An unlabelled or partially labelled target dataset is modelled with a machine learning model for classification (or regression). The target dataset is processed by the machine learning model; a subgroup of the target dataset is prepared for presentation to a user for labelling or label verification; label verification or user re-labelling or user labelling of the subgroup is received; and the updated target dataset is re-processed by the machine learning model. User labelling or label verification combined with modelling an unclassified or partially classified target dataset with a machine learning model aims to provide efficient labelling of an unlabelled component of the target dataset.
A user device includes a camera configured to capture a series of images of a vehicle and a processor configured to receive a first image of the vehicle from a first viewpoint, classify, for the first image, one or more parts of the vehicle captured in the first image, generate, for the first image, a first graphic indicating the parts of the vehicle being displayed in the first image and a display configured to receive the first image and the first graphic from the processor and display the first image of the vehicle with the first graphic indicating the parts of the vehicle being displayed in the first image.
A method for inspecting a vehicle, comprising capturing one or more segments of video of the vehicle comprising a plurality of parts, identifying, using one or more classifiers, one or more parts of the vehicle captured in the one or more segments of video, generating feedback related to capturing the one or more segments of video and displaying an interface comprising the feedback and video data being captured.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
An artificial intelligence (AI) system is configured to receive a series of images of a vehicle from one or more viewpoints, identify, for at least a first image from the series of images using a machine learning model, one or more parts of the vehicle captured in the image using a first classifier for identifying parts of the vehicle and identifying, for at least the first image, that one of the parts of the one or more parts of the vehicle incurred damage.
A system is configured to capture images of a vehicle, perform, by one or more machine learning models, a visual inspection of the vehicle based on the images, determine, by the one or more machine learning models, inspection results based on the visual inspection and determine, by the one or more machine learning models, a confidence value for the inspection results.
A system is configured to receive image data, identify, using a first set of one or more machine learning models, multiple objects related to real property that are shown in the image data, determine a number of unique objects that are shown in the image data and generate, using a second set of one or more machine learning models, an assessment of a state of the real property.
A system is configured to receive image data, identify, using a first set of one or more machine learning models, multiple objects related to real property that are shown in the image data, determine a number of unique objects that are shown in the image data and generate, using a second set of one or more machine learning models, an assessment of a state of the real property.
A method for inspecting a vehicle, comprising capturing one or more segments of video of the vehicle comprising a plurality of parts, identifying, using one or more classifiers, one or more parts of the vehicle captured in the one or more segments of video, generating feedback related to capturing the one or more segments of video and displaying an interface comprising the feedback and video data being captured.
G06V 20/40 - ScenesScene-specific elements in video content
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
G07C 5/00 - Registering or indicating the working of vehicles
An artificial intelligence (AI) system is configured to receive a series of images of a vehicle from one or more viewpoints, identify, for at least a first image from the series of images using a machine learning model, one or more parts of the vehicle captured in the image using a first classifier for identifying parts of the vehicle and identifying, for at least the first image, that one of the parts of the one or more parts of the vehicle incurred damage.
An artificial intelligence (AI) system is configured to receive first historical data for an entity related to an insurance claims operation, the first historical data including performance-related parameters and at least one associated performance metric for claims processed by the entity during a first duration of time, wherein the first historical data is parameterized for input into one or more artificial intelligence (AI) models, identify, from the AI model fit to the first historical data, one or more of the performance-related parameters that influenced the at least one associated performance metric, determine, from one or more performance-related parameters, a recommendation to improve the at least one associated performance metric and provide a notification of the recommendation.
A system is configured to capture images of a vehicle, perform, by one or more machine learning models, a visual inspection of the vehicle based on the images, determine, by the one or more machine learning models, inspection results based on the visual inspection and determine, by the one or more machine learning models, a confidence value for the inspection results.
The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate.
The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate.
Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06F 18/243 - Classification techniques relating to the number of classes
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
The present invention relates to the determination of repair operations for a damaged vehicle. More particularly, the present invention relates to determining repair operations, for example whether to repair or replace parts of a damaged vehicle and associated labour time required, for a damaged vehicle using images of the damage to the vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining repair operations that are required to repair a damaged vehicle, using images of the damage to the damaged vehicle.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06F 18/243 - Classification techniques relating to the number of classes
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
A method of determining one or more damage states of one or more auxiliary parts of a damaged vehicle, the vehicle comprising a plurality of normalized parts and at least some of the normalized parts further comprising one or more auxiliary parts. The method includes receiving one or more images of the vehicle, using a plurality of classifiers, each determining at least one classification of damage to the vehicle, each said classification being determined for each of a plurality of normalized parts of the vehicle, determining one or more classifications for the plurality of auxiliary parts using one or more trained models, wherein each classification comprises at least one indication of damage to at least one auxiliary part and outputting the determined damage states of the one or more auxiliary parts.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
A user device includes a camera configured to capture a series of images of a vehicle and a processor configured to receive a first image of the vehicle from a first viewpoint, classify, for the first image, one or more parts of the vehicle captured in the first image, generate, for the first image, a first graphic indicating the parts of the vehicle being displayed in the first image and a display configured to receive the first image and the first graphic from the processor and display the first image of the vehicle with the first graphic indicating the parts of the vehicle being displayed in the first image.
A user device includes a camera configured to capture a series of images of a vehicle and a processor configured to receive a first image of the vehicle from a first viewpoint, classify, for the first image, one or more parts of the vehicle captured in the first image, generate, for the first image, a first graphic indicating the parts of the vehicle being displayed in the first image and a display configured to receive the first image and the first graphic from the processor and display the first image of the vehicle with the first graphic indicating the parts of the vehicle being displayed in the first image
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/96 - Management of image or video recognition tasks
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) services featuring software in
the fields of artificial intelligence and machine learning
for use in the vision-based inspection of damages, the
identification, analysis and appraisal of repair costs and
the automated processing of insurance claims; software as a
service (SAAS) services featuring software for assessing,
estimating and appraising damages and associated repair
costs for homes, vehicles and other insured items; software
as a service (SAAS) services featuring software for
automating insurance claim processing and expediting
insurance claims.
19.
Universal car damage determination with make/model invariance
The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate.
Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
A method, system and apparatus for determining requirements for painting a vehicle, including receiving images of the vehicle, determining, using classifiers, one or more classifications for parts of the vehicle based on the images, wherein each classifier processes the same images and is trained to identify damage to only one part of the parts of the vehicle, wherein each classifier is trained to identify a different part of the vehicle and be generic with respect to a make and model and year of the vehicle, determining, for at least one of the parts of the vehicle, one or more paint areas, wherein each paint area is an area of damage to the vehicle requiring painting, determining one or more operations and materials required to paint at least one of the one or more paint areas and outputting the determined one or more operations and materials required.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
G06F 18/243 - Classification techniques relating to the number of classes
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.
A method of determining, one or more damage states of one or more auxiliary parts of a damaged vehicle, the vehicle comprising a plurality of normalized parts and at least some of the normalized parts further comprising one or more auxiliary parts. The method includes receiving one or more images of the vehicle, using a plurality of classifiers, each determining at least one classification of damage to the vehicle, each said classification being determined for each of a plurality of normalized parts of the vehicle, determining one or more classifications for the plurality of auxiliary parts using one or more trained models, wherein each classification comprises at least one indication of damage to at least one auxiliary part and outputting the determined damage states of the one or more auxiliary parts.
The present invention relates to the determination of repair operations for a damaged vehicle. More particularly, the present invention relates to determining repair operations, for example whether to repair or replace parts of a damaged vehicle and associated labour time required, for a damaged vehicle using images of the damage to the vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining repair operations that are required to repair a damaged vehicle, using images of the damage to the damaged vehicle.
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including preserving the quality of the input images of the damage to the vehicle.
Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including preserving the quality and/or resolution of the images of the damaged vehicle.
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including segmenting the input images.
Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including performing segmentation of the images to create richer input data.
The present invention relates to assessing the damage and repairs needed to damaged vehicles. More particularly, the present invention relates to assessing vehicle damage using primarily photos of damaged vehicles and information provided by drivers or insurers, to determine whether vehicle body parts to be replaced or repaired require paint blending.
Aspects and/or embodiments seek to provide a method and system to determine whether a part of a damaged vehicle requires paint blending.
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining damage to auxiliary parts of a vehicle from images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage to auxiliary parts of a vehicle using images of the damage to the vehicle.
The present invention relates to verification of damage to vehicles. More particularly, the present invention relates to a universal approach to automated generation of a damage estimate to a vehicle using images of the vehicle and verification of a manually-generated damage repair proposals using the automatically generated damage estimate. Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more repair estimates from one or more photos of a damaged vehicle and comparing the generated estimate(s) to one or more input repair estimates to verify the one or more input repair estimates.
The present invention relates to assessing, against reference data and predetermined criteria, vehicle repair work estimates for damaged vehicles. More particularly, the present invention relates to verifying input data detailing the estimated repair work and labour using historical repair work data and assessing the data supplied to support the vehicle work estimate to verify estimates of damage and repair required. Aspects and/or embodiments seek to provide a method for assessing, against reference data and predetermined criteria, vehicle repair work estimates for damaged vehicles.
The prevent invention relates to assessing the damage and repairs needed to damaged vehicles. More particularly, the present invention relates to assessing vehicle damage using primarily photos of damaged vehicles and information provided by drivers or insurers, to determine an estimate of the damage to the vehicle, and repairs needed to the vehicle. Aspects and/or embodiments seek to provide a method and system to determine an estimate of the damage to a vehicle.
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including segmenting the input images. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including performing segmentation of the images to create richer input data.
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including preserving the quality of the input images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including preserving the quality and/or resolution of the images of the damaged vehicle.
The present invention relates to the determination of repair operations for a damaged vehicle. More particularly, the present invention relates to determining repair operations, for example whether to repair or replace parts of a damaged vehicle and associated labour time required, for a damaged vehicle using images of the damage to the vehicle, using domain confusion loss techniques. Aspects and/or embodiments seek to provide a computer-implemented method for determining repair operations that are required to repair a damaged vehicle, using images of the damage to the damaged vehicle and domain confusion loss techniques.
The present invention relates to the determination of repair operations for a damaged vehicle. More particularly, the present invention relates to determining repair operations, for example whether to repair or replace parts of a damaged vehicle and associated labour time required, for a damaged vehicle using images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining repair operations that are required to repair a damaged vehicle, using images of the damage to the damaged vehicle.
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.
The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle.
The present invention relates to the determination of painting requirements for damaged vehicles. More particularly, the present invention relates to determining paint materials and/or operations required to repair a damaged vehicle using images of the damaged vehicle. Aspects and/or embodiments seek to provide a computer-implemented method of generating one or more estimates for painting required to a damaged vehicle from one or more photos of a damaged vehicle, including paint operations and/or materials required.
The present invention relates to assessing the damage and repairs needed to damaged vehicles. More particularly, the present invention relates to assessing vehicle damage using primarily photos of damaged vehicles and information provided by drivers or insurers, to determine whether vehicle body parts to be replaced or repaired require paint blending. Aspects and/or embodiments seek to provide a method and system to determine whether a part of a damaged vehicle requires paint blending.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) services featuring software that contains artificial intelligence and machine learning capabilities for use in the vision-based inspection of damages, the identification, analysis and appraisal of repair costs and the automated processing of insurance claims; software as a service (SAAS) services featuring software for assessing, estimating and appraising damages and associated repair costs for homes, vehicles and other insured items; Software as a service (SAAS) services featuring software for automating insurance claim processing and expediting insurance claims
An unlabelled or partially labelled target dataset is modelled with a machine learning model for classification (or regression). The target dataset is processed by the machine learning model; a subgroup of the target dataset is prepared for presentation to a user for labelling or label verification; label verification or user re-labelling or user labelling of the subgroup is received; and the updated target dataset is re-processed by the machine learning model. User labelling or label verification combined with modelling an unclassified or partially classified target dataset with a machine learning model aims to provide efficient labelling of an unlabelled component of the target dataset.
