# US 2021/0241439 A1 · Repair Estimation Based on Images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241439 A1 - Document ID: US20210241439A1 - Status: Published application - Kind: A1 - Application number: 16/779,105 - Filed: 2020-01-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: Sachcontrol GMBH - Applicants: Sachcontrol GmbH - Inventors: Andrey Lutich; Lars Rietzschel - Web page: https://patentgrep.com/patent/US20210241439A1 ## CPC - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06F 16/5854: {using shape and object relationship} - G06F 18/2163: {Partitioning the feature space} - G06Q 10/20: Administration of product repair or maintenance - G06T 7/0004: {Industrial image inspection} - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/764: using classification, e.g. of video objects - G06V 10/766: using regression, e.g. by projecting features on hyperplanes - G06V 10/82: using neural networks - G06V 20/00: Scenes; Scene-specific elements (control of digital cameras H04N23/60) - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30132: Masonry; Concrete - G06V 10/247: {by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids} - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device. ## Claims ### Claim 1 A system comprising: one or more processors; and one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to: access an image of a damaged object; determine, using a plurality of image segmentation models, a plurality of objects in the image; determine, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image, the repair-relevant property vector comprising a plurality of damaged object properties; generate a repair report using the repair-relevant property vector and a price catalogue, the repair report comprising an indication of the damaged object and a price associated with the repair or replacement of the damaged object; and provide the generated report for display on an electronic display device. ### Claim 2 (depends on 1) The system of claim 1, wherein the repair report further comprises a state of each of the objects determined from the plurality of image segmentation models. ### Claim 3 (depends on 1) The system of claim 1, wherein generating the repair report using the repair-relevant property vector and the price catalogue comprises comparing the plurality of damaged object properties of the repair-relevant property vector with a plurality of items in the price catalogue. ### Claim 4 (depends on 1) The system of claim 1, wherein determining the plurality of objects in the image using the plurality of image segmentation models comprises applying a label from a plurality of labels to individual pixels of the image of the damaged object. ### Claim 5 (depends on 1) The system of claim 1, wherein the plurality of image segmentation models comprises DeepLab implemented in TensorFlow. ### Claim 6 (depends on 1) The system of claim 1, wherein the plurality of visual inference models are: regressor type models; or classifier type models. ### Claim 7 (depends on 1) The system of claim 1, wherein determining the repair-relevant property vector further comprises using an image query language engine to find spatial relationships between the objects determined from the plurality of image segmentation models. ### Claim 8 A method comprising: by a computing device, accessing an image of a damaged object; by the computing device, determining, using a plurality of image segmentation models, a plurality of objects in the image; by the computing device, determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image, the repair-relevant property vector comprising a plurality of damaged object properties; by the computing device, generating a repair report using the repair-relevant property vector and a price catalogue, the repair report comprising an indication of the damaged object and a price associated with the repair or replacement of the damaged object; and by the computing device, providing the generated report for display on an electronic display device. ### Claim 9 (depends on 8) The method of claim 8, wherein the repair report further comprises a state of each of the objects determined from the plurality of image segmentation models. ### Claim 10 (depends on 8) The method of claim 8, wherein generating the repair report using the repair-relevant property vector and the price catalogue comprises comparing the plurality of damaged object properties of the repair-relevant property vector with a plurality of items in the price catalogue. ### Claim 11 (depends on 8) The method of claim 8, wherein determining the plurality of objects in the image using the plurality of image segmentation models comprises applying a label from a plurality of labels to individual pixels of the image of the damaged object. ### Claim 12 (depends on 8) The method of claim 8, wherein the plurality of image segmentation models comprises DeepLab implemented in TensorFlow. ### Claim 13 (depends on 8) The method of claim 8, wherein the plurality of visual inference models are: regressor type models; or classifier type models. ### Claim 14 (depends on 8) The method of claim 8, wherein determining the repair-relevant property vector further comprises using an image query language engine to find spatial relationships between the objects determined from the plurality of image segmentation models. ### Claim 15 One or more computer-readable non-transitory storage media embodying software that is operable when executed to: access an image of a damaged object; determine, using a plurality of image segmentation models, a plurality of objects in the image; determine, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image, the repair-relevant property vector comprising a plurality of damaged object properties; generate a repair report using the repair-relevant property vector and a price catalogue, the repair report comprising an indication of the damaged object and a price associated with the repair or replacement of the damaged object; and provide the generated report for display on an electronic display device. ### Claim 16 (depends on 15) The media of claim 15, wherein the repair report further comprises a state of each of the objects determined from the plurality of image segmentation models. ### Claim 17 (depends on 15) The media of claim 15, wherein generating the repair report using the repair-relevant property vector and the price catalogue comprises comparing the plurality of damaged object properties of the repair-relevant property vector with a plurality of items in the price catalogue. ### Claim 18 (depends on 15) The media of claim 15, wherein determining the plurality of objects in the image using the plurality of image segmentation models comprises applying a label from a plurality of labels to individual pixels of the image of the damaged object. ### Claim 19 (depends on 15) The media of claim 15, wherein the plurality of image segmentation models comprises DeepLab implemented in TensorFlow. ### Claim 20 (depends on 15) The media of claim 15, wherein the plurality of visual inference models are: regressor type models; or classifier type models.