Patent Application
20140278568
1. Technical Field
This disclosure is in the field of aggregating pricing information from many different sources in real time from different geographical areas into a centralized database that provides accurate estimates for material and labor costs for insurance claims adjusting and contractor bid estimates.
2. Description of the Related Art
When insured property is damaged and requires repair, a claims adjuster will refer to costing information to in an effort to determine an accurate repair cost. For example, the claims adjuster will wish to calculate the cost of materials, labor, markup and other costs in order to assign a correct overall value to settle the claim. If estimates are too low the insurance company risks customer dissatisfaction by underpaying the claim. If estimates are too high the company will pay out more money than required to repair the property, hurting the company's profits. Similarly, contractors bidding for a repair contract wish to have accurate costing information to make estimates that are high enough to cover their costs and make a profit, yet not too high so that the contractors bid is uncompetitive. Traditionally, insurance companies and contractors have based costing information on previous repair claims and estimates or on catalog prices for materials from distributors, both of which may be out of date, and may not apply to where the damaged property is located.
The present disclosure proposes systems and methods to create an integrated centralized database that aggregates costing data in real time and provides an accurate repair cost estimating platform. In one or more embodiments, this data is based on data collected from repair estimates that have been done by actual roofing contractors who have actually performed repairs; data from third-party data sets such; data from material suppliers from companies like Home Depot, Lowes, and others; data from specialty providers of selected products; and materials description information from manufacturers. Some of this data is regularly published, and some is collected in real time. The data might also be associated with a unique geographic area to provide accurate costing information for repairs in that area.
Recently, it has become possible to improve the accuracy of roof measurements through remote image acquisition, using a computer assisted roof estimation system. Thus, it is now possible to obtain actual roof dimensions and generate a roof estimation report without relying on a human estimator to be present at a building site. Even if a building is significantly damaged, satellite photos or aerial images saved in a database can provide accurate views of the building as it was, prior to a damage incident. Furthermore, if two or more current or previous views of the same roof are available, for example, an orthogonal (top plan) view and at least one oblique (perspective) view, a 3D image of the roof can be computer-generated. Such a 3D rendering allows obtaining actual dimensions of a complex roof that can be used to accurately calculate replacement costs. Such methods are described in U.S. Pat. Nos. 8,088,436 and 8,170,840.
As described below, actual roof dimensions can then be used to provide a more accurate computer assisted estimate of the full replacement costs. Instead of simply applying a generic estimate based on roof size, shape and expected labor and material costs, this computer collection and centralized data base approach can take into account more of the relevant factors, including the current labor price in a specific geographic area, the most current actual material costs, any expect mark up the roofing contractor on material and labor, which may vary from area to area and from time to time, along with many other factors. The resulting estimate for replacement costs is can therefore be more accurate, less expensive, and faster than using material cost and labor prices that are updated once per quarter or collected only from sellers and not from buyers.
The fully integrated method can be summarized as including the acts of receiving a data set from a large number of the actual roofing contractors as they prepare an live roof replacement estimate, any entries made by these roofing contractors for their estimate costs, including labor and materials, any mark ups of each, then, if the estimates costs change on a particular project, receiving immediately the update estimated costs, then once the actual costs known, obtain the actual costs, in addition, obtain both actual selling material costs form sellers and the buying material costs from the buyer.
Embodiments of a method of computing an expected replacement cost can include a model that takes into account different most expense estimate along with least expensive case factors for the amount and type of each roof material used and labor, together with an actual average and also obtain an expected range of replacement costs with a reasonable tolerance factor. The calculation can also take into account expected waste of material to be incurred during installation of the building material.
Accurate costing estimates for materials and labor are important to the insurance industry for properly adjusting property damage claims and also to individual contractors who are bidding for contacts to repair the property damage. It is often difficult to obtain an accurate cost estimate of the actual price for each of the large number of individual materials that go into a repair bid. Although distributors commonly have catalogs that describe the prices of the materials they sell, these prices may be out of date when used at a later time by the contractor or insurance adjuster, and in addition the quantities available for each desired item may be limited. Similarly, the cost for labor may also be difficult to estimate due to fluctuating supply and demand in a particular geographic area or within a particular specialty area such as roofing, sheet rock, or tiling.
In addition, events within a particular geographic area can quickly cause unforeseen movements in the costs for materials, labor, and contractor markup. For example, a huge hailstorm or tornado in a particular geographic area may destroy many thousands of roofs on residential and commercial buildings. In such a case, it would be very difficult to purchase shingles or roof nails in the coming days and weeks since the local supplies would be consumed quickly. As a result of such natural disasters, the cost of acquiring materials and labor for repair contracts would quickly become very high. Furthermore, as an influx of materials and labor made its way to the damaged area, pricing for materials and labor would likely remain volatile for a long period of time. To know the accurate cost to timely replace a roof, an insurance company would need access to current, accurate pricing data in order to properly adjust property damage claims. Similarly, contractors bidding on repair contracts would need to understand the spot market prices for materials and labor and how those prices may adjust over the next several days or weeks in order to place competitive bids that will also make money for the contractor.
An insurance company also realizes that completing a repair quickly, even if it needs to pay a premium price, may have significant benefits. Not only will their customers be pleased and keep their business with the insurance company, they will refer their friend to the same agent. Further, if a roof is repaired quickly, before the next heavy rainstorm, this will avert a higher claim that might include carpets, ovens, appliances, kitchen cabinets and other household items. It will also the avoid the costly and time consuming process of having to adjust a claim payment that was based on a first lower price as an estimate but ended up costing significantly more when the work was completed. Thus, if an insurance company is aware that the price of goods and services has truly increased greatly in a short period of time, they will be willing to pay the higher price to ensure that the repair is timely completed and properly paid for to obtain a number of benefits, even though the price they are asked to pay is outside their expected pricing models. On the other hand, the insurance company does not wish to pay excessive fees for materials and labor that are outside the normal for the market and are unwarranted. The insurance company therefore has significant benefits in knowing current real price models as well as trends.
Time of year also plays a role in price fluctuations. For example, in certain geographic areas such as North Dakota and northern Montana with harsh winters, the price of labor may rise considerably during the wintertime because of the difficulty of performing outdoor projects in the cold and snow and the shortage of labor. Areas in the southern United States in more temperate climates, such as Texas and Arizona, might see the price of labor drop in the winter time as more workers travel south in the winter and rise in the summer when there is very hot weather outside and the labor pool is spread out over a wider area. Some areas will see less variations in labor rates.
In a preferred embodiment, the ZIP code of the construction site is the area by which the reports and data are sorted. They can be up to about 90,000 ZIP codes in the U.S. and currently there are about 45,000 distinct ZIP codes in the U.S. The data is sorted as ZIP code of the construction location rather than the material seller's ZIP code or the contractor's ZIP code. The goal of the insurance adjustor is to understand the accurate cost to replace or repair the damaged building. Therefore, the data that is sorted according to the location of the damage building will be the most reliable and likely to result in the most accurate repair estimate, even though the contractor and the seller of materials may live in different ZIP codes.
In another embodiment there may be over 450 such geographic areas within the United States. Each geographic area can be selected, grouped and sorted based on market boundaries such that the pricing for individual materials, labor, and other related charges, for example contractor overhead and profit, are generally consistent within that area. This can be determined by monitoring the cost and pricing trends across the United States and then selecting the boundaries and size of the areas based on those geographic locations which have common features in a second embodiment.
Database 120 receives its data from a number of different sources, some of which are updated in real time and some of which are updated on a less frequent, but periodic basis. Examples and sources of different data that are input to data base 120 will now be provided
One source of data of construction and material costs comes from third party data sets 126. These are industry-wide data providers that publish material and labor costs on a regular schedule, such as 4 times year, that covers certain for geographic areas. This published data represents cost figures that were as accurate as possible at time they were published, however may be anywhere from a few days to several weeks out of date. Although this data may provide a useful baseline for producing a cost estimate, it may not take into account current market forces within an area that have caused recent cost volatility.
Another source of data for database 120 comes from actual material costs from the sellers of products. These might be in the form of price feeds 128 from materials distributors such as Home Depot, Allied, Lowes and others who sell products in the construction industry. Distributors acquire products from multiple manufacturers and resell those products to end consumers, contractors, or other businesses. These distributors have large databases that manage and track the inventory levels and pricing data for every item that is sold in any store operated by the distributor. In some embodiments this pricing data includes the advertised price for the item as well as the actual price for which the item was sold. The data might be available for an individual store, a group of stores in a geographic area of the same chain, or many types of stores in that area. Many distributors regularly publish a materials price report feed that discloses the distributor's complete pricing data information for products it sells to the construction industry. This published data from multiple distributors is also put into the data in material price feed database 128. This database might be updated monthly, weekly, daily, depending on the database, the geographic location and number stores and other factors. It might, in some cases, be available in real time as prices within a distributors individual store changes.
Another source of data for database 120 comes from purchaser data base, which could be consider a user data 130 since they are the users of the material in the labor repair. In a preferred embodiment, user data 130 is data taken directly from contractors who are either preparing a bid for a repair contract or have finished performing a repair contract and are entering in final cost data. In one example, contractor 112 is preparing a bid to submit to a customer to repair damage to the customer's property. In the bid, the contractor would typically describe the scope of the work, timeframe for completing the work, a list of materials needed to complete the project, a quote for the cost of materials, an estimate of the labor needed to complete the project, a quote for the cost of that labor, and other costs including tax, permit fees, and other related costs. The bid amount also includes a markup amount on some of the products and labor in order to cover the contractors overhead. It might also include some factor to provide a profit to the contractor. Typically, overhead and profit are included as a multiplier mark-up to the material and labor cost. For example, if a bid has $5,000 in material costs and $10,000 in labor costs and the markup multiplier is 1.2, or 20%, then the bid will list the materials estimate as $6,000 and labor estimate as $12,000.
The assignee of the present application, Eagle View Technologies, has filed a number of applications on various software products that assist contractors in preparing bids to repair roofs, install siding and perform construction products. The issued patents include U.S. Pat. No. 8,170,840; U.S. Pat. No. 8,078,436 and the pending applications include Ser. No. 13/757,694 and Ser. No. 13/757,712 both of them filed on Feb. 1, 2013 and naming Chris Pershing as an inventor. The patents and applications provide examples of reports that are supplied to contractors to assist them in preparing construction bids. According to one embodiment of the present invention, the contractor can receive these reports as an active computer data file rather than a .pdf or paper printout. With the active computer file, he can enter data regarding the bid he is providing the home owner and the insurance company. The contractor will use graphics user interface screen 112a to enter this estimated bid data into the user data database 130. Examples of such user interface screens are explained in more detail later herein and provided as
There is a significant advantage of using actual material and price cost data from contractors who have actually purchased materials and labor, and have actually completed repairs on a property within a geographic area. This cost data represents the closest approximation to a “spot” price for materials and labor in that geographic area, and also provides additional related data such as contractor markup, permit fee amounts, and other expenses actually incurred by the contractor. An advantage of incorporating purchasers' data 130 as user data into the integrated costing database 120 is that the more frequently contractors enter their material and labor cost into this data feed, the more accurate contractor repair estimates will become for a geographic area. In addition, the greater the numbers that other contractors enter data into this data feed, the more reliable it will be.
The user data base 130 will therefore usually include data that has been input from three different sources, the contractor 112 preparing the bid, the contractor 114 who is placing an order and the contractor 116 who has finished a job and is preparing a report to be paid for his work. In some cases, these will be different groups and represent different data sets. For example, many contractors might prepare bids for the same project, but usually only one will get the job. Similarly, a contractor might bid on dozens of projects in a single day, but only win the contract and place the order for a few contracts. The data in which the contractor bid on a project, but did not get the work is valuable data, but should be viewed differently, and weighted differently, then data entered by a contractor who made the bid and got the work. It is usually the case that the contractor who placed the order will also be the contractor who finishes the job and inputs the actual, end of project data.
In one embodiment, there are separate software engines for the contractor and insurance carrier to interact with the EPIC database 120. The user interface 110a will have different entries and data search capabilities that the user interfaces 112a, 114a and 116a. The insurance adjustor will generally have a additional access to more data sets, grouping of data, weighting factors and all the may have the ability to vary the weighting factors. The insurance company and employees thereof, as represented by 110, will be able to select specific ZIP codes for the data to be provided, based on the construction location. They may also have the ability to modify it to sort by the ZIP code of the contractor, the product distributor. In one embodiment, they can look at adjacent ZIP codes to understand pricing patterns and also save money by having contractors work on projects in a ZIP codes, but use a pricing model for an adjacent ZIP which has a lower construction cost.
In one embodiment, the data input to the data base 130 will also be from the insurance adjustor 110 via his computer report interface 110a. This would be in the form of the payment actually approved by the insurance company and the amount paid out for the work performed. Thus, in one embodiment, the insurance adjustor 110 only views the data and makes decisions regarding the payment of claims, in other embodiments, the decision to accept a bid and price that was accepted, together with the cost data of each item on the winning bid is returned to the system from the insurance adjustor interface 110a to the user data base 130. This can therefore be an important source of data to future insurance adjustors 110 or to contractors 112 who are preparing bids and wish to see which bids have been winning bids in the past.
The collection of data from all these sources into a single data base is a significant benefit in providing a more accurate output report to the viewer and has significantly more value than is available to insurance adjustors and other viewers in the market today.
Database 120 can therefore contain at least four types of cost related data, all of which can be organized based on geographic areas. The four types of cost related data include the price at which the supplier sells the goods to buyers, the price that buyers estimate they will need to pay for these goods, the actual price that the buyers to pay for the goods and then the price the buyers, sold the goods to the end customer, the home owner. In the construction and roofing business, the seller or supplier might be at any one of the manufacturer, wholesale, distributor level or retail level. The buyer might be a contractor or large construction company and the buyer might be the home owner or insurance company. Since a contractor in most cases first provides an estimate of the bid before doing the work and then an actual work report with receipts after the job is completed, these are two types of cost data than can be compared and used to more accurately understand the market as well as market dynamics. In addition, since price data from both the seller of goods and buyer of goods is being received and tracked, these provide additional types of data that can be compared and organized in a useful manner, as described and claimed herein. The inventors have therefore recognized that organizing data according to geographic regions that includes the price at which the product was sold as one data entry, the price at which it is expected to be bought as a different data entry, the price at which it was really bought as a different data entry, the price at which it was sold again to the end user as a different data entry has particular benefits in being able to understand the market and also pricing trends in the market.
While it would be expected that the price at which a product is sold and the price at which it is bought will be same, this is not always the case when large amounts of data are concerned, particularly when individual purchases are not tracked but rather the but rather a large amount of sell data and buy data are obtained and compared. Obtaining this data from both the buyer and the seller who are each entering it into a common data base, grouped by geographic area thus provides insights into the market, including actual costs and price pressures and market dynamics. Another source of data for database 120 comes from specialty feeds 132. These specialty feeds represent subcontractors that provide specialty services for a repair contract. For example, there are some organizations that provide specific construction and restoration work. There is an organization of textile restoration experts that inventory and restore garments and fabric items affected by fire, smoke, water, mold or other contaminants. This type of restoration work is typically not done by a contractor bidding for a repair job; rather the contractor works directly with a local certified restoration drycleaner as a subcontractor. There are other contractors who provide restoration of water damaged basements, smoked damage kitchens, ceiling or other household items. In some embodiments, data in the specialty feeds 132 comes from two principal sources, data including prices for each geographic area received periodically from various specialty publications as part of the data feed, and data including prices paid by individual contractors for work subcontracted to specific certified restoration companies. Yet another potential source of data for database 120 comes from manufacturer feeds 134. This data typically does not include cost or pricing data, but does include individual product descriptions, product information, SKU numbers, product pictures, and the like. In some embodiments this data is used within database 120 to provide additional descriptive product information along with associated cost information. The data might, in some cases, include a price at which the manufacturer sold the product to its customer, which might be wholesaler, distributor or retail store.
In a preferred embodiment, a weighting 124 is applied to one or more of the data sources to determine an expected final cost estimate amount for a material item cost stored in the integrated costing database 120. The purpose of the weighting is to create a more accurate material and labor estimates for a geographic area by making adjustments of the data sources depending on data characteristics, for example how current the data from the data feed is and recent changes and trends in that same type or related types of data.
For example,
In a preferred embodiment, these weighting factors are customizable, and can be varied, In one embodiment, the weighting factors are varied based on the age of the data. They might be initially set depending on the real-time status of each of the data sources. For example, if there data that is available from third-party data sets 126 is 1 week since it was current and the material price feeds 128 are 14 weeks since they were current and there are few or no user price feeds, the integrated costing database 120 estimate for a material item could be determined entirely (100%) from the most recent third party data set 126. In another example, if there is no real time third party data available, but it is 11 weeks old and user data 130 is available that is 3 days old, then the database 120 estimate for a material item could be 20% from the most recent third-party data set 126, and 80% from user data 130.
The weighting of accepted bids from insurance adjustors 110 might also be of a high value, depending on the viewer. For example, a contractor who is preparing a bid may wish to see an aggregation of accepted bids sorted by insurance companies, by individual adjustors, by geographic region or other sort. The contractor would wish to know the time difference between a bid was submitted and when it was accepted to understand how current each of them are, as well as the actual date of approval compared to the date of new bid he is now submitting. For example, a bidding contractor 112 may benefit from viewing insurance adjustor 110 accepted bids from a geographic region that is less than seven days old, but may gain little to no benefit from accepted bids in a different geographic region or that is more than 20 days old in a rapidly moving market.
The data base also recognizes the difference between data that was current, live data when input but is now several weeks old. For example, user purchase data will generally always be current on the date it is entered. Two weeks later, the very same data will be recognized as data of the type that is current data, but it is two weeks old. This is different from data that was two weeks old on the date it was first entered. For example, if a quarterly data report issues on October 15, to include the data collect in the third quarter, July 1 to September 30, it was two weeks old as aggregated data on the date it was entered, but in fact some of the data in the set is three months and two weeks old, some is two months old and some is a month old since some of the data would have been collected on July 1, some on July 15, and some in August and September. Thus, actual third quarter data entered on October 15 might be considered by the supplier to be current data, but in fact it was, on average, over two months old on the date it was entered. Thus, there will be recognition that data which is newly entered as current data is not the same as data that is newly entered as several weeks old data. The meaning of data that is two weeks old can vary, depending on whether the date is regarded as date it was entered or what the date it represented when it was entered. These characteristics can also be used in determining the weighting factors.
In other embodiments, these weighting factors can be determined, customized, and used in various ways. For example, the weighting factors may differ based on geographic area where distributor coverage may not be as broad, material type where certain materials such as a special grade of plywood may have a greater price variability, time of year such as the beginning of summer when labor rates rapidly increase, known natural disasters such as hailstorm's or hurricanes within one or more geographic areas quickly raising the price of roofing materials, and the like.
In one embodiment, data from specialty feeds 132 is not weighted, rather the individual items and costs are sent directly to the integrated costing database 120 and provide as raw data at the output. In this case, the cost data provided for subcontractor services, such as for certified restoration companies will be best represented by the latest subcontractor quote in that geographic area.
Whether the data is weighted or not is also tracked and can be reported (but is not required to be reported) as part of the output so that the viewer has information that assists him in making a decision.
Whether the data has been weighted is supplied as information to the viewer of the output, whether it be the insurance adjustor 110, the contractor 112, the contractor 114 and the contractor 116.
The weighting is illustrated in
In one or more embodiments, when contractor 114 has won a bid for a repair project that was previously submitted to a property owner or an insurance adjuster, the contractor uses graphical user interface screen 114a to access database 120 for final pricing information and to order materials.
The data can also be output as data trends for particular types of data. For example, one output can be user data 130 that is listed on the output screen as being input as current data some of which is now two weeks, some is three weeks old and some is four weeks old.
The data output from the EPIC database 120 can be obtained at a number of different levels and sorted by different characteristics and weighting factors. One example of an output is the report obtained at 110a by the insurance adjustor 110. The insurance adjustor can ask for report 110a that contains the data from each of the various sources 126-134 with equal weights applied to them. He can also obtain a report that contains data that was considered current data when it was newly entered, and obtain this data that has been input over a several week time span. In other embodiments, the insurance adjustor 110 has the ability to modify the requested report 110a to obtain only user data 130, just material price feed data 128 or various weightings of these in a single report, with the weighting being accomplished based on the characteristics as explained herein.
The insurance adjustor may also go into the system and perform a reconciliation for a construction project. In some cases, the repair is started or even completed and then it is learned the bid approved is not correct for the actual job costs. For example, the price of roofing shingles might have spiked recently so that the insurance price is low. The inventive system will greatly reduce the occurrence of such reconciliation reports since the insurance adjuster 110 will have more accurate, real time data. Even so, there might be incorrect costs approved. Therefore, the insurance adjustor can enter the interface 110a and perform a reconciliation. Any such reconciliation that is done by the insurance adjustor will be input to the database 120, either directly or through the user database 130. The insurance company will be able to acquire the data for reconciliations, even by ZIP code, so that they can better understand the accurate price of a construction project. Thus, when a reconciliation is requested, prior to it being approved, the insurance adjuster 110 can consult the data base 120 and determine whether they have been any or many such reconciliations in this ZIP code or adjacent ZIP codes. He can give these a high weighting, for example 100%. He can then study this data to determine whether a reconciliation on the product under question is appropriate and if so, approve one. The ability to input, store, weight and sort reconciliation data with original bid data, order data and finishing data provides significant benefits over those obtainable in the prior art.
As can also be seen in
In these other tabs, such as Order, Report, Estimate info, Labor and the like, different options and descriptions are provided to permit the viewer, in this case the contractor, to obtain the data that has been described herein or to input data, place an order for goods or submit a bid to an insurance adjustor. Thus, this single, unitary computer and database system, as shown in more detail in
One or more general purpose or special purpose computing systems may be used to implement the computer- and network-based methods, techniques, and systems for point pattern matching computation described herein and for practicing embodiments of a building structure estimation system based on the point pattern matching. More specifically, the computing system 600 may comprise one or more distinct computing systems present at distributed locations. In addition, each block shown may represent one or more such blocks as appropriate to a specific embodiment or may be combined with other blocks. Moreover, in one example embodiment, the various components of a Building structure estimation system 614 may physically reside on one or more machines, which use standard inter-process communication mechanisms (e.g., TCP/IP) to communicate with each other. Further, the Building structure estimation system 614 may be implemented in software, hardware, firmware, or in some combination to achieve the capabilities described herein.
Examples of computing systems and methods to obtain a roof report are shown and described in detail in U.S. Pat. Nos. 8,078,436 and 8,170,840 and these can be used as one component of the present embodiment, as well as other roof report generation systems. For completeness, one potential system for creating such a report will be described herein as follows.
In the embodiment shown, the computing system 100 comprises a computer memory (“memory”) 602, a display 604, one or more Central Processing Units (“CPU”) 606, Input/Output devices 608 (e.g., keyboard, mouse, joystick, track pad, CRT or LCD display, and the like), other computer-readable media 610, and network connections 612. A building structure estimation system 614 is shown residing in the memory 602. In other embodiments, some portion of the contents or some or all of the components of the building structure estimation system 614 may be stored on and/or transmitted over the other computer-readable media 610. The components of the building structure estimation system 614 preferably execute on one or more CPUs 606 and generate roof estimate reports, as described herein. Other code or programs 616 (e.g., a Web server, a database management system, and the like) and potentially other data repositories, such as data repository 618, also reside in the memory 602, and preferably execute on one or more CPUs 606. Not all of the components in
In a typical embodiment, the building structure estimation system 614 includes an image acquisition engine 620; a roof modeling engine 622; a point pattern matching computation engine 624, and a roof materials overage computation engine 625 within, or as part of, the roof modeling engine 622; a report generation engine 626, an interface engine 628, and a data repository 630. Other and/or different modules may be implemented. In addition, the building structure estimation system 614 interacts via a network 632 with an image source computing system 634, an operator computing system 636, and/or a customer computing system 638. Communication system 632 may utilize one or more protocols to communicate via one or more physical networks, including local area networks, wireless networks, dedicated lines, intranets, the Internet, and the like.
The image acquisition engine 620 performs at least some of the functions described herein, with respect to the processes described herein. In particular, the image acquisition engine 620 interacts with the image source computing system 634 to obtain one or more images of a building, and stores those images in the building structure estimation system data repository 630 for processing by other components of the building structure estimation system 614.
The roof modeling engine 622 performs at least some of the functions described with reference to
In addition, at least some aspects of the model generation may be performed automatically. In particular, to generate a 3D model, the roof modeling engine 622 may use output from the point pattern matching computation engine 624 which employs variational analysis to compute a point-to-point probability spread function. The point-to-point probability spread function can be used to estimate which individual points on one image of the building most likely match corresponding points on another image of the building (i.e., the point pattern matching computation engine endeavors to “optimize” point matching associations). This estimation may be based on adaptive predominance voting probabilities generated from shape pattern matches. The shape pattern matches can be created by comparing combinations of points on an orthogonal view of the building with specific other points on an oblique view of the building, and as further described herein.
The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments.
These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.
