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.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06N 5/046 - Inférence en avantSystèmes de production
G06N 7/08 - Agencements informatiques fondés sur des modèles mathématiques spécifiques utilisant des modèles de chaos ou des modèles de systèmes non linéaires
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 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
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.
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06T 7/143 - DécoupageDétection de bords impliquant des approches probabilistes, p. ex. la modélisation à "champs aléatoires de Markov [MRF]"
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.
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
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 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
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.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
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 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06F 18/243 - Techniques de classification relatives au nombre de classes
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
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 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06F 18/243 - Techniques de classification relatives au nombre de classes
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
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.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
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 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/96 - Gestion de tâches de reconnaissance d’images ou de vidéos
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
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 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06F 18/243 - Techniques de classification relatives au nombre de classes
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
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 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
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.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
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 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
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.
G06Q 30/0283 - Estimation ou détermination de prix
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
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.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 30/0283 - Estimation ou détermination de prix
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
26.
Detailed damage determination with image segmentation
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.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
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.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
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.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
34.
METHOD OF DETERMINING REPAIR OPERATIONS FOR A DAMAGED VEHICLE INCLUDING USING 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, 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 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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.
