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 Title: |
US6748369:
Method and system for automated property valuation
[ Derwent Title ]
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| Country: |
US United States of America
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| Inventor: |
Khedkar, Pratap Shankar; Philadelphia, PA
Bonissone, Piero Patrone; Schenectady, NY
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| Assignee: |
General Electric Company, Niskayuna, NY
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| Published / Filed: |
2004-06-08
/ 1999-06-21
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| Application Number: |
US1999000337285
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| IPC Code: |
Advanced:
G06N 3/04;
G06N 5/04;
Core:
G06N 3/00;
G06N 5/00;
IPC-7:
G06F 15/18;
G06G 7/00;
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| ECLA Code: |
G06N3/04F; G06N5/04G;
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| U.S. Class: |
706/006;
706/052;
706/059;
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| Field of Search: |
706/006,52,59,25
705/010
382/224
708/801
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| Priority Number: |
| 1999-06-21 |
US1999000337285 |
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| Abstract: |
A method and system for automating a process for valuing a property that produces an estimated value of a subject property, and a reliability assessment of the estimated value. The process is a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property's value. A network-based implementation of fuzzy inference is based on a system that implements a fuzzy system as a five-layer neural network so that the structure of the network can be interpreted in terms of high-level rules. The neural network is trained automatically from data. IF/THEN rules are used to map inputs to outputs by a fuzzy logic inference system. Different models for the same problem can be obtained by changing the inputs to the neuro-fuzzy network, or by varying its architecture.
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| Attorney, Agent or Firm: |
Goldman, David C. ;
Patnode, Patrick K. ;
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| Primary / Asst. Examiners: |
Patel, Ramesh; Hirl, Joseph P.
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| Maintenance Status: |
E1 Expired Check current status
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| Family: |
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First Claim:
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What is claimed is:
1. A computer-implemented method embedded in a recordable medium for estimating value of a subject property and providing a reliability measure of the estimated value, said method comprising the steps of:
- providing a generative system based on a combination of fuzzy logic systems and neural networks capable of accepting multiple inputs and providing a single output;
- retrieving data for multiple properties, the data for each property comprising multiple attributes and a selling price of the property;
- training said generative system using the retrieved data, using the attribute information of the data as inputs and adjusting said generative system to provide an output conforming with selling price of the property associated with the attribute information;
- providing attribute information regarding the subject property to the trained generative system;
- receiving the estimated value of the subject property from the trained generative system; and
- receiving the reliability measure of the estimated value from the trained generative system.
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| Background / Summary: |
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| Drawing Descriptions: |
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| Description: |
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| Forward References: |
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