Source: http://www.google.com/patents/US7987149?dq=Frischling
Timestamp: 2016-07-25 01:00:18
Document Index: 629066584

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'art1', 'art2', 'art1', 'art2']

Patent US7987149 - Configuration mapping using a multi-dimensional rule space and rule ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA configuration mapping system and method increase the effectiveness of mapping of information from an established product line to a new product offering. In at least one embodiment, the configuration mapping system herein uses configuration mapping rules to map individual product features and entire...http://www.google.com/patents/US7987149?utm_source=gb-gplus-sharePatent US7987149 - Configuration mapping using a multi-dimensional rule space and rule consolidationAdvanced Patent SearchPublication numberUS7987149 B2Publication typeGrantApplication numberUS 11/684,904Publication dateJul 26, 2011Filing dateMar 12, 2007Priority dateMar 10, 2006Fee statusPaidAlso published asUS8364628, US20070233630, US20110264255, US20130132701Publication number11684904, 684904, US 7987149 B2, US 7987149B2, US-B2-7987149, US7987149 B2, US7987149B2InventorsAditya P. Kulkarni, Sourabh KukrejaOriginal AssigneeTrilogy Intellectual Property Holdings, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (32), Non-Patent Citations (1), Referenced by (2), Classifications (16), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetConfiguration mapping using a multi-dimensional rule space and rule consolidation
This application claims the benefit under 35 U.S.C. �119(e) and 37 C.F.R. �1.78 of U.S. Provisional Application No. 60/780,965, filed Mar. 10, 2006 and entitled “Configuration Mapping Using a Multi-Dimensional Rule Space and Rule Consolidation”. U.S. Provisional Application No. 60/780,965 includes example systems and methods and is incorporated herein by reference in its entirety.
Product configurations implemented in software use product models to define products. Examples of product configuration and product configuration models are described in (i) U.S. Pat. No. 5,515,524, issued May 7, 1996, entitled “Method and Apparatus for Configuring Systems”, inventors John Lynch and David Franke, and assignee Trilogy Development Group of Austin, Tex. (referred to herein as the “Lynch Patent”) and (ii) U.S. Pat. No. 5,825,651, issued Oct. 20, 1998, entitled “Method and apparatus for maintaining and configuring systems”, inventors Neeraj Gupta, Venky Veeraraghavan, and Ajay Agarwal, and assignee Trilogy Development Group, Inc. of Austin, Tex. (referred to herein as the “Gupta Patent”). The Lynch Patent and the Gupta Patent are incorporated herein by reference.
Product configuration model data has been used to determine and correlate product demand data with actual products as, for example, described in U.S. patent application Ser. No. 10/699,148, entitled “Identifying Quality User Sessions and Determining Product Demand with High Resolution Capabilities”, inventor Paul Daniel Karipides, filed date Oct. 31, 2003, and assignee Trilogy Development Group, Inc. of Austin, Tex., (referred to herein as “Karipides I”) which is herein incorporated by reference. The demand data can be obtained, for example, from web sessions as, for example, described in U.S. patent application Ser. No. 10/324,764, inventors Karipides et al., entitled “Generating Contextual User Network Session History in a Dynamic Content Environment”, filed Dec. 20, 2002, and assignee Trilogy Development Group, Inc. of Austin, Tex., which is herein incorporated by reference.
Configuration models can change over time but still relate to essentially the same product. For example, in the automotive context, an ‘old’ configuration model could refer to the color “navy blue” and a subsequent configuration model could refer to “midnight blue”. Midnight blue is intended as a replacement for navy blue but is essentially the same color. When configuration model changes, it has been conventionally difficult to correlate data, such as product demand data, associated with an ‘old’ configuration model with a new configuration model.
In at least one embodiment, product demand data is correlated with a then-current product definition (i.e. product model). However, product definitions often change. The systems and methods herein can map the changes in the product definition to extend the applicability of the ADI demand data to new product definitions. Karipides I describes exemplary “ADI Demand Data”.
Accessories: Smaller options, such as floor mats. Config: Config (also referred to as “Configuration”) is a valid configuration of parts from which a product, such as a vehicle, can be built. In at least one embodiment, the difference between a Config and a Product Model is that Product Model includes a subset of the features of Config. For example, in at least one embodiment, a Product Model does not include color and Accessories whereas Config does. Config can be represented as:
Config.: Model+Trim+Emission+Transmission+Drive Train+Int Color+Ext Color+Part1+Part2+Acc1+Acc2+ . . . , where “Model” represents the Model of the Product, “Trim” represents the Trim of the Product, and so on . . . Drive Train: Specifies whether a vehicle is two wheel drive or four wheel drive. Emission Type: Emission types for specific regions. There are many types of emission codes specific to regions, such as a “50 State Emission” code and a “California Emission” code. Exterior Color: Exterior color of the Product Model. Feature: Part. Interior Color: Interior color of the Product Model. Model Line: A model line is uniquely identified by the Model name and the model year, such as “Apollo 2004”. Package/Options: Various parts from which a product can be built. Product Model: Product Model (also referred to as an “End Item Model”). Product models are a combination of parts. Many combination of parts are often possible. For example, a large number of possible parts can be combined into millions of different combinations. In at least one embodiment, the Product Model does not include all parts. For example, in at least one embodiment, the Product Model does not include color and accessories. An example Product Model includes attributes for specific features included in the Product Model. For example, a Product Model can be represented as:
Product Model: Model+Trim+Emission+Transmission+Drive Train+Part1+Part2 . . . , where “Model” represents the Model of the Product, “Trim” represents the Trim of the Product, and so on. Trim: A complete configuration of a model line catering to a segment of buyers, such as “Apollo 2.5 S”. Transmission: Type of transmission in the model, such as “5 speed Manual Transmission” or “Automatic Transmission”. Configuration Mapping and Configuration Rules. In at least on embodiment, the configuration mapping system and method described herein use configuration mapping rules to map individual product features and entire configurations from established products to a new product offering. In at least one embodiment, the configuration mapping system and method described herein also provide a way to appropriately map demand and sales information for the purpose of demand estimation and sales prediction.
In at least one embodiment, configuration mapping rules are described on a per-part basis. In at least one embodiment, configuration rules represent a relationship between a left hand side part of attribute (LHS) and a right hand side part or attribute (RHS). In at least one embodiment, the relationship in the configuration rules includes “included”, “excluded”, “removed”, and “requires choice”. For example, in the context of Trilogy Automotive Demand Intelligence (ADI) described, for example, in Karipides I, configuration mapping rules cover the following parts of a car:
Configuration Mapping Rules Specific To Time Periods. In at least one embodiment of ADI, the configuration mapping rules are described independently each month, but the system and method described herein analyzes the effect of the configuration mapping rules across time. The mappings may not only be changed every month, but they may also be chained. For example, a mapping rule {A->B} may be defined in month 1, but it may be changed to {A->C} in month 2 as a correction. The symbols “->” mean “maps to”. Thus, {A->B} is an example of a part-to-part mapping rule. For example, if A is the part “color =dark blue” and B is the part “color=midnight blue, then the part-to-part mapping rule {A->B} means that the part “color=dark blue” maps to the part “color=midnight blue”. Another example is that part-to-part mapping rules {A->B} and {B->C} may be defined in successive months.
Operation 102 removes unnecessary parts from the Product Model Configuration Mapping Rules. In at least one embodiment, parts are “unnecessary” when the intended use of the Product Model does not need the particular part. For example, in at least one embodiment of ADI, emission standards are not used to determine demand. Thus, in this embodiment, emission standards are not necessary and, thus, do not need to be mapped from one Product Model to another Product Model.
Operation 104 combines configuration mapping rules from a set of time periods, such as all time periods from which demand and sales data is available, and consolidates the configuration mapping rules to view the effect of the configuration mapping rules across time. Configuration mapping rules from later months override configuration mapping rules from earlier months. In at least one embodiment, operation 104 performs “rule-chaining” to see the effect of configuration mapping rules across time. For example, if two configuration mapping rules {A->B} and {B->C} exist, operation 104 chains the two configuration mapping rules to obtain both {A->C} and {B->C}, since both A and B configurations can exist, and the latest equivalent of A and B is the “C” configuration. The configuration mapping rules may be chained to any depth, and operation 104 creates configuration mapping rules that map all the configurations that can exist to the latest configuration.
The “rule-chaining” procedure described above can be used to obtain a single set of configuration mapping rules that map all product configurations from the established product to the latest configuration of the new product.
Following is an example of Configuration Rule Mapping using ADI data for an example vehicle referred to as the “Apollo”.
Note: “New Part” in the mapped model may or may not be same as the part in the original model.
As, for example, described in Karipides I, demand for a new product offering can be estimated by studying the demand for the product's constituent features when the features occur as a part of established products. The demand and sales data in ADI database 206 can be “sliced” e.g. arranged, in various ways so that configuration rule mapping process 100 can identify the metrics for any combination of “dimensions” to allow correlating demand data with mapped configurations. For example, if a car has 3 dimensions: Trim, Color, and Moonroof, and if Trim can contain “2.5 S”, “3.5 S” and “3.5 SE”:
Color can contain “Black”, “Blue” or “Red”
Moonroof can contain “Moonroof” or “No Moonroof”
Calculation of metrics. To determine how many cars were sold with Black color, sum up the “Sales” column for all rows with “Black” in the Color column of Table 1. In this example, the number of Black cars sold is 4, i.e. 1+3=4. To get the “mix”, divide the number of Black cars by total sales. (Total =10). The mix in this example is 40%, i.e. 4/10=40%.
Demand score of rows with Moonroof that also have Trim 2.5S=30+40=70. Total Demand Score of rows with a Trim of 2.5S=30+40+30=100. Therefore, the Demand mix percentage of a Moonroof with Trim 2.5 S=70/100=70%. Configuration mapping preserves the “sliceability” property of the ADI database. The end effect of the rule consolidation process is to produce mappings of end configurations. Table 2 includes a final set of configuration mapping rules obtained after completing all the steps in the configuration mapping process 100, i.e. the black and blue colors were merged into “Metallic Black” in 2006 Apollo, the “3.5S” trim was renamed to “3.5 Super-edition”, and the Moonroof remained unchanged.
Configuration rule mapping process 100 can apply the configuration mapping rules from “Table 2” to the ADI database (Table 1), the new Table 3 is:
The demand mix can now be computed using the consolidated model data. Determining the sales of “Metallic Black” yields 8, i.e. 8=1+2+1+3+1. The Total Cars sold is 10, 10=1+2+1+2+3+1. Therefore, the sales mix of “Metallic Black” equals 8/10=80%. Similarly, the demand mix percentage of “Metallic Black” equals 77%, i.e. [(30+40+30+40+30)/Total(DemandScore)]=170/220=77%. Accordingly, the demand mix percentage of a new color (“Metallic Black”=77%) can be calculated based on data of old configurations (configurations with “Black” and “Blue”) through the rule-consolidation process.
Client computer systems 306(1)-(N) and/or server computer systems 304(1)-(N) may be, for example, computer systems of any appropriate design, including a mainframe, a mini-computer, a personal computer system, or a wireless, mobile computing device. These computer systems are typically information handling systems, which are designed to provide computing power to one or more users, either locally or remotely. Such a computer system may also include one or a plurality of input/output (“I/O”) devices coupled to the system processor to perform specialized functions. Mass storage devices such as hard disks, CD-ROM drives and magneto-optical drives may also be provided, either as an integrated or peripheral device. One such example computer system is shown in detail in FIG. 4.
Embodiments of the configuration rule mapping process 100 and configuration rule mapping system 200 can be implemented using software processing engine executable using a processor in a computer system such as a general-purpose computer 400 illustrated in FIG. 4. Input user device(s) 410, such as a keyboard and/or mouse, are coupled to a bi-directional system bus 418. The input user device(s) 410 are for introducing user input to the computer system and communicating that user input to processor 413. The computer system of FIG. 4 also includes a video memory 414, main memory 415 and mass storage 409, all coupled to bi-directional system bus 418 along with input user device(s) 410 and processor 413. The mass storage 409 may include both fixed and removable media, such as other available mass storage technology. Bus 418 may contain, for example, 32 address lines for addressing video memory 414 or main memory 415. The system bus 418 also includes, for example, an n-bit DATA bus for transferring DATA between and among the components, such as CPU 409, main memory 415, video memory 414 and mass storage 409, where “n” is, for example, 32 or 64. Alternatively, multiplex DATA/address lines may be used instead of separate DATA and address lines.
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Porsche AktiengesellschaftMethod and apparatus for transparently displaying information relating to development of a motor vehicleUS20110172795 *Jul 14, 2011The Boeing CompanyConsideration of manufacturing capability during aircraft design* Cited by examinerClassifications U.S. Classification706/47, 706/46, 703/7, 706/62, 706/45, 703/6, 706/48, 703/1, 706/52International ClassificationG06N5/02Cooperative ClassificationG06N5/022, G06Q10/06, G06N5/04, G06N5/025, G06F12/0646European ClassificationG06Q10/06Legal EventsDateCodeEventDescriptionMar 27, 2007ASAssignmentOwner name: TRILOGY INTELLECTUAL PROPERTY HOLDINGS, INC., TEXAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KULKARNI, ADITYA P.;KUKREJA, SOURABH;REEL/FRAME:019067/0966;SIGNING DATES FROM 20070321 TO 20070322Owner name: TRILOGY INTELLECTUAL PROPERTY HOLDINGS, INC., TEXAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KULKARNI, ADITYA P.;KUKREJA, SOURABH;SIGNING DATES FROM 20070321 TO 20070322;REEL/FRAME:019067/0966Jan 7, 2015FPAYFee paymentYear of fee payment: 4RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services