- 专利标题: Deep learning image processing method for determining vehicle damage
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申请号: US16937318申请日: 2020-07-23
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公开(公告)号: US11610074B1公开(公告)日: 2023-03-21
- 发明人: He Yang , Bradley A. Sliz , Carlee A. Clymer , Jennifer Malia Andrus
- 申请人: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- 申请人地址: US IL Bloomington
- 专利权人: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- 当前专利权人: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
- 当前专利权人地址: US IL Bloomington
- 代理机构: Marshall, Gerstein & Borun LLP
- 主分类号: G06K9/62
- IPC分类号: G06K9/62 ; G06F16/583 ; G06Q40/08 ; G06N3/08
摘要:
In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.
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