to identify said at least one product/service and said
corresponding building elements.
21. The method of claim 20 further including a step: generating a
simulated remediation of said first target building structure in
visual form, and presenting said visual form in a printed or
electronic advertisement.
22. The method of claim 20 further including a step: generating a
physical mailer or electronic mail wrapper containing address data
for an owner of such structure, and image data corresponding to
said first target building structure.
23. The method of claim 1 further including a step: providing an
electronic query engine which generates a report set of result
building structures matching a formatted query directed to said
individual building elements, types and/or associated
conditions.
24. The method of claim 23 wherein said report set includes only
partial address or image data for said set of result building
structures.
25. The method of claim 1 further including a step: providing an
electronic query engine which generates a report set of result
building structures matching a formatted query directed to products
or services corresponding to said individual building elements,
types and/or associated conditions.
26. The method of claim 25 wherein said query engine uses an
electronic classifier customized for a vendor generating said query
which is based on products or services calculated by a computing
system as corresponding to said individual building elements, types
and/or associated conditions.
27. The method of claim 1 further including a step: monitoring
engagements and redemptions of recipients of said first
advertisement with the computing system.
28. The method of claim 1 wherein additional second advertising
content for a second vendor is included in said first
advertisement, which second advertising content is directed to a
second product/service matched to features of said first target
structure characterization profile other than those matched by said
first advertising content.
29. A method of targeting advertisements based on features of
building structures with a computing system comprising: a.
providing a first reference file containing first image data for an
exterior of a first target building structure; b. repeating step
(a) for a first set of target building structures in a first target
area; c. generating a second reference file using the computing
system, and said first reference file, said second reference file
containing: i. a plurality of individual building elements
identified in said first set of target building structures in said
first target area based on said first image data; ii. a condition
associated with each of said plurality of individual building
elements identified from said first image data; iii. wherein said
first and second reference files are processed to generate a set of
first target structure characterization profiles that identifies at
least a set of structure features and conditions for each of said
first set of target building structures; d. generating at least a
first target subset of target building structures in said first
target area, which first target subset is determined to be those
structures related through a common feature and associated common
condition; e. generating a first advertisement targeted to said
first target subset based on step (d), which first advertisement
contains personalized advertising consisting of a combination of at
least some of said first image data, said first target structure
characterization profile and first advertising content.
Description
RELATED APPLICATION DATA
[0001] The present application claims the benefit under 35 U.S.C.
119(e) of the priority date of Provisional Application Ser. Nos.
61/990,429 filed May 8, 2014 and 61/883,609 filed Sep. 27, 2013
both of which are hereby incorporated by reference herein; the
instant application is further related to Ser. No. 14/499,061 also
filed this instant date and which is also incorporated by reference
herein.
FIELD OF THE INVENTION
[0002] The present invention relates to automated tools, methods
and systems which assess the condition of living structures and
other appurtenant real property features. The invention has
particular utility in the areas of real estate prospecting,
appraisals, insurance, targeted marketing, and similar domains.
BACKGROUND
[0003] Competition for housing stock is rapidly increasing in the
United States. In some areas turnover of housing is extremely small
and cannot satisfy demand. The problem is exacerbated as people
live longer and stay in their residences for longer periods than in
the past. Young families have significant difficulties finding
suitable existing homes for rent or purchase in many desirable
areas.
[0004] Current and useful information about housing stock is often
both incomplete and inaccurate. While some details can be found at
governmental websites (tax authorities, planning departments) and
at sites such as Zillow, Trulia, Redin, etc., there is no easy
mechanism by which a prospective renter or purchaser can search and
locate properties that--while not in perfect condition--may be good
leads. For example many homes are dilapidated or in poor condition
as a result of owners being unable to maintain such properties (or
attendant grounds) because of age, poor health, etc. In some
instances the structure is not even inhabited. These homes would be
excellent leads for housing opportunities but currently go
undiscovered and thus unexploited due to inadequate research and
assessment tools.
[0005] In a similar vein other interested parties would benefit
from more detailed knowledge on the types and conditions of housing
stock in their areas. For example public agencies should be kept
aware of the health and welfare of citizens who may be too elderly
to travel on their own, or respond to phone calls. Construction and
home building supply, insurance and other providers are also
similarly unable to quickly and accurately assess the health of
housing stock with current tools.
[0006] While some tools have been used in the past to assess
buildings, these have been limited and do not address the problems
above. For example, generic image databases of real estate
properties are shown in U.S. Pat. No. 5,794,216. U.S. Pat. No.
8,078,436 to Pershing et al., and US Publication No. 2003/0171957
to Watrous (both incorporated by reference herein) are all directed
to simple overhead, top down aerial inspections of the roofs of
structures. Such system typically rely on satellite or other image
databases. Billman (U.S. Pat. No. 8,289,160) requires a number of
sensors in a house from which he records data such as temperature,
water pressure, humidity, etc. to assess a future risk of damage or
destruction of the structure. Schwartz (US Publication No.
2004/0162710) requires a manual inspection form for rating a risk
of mold. Similarly, Pendergast et al. (U.S. Pat. No. 5,842,148)
incorporates a manual inspection form that is used to assess a risk
of damage to a house as a result of physical disturbance such as
wind, earthquakes, etc.
[0007] U.S. Pat. No. 5,742,335 issued in 1998 to Cannon
(incorporated by reference herein) teaches the use of a camera
system for surveying a property. The setup is quite complicated,
however, and requires extensive manpower to implement. Maciejczak
(U.S. Pat. No. 4,550,376 incorporated by reference) similarly uses
a complex unmanned apparatus for capturing condition information
for a structure. However, in both references little or no automated
processing is done of the captured structure information. Cannon
for example teaches only that recordings capture over time can be
manually examined to detect weathering changes. Despite these older
teaching, the state of the art has not improved beyond what is
shown therein.
[0008] In addition there is a considerable market in the US for
home improvement goods and services, such as for example, windows,
landscaping, siding, paint, roofing, plumbing and similar products
to name a few. Companies in this space have traditionally used
generic flyers for marketing purposes, as they have little or no
specific structural information for a specific property. Current
hard copy advertising materials therefore are represented by the
examples shown in FIG. 9. Furthermore, to date such types of
entities have not targeted groups of homes in a neighborhood by
identifying common issues with structures that could induce or at
least incentivize group purchasing behavior.
SUMMARY OF THE INVENTION
[0009] An object of the present invention, therefore, is to reduce
and/or overcome the aforementioned limitations of the prior
art.
DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is an illustration of components of an embodiment of
a real estate assessment system of the present invention;
[0011] FIG. 2 illustrates an exemplary method used for identifying
and assessing real estate in accordance with the present
teachings;
[0012] FIGS. 3A-3C illustrate an exemplary method used for
identifying and reporting on real estate leads in accordance with
the present teachings;
[0013] FIG. 4 illustrates an exemplary structure/format used for
collecting and storing parameters associated with real estate leads
in accordance with the present teachings;
[0014] FIG. 5 illustrates an exemplary method used for processing
real estate image information to identify and log key features in
accordance with the present teachings;
[0015] FIGS. 6A-6B illustrate examples of images and features that
can be identified and rated in accordance with the teachings of the
present disclosure;
[0016] FIGS. 7A-7E illustrate examples of image processing, feature
processing and reporting for a typical building structure that can
be performed in accordance with the teachings of the present
disclosure;
[0017] FIG. 8 illustrates an example of a reference structure and
image/feature processing that can be employed with embodiments of
the present invention.
[0018] FIG. 9 illustrates examples of prior art advertising and
marketing to home/property owners for products and services;
[0019] FIG. 10 shows a typical city sized block in a residential
neighborhood which can be assessed and targeted in accordance with
embodiments of the present teachings;
[0020] FIG. 11 identifies examples of structural features,
parameters, conditions, etc. that can be identified, assessed,
tagged, coded and stored for a particular building structure in a
city block in accordance with embodiments of the present
teachings;
[0021] FIG. 12 identifies further examples of structural features,
parameters, conditions, etc. that can be identified, assessed,
tagged, coded and stored for another structure in a city block in
accordance with embodiments of the present teachings;
[0022] FIG. 13 identifies other aspects of structural features that
can be classified in accordance with embodiments of the present
teachings;
[0023] FIG. 14 provides an exemplary embodiment of a targeted
advertisement generated for a property owner identifying a specific
structure and specific improvements identified by a classification
system of the present invention;
[0024] FIG. 15A provides an exemplary embodiment of a second
variant of a targeted advertisement, customized delivery envelope
and customized coupons generated for a property owner for a
specific structure, products, services, etc. in accordance with
embodiments of the present invention;
[0025] FIG. 15B provides an exemplary embodiment of a third variant
of a targeted advertisement for a property owner for a specific
structure, products, services, etc. in accordance with embodiments
of the present invention;
[0026] FIG. 16 illustrates a preferred embodiment of a data
acquisition process used by a classifier of the present invention
for building structures in a target city;
[0027] FIG. 17A illustrates a preferred embodiment of a structure
coding process used by a classifier of the present invention for
building structures in a target city;
[0028] FIG. 17B illustrates a typical structure coding as it would
be performed in accordance with embodiments of the present
invention;
[0029] FIG. 18A depicts an exemplary embodiment of a query engine
and interface that can be implemented in accordance with the
present teachings to facilitate identifying relevant properties
matching a particular target structural profile;
[0030] FIG. 18B depicts an exemplary embodiment of a query result
list for a vendor can be implemented in accordance with the present
teachings to facilitate identifying relevant properties matching a
particular target structural profile;
[0031] FIG. 19 depicts an exemplary embodiment of a query engine
and interface that can be implemented in accordance with the
present teachings to facilitate identifying relevant properties
matching a particular target product profile;
[0032] FIG. 20A depicts an exemplary taxonomy that can be employed
to map structure features, impairments, etc., categories to
respective product/service categories, or vice versa to facilitate
responding to queries and identifying prospects for customized
advertising;
[0033] FIG. 20B depicts an exemplary graphical interface that may
be presented to a user seeking to make home improvements;
[0034] FIG. 20C shows a block diagram of an exemplary automated
computing process that can be employed to assist homeowners and
merchants coordinate for remodeling and renovation projects;
[0035] FIG. 21 depicts a preferred embodiment of a preferred
tailored advertisement marketing engine implement in accordance
with the present teachings;
[0036] FIG. 22 shows a block diagram of a preferred embodiment of
an overall direct marketing system implemented in accordance with
the present teachings;
[0037] FIG. 23 shows a preferred embodiment of a property/structure
owner verification process that can be implemented in accordance
with the present teachings;
[0038] FIG. 24 shows a preferred embodiment of a vendor interface
that can be used by a vendor to identify, create and target
particular property structures in a geographic area; and
[0039] FIGS. 25A and 25B depict an exemplary auction process for
matching vendor products to targeted structures in response to
queries, including general keyword queries at a conventional search
engine.
DETAILED DESCRIPTION
[0040] A preferred embodiment of a system 100 for identifying,
assessing, rating and reporting on real estate properties, building
structures, etc. is depicted in
[0041] FIG. 1. While the preferred embodiments are presented in the
context of single family residential housing structures, it will be
understood that the invention has applicability to other types of
building structures, such as commercial structures, or any other
structure for which there is sufficient visual/machine perceptible
information to perform the processes described herein. Since SFR
have a high degree of variability--architecturally, aesthetically,
etc.--their wear, aging and weathering characteristics are also
variable and thus allow for significant differentiation
statistically. The terms "property" or "real estate" used herein
are intended broadly to denote the entirety of a property
opportunity present at a particular site, including any building
structures, fencing, walls, landscaping, foliage, trees, public
sidewalk features, vehicles, appertunant structures, etc., the
owners/inhabitants, and neighborhood related factors as well such
as crime rates, schools, access/convenience to shopping,
demographics, economics, and other factors known in the art.
[0042] As seen herein, a property assessment server computing
system 110 is preferably an online collection of computing machines
and accompanying software modules suitable and configured
particularly for performing the operations described below. The
preferred system 100 interacts through interface logic 120 with
outside data sources including a building stock data collection
system 113 and an external reference database 114. Building Stock
Data Collection system 113 can be any known provider of information
(such as for example Google through Google Maps image data) about
the properties being assessed, and may be accessed through an API
in appropriate instances. At a minimum such entities provide real
estate information (images, videos, etc.) sufficient to identify a
property at a particular location and perform an assessment such as
described below. In other instances the external databases 114 may
contain further information concerning the property, such as
owner/inhabitant identifications, gps coordinates, liens, taxes,
deed recordings, sales transactions, valuations, trends, and other
similar types of data maintained at governmental systems and/or
conventional real estate sites such as Zillow, Trulia, Redfin, etc.
Other types and sources data such as described in the art above can
of course be utilized and the invention is not limited in this
respect. It will be further understood that while FIG. 1 shows that
this data is obtained from third parties, it can of course be
collected and retained internally if desired.
[0043] System 110 engages with users employing computing devices
112 through interface logic 120 as well. These computing devices
112 may range and include smartphones, PDAs, notebooks, tablets,
laptops, desktops, etc. In a preferred embodiment system 110 is
part of a website which can be accessed through a conventional
browser running on such devices, or alternatively through an app on
Android or 105 device.
[0044] System 110 includes a number of specialized software modules
and storage modules which implement the processes described below,
including an Inventory Intake Manager 130, a Building Stock Data
Manager 140, a database of Structure Images 142, a Building
Classifier Engine 150, a Structure/Feature Reference database 152,
a Lead Generator Engine 160, Report Logic 170, and Vendor/Customer
Account Admin module 180. Some of the main functions of these
modules is as follows:
[0045] Inventory Intake Manager 130--preferably includes logic to
programmably and periodically locate and catalog new/updated
building stock images, new updated property records, etc.
[0046] Building Stock Data Manager 140--preferably includes data on
each property, including location, style, condition, owners, etc.
as gleaned from external systems 113, 114 and as derived from
internal classifications/assessments performed internally;
[0047] Structure Images 142--preferably includes raw and/or
annotated image data of at least outside portions of the structures
for the properties in question;
[0048] Reference Images 144--preferably includes
exemplar--reference image data for a reference set of building
attributes/elements and associated conditions, and that is used by
a classification engine described below;
[0049] Building Classifier Engine 150--preferably processes and
evaluates property data, including image data, to identify/assess
structures;
[0050] Structure/Attribute/Condition Reference database
152--preferably contains reference list of structure types,
attribute types, associated economic/physical impairments, scores,
etc. to be discovered in target structures;
[0051] Attribute/Condition--Feature databases/network
154--preferably contains computed models, templates or patterns
developed by a classifier to identify correlations between specific
structure attributes, conditions, and image features which can be
used to identify specific attribute/condition associated with a
particular structure;
[0052] Lead Generator Engine 160--preferably interacts with
customers and back end systems to identify properties of interest
based on selected query parameter criteria;
[0053] Report Logic 170--preferably cooperates with lead engine to
provide actual report organized and or composed in part under
control of a user, a vendor, etc.
[0054] Remediation Simulation Logic 175--uses specialized image
filtering and other image processing to remediate or simulate
visual improvements to building elements in a target structure for
the benefit of users;
[0055] Vendor/Customer Account Admin module 180: preferably
coordinates and manages vendor and customer accounts, including
billing, alerts, etc. The functions and features of each is
discussed in more detail where appropriate below.
[0056] It will be understood that system 110 will likely have other
components, modules, etc., and so as to better highlight the key
features of the present invention only those aspects most germane
to such are presented. Moreover the software modules described
(referenced usually in the form of a functional engine) can be
implemented from the present teachings using any one of many known
programming languages suitable for creating applications that can
run on client systems, and large scale computing systems, including
servers connected to a network (such as the Internet). Such
applications can be embodied in tangible, machine readable form for
causing a computing system to execute appropriate operations in
accordance with the present teachings. The details of the specific
implementation of the present invention will vary depending on the
programming language(s) used to embody the above principles, and
are not essential to an understanding of the present invention. To
the extent it is considered relevant to the present invention, the
Applicant specifically disclaims any coverage that may encompass
so-called "transitory" subject matter deemed unpatentable under 35
USC 101, including for example any coverage to transitory media or
bare transitory propagating signals and the like, or to any pure
"abstract" ideas.
[0057] FIG. 2 illustrates the main processes 200 used in preferred
embodiments of the disclosure, including broadly the two tasks of
1) training the Classifier Engine 150 (FIG. 1) and 2) using it to
assess and rate different new properties. A list of basic building
elements that can be identified and logged can be found at
nyc.gov/html/Ipc/html/glossary/glossary.shtml. Other online sources
can be consulted of course for a catalog of identified building
elements. In the most straightforward case examples of each basic
building attribute is captured, such as facade, eaves, windows,
balcony, porch, arch, piers, columns, lattices, false timbering,
ornamental, etc. along with specific types (i.e., facade (shingle,
siding, brick, stone, horizontal boards, vertical boards, etc.)
roof {pitched, double pitched, hipped, flat, metal, tile, shingle,
slate, parapet, dormer, mansard, fascia, brackets, eaves, pent,
pediment, etc.} and so on).
[0058] At step 210 a set of reference images for database 144 are
collected. The reference images can be captured by human
assistants, and/or obtained from a reference image database(s) such
as Google Maps (not shown) etc. Preferably a reference set is
established which includes sufficient exemplars representing
different building elements to be classified. The reference images
are also preferably tagged by human annotators to identify each
building attribute, an associated condition, a location in the
image, etc. Building Stock database 140 should include complete
data on each entry in the property database which identifies any
and all reference building elements or attributes associated with a
particular structure, as well as other data noted above.
[0059] Image database 142 preferably includes a current image of
the structure in question which is in a form that can be analyzed
for building elements. The images can also include annotations (see
below) identifying structure elements, defects, severity ratings,
locations of identified defects, etc. as annotated automatically by
a classifier and/or manually by a human operator.
[0060] In addition it is desirable to include image exemplars of
the building elements or attributes in various physical conditions
or impairment, which form part of reference image set in database
144. The conditions/impairments are each associated with a
particular building attribute. Each is also separately identified
and classified to make them amenable to query. Thus at step 215 one
or more examples of the following structure attributes or elements
and related conditions pairs are defined:
[0061] Roof {new, missing/damage tile, shingle, metal,
holes/cracks, unevenness, brightness (moss, mold)}
[0062] Fixtures {new, damaged eaves/chimney/gutters/downspouts,
leaves in gutters}
[0063] Faccade {new, missing shingles/siding, breaks, holes,
discontinuities, discoloration, warping, paint
bubbles/blistering/peeling--aging, weathering, water damage}
[0064] Body Structural {new, cracks/holes, exposed beams, fire
damage, warping, lean, foundation cracks, bricks missing/damaged,
missing plaster, damaged flashing, gaps, exposed insulation}
[0065] Windows/Skylights/ Doors {new, breaks, holes, warping,
sills, covering (or lack thereof)}
[0066] Support {leaning/damaged columns, retaining walls}
[0067] Surround {new, fence, wall (including retaining wall),
walkway (condition, overgrowth), garage, carport, mailbox,
chainlink}
[0068] Landscaping/grounds {Trees, shrubs, hedge, grass, debris
(mail, newspapers)}
[0069] Secondary objects {tools, toys}
[0070] It will be understood that this is a just a partial list,
and that a number of other individual structure attribute/condition
pairings can be identified as well, and/or that the attributes and
conditions can be logically associated in different ways. To build
out database 144 therefore a graphical image (photograph or
electronic rendering) of a structure (e.g., a house) with a roof
(feature) having missing tiles (condition) is preferably collected
and included in the reference image set. Examples of structures
with new and broken windows are collected, and so on. In some
instances it may be desirable to also include and assign severity
weightings (i.e., a scaling factor of any convenient range (1-10,
or the like) for the identified condition) as well as damage
location data (i.e., an indication in a coordinate grid of where
the condition exists for the element on the structure).
Consequently each individual reference record of a particular
structure attribute may contain a different condition, severity,
location, etc. It will be understood of course that a single
reference image may have more than one attribute that can be
tagged. As noted earlier, in most instances it will be preferable
for a human to create the tags for the reference images, including
an identification of each building attribute, a condition, and a
location thereof.
[0071] While the above presents a number of examples, it will be
understood that this is not the entirety of attributes that could
be extracted for a property, and that others could be captured as
desired for any particular application. In some instances the data
in the reference set will be augmented by additional data gleaned
from external sources and without reference to the images alone.
For some applications, rather than resort to actual image data, it
may be more convenient or effective to have a human artist creating
the renderings identifying a model or reference example of an
element, attribute, condition, severity, etc. In this manner the
reference set can be made more uniform and potentially yield more
predictable and consistent results across different condition
types.
[0072] Preferably the reference set 144 also includes data/images
from different architectural types, so that a nominal House Type
217 including from Victorian, Craftsman, Colonial, Ranch, and other
common types can be represented. This allows for embodiments in
which users can query and select for distinct housing architectural
types.
[0073] Since vehicle data can also be processed as part of property
assessment, data for such items should also be captured by
reference to vehicle types 218. The state of image recognition
software at this point is such that identifying vehicle makes,
models from photographs is a relatively straightforward task. Other
items or objects such as living organisms (persons, animals (pets))
personal property items (bicycles, strollers, carriages, lawn
mowers, children's toys, tools, decorations, signs) etc., may be
identified in images as well, and can be positively associated and
predictive of whether a structure is inhabited. Negatively
correlated items such as chain link fences, debris, garbage,
newspapers, mail, etc., can also be identified and recorded. Again,
it is not necessary to identify the identity of persons, or the
object, only a high likelihood of the presence of such item or
object. As will be apparent some items/objects may be simply
identified as being present and not have a corresponding condition
that needs to be logged. Finally it may be possible in some
instances to automatically identify and classify types of trees,
flowers, plants, etc. from image data alone and by comparison to
reference images of such foliage.
[0074] The identification/labeling or tagging of the reference
images (including any appurtenant data for vehicles, foliage, etc.)
at step 219 is preferably done by humans, since they can more
quickly identify and log the corresponding architecture type,
attribute/condition, and/or augmented data set including severity
and location. Nonetheless a machine classifier can provide
preliminary or tentative coding based on pre-processing as
described herein, to give a significant leg up in such annotation
process. This data is preferably stored as part of a
structure/attribute/condition database 152 which is correlated to
reference images 144 and which is accessible to Classifier Engine
150.
[0075] It will be understood that with improved image and computing
processing it is also likely to be a task that can be automated
with a computing system as well. In some cases it may be possible
to crowd source the tagging of reference images at step 222,
including through implementing them as part of a human interactive
proof test, or CAPTCHA. For example to gain access to a resource
(device, data, etc.), a human may be asked by a computing system to
identify (with a mouse or other pointer) where there is a cracked
window in a presented image of a house, or if a particular
attribute can be found in a particular house in a set of images
presented. The system can then sense if the user has identified the
feature correctly and use such information as part of the tagging
process.
[0076] In some applications it may be desirable to scale or
normalize the size of the structures in the reference image sets
144 to some optimal processing size (e.g.. N pixels by M pixels for
a particular structure) prior to training, which can be determined
through routine experimentation. Accordingly the reference images
in database 144 may be cropped, shrunk or expanded depending on the
target desired comparison size. Other customized image processing
operations (rotations, noise clean up, etc.) may also be performed
to derive and generate an image reference set.
[0077] At step 220 the Classifier Engine 150 is trained with the
template consisting of the training set of reference images 144 and
tags as created and logged in database 152. Objects (and their
reduced feature set) can be analyzed in image patterns using a
variety of techniques, including statistical processing, neural
networks, etc., which can be used to detect and extract features of
such attributes. In this instance the attributes identified by the
tagging are analyzed in the images to break them down and reduce
them into (or extract) smaller distinct features that can be more
readily detected. This attribute--condition--feature dataset is
stored in the form of models, template or other suitable form in a
database or network 154.
[0078] Using conventional image processing, each attribute (and
related condition qualifier) may be reduced in dimensionality and
characterized by a distinct set of pixels, shapes, sizes,
proportions, curves, textures, or other mathematically derivable
feature from the image. The training results in extraction and
optimization of specific features most appropriate for a particular
element, so as to minimize classification errors for the attribute
in question.
[0079] Feature extraction algorithms are well-known in the art, and
examples of such that are suitable and/or can be adapted easily for
use in the present embodiments is set out in literature such as A
Survey of Face Recognition Techniques, by Jafri et al. appearing in
Journal of Information Processing Systems, Vol. 5, No. 2, June 2009
and incorporated by reference herein. Commercial systems for
identifying defects in manufacturing materials could also be
adapted for this purpose, such as those offered by Camea's Visual
Inspection System and as disclosed in references such as Surface
Defects Detection for Ceramic Tiles Using Image Processing and
Morphological Techniques by Elbehiery et al. appearing in World
Academy of Science, Engineering and Technology 5 2007 and
incorporated by reference herein. Again it will be understood that
the type and amount of training may vary according to the
particular attribute and condition to be identified, and thus will
be the subject of routine experimentation.
[0080] In the end the classifier(s) can be configured to use some
form of similar or template matching, probabilistic (Bayesian
logic) decision, or a combination thereof. Consequently at step 230
the classifier preferably outputs a score for each entry in a
Building Stock images 142 (or other particular unknown target image
presented in a list 232) along with a confidence score for each of
N possible attributes, M possible conditions for each, and
additional information such as an estimated location in the target
image. Tentative structure classifications (architecture type,
attributes, conditions, etc.) are identified at step 240 and then
stored at step 250 along with unique structure id in database 140.
For better accuracy it may be useful to employ multiple classifiers
trained with different algorithms to give a combined or averaged
score to identify the attributes and classify the structure.
[0081] As part of the training step 220 noted earlier, a classifier
may also be provided with additional data for the reference image
structures 210 in question which can be later correlated, including
any recorded/estimated economic details (value of the property),
occupancy data (inhabited/not inhabited, rented/owned), internal
details (size, # of bedrooms, # rooms, etc.) This type of data is
available from a number of sources, including commercial real
estate entities, government agencies, etc. An output 240 therefore
comparing a target structure to a reference structure can also
generate a correlation indicating a tentative or predicted value of
the structure, an occupancy score/rating, and other similar useful
metrics. All of this property related data can be stored as part of
prospect database 142 noted above.
[0082] In some instances the attribute and/or condition may be
determined geometrically and without strict reference to a template
or pattern. For example significant aging, extreme weathering,
paint peeling, and other types of deformities or damage may be
detectable with image filtering and processing without resort to
templates per se. An article by Winkler et al. titled Visibility of
Noise in Natural Images, Proc. IS&T/SPIE Electronic Imaging
2004: Human Vision and Electronic Imaging IX, vol. 5292, p. 121
incorporated by reference herein explains how noise can be inserted
into or filtered from images. The pattern of aging,
weathering/facade damage for many structures appears as and mimics
the effects of general image noise and thus could be detected in a
similar fashion. A similar noise reduction process is identified in
the aforementioned Elhebierry et al. reference for detecting
defects in tile materials and could be adapted for a similar
purpose here.
[0083] A reference by Lin et al. titled Salt-Pepper Impulse Noise
Detection and Removal Using Multiple Thresholds for Image
Restoration appearing in the Journal of Information Science and
Engineering, Vol. 22, pp. 189-198 (2006) incorporated by reference,
discloses optimal techniques for removing certain specific types of
noise from images. As this noise again mimics the appearance of
aging and/or weathering, one option for detecting aging/weathering
in building facades proposed by the inventor therefore is to noise
treat the images, and generate a noise reduced version of the same.
The amount of "noise" detected in the image can be treated as a
proxy for the degree of aging and/or weathering of the building
structure. In general preferred embodiments of an image processing
process are configured to mimic a human eye's capability of
discerning errors, defects and other irregularities in an otherwise
homogeneous or regularly textured object.
[0084] Examples of the types of attributes and conditions that can
be used for training the classifier are illustrated in FIGS. 6A-6B.
As seen there, a first structure is in very distressed condition
and is boarded up in some places, as evidenced by elements 605
which are boards superimposed over a window. This element 605 has
both an irregular orientation, and overlaps with a window element,
making it an unusual feature that can be detected. Elements 610
illustrate examples of broken windows which can also be easily
identified by distinct and detectable image features. Other
examples are shown as well in FIGS. 6A-6B including:
[0085] damaged/exposed roof elements 615
[0086] chain link fencing 618
[0087] burned out areas 620
[0088] overgrown weeds/plant growth 625
[0089] peeling paint 630
[0090] damaged--deformed porch and patio 635
[0091] exposed--broken facade 640
[0092] water stains (abrupt changes in color)--rotting 645
[0093] As is apparent from these clear examples, these elements
represent tell tale signs or signatures of damage, aging,
weathering, neglect, etc. to a building structure, and which can be
readily identified in image data. In general, since humans are very
adept at picking out inconsistencies or visual errors in an
otherwise homogeneous texture, it is expected that relevant
training samples are easy to obtain. Other examples will be
apparent to those skilled in the art from the present
teachings.
[0094] FIG. 3A depicts a process 300 by which users can search for
real estate/building stock that meets particular criteria of
interest, including certain visual aesthetics, architectural
features, predicted occupancy criteria, etc. This is done
preferably through providing search parameters to Lead Generator
Engine 160 (FIG. 1) which then identifies matching properties and
outputs reports through Report Logic 170 to a client device
112.
[0095] As seen in FIG. 3A a target location is optionally provided
at step 310, such as a City, neighborhood, zip code, street, or any
other desired geographical qualifier. Other attributes,
characteristics, categories, etc., can be specified at step 315 to
the Lead Generator Engine 160 as desired to filter appropriate
results. Matching leads are then identified at step 320 from
property prospect database 142 along with building images in
appropriate cases.
[0096] At step 330 a report of the results is presented to the user
in accordance with the filtering parameters specified, and any
desired formatting, sorting, etc. For example results presented on
a mobile handset may vary dramatically from that shown on a webpage
to a desktop user. The output results can be tailored for a
particular platform using known techniques. If the user wishes to
refine the results or search with different parameters, they can
refine the query appropriately.
[0097] One further aspect that can be optionally employed in some
embodiments is a remediation simulation function implemented by
module 175. For example a user may find a target property that is
in dilapidated condition, and may desire to understand better what
such structure would look like if it were improved. As noted above
the present invention preferably stores reference images of
structures similar to the target images reviewed by the user, and
is also able to characterize or model the effects of aging or
weathering. Alternatively different types of noise reduction,
removal or image enhancement can be performed to smooth out and
improve the image. Consequently it is a simple matter to "reverse"
some of the effects of such aging or weathering and/or simulate
correcting many of the attribute impairments in the target image
using conventional image filtering and processing. The simulation
can be controlled selectively to correct particular damage or
attributes, such as facade/siding cracks, paint irregularities,
roof damage, etc. or other basic building elements. Note that this
feature is handy and could be employed by painters, contractors,
etc., who are presenting or bidding on remodeling of a property.
The Remediation Simulation Logic 175 thus outputs a simulated image
of a remediated version of the target structure. The corrected
image version of the building structure can be shared, emailed,
etc. in any conventional fashion.
[0098] The selection of search parameters shown in process 300 can
be achieved through an interactive interface 350 seen in FIG. 3B,
which in a preferred embodiment is implemented as part of an
interactive web page presented by interface logic 120 (FIG. 1) and
viewable within a browser on device 112. Nonetheless it could also
be implemented as part of an app executing on a smartphone device
as alluded to above.
[0099] This selection screen 350 includes a number of query
selection boxes, buttons, pull-down elements, etc. which may become
active with mouse-overs and other techniques known in the art. The
user can specify a location for the property in query box 360, in
this case selected to be San Francisco, or some other geographical
region (city, state, neighborhood, zip code, etc.) In addition the
user can qualify whether the property in question is in fact for
sale or not in box 361. Other options may specify that other
properties not on the market should be included so as to increase
the range of prospects. The condition of the property can also be
specified in query box 362 which may be presented in the form of a
sliding scale, descriptors, etc. which permit a wide range of
building stock to be searched, from "new" all the way to "extreme
fixer upper" or some other similar moniker.
[0100] Occupancy rating query box 363 allows the user to again
filter or control the types of properties presented, based on a
calculated occupancy score for the properties in question. As noted
above, in some instances a potential purchaser may be interested in
pursuing unconventional or otherwise unexploited opportunities by
looking for properties that appear to be unoccupied. The type of
ratings, scores and selection mechanisms for this function can be
varied in accordance with any desired capability or performance.
For example a degree of confidence in the occupancy of the
structure could range from "known occupied" to "known vacant" and
several grades in between. As seen in FIG. 3B the user can select
from a slide bar, a set of designated buttons, or any other
convenient scheme.
[0101] In query box 364 users can further specify an architectural
"style" for the house as well if they wish. To make the query
easier to formulate, it can include visual clues or objects as
parameters as many people do not know the names of building
elements, or the types of architecture, etc., but do know what they
like aesthetically when they see it. In this instance the user has
selected "Victorian" as the style of house they wish to peruse.
[0102] Query box 366 allows a user to specify other property
elements, including architectural subtypes (there are many types of
Victorians, including Eastlake Stick for example), specific
attribute styles for facades (clapboard style, fishscale, style,
etc.) ornamental features common to that architectural style
(finials, sunbursts, etc.) and other desirable property features
(landscaping, trees, lawn, etc.) In this instance the user has
indicated that they are interested in properties that have a
sunburst element, which was a common ornamental embellishment in
housing stock built in the late 19.sup.th century.
[0103] An output 370 in interface 350 can take any convenient form
known in the art, including by identifying the locations of
listings on a map (virtual pins) or by presenting visual listings
with building images, addresses, and similar real estate data as
alluded above. Information about a listing agent may be included as
well, and the search results can be shared, emailed, saved etc. in
any convention manner. By selecting one of the entries the user can
be provided with any other useful details pertaining to the
property, including the type of information maintained by such
entities as Zillow, Trulia, Redfin and/or a listing broker/agent.
In addition the user can be provided with additional classifying
details about the building structure such as highlighted, tagged or
annotated version of the building structure identified in FIG. 7E.
This allows the user to quickly see the assessment of the property
as performed by Building Classifier Engine 150 and/or human
editors.
[0104] Consequently embodiments of the invention include a visual
search engine (FIG. 3B) that assists a user in constructing and
configuring a virtual target exemplar building in accordance with
user criteria. The user can construct a model of a desired house
from various building parameters (architectural types, roof types,
facade types, etc.) color, condition, etc., and then query against
database 142 to find one or more closest matches. In some instances
it may be desirable to set aside a portion of the interface 350 to
behave as a virtual canvass (not shown) so the user is permitted to
see a virtual model of the housing structure they are creating for
reference purposes.
[0105] To make the query process easier, the options presented for
additional structural elements presented in box 366 can be
selected/filtered automatically within the interface so that only
appropriate choices for a selected structure from box 364 are
presented. For example in selecting a Victorian there would be
different facade and structural elements than those presented for a
Colonial, Craftsman, etc.
[0106] While the selection parameters/icons are shown as thumbnail
photos for simplicity, it may be desirable to use uniform sized
black/white artist-rendered impressions of the various attributes,
or to create more isolated/focussed versions of the attributes
instead. This would give a more consistent look to the options
although it may be less informative absent other contextual details
provided by a complete image of a building structure. Again a
default of ALL can also be included as an option in most instances
to allow a wider range of search results.
[0107] It will be understood that these are just examples of a
query interface and accompanying query tools, and that other
variants with different forms, formats and variables could be
implemented as well consistent with the present teachings.
[0108] FIG. 3C shows an interactive interface 380 presented within
a mobile computing device such as a smartphone, along with an
additional level of functionality that permits rapid, on the spot
identification and review of property details. In this embodiment
Lead Generator Engine 160 is distributed and incorporated (at least
in part) within code and part of an app running on a smartphone.
The app integrates with the device's built-in camera (not shown) so
that the user simply captures an image of a target property 382 as
they see it on location. This can include both interior and
exterior images of course. The image and other useful data (such as
location data) is uploaded to a server side component of Lead
Generator Engine 160, which queries database 142 to locate a match.
As noted above, image matching for building structures can be
accomplished in any number of ways by adapting existing recognition
algorithms to recognize the types of objects of interest here.
Further details about the target property 382 can then be presented
within portion 385 or 390 of an interface 380 presented on their
respective computing device.
[0109] As most smartphones also capture geolocation data, the
present system can utilize such data to better match or correlate
the captured structure image to entries in databases 142 and 144.
If the user's location is known, then the range of image data that
must be scanned to identify the property is significantly reduced,
and the accuracy is also significantly enhanced. Nevertheless even
in the absence of explicit geolocation data the user's image data
of the property can easily be broken down and analyzed for
attributes, features, etc. by Classifier Engine 150 to identify
likely matches from the preexisting stock of building structures in
database 142 in a similar manner as discussed above, even if it
takes more time.
[0110] The match is retrieved for the user within portion 385 of an
interface of their mobile computing device. The user can be
prompted to confirm that the match is correct by checking selection
box 384. Other aspects of the records in database 142 can also be
confirmed directly from the user on site, including by presenting
them with an output such as seen in FIG. 7E, and asking them to
confirm, modify or reject any condition designated for a particular
building element. In such embodiments the database can be populated
effectively through user contributed content, or so-called crowd
sourced data.
[0111] Again after confirming the identification of the target
property the user can be prompted to see if they want to see more
details (box 386) which would permit them to see the kinds of data
discussed earlier for FIG. 3B. Alternatively since the property
attributes are known from database 142, the user can also query
against such records to find other matches (filtered by location,
availability, condition as before) that are aesthetically similar
to the target property image they have captured. These can be
presented within section 390 to permit the user to find similar
leads based on their personal tastes. As further refinements the
user can be provided with additional filtering options such as
noted above (box 366) f they want to constrain the search result
list further by building characteristic/element, property features,
distance from the target property/user's location, etc.
[0112] The query "input" to a property prospect engine therefore
can be in the form of a direct visual image provided by the user,
so that the process operates to locate other building structures
that are most similar to the one identified by the user. This is
useful because in many instances users/purchasers frequently desire
a particular visual look in a house, and the invention can help
them find other stock that matches their target appearance, and
which may be available (or more likely to be available) for
purchase.
[0113] In some embodiments the retrieved entry from database 142
and 144 is presented in interface 380 along with visual tags or
selectable overlays, so that the user can further define or refine
a target building structure. For example the user may desire a
different type of facade (stucco instead of shingle) a different
color (white instead of dark brown) or a different roof type (slate
instead of shingle), etc. These can be presented as drop-down menus
to form a final query is that is then processed by Lead Generation
Engine 160 to retrieve corresponding entries. The entries can then
be presented as noted earlier in map form, listing form, etc. with
any desired accompanying data. The user can also be prompted to
confirm information in database 142 concerning building attributes,
associated conditions, etc., for a particular target property.
[0114] In addition it may be useful to log and compile data on
particular properties that are the subject of such data captures,
by property identification, building type, neighborhood, city, etc.
to glean insights into the current mindset of prospective home
buyers, and for other similar marketing or research purposes. To
collect information first hand on building stock inventory, mobile
handset users can be solicited to directly rate the quality or
aesthetic appeal of a building structure that they are viewing on
location within interface 380 as well using any convenient scale. A
frequency, average score, or popularity of buildings within a City
or neighborhood captured in images can be identified with a heat
map or other convenient visual indicator. Similarly the queries
made by users with Lead Generator Engine 160 can be logged,
categorized and mined to identify trends in tastes for
architectural types, building elements, building aesthetics,
etc.
[0115] FIG. 4 illustrates an example of a record 400 containing a
number of relevant data fields generated by Classifier Engine 150
(or which can include human annotations in some instances) and
maintained in database 142 for each property. Each property
preferably has a unique ID stored in field 402. The property ID
could also store data logging details such as the dates of image
captures, which helps to understand a currency of any recorded
data.
[0116] A property structure style field 405 identifies an
architectural type (Victorian, Craftsman, etc.) as discussed
earlier. Structural element fields 410 including an identification
of each structural element presented in the property, a condition
of such element, a rating/weighting of such condition, and an image
location for such particular element. It will be understood that a
particular property ID may have multiple structural elements, and
in fact several of the same type of structural element, each of
which can have its own pertinent particulars. The same information
is made for appurtenant elements with fields 412, including for
example ancillary buildings, fences, garages, vehicles, foliage,
etc.
[0117] One or more overall structure ratings is provided in field
415 which is generated by Building Classifier Engine 150 using a
number of factors in accordance with features desired for a
particular application. For example structure ratings may be
derived on a granular, attribute-by-attribute basis, or based on an
entire collection of elements present in a structure. Since each
attribute may be considered a different dimension of the property
data, the ratings may be based on or constitute multi-dimensional
vectors representing a particular structural element, or group of
elements, or of an entire structure. In this form they can be more
easily compared to other property structures as well for purposes
of grouping, classification and querying. Those skilled in the art
will appreciate that many variations of ratings can be employed by
a system 100 for purposes of improving and optimizing individual
property assessments and comparisons.
[0118] Occupancy Prediction field 420 stores a score (of any
convenient range) generated by system 100 identifying a likelihood
of current occupancy of the structure. As noted earlier this score
may be associated or mapped to other content labels, such as
"confirmed vacant" or "confirmed occupied," etc. Preferably the
prediction should have some reasonable range to help differentiate
structures, as well as a confidence score. Thus there may be other
ratings or predictions such as likely vacant, uncertain, likely
occupied, and so on. As noted above where this information is
already known with certainty by reference to public records it can
be included here. Since it is not usually known, however, the
invention derives a prediction of occupancy by comparing the
property structural element conditions, scores, etc. against other
known examples for properties in which the properties are confirmed
occupied (at one end of the spectrum) and other examples in which
the properties are confirmed vacant, abandoned, etc.
[0119] Data fields 442 (address, geolocation) 444 (owner, occupant
data) 446 (image links) and 448 (transaction, tax records) can be
obtained usually from any number of public records or commercial
resources. In other instances, as noted below, it is expected that
interested persons will capture and communicate this type of data
(including image data, aesthetic ratings, attribute ratings, etc.)
from mobile devices directly. While not shown here it is possible
of course to integrate or cross-reference to other data tables to
indicate a registered agent/broker associated with the property. It
will be understood that the database could be adapted differently
and that any particular commercial implementation is likely to have
significant variations.
[0120] FIG. 5 depicts an exemplary building attribute/condition
assessment process 500 that employs image processing that is
suitable for embodiments of the present invention. General aspects
of the image processing are also shown in FIGS. 7A-7D.
[0121] As seen in FIG. 7A an image 700 is retrieved from database
144 (or derived from other source) and is divided into distinct
regions or blocks 701 at step 510 (FIG. 5). While only one block is
shown it will be understood that any and all parts of the image can
be divided and treated this way. The size and shape of the blocks
701 can be varied of course using conventional techniques such as
discussed above and it will be understood that FIG. 7A's depiction
is not necessarily drawn to scale and is merely illustrative to
help facilitate understanding of the invention.
[0122] A building envelope 702 (FIG. 7B) is identified at step 520
(FIG. 5) by image processing adapted to detect edges of structures.
The envelope information (and similar profile data) is useful and
can be used for identifying an architectural type descriptor for
the target structure, for defining regions of blocks to be used
within the envelope for further evaluation, etc. The building
envelope information can be employed to define a building structure
grid (not shown) from which the individual examination blocks can
then be derived. Since different architectural types can have
different grids, this grid information can be used as well to
improve block definition, attribute identification, etc., as it
will be determined statistically that a particular grid element in
a first building type will contain very different structure
attributes than a corresponding element in a second building type.
Other boundaries for other objects such as landscaping 706, trees
708, etc., can also be determined and logged.
[0123] At step 525 an identification is performed to determine
structural elements presented in a first block 701, including such
attributes as roof elements, window elements, facade elements, etc.
Again the identification of such attributes (and their types) can
be performed in any number of ways by Classifier Engine 150 after
it has been trained, including by pattern recognition techniques
and statistical image processing using models, templates, etc..
Preferably each block is assessed to see if it contains one or more
of a set of target structural elements or attributes. For example
the first block 701 may be coded to denote that an attribute {roof}
with type {pitched} and {shingle} is contained, and so on. The
first block can also be color coded as well, so that abrupt changes
within the block or between blocks can also be used as an indicator
of an irregularity, defect, damage or the like.
[0124] Confidence scores and similar measures can be employed and
recorded with each attribute/type pair to improve performance.
Attribute characteristics can be identified, generated and stored
as part of such process. For example an attribute {roof} would be
expected to have a certain attribute size/shape relative to the
building structure and an attribute orientation. These attribute
codings can be used to identify and confirm the existence and
extent of an element in the target building structure. Consequently
data from adjacent blocks (of which 701' is an example) can be used
to influence and score a confidence rating as well. Thus, for
instance if a first block is tentatively coded as noted above, and
a second adjacent block is also tentatively coded the same way, the
likelihood is high that the attribute identified is correct because
the {roof} attribute in question is known to have characteristics
matching the observed block data.
[0125] Similarly as additional blocks of the property image are
processed, and depending on an identified architectural type, it
might be unlikely to have an attribute in a particular block. For
example a roof attribute would be uncommon below a certain level
(line 705) in a building structure. Conversely, doors are uncommon
at a rooftop level, and so on. These statistical observations can
be gleaned and used to train the classifier as well so that it
weights the presence of attributes in each block in accordance with
the location of the attribute, the type of architectural type
identified, and so on.
[0126] At step 530 a tentative condition score is computed for the
detected attribute. Again it is preferable to assess the condition
of the attribute against a reference set of conditions to determine
an appropriate rating. In this instance the dark or irregular color
may cause a roof element to score poorly. As before it is possible
in some embodiments to use adjacent block coding scores, so that
the detection of a roof in poor condition in one block is likely to
mean that a second adjacent block with such attribute is likely to
be scored in a poor condition. A window element may be detected
with reference to blocks at 704, and with a condition of {no
covering} which may be an indication of abandonment (since most
people prefer some kind of privacy/covering if they inhabit a
structure). Landscaping 706 occludes structure 700 and is
irregular, and thus may be classified in this manner along with
other appropriate plant/tree identifiers and condition descriptors
for the same as noted earlier.
[0127] If Classifier Engine 150 identifies an attribute, condition
and location with an acceptable confidence in block 701 (which
again can be calibrated as needed) at step 535 then the data is
coded in a tentative tag table at step 540, along with an
indication of the block id, location id, or similar coding. If the
attribute is not confirmed, the process simply loops around and
looks to see if another element may be present. This is done
repeatedly (see FIG. 7C) to identify any number of elements 712
(siding, dormers, fascias, overgrowth, water stains, missing
windows, etc.) in poor condition. As seen in FIG.7D information
concerning the existence of a vehicle 714 vehicle models, types,
etc. can also be captured as part of the process.
[0128] While the blocks do not have to overlap, in some embodiments
the process can be repeated with different sized blocks, with
overlapping blocks, etc., so as to increase a confidence in the
attribute tagging process. Furthermore it is expected that in many
instances there will be multiple images of the same target property
and each can be assessed individually to contribute to the overall
structural rating. This may be desirable particularly in instances
where shading, light intensity, etc. may vary significantly between
images.
[0129] At step 545 the entire table of attributes is reviewed in
automated process for consistency, final scoring, etc. As alluded
above some smoothing of the data or the like may be performed.
[0130] A visual assessment report 730 can be generated (see FIG.
7E) at step 550 (FIG. 5) which preferably identifies at least those
attributes identified by the system as having some measure of
damage, impairment, aging, weathering, etc., This data is presented
in a list and visual form for a human reviewer, and within an
interface may have interactive tags so that the tagged
elements/conditions may be further reviewed, modified, etc. by the
human operator. Intensity information can be provided by way of
color coding to denote a degree of severity of the identified
condition, along with location information.
[0131] An initial tentative architectural designation is also
identified in field 735, along with a tentative inhabited score
field 740. This latter score may be derived by examining and
comparing any number of identified defects in the property
structure against reference examples. Correlations may be derived
based on individual elements, combinations of elements, etc.--for
example absence of window coverings on multiple windows may be
strongly correlated with abandonment or vacancy. Overgrowth of
landscaping or extremely weathered siding may be correlated with
aged occupants, and so on. A number of statistical observations can
be derived and used to determine an estimate of the likelihood of
occupancy of the structure, and whether is occupied by a renter or
owner, etc. Other fields, data can be presented of course and the
invention is not limited in this respect.
[0132] During step 550 (FIG. 5) a human reviewer can accept or
modify the initial classification presented in visual assessment
report 730. As alluded to earlier in some embodiments the visual
information report may be incorporated as part of a CAPTCHA so that
a human user is requested to confirm or verify the presence (and/or
location) of certain building elements in the image that are
impaired/damaged/aged/weathered, etc., to crowd-source the
assessment of the target properties, or the reference templates
used to rate the target properties.
[0133] The final structure assessment data is then recorded at step
555 with the property information in database 142 as noted above.
Based on the assessment of individual elements, their condition,
etc., and collectively over all the elements, an overall assessment
or rating of the exterior physical condition can be assigned to the
building structure. This rating or score can be normalized by
reference to other specific buildings have the same architectural
type as well for better comparison. A structure may be ranked or
rated for condition relative to peer structures in an immediate,
specified target region. "Peer" structures may include all
structures, or a subset having the same architectural style, or a
predetermined number of common features, etc. A "target region" may
include a street, block, zip code, city, or any other desired
benchmark.
[0134] By correlating each of the impairments to repair or
improvement figures, and summing over all the attribute conditions,
an overall estimate can also be generated to identify a cost to
restore the building structure to a nominal target state. Using
sales data for similar structures in a similar condition, and other
similar parameters a purchase prospect score can also be assigned.
This and similar data can be stored in database 142 as part of a
structure rating 415. Since the image data is regularly updated,
long term evaluations over defined time periods can be made as well
to identify changes in a property condition.
[0135] FIG. 8 illustrates further examples of reference property
structure images that can be used in embodiments of the present
invention and coded in database 142. The attributes of reference
property 830 identified here include examples of siding, windows,
downspouts, walkways, fencing, lawns which can be classified as
corresponding to a positive or high end of a condition scale, i.e.,
being in top condition and lacking any noticeable weathering or
aging. In contrast reference property's walkway is notably less
"clean" (i.e., includes plant overgrowth) and the fence is
discolored and weather. Extreme examples of each building element
can be captured and analyzed statistically in this way to help
characterize elements having opposite valued ratings. In addition,
botanical elements such as particular trees (maple, willow) can be
seen in these images along with particular notable flowering and
climbing plants that could be identified and logged as part of a
reference database.
[0136] FIG. 9 illustrates different examples of prior art
advertising materials in the general areas of home improvement. As
is apparent from a cursory glance, these marketing materials are at
best relevant on a macro scale approaching a city or zip code
level, but little if anything about these materials is tailored or
customized for any particular neighborhood block, let alone for a
specific domicile, residence or building. For this reason these
flyers or circulars have little appeal or relevance to any
particular homeowner, and the targeting appears to be little more
than hit or miss. At best it is based on "seasonal" improvements
generally seen in particular geographic regions, meaning it is more
likely a resident of Chicago will see a pool supply ad in early
summer rather than mid winter. Similar generalized advertising is
presented online as well in response to search queries, so that a
homeowner querying "windows" is given at most generic advertising
from a vendor near their location.
[0137] FIG. 10 shows a typical city sized block in a residential
neighborhood which can be assessed and targeted with better
marketing materials in accordance with embodiments of the present
teachings. The individual buildings and lots have been identified
with unique numbers in this block to make it easier to understand
the present discussion. It will be understood that the residential
and commercial housing stock of any particular city, town, etc.,
can be divided in this fashion by the computing system of FIG. 1
using a number of automated procedures based on computer records,
address data, etc. and/or using some other convenient scheme for
purposes of achieving the objectives set forth herein.
[0138] Furthermore it will be apparent that the size of the block
can be adjusted as needed or desired for any particular assessment,
advertising campaign or group offer. For example it may be
determined that as concerns marketing for window products, the
target size of a group block or offer should include about N
separate buildings (see logical block encompassing structures 3-6)
while the target size of a group block for landscape or roofing
services should be about M separate structures (see logical block
encompassing structures 14-21). Other logical groupings that do not
require contiguous boundaries are also possible of course. The
present teachings can be used to glean such optimal group sizings
and clustering to better increase an adoption rate for particular
campaigns.
[0139] Furthermore by observing and logging participation rates by
individual homeowners embodiments of the present invention can
identify and optimize logical groupings in any block for any
particular product or service. For example it may be determined by
a computing system that homeowners in lots 7, 10 and 12 are
frequent purchasers of paint products. A database 140 of
structures, owners and specific products can be maintained to log
such purchases. If all three purchased products within a period
T1-T2, and a predetermined or calculated update period T3 for such
product is approaching or has expired, these individuals can be
targeted with a group discount or coupon to increase their odds of
participation. By analyzing owner purchase histories and expected
product lifetimes the computing system 100 can pair and aggregate
similarly behaving owners in a particular block with similar needs
to create customized targeted group advertising. On a larger level
of course groups of blocks too can be analyzed for optimal
targeting.
[0140] FIG. 11 identifies examples of structural features,
parameters, conditions, etc. that can be identified, assessed,
tagged, coded and stored for a particular building structure in a
city block in accordance with embodiments of the present teachings.
It will be understood that these are but examples of course, and
other features could be classified as well. In a preferred
embodiment, each structure is classified in accordance with at
least N different mandatory features, and potentially with an
additional M optional features. For example it may be required to
capture at least the street address from a sign on the structure,
from sidewalk markings or other similar indicators. The number of
distinct visible stories can be logged (1, 1.5, 2, etc.)
Landscaping and vehicle data may be optional, and so forth. The
number of features to be coded will vary in accordance with a
desired purpose of the data being captured. In some instances a
particular entity may want to capture additional customized data,
such as the presence of a sign indicating an alarm system. In other
instances a homeowner can be encouraged or induced (including
through online surveys) to contribute interior photographs as well
of specific rooms. Thus images of kitchens, bathrooms, bedrooms,
and surroundings--floors, walls, ceilings, fixtures (lights,
appliances) can be captured and coded as well. This can be used as
a feeder to an online user home improvement interface. For example
a property owner desiring a kitchen renovation could upload photos
of a current kitchen to get an assessment, evaluation, etc. by
competing vendors or contractors desiring to take the job. A
homeowner may similarly contribute such interior information as
quid pro quo for access to a full exterior report as described
herein.
[0141] As noted above, preferably the presence of the feature, type
and condition is coded and collected and stored in digital form.
For example, feature "facade" has a type "stucco" and a condition
"good." Other forms of classification and annotating will be
apparent to those skilled in the art. Information on the type of
structure, the presence, type and condition of facades, roofs,
awnings, porticos, landscaping, flags, exterior fixtures, vehicles,
yards, articles, garages, building types, air conditioners, fuel
storage tanks, window bars, security signs, security lights, flower
pots, flower boxes, fire escapes, amount of tree/building shading,
street obstruction, and even colors of objects can be collected.
For commercial establishments, the type of business can be tagged
and stored as well (dry cleaner, restaurant, bar, convenience
store, etc.) Grafitti can also be identified in this manner. The
types of private --public trees, to the extent discernable, can
also be collected as such data can be used for a number of purposes
as well. For example certain types of trees produce sap or other
droppings that cause damage to vehicles on a seasonal basis, and
cluttering of gutters. Knowing the time(s) of year when trees
flower or are likely to produce droppings is another item of
information that can be exploited for targeting optimal structures,
homeowners and times for marketing cleaning products, car wash
entities, gutter/window cleaning, etc.
[0142] The existence of public or private utility poles, boxes
(lights, phone, cable) and cable wires, telephone wires, common
fences or open areas between structures can be noted for each
structure. In some instances the existence of open parking spaces,
and public street signage can be identified and logged as well. An
overall density, availability, etc. of street parking relative to
private driveway parking can be estimated as well. This data can be
aggregated and used for determining potential parking places for
persons unfamiliar with an area. The existence and condition of
fire hydrants, street parking signs, school signs, can be compiled
for any convenient purpose.
[0143] Relative sizes and areas of objects in the image data can
also be collected if desired. This data can have further utility in
assessing the overall features of a structure and potential for
different products/services. For example, dimensions such as a size
of a roof area, amount of landscaping, height/size of trees,
hedges, a size of a driveway, size of window openings, sidewalks,
etc. can also be collected and stored for each structure. To
facilitate such measurements, additional scales, tools, etc., could
be included in the interface of FIG. 11 to assist a human
rater/coder. An amount of height displacement from street level can
also be measured if desired. While some images may allow only for
one/two dimensional measurements it is expected that additional
data could be gleaned from other perspectives obtained by other
image capturing systems, or from public records which identify the
layout of building envelopes on each lot. Accordingly a front-on
shot may identify a dimension of X feet width for a yard, and a
public record may indicate a setback of Y feet from a public
street. From such combined data it is possible therefor to glean
additional site characteristics.
[0144] An overall structural rating can be identified as well,
along with a relative target area rating indicating a comparison
other structures in the area. Many times prospective investors,
home owners, etc., want to know a ranking of a structure relative
to other homes in a particular neighborhood.
[0145] To improve data accuracy it is expected that multiple human
coders could be employed to review any particular structure. This
will increase accuracy and coverage through crowdsourcing of such
tasks. A voting algorithm can weigh the contributions from
individual coders in assessing and attributing the presence of
features, their type and condition. Since the image data is in
electronic form it is expected that such human classifiers could be
trained and do such work from any location that has computer
access, including in remote or foreign locations where labor costs
may be significantly lower. As noted above it is expected that with
sufficient training an automated classifier could participate in
the process, if not perform the entire process of classifying
structural features. To make it easier for human coders, a visual
palette can be presented with the features on the screen. As a
coder completes one feature, a corresponding box could turn from
red to green. Pulldown menus can be employed as seen in FIG. 11 to
assist with the coding parameters. Again, as noted above in some
instances these features can be pre-computed by a preprocessing
operation and given a tentative designation for human
confirmation.
[0146] FIG. 12 identifies further examples of structural features,
parameters, conditions, etc. that can be identified, assessed,
tagged, coded and stored for another structure in a city block in
accordance with embodiments of the present teachings. Information
on the type of structure, the presence, type and condition of
yards, articles, garages, number of stories, and building types can
be collected. An electronic interface may be optionally configured
primarily or solely for the purpose of identifying defects, wear or
other hazards. Alternatively the coding process may be targeted
primarily or solely for identifying structural improvements, such
as the presence of a new fence, new roof, new paint job, etc.. This
may be preferable for some implementations in which it is desired
to get a first pass at a set of building stock, and/or where it is
not necessary to capture more than a few features. Tags and other
annotations can be conveniently added and stored using an automated
computing system programmed in accordance with the present
teachings.
[0147] FIG. 17B illustrates a typical structure coding as it would
be performed in accordance with embodiments of the present
invention. In this instance both defects and property improvements
have been annotated and highlighted. Again other variations for
items to be coded, and tools for doing so will be apparent to those
skilled in the art. The implementation of such customized
electronic tool can be achieved in any number of ways known in the
art based on the present teachings.
[0148] FIG. 13 identifies other aspects of structural features that
can be classified in accordance with embodiments of the present
teachings. For example the presence of chimneys, fire damage,
defective windows, inferior fencing, weathered paint, overgrown or
unkempt yards, etc. can all be tagged and logged into a database.
These structures correspond to other numbered lots in the block of
FIG. 10. While the preferred embodiment uses an existing database
of images from a third party supplier, it will be apparent that the
images could be obtained directly using conventional processes and
sources as well at different angles, elevations and profiles to
increase building coverage and feature currency/accuracy. For
example, as noted below, it is contemplated that crowdsourcing
and/or aerial drone, satellite and/or low altitude dirigible
technology could be employed usefully for such purposes.
[0149] FIG. 14 provides an exemplary embodiment of a targeted
advertisement 1400 (preferably generated by the computing system of
FIG. 1) for a property owner containing multiple targeted and
customizable content sections. Among other things these separate
sections identify a specific structure and specific improvements
identified by a classification system of the present invention. The
content for this marketing material can be synthesized from a
variety of sources, including the original image database, the
annotations added by human coders, and other tailored content
appropriate to the features and conditions of the building
structure. Preferably the marketing and/or advertisement 1400
includes a first section containing a current image of the
structure, an identification section for the owner's name or other
identifier, a targeted message section to the owner identifying the
address of the structure, identification of defects or
imperfections at the site, and additional sections for customized
offers and/or messages addressing such defects/imperfections.
Correspondence and contact information would preferably be included
as well in these or other sections of the targeted material. While
this material is shown here in printed form, it will be understood
of course that a full report containing such information may be
made accessible to a user online, and/or presented as part of a
targeted electronic ad.
[0150] In addition the targeted ad may include a "group" discount
coupon portion (see bottom left of FIG. 14) that informs the
structure owner of group discounts, including other specific
building owners in their area that can be solicited to achieve a
group discount rate for a qualified set of participants. In this
manner the homeowner can be engaged and motivated to interact and
obtain group discounts by cooperating with their neighbors who are
determined by the computing system to have similar needs or
interests. A group offer can be specified in detail in another
section of the targeted ad, and can include any number of criteria
tailored by a vendor. For example it may require that at least X
participants purchase $Y of products within a period P to obtain a
discount D. For example if two neighbors purchase products
exceeding $1000 within 30 days they may achieve 20%
discount/refund, and so on. The format of the group offer and
extent of discount may be adjusted concomitantly with the number of
participants, type of products, etc.
[0151] In a preferred approach the computing system identified
above (FIG. 1) keeps track of a group of owners {S1, S2 . . . } who
"opt in" to a proposed discount, and gives them a grace period (T)
of a certain number of days to solicit commitments by other
participants so as to qualify for the group offer. Small refundable
deposits are collected from each participant to secure
participation in the group discounts. As the period expires the
system can send reminders to the non-opting participants,
additional enhancements or discounts, etc. or qualify the existing
set of participants for the discount. The deposits are then applied
towards purchase of the goods or services. If the group does not
achieve the target size or set the deposits are simply refunded in
part or whole. Other variants will be apparent to those skilled in
the art.
[0152] This type of targeted neighborhood group coupon should have
reasonable and improved adoption rates and benefits since the needs
of the individual owners are accounted for and aggregated in the
grouping of the offers. Stated another way, rather than simply
targeting every house in the block with the same random offer by
mailers or emails (as is done by most group advertising
technology), the present invention can identify particular houses
with particular needs, group such entities, and present a specific
offer to such entities based on stored profiles for such structures
and owners. It will be understood that this is only an example, and
the format of such advertisement could take any number of forms,
styles in accordance with the present teachings.
[0153] Also shown in the bottom right of FIG. 14 (in thumbnail
form) is a remediation simulation segment or portion of the
marketing materials 1400. In this section the computing system
provides a visual simulated version of the homeowner's structure as
it could appear if remediated using the products/services proffered
in the marketing materials. The remediation and/or rendering
simulation software can take as an input the image file for the
structure in question and using conventional image processing
techniques imitate the effects of different types of
improvements.
[0154] Other examples and formats of the simulation/remediation
section and other sections of the targeted advertising will be
apparent to those skilled in the art from the present teachings.
Again while shown in hard copy form it is apparent that such
targeted advertising 1400 could be created on a structure by
structure basis and maintained/presented electronically. A virtual
flyer/ad could thus be constructed and viewed online at a website
by a homeowner or other authorized user, with the same sections
noted in FIG. 14. This information could be accessed online by a
homeowner in the same manner that they can currently access or edit
information online for certain real estate listing sites. By
specifying a particular address, and providing suitable
credentials, a user/homeowner could access his/her
tailored/customized data for their structure using a general query
engine. The annotated structure data and all other sections would
be presented within a conventional browser or mobile equivalent for
perusal. The tags, coupons and other sections could similarly
include active link portions to engage with the owner directly
through more interactive electronic tools.
[0155] FIG. 15A provides an exemplary embodiment of a second
variant of a targeted advertisement 1500 with numerous customized
sections including customized coupons generated for a property
owner for a specific structure, products, services, etc. in
accordance with embodiments of the present invention. In addition,
a separate customized delivery envelope can be employed as well
(see bottom left of FIG. 15) to further personalize the message. As
with FIG. 14 and the other embodiments, this information could be
accessed and presented electronically as well within a conventional
Internet-accessible interface.
[0156] This figure illustrates further that different components
and aspects of the coded data can be customized and monetized for
use by different service/product companies. For example information
on chimneys, roofs, landscaping, windows, etc. can be captured and
segmented for analysis and targeted marketing. Interior features
can be captured and coded in the same fashion (hardware floors,
carpeting, linoleum, tiles, etc.) As seen at the top section of the
targeted ad, a coupon can be customized and generated with offers
and discounts matched to particular conditions observed and
identified at the particular structure. The content can be further
tailored based on prior purchase and/or engagement behavior of the
owner.
[0157] An owner of such property, therefore, can receive a
different flyer and targeted advertisement based on the particular
condition of their living structure, which may be entirely
different than their adjacent neighbor(s). Each flyer or targeted
advertisement may have different sections (identification,
structure details, coupons, remediations, etc.) and with different
content in each section. In this manner the present invention can
micro-target advertising for specific individuals on a building by
building basis to achieve superior results over generic mass
marketing techniques. Conversely product and service companies can
quickly and accurately identify promising leads for their business
using more relevant information.
[0158] Other interaction mechanisms with the owner can be included
in the advertisement as well, including URLs, barcodes and QR codes
in another section of the advertisement (see bottom right of FIG.
15) that can be scanned by a smartphone to access content, and web
based codes useable at an entity's website as well. These
additional access points allow an owner to quickly and rapidly see
additional targeted and tailored materials appropriate for their
structure. Again group offers can be presented on such
advertisement as well.
[0159] As alluded to above the marketing materials are preferably
further customized for the homeowner by including a small graphic,
image or icon of their structure directly on an envelope or similar
mailer/flyer. This further reinforces the personalization factor
and attractiveness of the materials for the individuals being
targeted. Rather than receiving a generic flyer with their name and
address, the present invention can present high quality,
structure-specific content appropriate for their situation.
[0160] FIG. 15A provides an exemplary embodiment of a second
variant of a targeted advertisement 1500 with numerous customized
sections including customized coupons generated for a property
owner for a specific structure, products, services, etc. in
accordance with embodiments of the present invention. In addition,
a separate customized delivery envelope can be employed as well
(see bottom left of FIG. 15) to further personalize the message. As
with FIG. 14 and the other embodiments, this information could be
accessed and presented electronically as well within a conventional
Internet-accessible interface.
[0161] FIG. 16 illustrates a preferred embodiment of a data
acquisition process 1600 used by a classifier of the present
invention for building structures in a target city as it would be
implemented on a customized structure assessment--targeted
marketing computing system. The general purpose of this specialized
computing module is to acquire appropriate image data for
structures within a target area, along with accompanying address
and owner biographical and purchase profile data if available. It
is expected that the critical steps identified in this process can
be implemented into executable software routines and modules using
any number of ways by skilled artisans based on the present
teachings.
[0162] At step 1610 a target city (or other convenient population
unit) is divided into grids, blocks and streets by a human and/or
an automated software program. Information identifying a beginning
and end of each individual street, road, alley, etc, is used as
well at step 1615 from any convenient database or similar
source.
[0163] At step 1620 customized logical blocks are constructed by
the computing system either from actual physical
residential/commercial blocks, from boundaries established by
street address ranges, or by any other convenient scheme that
facilitates the present objectives. An automated scheduler/logger
routine is also used at step 1625 to keep track of the progress and
status of processing of each street, block, etc.
[0164] During step 1630 image data (and other similar machine
captured data) for the building structure is retrieved for the
target address in question. Again in a preferred approach this data
is obtained from a third party vendor, but it can be generated as
needed as well using any conventional techniques. For example it is
expected that aerial drones, satellite, balloon and similar
technology can be used in certain areas to easily capture structure
image data from a variety of perspectives, and at different times.
Because such devices can obtain image data different elevations,
this will also facilitate building out a comprehensive image
database. By taking pictures at later hours (including at night)
such devices could also identify whether structures are inhabited
or not based on the presence of lighting and other similar
signatures. Appropriate safeguards could be implemented of course
to ameliorate or at least reduce privacy concerns.
[0165] The building image and tentative address are tentatively
tagged and stored as part of a set of master data tables 1650,
including in a structure image database 1652. The structure image
database can also include structure sub-images, which are based on
automatically dividing the original image into separate blocks, or
separate areas corresponding to distinct building elements using an
image computing device. The subimages can then be used in targeted
marketing for the target property instead of an entire structure
image in some circumstances where it is desirable to highlight or
focus on one or more particular elements, or where it may be
considered less intrusive to the homeowner's privacy.
[0166] Owner data for such structure can be accessed automatically
and stored in a database 1656 as well, along with optional prior
home improvement data (building permits), vendor historical
purchase data, line of credit data where it is available, etc.
Metadata tags for each structure are stored in a database 1654 as
they are coded. It will be understood that the format and routines
required to access and store such data can be implemented in any
number of ways based on the present teachings.
[0167] The automated process continues at step 1640 by proceeding
to a subsequent address. Again this may be done programmatically or
can even be done manually by a human operator navigating and
accessing an image view of a street under consideration. When a
street is completed at step 1645 the process can continue by
selecting a different street until an entire target area is
completed. To optimize targeting the scheduler logic may be
programmed to discontinue image and data access when a density of
structures falls below some threshold minimum. For example, in some
suburbs and rural areas the benefits of logging and assessing
specific structures may be less because of a lack of critical
targeting mass. Conversely in large cities it may be less desirable
to analyze large apartment buildings, and instead prioritize based
on single family residences and small businesses. This approach may
yield less comprehensive coverage for some areas, but can be
employed to prioritize assessment and marketing. It will be
understood that some steps are simplified for purposes of
elucidating the key points of the present teachings and that many
other steps could be implemented in accordance with any particular
commercial application.
[0168] FIG. 17A illustrates a preferred embodiment of a structure
coding process 1700 used by a classifier of the present invention
for analyzing building structures in a target city. This process is
used to capture and annotate data within an interface such as shown
in FIGS. 10-13. It mirrors the process of FIG. 16 in many respects,
and like reference numbers are intended to refer to like processes
and structures. For example structures 1650, 1652 (image data),
1654 (metadata tags) and 1656 (owner data, profiles) are the
same.
[0169] At step 1710 a coding process initiates preferably at one
endpoint of an identified street. A scheduler/logging step 1720
keeps track of a completion process for any particular target area
and street--address range set.
[0170] The image data for a particular target address is obtained
at step 1725, along with a tentative address tag. Preferably this
address information for the structure is confirmed at step 1730 to
ensure that the targeted marketing materials (ads, flyers and
envelopes) contain accurate information for a particular
address.
[0171] At step 1740 an input coding overlay or coding template is
presented to a human coder to facilitate annotating, scoring, etc.
of a target structure image. This template tool can take any
convenient form suitable for assisting a coder, and may have a
number of pop up fields, pre-designated tags, and image recognition
capability, etc. for performing a coding process. For example when
a coder places a mouse over a roof portion of the structure, the
image data may include some pre-processing areas with preexisting
tentative feature designations to facilitate data input. Other
features may already be automatically tentatively classified as
noted above, so that the human coder is mostly used in a
verification role. When a coder selects a portion of this area the
template can present a tag already populated with the appropriate
feature label, or a set of labels predicted to be present in the
designated region. Preferably the tool includes predictive and
error-control logic so that the user is constrained to use
predesignated labels for features, types and conditions that become
active as the user enters data into particular fields. It will be
understood that any number of different techniques can be employed
to collect the image feature data and the invention is not limited
in this respect.
[0172] During step 1750 the input template is used by a coder to
identify, classify and rate a condition of features in an image for
a structure. Again in a first pass it may be desirable simply to
identify only defects or only improvements. In some embodiments it
may be desirable to code each image with contributions from
multiple observers. For some applications it may be sufficient to
collect data from volunteers contributing information ad hoc based
on their informal surveys of structures conducted on a portable
device such as a smartphone, tablet, etc., while they are in the
vicinity or in the location of the structure in question. Any
number of techniques can be used for this purpose.
[0173] During step 1760 a coder provides annotation tags as
required by the template, and according to their visual inspection
of the structure in question. Again because a significant amount of
structural information--particularly defects or impairments--can be
gleaned rapidly and easily by the human eye, the coding is expected
to be relatively easy to perform, even for unskilled workers.
[0174] As alluded to above a structure image database 1652 can also
include structure sub-images. During or after the coding process,
the image for the structure can be automatically divided by an
image processing system into different sub-images of different
size, location, etc., which correspond to distinct building
elements. Thus the coding database preferably includes both a tag,
as well as a corresponding sub-image of a desired size to identify
the element and its condition. The size and content of the image
can be made uniform, or it can adjusted based on the type of
element, selected by the coder manually using a conventional image
cropping tool, or automatically identified and bounded by an
automated classifier as noted above. Again these subimages can then
be used in reports, responding to queries, creating targeted
marketing for the target property (instead of an entire structure
image) and so on.
[0175] The automated process then proceeds to the next address at
step 1765 to facilitate further data entry by the coder. This is
repeated by until each structure is coded as needed for a
particular application. Again in some instances it may be desirable
to divide the coding of structural information into distinct coder
"experts" so that individuals with experience and understanding of
facades may be employed to do that kind of work, while persons
familiar with landscaping or roofing could be used for other
components, and so on. By hyper-segmenting the
identification/classification task, it may be faster and easier for
certain coders to obtain proficiency at certain tasks and improve
accuracy, rather than requiring them to master all identification
tasks. A first team of coders may be dedicated to roofs, with
another team to fences, landscaping, facades, vehicles, etc.
Accordingly, a number of different coders can work on the same
image data and provide a number of separate tags and annotations
for the same structure serially or in parallel. The data can then
be aggregated and updated as needed in metadata tag database
1654.
[0176] As alluded to above, in some instances an automated
classifier can be trained to locate the features of interest,
either as a complete data capture, or even simply as an initial
pre-coded template that is reviewed by a human coder for accuracy
and completeness. The final meta data template can be tweaked,
edited, augmented etc. by a human operator. In this cooperative
approach a machine can perform the bulk of difficult
annotating/tagging and a human can do more of the fine-tuning of
the results.
[0177] FIG. 18A depicts an exemplary embodiment of a query engine
and interface 1800 that can be implemented in accordance with the
present teachings to facilitate identifying relevant properties and
homeowners matching a particular target structural profile. It is
expected that this type of search tool can be used online over the
Internet or some other network by vendors and service providers in
any number of different industries to identify leads, generate
reports and customized, targeted advertising such as seen in FIGS.
14 and 15.
[0178] As seen in FIG. 18A, a location 1810 can be specified, and,
if desired, a particular zip code, city block, street, etc. Other
query parameters can be provided within a search field 1820 and the
invention is only limited by the features that are captured or
derivable from the coding data. For example a vendor or user may
filter leads by whether they are already existing customers or not
of such vendor. Other income, demographic and similar owner profile
data can be incorporated as desired as well.
[0179] To facilitate use, the search engine may include a number of
predefined fields 1830 corresponding to coded searchable features
in a building--structure set. The features can be associated and
filtered by type field 1840, as well as preferably by an impairment
1844, condition 1846, etc. In some applications a vendor can be
given a visual search field for a query based on impairments 1844
and/or condition 1846, so that a variety of exemplar images are
presented corresponding to each categorized condition. The vendor
therefore can thus easily glean what types of conditions are
associated with a particular feature across a spectrum of
classified values--i.e., what constitutes a poor condition shingle
(level 1) an excellent condition shingle (level 10) and so on.
[0180] For example a user can specify that they would like a result
set of all structures in Berkeley, zip code 94709 which have
shingle facades and that are below average condition (level 5).
Other default values could be used to review all structures in a
particular area, with sorting and reporting capability as well.
[0181] Furthermore a vendor can specify that a result set should be
grouped using a query construct selector 1850. For example the
vendor can specify that a result set from the query engine should
logically group structures within a particular area (e.g., a
cluster of 4-5 houses or an entire city block) and in a particular
number (say 10) which share a common feature, type and/or condition
or rating. This information can be used as leads for developing
group discounts and promotions.
[0182] FIG. 18B depicts a typical report 1860 as it could be
generated by a query engine 1800, in response to a basic query such
as identifying all structures in a particular city block that have
shingles as a facade, and which are below average in condition. The
report can identify the fields listed, as well as any other desired
data maintained by the platform for vendors. Preferably the
specific address or other contact information is masked in most
instances to prevent poaching of the data by competitors or the
vendors. In some instances it may be desirable to allow vendors to
see at least partial image information to confirm that they want to
target the customer in question. This depiction of a sample report
1860 is not intended to be exhaustive of course, and other formats,
fields, and features will be apparent to those skilled in the art
from the present teachings.
[0183] The specialized interface and functions of structure feature
query engine 1800 and report generation can be implemented using
appropriate computing systems adapted with software to perform such
functions in accordance with the present teachings.
[0184] Looking at it from another perspective, FIG. 19 depicts an
exemplary embodiment of a query engine and interface 1900 that can
be implemented in accordance with the present teachings to
facilitate identifying relevant properties matching a particular
target product profile. Where apparent, like reference numbers are
intended to refer to similar structures and functions identified in
FIG. 18. For example a vendor/service provider could specify a
particular location 1910 (a city) and/or narrowed to a particular
area 1915 (zip code, street, block) and specify a variety of search
parameters 1920. In the example of a home improvement entity, they
could request a result list that included leads for product field
1930 such as "paint" with a type field 1935 of "all," or structures
that require siding facades, and so on. As with the interface of
FIG. 18, this logic could be implemented on a webpage at a website
in any convenient form and accessed through a browser or mobile
device. Similar queries for building stock matching other criteria
can be solicited according to vendor product categories, such as
weatherproofing, raw materials, etc. A result set could look like
that shown in FIG. 18B, but instead include a ranked listing of
structures in the designated area according to a predicted need for
the product in question.
[0185] Furthermore as alluded to above, a vendor can specify that
the result set should be grouped using a query construct selector
1950. For example the vendor can specify that a result set should
logically group structures within a particular area (e.g., a
cluster of 4-5 houses or an entire city block) and in a particular
number (say 10) which may be good leads for a specified product or
service. This information can be used as leads for developing group
discounts and promotions. The interface and functions of structure
feature query engine 1900 similarly can be implemented using
appropriate computing systems adapted with software to perform such
functions in accordance with the present teachings. It will be
understood of course that other features could be implemented in
such interface(s) as well.
[0186] To facilitate the operations of search engines 1800 and
1900, FIG. 20A depicts an exemplary taxonomy that can be employed
to map structure features, impairments, etc., categories to
respective product/service categories, or vice versa to facilitate
responding to queries and identifying prospects for customized
advertising. This taxonomy may be centralized and made generic for
basic mappings, but it is expected that each vendor will customize
or tailor a mapping of a set of features, types and conditions to
their particular product line. This can be gleaned by such vendor
using their own proprietary logic for ascertaining the correlation
of identified features and their product line(s). For example a
landscape company may determine through correlating products and
features that their targeting should be made to specific structures
which have certain landscape annotations, include certain articles
(garden tools) and/or which have particular exterior features
(sheds, gazebos, ponds, trellis) etc. A roofing company may
consider not only a type and state of a roof but also whether other
prominent improvements are present, potentially indicating a
homeowner predisposed to invest in improving their property. An
insurance company may determine that owners with well-kept
properties file fewer claims, and so on. Other companies may employ
their own taxonomy and correlations to define appropriate queries
that best map to their customer intelligence data. The present
invention enables a large ecosystem of prediction --recommendation
approaches to be used in the final matching process because it
collects a large number of diverse feature items which can be
analyzed in a myriad of ways.
[0187] In addition to exterior features it is possible of course to
identify and map opportunities for interior projects using
embodiments of the present invention. For example, a homeowner may
be interested in a new kitchen floor, new cabinets, new
countertops, new fixtures, etc., or a new bathroom, bedroom,
etc.
[0188] To assist the homeowner, a pre-configured digital image
template can be presented, with all necessary or available features
presented and coded for the user's review and input online. As seen
in FIG. 20B for example a user wishing to do a remodel is offered
either to start with a brand new model kitchen, or to work from an
existing kitchen. A mock-up is the presented to the user to allow
them to see what items can be replaced, upgraded, etc. The user
simply has to identify each feature in their own particular
situation that they want to address, and provide some basic
information on type, condition, etc. For example a user can
identify that a current flooring is linoleum, and a desired
replacement is tile. This data can be implemented much in the same
way the coding tool described above is implemented for capturing
exterior condition data. The homeowner can be directed or walked
through an image capture process that is tailored to the particular
project or room. The precise parameters for each project can be
specified by the merchants or service providers, or the ad serving
platform.
[0189] An example is shown in FIG. 20C of an exemplary automated
computing process that can be employed to assist homeowners and
merchants coordinate for remodeling and renovation projects. All of
these steps, again, can be implemented on a customized computing
system adapted with appropriate software modules to execute code to
effectuate the steps noted in FIG. 20B and render customized,
targeted advertising material for the user. The result is a report,
an estimate, or an offer from a merchant that is again custom
tailored to the user's particular circumstances.
[0190] As seen in FIG. 20C a user selects a desired project at step
2010, which may be an interior project, or an exterior project. In
this example the user may select to remodel a kitchen as seen in
FIG. 20B. A template is then automatically loaded within the user's
browser or other viewing platform to permit them to code their
desired remodel with appropriate parameters. Images from the user's
existing property may be collected at step 2027, either directly
from the user (through a mobile device, a local camera or other
means) and/or from a structure database 2050. The latter may be
supplemented with online blueprint data 2040 as discussed
below.
[0191] The user then preferably identifies specifically both an
existing feature, condition, etc., and a target or desired feature,
type, condition, etc. This is repeated until the user has coded all
the features they wish to be targeted and addressed by merchants
and contractors. During step 2029 the user submits the project
(preferably online) which updates all structure tables as well.
[0192] During step 2030, which may be done in real-time or
off-line, a series of one or more project auctions are conducted
(see discussion below for FIG. 25, process 2530) to identify
winning merchants, contractors etc. who are permitted or qualified
to view or bid on the user's project. The merchants may bid against
each other in the manner described below for FIG. 25. Merchants who
participate and succeed in such auction can then process the
project data to make assessment at step 2060. For example a vendor
may determine that the project exceeds a certain desired threshold,
or alternatively is too small and may decline to participate
further. In any event the processing of the project data may be
done with reference to any number of standard techniques that are
used for estimating renovation costs.
[0193] Should the merchant wish to propose an offer, they may elect
to do so at step 2065. This offer is then presented to the client
and follow up can commence at step 2070. It will be understood of
course that multiple merchants may bid on the same project, and/or
that the projects may be partitioned automatically into separate
pieces or categories depending on the renovation. For example a
floor job and materials may be separated out and bid on separately
from a fixture renovation, cabinet replacement, etc.
[0194] While the example of a kitchen renovation is given, it will
be apparent that improvements for other interior aspects can be
similarly designed in a manner that permits a homeowner or user to
quickly identify and define a condition of an existing structure,
and desired remediation. For example to provide a remodel or closet
upgrade, photos or data of the existing closets can be uploaded,
with sufficient onsite information (size, shape, etc.) to assist
user in capturing relevant data in an interface (see FIG. 20B) and
assist a merchant/contractor in assessing the lead and providing a
reasonable estimate of repair or renovation. By capturing and
analyzing the information ahead of time, a merchant and/or local
contractor can rapidly assess and present a meaningful review and
proposal to a homeowner at a first meeting, rather than waste time
collecting onsite information during an initial visit.
[0195] In some instances where online information exists for the
structure in question--such as electronic blueprints--it may be
possible to process and consult such data at step 2040 as well as
part of an estimation service. Many local agencies require that
homeowners provide such detailed drawings as part of remodels. From
this layout/schematic information the identity, size and shape of
rooms is typically identified for individual property structures.
If metadata or other data is available and/or can be derived from
such repositories, a merchant or service provider can better assess
opportunities as well by calculating a number of square feet of
each room, a number of windows, number and size of closets, patios,
and so forth. A layout database of such features can be compiled,
either directly from such blueprint data, and/or from other sites
that have interior information contributed by occupants of such
structures (homeowners or tenants).
[0196] In other instances a user's social networking account can be
mined for relevant interests and possessions. For example pictures
of an individual may include background scenes, identifiable
objects, etc. With the user's permission these items can be image
processed, and tagged to identify relevant items. Any form of image
data associated with a user profile, or user collected data, can be
compiled and targeted by an advertiser. For example user or member
photos on a social networking site can be analyzed, dissected,
etc., to identify relevant objects, concepts, etc.
[0197] An advertiser on such network can designate to be matched
against such recognized objects, and/or to be matched (based on
some threshold) by comparing the advertiser's reference object
image to a potential customer's captured image (or sub-image). For
example an advertiser may want to target homeowners/users who have
certain breeds of dogs; by analyzing photos of users' dogs, and
matching them to a profile provided by the advertiser, an index can
be determined of potential candidate matches. Similarly in most
advertising contexts an advertiser knows the value of the item they
are promoting to the user. In the case of an unconfirmed property
prospect, a vendor must rely on estimates of the economic advantage
afforded by the lead. Accordingly a property prospect file should
contain sufficient information to permit a vendor to accurately
estimate a value of an opportunity presented.
[0198] FIG. 21 depicts a preferred embodiment of a preferred
tailored advertisement marketing process 2100 implement in
accordance with the present teachings. A marketing engine
preferably runs on a customized computing system adapted with
appropriate software modules to execute code to effectuate the
steps noted in FIG. 21 and render customized, targeted advertising
material such as shown in FIGS. 14-15.
[0199] As see in FIG. 21 target locations 2100 are specified by a
vendor. Desired structure features (FIG. 18) and/or product
attributes (FIG. 19) are specified as well at step 2110. For each
targeted feature, a corresponding product or service can be
automatically associated by the vendor using step 2112. An
automated process then retrieves a matching set of prospects at
step 2115. The matching set can take any form, including with
details on the structures such as size, type of house, a listing of
impairments, partial image information, etc., broken down by any
convenient field, including location (city, block, zip code,
etc.)
[0200] For some or all of these prospects a customized ad and
mailing envelope is generated and prepared at step 2120, with
exemplary embodiments shown in FIGS. 14 and 15 or any other
suitable form. The marketing materials are synthesized from a set
of structure image data 2124, and include a set of customized
--tailored coupons 2122. The content for the ads, coupons, etc., is
preferably provided by the individual vendors at step 2123, so that
it can be integrated and presented in a form suitable and
compatible with their branding, marketing look etc. If desired a
simulated remediation image can be generated at step 2126 and
presented with the marketing materials, to help a homeowner
visualize a proposed new state based on the offer provided.
Neighborhood or other group offers can be generated as discussed
herein and incorporated at step 2128 as shown as well.
[0201] The advertising-marketing materials are then distributed in
any convenient form, including hard copy, electronically, through
email, etc. Redemption monitoring logic preferably identifies
engagements made by homeowners, records types and dates of
product/service purchases, group behaviors, and develops homeowner
profiles during a step 2130. These interactions can be measured and
reported on by another automated process at step 2140 to provide
feedback to vendors and to update structure and homeowner
profiles.
[0202] FIG. 22 shows an overall architecture of a direct
customized--targeted marketing system 2200 implemented in
accordance with the present teachings. In this system there are
three main participants: 1) customers; 2) targeted advertising
providers; 3) vendors. While these labels are used for purposes of
explaining the present invention(s) it will be apparent that other
identifiers could be used for these entities.
[0203] As seen in this diagram, Customers interact through their
computing devices 2212 with a vendor computing system 2210 and a
direct targeted advertising platform 2250. These computing systems
are include servers, routers, storage devices, databases and
customized program code adapted to implement the functions noted
herein.
[0204] A vendor computing platform 2210 includes a number of
components, and may be coupled or associated to a vendor website.
As noted above, Vendors may be providers of products and services
as noted above. As such they have their own proprietary database
2225 of customers, transactions, etc., which can take any number of
forms.
[0205] A customer targeting engine 2220 is configured with inputs
and analytics that are unique to the vendor, in that they identify,
quantify and correlate customer behavior, adoption and engagement
with that vendor's products/services. This automated logic informs
and drives a vendor's marketing logic, in that it identifies and
optimizes marketing and advertising for an existing and targeted
customer base. This information may be derived from external
sources as well, including surveys. For example, a company selling
high end window products may target existing homes in particular
zip codes based on weather characteristics, home owner income, time
of year, etc. Other forms of targeting can be considered as
well.
[0206] In some embodiments a vendor may be given other options as
well to "piggyback" on a targeted flyer or advertisement with
vendors of other products that do not directly compete. To achieve
this a vendor may be given a white list of products that are
acceptable for co-marketing, or even a set of products or
co-vendors that are preferable for partnering. As an example a
provider of pool products may designate that they prefer to partner
with providers of landscaping products, and so on. Conversely a
vendor may be given an option to exclude co-marketing with specific
entities, products, etc. in accordance with their own advertising
campaign(s).
[0207] A vendor can engage with a direct marketing platform 2250
with a query logic engine 2215 (see FIGS. 18, 19) that is informed
and programmed with inputs from the targeting engine. That is, the
query logic can be configured to automatically solicit leads from
platform 2250 through prospector interface 2255 that match one or
more vendor specific criteria. For example, a query may specify
that the desired lead or result list should include houses with new
roofs but broken fences in a particular city, and so on. This
information is extracted from prospector structure/feature dB 2260
in the manner noted above. Again the variety of queries and
targeting options is expected to vary in accordance with each
vendor's specific knowledge set of its customers' behavior, needs,
purchases, etc. Regularly updated feeds of lead data may be
provided by platform 2250 as well. As will be apparent to those
skilled in the art, the vendor platform 2210 may be repeated in
distinct discrete installations or as part of a grouped cloud
configuration servicing a number of different vendors.
[0208] Platform 2250 facilitates and informs operation of targeting
engine 2220 and the two can cooperate to educate and optimize a
vendor's marketing. Access to platform 2250 is preferably
controlled and monetized with each vendor on a query basis, a
subscription basis, a lead/result basis, a type of query, etc. For
example queries directed to certain types of high end products may
be priced differently than for less expensive bulk products. Note
that if the results of the marketing, advertising efforts are
successful, an operator of platform 2250 may be further compensated
in accordance with monetization events achieved from direct
marketing efforts made on behalf of vendors, including through flat
rate payments, commissions, etc.
[0209] Note further that to preserve its proprietary data and
correlation intelligence an independent marketing platform 2250
operator can provide a sanitized or redacted lead list to a vendor,
which report contains sufficient information to inform the latter
of a matching list of structures that meet desired criteria, but
does not include all address, structure or owner information. This
is prevents usurpation of the efforts and systems of the direct
marketer. For example a vendor of facade products could be given a
list identifying a number and ID of houses in a particular zip
code, block or city that uses shingles, or both shingles and
stucco, and so on. In some instances partial or whole images of the
leads could be presented as it may be difficult or at least not
commercially impractical for a vendor to reverse engineer an
address from an image alone, especially if it has been masked
appropriately to prevent identification of an address. This
information nonetheless informs and permits a vendor to determine
if such leads should be targeted, and, if so, how. The vendor can
further generate their own correlations, as well, based on such
reports and feedback from redemptions, to determine how to optimize
their marketing.
[0210] In addition a vendor can elect to share at least limited
portions of its own customer db 2225 with the advertising platform
to improve correlations and targeting. By providing such data to an
advertising platform 2250, the latter can correlate specific
customers to specific purchases, behavior, etc. and provide
additional insights. For example a vendor may want to know that in
a result set of structures in a city that have shingles and stucco,
a significant portion of such owners also purchased another set of
one or more specific products from such vendor. Absent being able
to cross-correlate address and owner information, it would be much
harder to glean such useful insights.
[0211] Over time of course a marketing platform 2250 will build and
construct its own redemption/behavior database as a result of
engagements with customers of the vendor. Thus it is expected that
the two operations and collections will overlap to some degree over
time and they do not have to be mutually exclusive. By sharing
selected information the two entities can achieve significant
synergy.
[0212] To create a desired look and feel for targeted offers 2280
(see FIGS. 14 and 15) a vendor may have their own customized
content 2227 that they can insert into the targeted sections shown
in these mailers, flyers (whether in hard copy or electronic form).
For example the wording of a marketing pitch, specific
images/graphics, etc. can be specified by a vendor for inclusion in
a targeted advertisement. This information is then used by an
advertising synthesis engine 2265 which combines the data from the
vendor (and potentially other compatible co-marketing vendors) to
generate a tentative targeted offer (not shown) for entities in a
particular result set. In some implementations a vendor is then
permitted to inspect the content for the targeted material in
advance to approve or veto the final product. This process can be
iterated until a desired format and substance is achieved. In the
end targeted offers 2280 (in the form shown in FIGS. 14, 15, etc.)
are delivered by mail, electronically, etc. to individual
customers.
[0213] In some embodiments an advertising synthesis engine 2265
includes an automated auction component. This auction logic can be
implemented in applications where multiple vendors are attempting
to target the same or similar products to the same or similar
structure prospects. For example two different vendors A and B may
desire to target window products X and Y respectively to
structures/owners which meet a certain threshold condition. The
auction logic can be programmed so that for any particular time
window, or batch of targeted offers, only one vendor is permitted
to be included in such 2280. Alternatively the offers may be time
or batch "blended" so that multiple vendors can pitch the same
product to different structures in a target set, or even the same
structure. Thus an offer 2280 in hard copy form may include
targeted advertising from multiple vendors for the same product
matched to the same building element/condition. In another instance
an offer 2280 may make reference only to the structural
element/impairment in the structure, and invite the owner with a
designated code to visit an online site to see further information.
This designated code is used to identify the owner, the structure,
etc., and helps to facilitate targeted advertising by one or more
interested vendors.
[0214] An auction process 2500 for matching vendor products to
targeted structures is depicted in FIG. 25 and may be implemented
in a similar process to that used in other environments which
include automated bidding for ad impressions to users. For example
Google's E-commerce Platform allows vendors to list items on
Google's shopping engine in exchange for such entities bidding on
keywords in queries.
[0215] In the present embodiments if the user is already registered
as the owner (see FIG. 23) they can identify themselves directly at
step 2510 and the auction engine can use this data to determine the
target structure profile at step 2520. In instances where the user
has received a targeted printed flyer, one or more control codes
can be used to identify the user and property uniquely. In still
other instances, a user's exact location can be determined with
geolocation data from their smartphone or other mobile device, and
this can be converted into address--property information.
Accordingly a target structure, owner, etc., can be identified
during servicing of a generic query presented at a conventional
search engine site that is directed to home improvement products,
even in the absence of direct knowledge of the user's identity.
Note that customized ads generated in accordance with the present
teachings can be presented as ancillary or supplemental property
specific ads for a user, in the instance of a generic search query
made by a user directed to other product data that may not even be
directly related to home improvements. In other words a user making
a query for product X (where X may be clothing or some ancillary
item) at a search engine may be presented with personalized ads for
home improvement products created based on the present teachings
and which are micro-targeted to their particular living
domicile--habitat. Alternatively the user may be targeted while
they are on a third party site, a social networking site, etc. and
their location is determined and used, and so on. This can be done
even if the user is merely a tenant, as he/she may still then be
motivated to implement the proffered improvements, and/or to
alert/notify an owner of such offers so that they are followed
up.
[0216] A preexisting tentative list of matching products may be
pre-computed and used to classify target structure as well at step
2512 using the classifications and taxonomy described earlier (FIG.
20). At step 2515 vendors provide electronic bid inputs to the
auction engine for specific products, structure/property
attributes, conditions, or some combination thereof.
[0217] Given the identity of the structure, an auction engine then
conducts an auction amongst various vendors at step 2530. Unlike
prior art keyword auctions, the auction in this instance is
preferably based on bids made by vendors in accordance with: 1)
existence of a particular building element or feature derived
during the structure coding noted above; 2) a particular element
type; 3) a particular condition; 4) one or more identified products
predetermined and precomputed to be germane to the target
structure. For example a vendor may bid a price X to be permitted
to present an ad for product Y to criteria that include a target
structure meeting certain profile parameters, product needs, being
located in a particular area, and so on. In still other
embodiments, an estimated cost of repairs for a particular element,
or an aggregate cost of repairs for an entire structure can be used
as well for targeting. As noted above, estimate repair costs can be
stored for a structure in database 142 as part of a structure
rating 415. Thus a property profile may include information on a
potential commercial remediation score, or value, either on a
product-by-product or service basis. These individual scores can be
targeted, either alone or in aggregate. For example a vendor can
specify that they only want to target properties in which a
particular product estimated net return is $X, or a total
remediation estimate for the entire structure exceeds $Y and so on.
An auction process can divide and tier the structures into distinct
bins which can then be targeted to specifically by vendors and
service providers. A final auction price therefore can be based on
an expected potential return for the property lead in question.
During step 2540 the customized advertisement is served to the user
dynamically and on the fly in the form shown in FIGS. 14, 15A, 15B,
etc., i.e., preferably in a blended form which includes both
content from the owner's structure, and personalized content from
the vendor integrated into a single document (here, a webpage).
[0218] In this manner embodiments of the present invention can
effectuate new and unique forms of targeted advertising to
users/consumers using criteria not used before. Other parameters
will be apparent to those skilled in the art. The user can also
optionally directly specify a query in the interface at step 2525
to receive offers on other products offered by such vendors. In
some applications a vendor may elect to present an electronic
coupon as part of the advertisement at step 2150, in the manner
noted above. Redemption logic 2560 and reporting logic 2570 include
similar functionality to their counterparts discussed below.
[0219] Returning to FIG. 22 a redemptions analytics logic engine
2270 monitors customer engagement with the offers, discounts, etc.
Conversions can be identified and recorded in a proprietary
database on platform 2250 and/or on vendor platform 2210. Because
platform 2250 sits between different types of vendors and their
customers, it is able to assemble and compile extensive competitive
intelligence in cross-trade services and products. For instance,
vendors who market pool products have little or no information or
insights into housing paint marketing techniques or insights. Over
time embodiments of the platform acquire massive amounts of
cross-trade data that can be mined and exploited for identifying
useful correlations and relationships which in turn can become a
source of monetization. As an example it may be identified that
purchase of certain products or services (P1, S2) presage or
predict (by some correlation value R) the adoption by product P2 or
service S2 within a mean time period T. The data may be further
filtered or correlated according to city, zip code, street, or
other geographic parameter. With such data in hand platform 2250
can optimize and speculatively market specific products to certain
owners ahead of competitors. Other useful correlations can be
gleaned of course as well.
[0220] While no system is foolproof or immune from data theft,
embodiments of a preferred marketing platform 2250 have enhanced
protection from commercial usurpations by customers or competitor
misappropriation of lead/prospect data.
[0221] In part this is due to the fact that the results reports
(FIG. 18B) cannot be easily correlated to later customer
redemptions. For example a window hardware vendor working from a
prospect list that identifies 10k leads in a small city may receive
500 responses. From these responses of course it determines an
address of the customer, but it cannot readily match such response
data to a corresponding lead on the prospect list (1860). Other
techniques for protecting the platform's unique data and analytics
(i.e.,monitoring for data mining, throttling, etc.) can be
implemented and/or will be apparent from the present teachings.
[0222] In some applications it may be desirable to let a user/owner
register a specific property to receive a customized/targeted
improvement package of proposed improvements, discounts, etc. for a
structure at such location in real-time. This obviates the need for
a user to request and then physically receive a solicitation at a
registered owner property address. More importantly, a homeowner
may wish to see a comprehensive evaluation that they cannot perform
on their own. The owner can be incentivized by a "free" evaluation
to engage with marketers of products and services they may never
have considered.
[0223] However since it may be difficult to determine if a
requesting user is indeed a property owner of the structure in
question, an optional verification process can be employed to
assist. Such procedures are already used by some online real estate
marketing companies, such as Zillow, Redfin, Trulia, and others.
Typically such verifications require a user to confirm or provide
details specific to a property that are most likely known to the
property owner but not to others. For example questions directed to
an amount of a tax bill, a purchase price/date, etc.
[0224] As seen in FIG. 23, embodiments of the present invention can
also implement a property/structure verification process 2300 to
increase engagement with owners. This process preferably implicates
multiple computing systems including a verification processor
computing system, a user's mobile device, and other network
connection devices (not shown). The verification system can be
located either within the mobile device, and/or in part at a remote
server computing system (not shown). Such systems can be programmed
or coded using conventional techniques to receive, process and
communicate to achieve the objectives set forth herein.
[0225] The preferred process primarily relies on an automated
"proof of presence" determination that confirms that a user (or
their device) is physically in or near the property of interest
within an acceptable threshold of accuracy or risk. While this is
of course not 100% reliable or determinative of ownership of a
particular structure, it is a reasonably useful test in combination
with other mechanisms to confirm or deny access to a customized
structure report. This proof of presence technique can be used for
other applications as well, including for verifying local residency
for public benefits (schooling, mental health, other public
programs).
[0226] At step 2310 a user identifies a particular property or
address for which they want to see or gain access to a customized
report such as those presented above in FIGS. 14-15 and so on. If
the user is already registered and authenticated they can supply a
password of course at step 2315 to an automated verification system
and achieve access that way. In the absence of an existing account
and verified access a user is prompted instead to provide contact
information for a mobile device, such as a smartphone. During step
2320 a verification challenge is then presented to the user by a
verification computing system, which challenge may take any number
of different forms and implicate different data types, user
knowledge base(s) and real-time feedback.
[0227] At step 2322 a cell/smartphone or other mobile device's
location is determined. This can be achieved by an automated
verification system using any number of techniques known in the
art, including through identifying and processing GPS, cell-tower
triangulations, Wifi network signals, etc. It is expected that in
some embodiments a location determination process may employ random
periodic sampling, meaning that the user is informed that the
verification process may not occur in real-time, and instead may
require a period of some fixed length, say 12-24 hours. In those
instances the device can be interrogated randomly during different
times, including at late hours when it is expected that the user
will not be at some other physical location. Thus by exploiting the
fact that the user is likely to be at home at such times (say 3-4
a.m.) the present verification process can confirm with reasonable
certainty that such person is probably an owner or at least an
occupant of the structure in question. Again this electronic
challenge can be used with other applications, such as confirming
that a person is residing in a particular school district. The user
can be prompted as well to complete and confirm receipt of such
confirmation codes to further bolster a robustness of the
challenge.
[0228] In other instances a user may be requested to complete a
challenge that involves a structured series or location varied set
of steps. For example the user is prompted to depress a "find me"
or "verify" virtual button on their device at different physical
locations of the property. By comparing the different signals
received at the different locations (for example at four corners of
a lot) based on the measured strength of different WiFi systems at
such different locations, or different GPS, etc.) a verifications
system at step 2324 can assess if the user is likely present at the
structure in question. Alternatively a user can be asked to provide
input at locations both inside and outside the structure in
question, which should again cause the signal strength to vary.
Other forms of inducing measurable and significant signal
variations unique to a location will be apparent to skilled
artisans.
[0229] In yet other variants a user can be solicited to provide
details about the structure (i.e., answer questions about features)
or alternatively provide one or more real-time, time stamped
photo(s) of the structure for verification purposes. A virtual
outline image of the property can be presented in the user's camera
viewfinder. The verification test can require the user to register
an actual outline of the structure within such template in the
viewfinder to confirm they are present at such location. Other
examples will be apparent to those skilled in the art. Since
embodiments of the present invention include data records
containing image and other feature data, a verification challenge
can leverage and incorporate comparisons to such preexisting data
for improved accuracy.
[0230] Accordingly at step 2325 a location verification process is
performed and completed using one or more of the above criteria,
tests, etc. Again this particular test can be supplemented with
additional conventional verification processes at step 2330, which
may take the form noted earlier used in the prior art. During step
2340 a final determination is made to grant or deny access to the
customized property report. Again in some implementations this may
be made in real time when there are supplemental corroborating
indicators of ownership, or it may be delayed pending resolution of
further checks.
[0231] While it is of course preferable to ensure that only an
authorized owner receives access to a customized structure report,
it should be noted that the degree or extent of risk is reduced in
the present applications because the discounts and/or promotions
are tied to the structure in question, and/or are personalized to
an owner of record. Accordingly there is little incentive for a
third party to engage maliciously to impersonate an owner of a
structure since they cannot avail themselves of the benefits of the
customized promotions.
[0232] FIG. 24 depicts a preferred embodiment of a vendor interface
that can be used by a vendor to identify, create and target
particular property structures with personalized content for
particular products in a particular geographic area. Preferably
this interface for creating personalized marketing campaigns is
presented within a browser of a conventional small client computing
device (desktop, laptop, tablet, etc.) and is supported and
implemented by a number of software routines operating on a
specially configured hardware computing system (such as shown in
FIG. 1) that includes functionality as described herein. It will be
understood that mobile versions of the interface can be implemented
as well on smaller screens.
[0233] As seen on sheet #1, a vendor's product offerings are
presented in textual and/or visual form in a first portion of the
interface. Any one (or all) of the products can be selected for
targeting to a particular geographic area. A campaign planner for
the vendor can preferably specify also in a second portion of the
interface whether they desire to run independent or integrated
campaigns for each product. For example, if the vendor selects
three products, they can choose to independently target the
prospects with separate campaigns for each product. In an
integrated campaign the vendor can elect to have multiple products
promoted and pitched in a single flyer. Similarly a vendor can
opt-in to a shared or co-marketing campaign with other vendors for
non-competing products as noted earlier.
[0234] In a third portion of the interface a result set is
presented to the vendor for leads in the particular area, here a
particular zip code. The leads can be presented visually in any
desired form, an example here shown as green (good leads), yellow
(average leads) or red (less promising leads). Other formats will
be apparent to those skilled in the art, and in this respect the
outputs of FIGS. 18A, 18B and 19 could be presented for example.
The interface preferably permits a vendor to simply click/select
any one or more of the regions in the lead spectrum to target such
result set. A bottom lead indicator provides feedback on a total
number of leads or prospects that match one or more of the targeted
products. In other implementations other logic could be used, so
that the lead list is constrained to those structures which require
two or all of the products, and so on. The third portion of the
interface is preferably capable of adapting dynamically to vendor
selections in the search query portion, so that they can
immediately get a sense of how many prospects are possible for
particular products.
[0235] As seen in the second sheet of FIG. 24 a vendor can also
specify group discount or coupon parameters in another portion of
the interface, on a product by product basis, as alluded to above.
In the example shown, the vendor can choose a Minimum/Maximum size
of the group clusterings, a group Proximity requirement, an
individual participant minimum purchase requirement, a group
minimum purchase requirement, a proffered discount, an opt-in time
limit, an opt-in fee, and so on. In other instances where multiple
products are combined, the group offer may require purchase of both
products/services. Other examples and parameters will be
apparent.
[0236] Finally an ad content builder can also be provided to a
vendor to allow them to visually see a simulated mock-up of their
content, and how it will be presented in final form by the
advertising platform 2250 to an end customer. Ad content builders
are available in other industries for creating customized
materials, and such tools could be integrated herein as well.
Preferably the advertising platform operator 2250 provides a
format, layout and arrangement of the different content regions. A
mock-up or placeholder for the particular structure-specific image
data that will accompany the advertisement is presented in a first
portion shown on the left. Additional areas for an address,
customer ID, etc., are preferably reserved at well in some
convention location. A vendor therefore can customize the
advertisement to their liking with particular customer-specific
personalized greetings, a product specific message (text, pricing,
availability, etc.) product image data, product QR codes, URLs, and
other references to help a customer perceive the relevance to the
particular targeted structure. If elected by the vendor other Group
promotional content is included as well, which may identify
neighbors generally in a particular area, and/or by specific
address in some cases. Again other examples of
advertising/marketing building tools which are suited for
exploiting the features and functions afforded by the invention(s)
will be apparent from the present teachings.
[0237] Embodiments of the invention thus permit assessment of and
predictions for building stock, including occupancy, individual and
aggregate element condition, prospects for purchase, etc. While the
main application is described in connection with assisting property
seekers, real estate personnel and others to assess and develop
leads for real estate prospects for single family residences, a
number of other uses can be made of the data captured and processed
by embodiments of the present invention, including: [0238] 1)
Insurance: policy premiums, risk assessments, etc., can be based on
an evaluation of an upkeep/maintenance evidenced for a particular
property; in this respect correlations may be developed between
property condition ratings, occupancy estimates and number of
claims filed, type of claim, severity, etc. For example a property
insurer is likely to be interested in knowing if a building is
vacant and thus more likely to be vandalized or have a higher risk
of arson, etc. Other potential hazards (trees that are too close or
overgrown, dilapidated ancillary structures adjacent to a
structure, undesirable and dangerous fixtures (trampolines etc.)
can be identified by insurers and used to adjust premiums on a
structure by structure basis. Other similar uses will be apparent
to skilled artisans; [0239] 2) Air quality/pollution estimation:
government agencies and other stakeholders are likely to benefit
from long term, longitudinal studies of building structure
appearances, as they can reflect air pollution and presence of
other chemicals in the air deleterious to building facades (and
potentially humans). The invention can be used to study and examine
differences in large numbers of structures located in particular
neighborhoods at different time intervals for this purpose. [0240]
3) Home improvement/construction: builders and suppliers of
building materials will benefit from direct access to a database of
building stock condition data. Queries can be made to determine
particular conditions in particular building elements for enhanced
targeted advertising. For example suppliers of paint products can
quickly develop a targeted list of prospects likely to need
renovation. Overall assessments and estimates can be made for
repairs/improvements to an entire building structure simply from
processing the image data. Other examples will be apparent from the
present teachings. [0241] 4) Banks/appraisers: property "comps" for
a particular target property can be based more accurately on other
properties having an identical building envelope, architectural
style, visual aesthetic, etc. [0242] 5) Aging of materials: if the
image stock for the properties is updated, long term evaluations of
wear/aging characteristics of individual building elements can be
assessed over time. Estimates and predictions can then be made of
the age of a particular facade element (paint, siding, roof) simply
by comparing such elements to reference norms of a known age.
[0243] 6) Plants/foliage: Frequently house seekers or other
similarly interested parties desire more information on landscaping
features of a property, such as the identity of particular trees,
flowers, plants or other foliage. Again such information can be
captured by the on site viewers using a camera and matched against
entries already logged in database 142, or some other database. For
example users may capture publicly viewable foliage information at
a location, tag it with appropriate descriptors, and make it
available to other persons. When a second user visits the site
later, there may be preexisting entries for the foliage in question
which can be queried against to identify plants, flowers, trees,
etc. Alternatively in some embodiments it may be possible to
perform an image match against a botanical image database (not
shown) which can determine the identify is of such plant items. In
this manner the natural elements of a neighborhood may also be
mapped out to allow for identification of particular types of
flowers, trees or plants of interest. For example walking/nature
tours could be divined from identifying specific property locations
of prominent rose plants, oak trees, or other foliage in particular
neighborhoods. This would facilitate further neighborhood
exploration by local citizens interested in mapping out the natural
elements of their environment and surroundings.
[0244] While the primary uses for some of the advertising materials
are expected to be structure-specific, it is entirely possible that
other providers of goods and services (doctors, dentists, etc.) may
be able to exploit competitive intelligence in the aforementioned
platform 2250 for purposes of piggybacking their own advertising
content.
[0245] In general, by comparing publicly recorded owner data,
including age and other demographics against building structure
condition data, additional insights and useful correlations can be
developed and exploited. It will be understood by those skilled in
the art that the above are merely examples and that countless
variations on the above can be implemented in accordance with the
present teachings. A number of other conventional steps that would
be included in a commercial application have been omitted, as well,
to better emphasize the present teachings.
[0246] It will also be apparent to those skilled in the art that
the modules of the present invention, including those illustrated
in the figures can be implemented using any one of many known
programming languages suitable for creating applications that can
run on large scale computing systems, including servers connected
to a network (such as the Internet). The details of the specific
implementation of the present invention will vary depending on the
programming language(s) used to embody the above principles, and
are not material to an understanding of the present invention.
Furthermore, in some instances, a portion of the hardware and
software will be contained locally to a member's computing system,
which can include a portable machine or a computing machine at the
users premises, such as a personal computer, a PDA, digital video
recorder, receiver, etc.
[0247] Furthermore it will be apparent to those skilled in the art
that this is not the entire set of software modules that can be
used, or an exhaustive list of all operations executed by such
modules. It is expected, in fact, that other features will be added
by system operators in accordance with customer preferences and/or
system performance requirements. Furthermore, while not explicitly
shown or described herein, the details of the various software
routines, executable code, etc., required to effectuate the
functionality discussed above in such modules are not material to
the present invention, and may be implemented in any number of ways
known to those skilled in the art. Such code, routines, etc. may be
stored in any number of forms of machine readable media. The above
descriptions are intended as merely illustrative embodiments of the
proposed inventions. It is understood that the protection afforded
the present invention also comprehends and extends to embodiments
different from those above, but which fall within the scope of the
present claims.
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