Automated vehicle repair estimation by voting ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets, each identifying (i) at least one component of a damaged vehicle, (ii) a recommended vehicle repair operation for each identified component, and (iii) a score and/or confidence percentage for each operation; when a plurality of the sets identify recommended operations for one of the components, selecting the operation having the highest score, and unselecting the other operations for the component; generating a composite vehicle repair recommendation set, wherein the composite vehicle repair recommendation set identifies the selected recommended vehicle repair operation, and wherein the composite vehicle repair recommendation set does not identify the unselected recommended vehicle repair operation; and providing the composite vehicle repair recommendation set to one or more claims management systems.
A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.
A computer-implemented method comprises obtaining an electronic claim record comprising claim data describing damage to a vehicle; selecting one or more of an obtained plurality of electronic vehicle diagnostic records by applying the records and claim data as inputs to a trained machine learning model, wherein responsive to the inference input the trained machine learning model selects one or more of the records; obtaining a vehicle repair estimate data structure having a plurality of fields; populating the fields of the vehicle repair estimate data structure with at least one of the claim data and the vehicle data from the selected one or more electronic vehicle diagnostic records; and generating a user interface for presentation to a user on a user device, wherein the user interface includes display elements that represent the populated fields of the generated vehicle repair estimate data structure.
Systems and methods are provided for a dynamic and iterative process for determining a weighted decision using a combination of weighted output from multiple, trained machine learning (ML) models. Key data can be identified and efficient decision-based processing can be achieved. In some examples, the system calculates a weighted decision of a repair or total loss determination for a motor vehicle, yet any industry or data set may be implemented with the use of the dynamic and iterative decision process.
G06N 5/022 - Ingénierie de la connaissance; Acquisition de la connaissance
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
5.
COMPREHENSIVE LIABILITY MANAGEMENT PLATFORM WITH INTEGRATION WITH PROVIDER NETWORKS AND PROVIDER NEGOTIATIONS SYSTEMS
A computer-implemented method comprises: receiving, from an insurer system, an electronic medical bill; generating a decision representing a workflow selected from a plurality of the workflows based on at least one of a plurality of decisioning factors describing the electronic medical bill by providing the electronic medical bill as an inference input to a trained machine learning model, wherein responsive to the inference input the trained machine learning model generates the decision representing a workflow, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding historical decisions, the workflows including a provider network workflow and a provider negotiations workflow, and routing the electronic medical bill accordingly.
A computer-implemented method comprises receiving an image of a vehicle having damage to a first exterior body panel; providing the image to one or more trained machine learning models that are configured to identify a first region of the first exterior body panel to be repaired and a second region to be paint-blended, when the second region contains a second exterior body panel of the vehicle other than the first exterior body panel, generating an exterior body panel repainting list that includes the identification of the first exterior body panel of the vehicle and the identification of the second exterior body panel of the vehicle; querying a repainting cost database using the exterior body panel repainting list; and receiving a repainting cost estimate from the repainting cost database responsive to the querying.
A computer-implemented method comprises obtaining an image of a first damaged vehicle; selecting a set of images of second damaged vehicles that are similar to the first damaged vehicle; finding a set of images of the second vehicles showing damage similar to the damage to the first vehicle; obtaining a set of vehicle repair claims corresponding to the set of one or more images of the second vehicles; adding a selected subset to a repair estimate data structure; presenting a user interface that represents the selected subset of line items; receiving first user input that represents line items chosen by the user; generating a vector that represents the chosen line items; and applying the vector to a trained machine learning model, wherein the trained machine learning model outputs a refined subset of line items.
A computer-implemented method comprises: generating a user interface operable by a user to generate one or more vehicle repair estimate lines for repairing a damaged vehicle and/or request an automated review of the line(s); generating one or more first vehicle repair estimate lines, adding the one or more first vehicle repair estimate lines to a vehicle repair estimate data structure, and presenting a first view of the data structure in the user interface; obtaining images of the damaged vehicle, providing the images to one or more trained machine learning (ML) models, which provide first output comprising second vehicle repair estimate lines for the vehicle repair estimate, adding second vehicle repair estimate lines to the data structure, and presenting a second view of the data structure in the user interface; and generating a vehicle repair estimation document based on the vehicle repair estimate data structure.
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a Workflow Module supports more accurate and timely decisions on vehicle repair. An advantage of this new automotive collision repair technology is that it enables proper and proven repairs, which in turns increases operation safety of repaired vehicle and people safety.
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
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
A method and computing apparatus for determining comprehensive vehicle information using an intelligent Vehicle Identification Number (“VIN”) decoder process is described. The method and computing apparatus obtains OEM marketing data, OEM engineering data, parts catalog data, uses a machine learning algorithm to determine relational dependencies between the obtained OEM data and standard comprehensive vehicle configuration data to generate complete vehicle data using VIN.
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
A method, non-transitory computer readable medium, and apparatus that identifies one of a plurality of diagnostic mapping tables based on a diagnostic code associated with one of a plurality of data environment formats in an electronic claim. The diagnostic code associated with one of the plurality of data environment formats is correlated to at least one of a plurality of parts and laterality associated with another one of the plurality of data environment formats based on the identified one of the plurality of diagnostic code mapping tables. One of a plurality of assessment ratings is determined based on the diagnostic code the correlated one of the plurality of parts and the laterality, and a categorization table associated with the another one of the plurality of data environment formats. Execution of one of a plurality of actions on the electronic claim in response to the determined one of the plurality of assessment ratings for the diagnostic code is initiated.
In general, one aspect disclosed features a system, comprising: a hardware processor; and a non-transitory machine-readable storage medium encoded with instructions executable by the hardware processor to perform operations comprising: receiving an electronic record, the electronic record representing a medical bill, the medical bill comprising a plurality of attributes; mapping each attribute in the medical bill to a single bucket of a predetermined second quantity of the buckets according to a predetermined correspondence between the attributes and the buckets, the first quantity exceeding the second quantity; and providing identifiers of the single buckets as input to a machine learning model, the machine learning model being trained according to historical correspondences between the buckets and decisions of whether human review was necessary, wherein responsive to the input, the machine learning model provides as output an indication of whether the medical bill should be reviewed by a human.
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
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A method for automatically determining injury treatment relation to a motor vehicle accident comprises obtaining a plurality of images of a damaged motor vehicle, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant; determining a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the set of images of the damaged motor vehicle and the vehicle data; determining an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data; and automatically adjudicating the electronic insurance claim.
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
14.
METHODS FOR ANALYZING INSURANCE DATA AND DEVICES THEREOF
Vehicle insurance claim data is categorized into a plurality of strata. The categorized vehicle insurance claim data is mapped to corresponding geographic regions and aggregated. When the number of samples in the aggregated data meets a sampling threshold size, the aggregated data is clustered into clusters based on certain criteria and sampled to generate component synthetic peer data sets. A synthetic peer data set is generated by applying a bootstrap aggregation machine learning algorithm on the plurality of component synthetic peer data sets. The performance of a target vehicle insurance company is analyzed by comparing target vehicle insurance claim data of the target vehicle insurance company with the synthetic peer data set. The results of the comparison between the target vehicle insurance claim data and the synthetic peer are presented in a graphical representation.
Systems, non-transitory machine-readable storage media, and computer-implemented methods are provided for automated scheduling of vehicle repair reinspections. In general, one aspect disclosed features a computer-implemented method comprising: obtaining an appraisal schedule that includes a plurality of appraisal appointments for appraisers, wherein each appointment identifies one of the appraisers, a vehicle repair to be appraised; receiving a reinspection notification that identifies a vehicle repair to be reinspected, a vehicle repair facility, and a duration for the reinspection; identifying availabilities in the appraisal schedule; selecting one of the availabilities in the appraisal schedule according to a predetermined assignment profile; modifying the appraisal schedule to schedule the reinspection during the selected availability, and sending an electronic message to an electronic device associated with the corresponding vehicle repair facility, wherein the electronic message identifies the vehicle repair to be reinspected, and the time of selected availability for the reinspection.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
Systems and methods are provided for integrating damage evidence with appraisal management system to create a unified user experience for improving a virtual damage appraisal process. The system may display a curated collection of evidence providing an overview of the vehicle and sections of a vehicle damaged during an adverse incident. A user may select a damaged section from a plurality of damaged sections to view damage evidence determined by machine learning algorithms to best reflect the selected damaged section may be displayed. The damage evidence may be displayed concurrently with vehicle part selection and repair line editing functionality in a configurable graphical user interface (GUI) of a virtual appraisal application. Additionally, the system may generate set of recommendations for repairing or replacing the damaged parts in the selected section. The user may add the recommendations to a repair estimate as repair estimate lines.
Systems and methods for processing paper bills are provided. In general, one aspect disclosed features a computer-implemented method, comprising: at a first computing device: receiving first data to be printed as text on a first form, encoding the received first data as a two-dimensional machine-readable code, and causing the first form to be printed on paper, including the text and the two-dimensional machine-readable code; and at a second computing device: receiving second data representing a scan of the printed two-dimensional machine-readable code, obtaining the first data from the received second data, and populating a second form according to the obtained first data.
G06Q 50/26 - Services gouvernementaux ou services publics
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p.ex. pour la gestion du personnel hospitalier ou de salles d’opération
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p.ex. lecture de la lumière blanche réfléchie
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
G06K 15/02 - Dispositions pour produire une présentation visuelle permanente des données de sortie utilisant des imprimantes
A method and computing apparatus for automatically identifying whether or not one or more identified injuries are related to the insurance claim which has been submitted is described. The method and computing apparatus identify injury data in an electronic medical claim data associated with a claimant based diagnosis code data, uses a classifier to determine an association between the identified injury data represented by the diagnosis code data and the initial injury data represented by the initial diagnosis code data is identified when historical claim data is determined to be present for the claimant.
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
19.
Systems and methods for managing associations between damaged parts and non-reusable parts in a collision repair estimate
Systems and methods are provided for automating the process of determining non-reusable parts associated with replacement or repair of a primary part of a vehicle involved in a collision event. The primary parts to be repaired may be indicated in repair estimate record. Notably, replacement of some primary parts may require replacement of certain non-reusable parts (NRPs) that were not damaged during the collision event and are not identified as such. The method determines which NRPs are required to be replaced when repairing the primary damaged part by using repair documents specifying repair instructions for repairing the primary part. Further, the repair estimate record is updated to include the information related to the primary pars and their associated NRPs.
B60S 5/00 - Maintenance, entretien, réparation ou révision des véhicules
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
System and methods for automated claim processing with endnote hierarchy are provided. In some embodiments, a computer-implemented method comprises: receiving an insurance claim, wherein the insurance claim comprises at least one line, and wherein each line comprises at least one procedure code and at least one explanation code; determining a first hierarchical rank of a plurality of the hierarchical ranks for a first explanation code in a first line based on predetermined associations between the explanation codes and the hierarchical ranks; determining, according to the determined first hierarchical rank for the first explanation code, whether the first line comprising the first code should be modified; modifying the first line comprising the first explanation code when it is determined that the first line comprising the first explanation code should be modified; and outputting the insurance claim after determining whether the first line comprising the first explanation code should be modified.
Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.
Vehicle repair workflow automation with natural language processing is disclosed. One computer-implemented method comprises: providing images of a damaged vehicle as first input to a computer vision machine learning model, wherein the computer vision machine learning model has been trained with images of other damaged vehicles and corresponding vehicle repair operations; receiving first output of the computer vision machine learning model responsive to the first input, wherein the first output represents a plurality of the vehicle repair operations; providing the first output of the computer vision machine learning model to a natural language processing (NLP) machine learning model, wherein the NLP machine learning model has been trained with vehicle repair content comprising a plurality of vehicle repair procedures; and receiving second output of the NLP machine learning model responsive to the second input, wherein the second output comprises a recommended one of the plurality of the vehicle repair procedures.
Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.
Systems and methods are provided for automating the process of mapping repair documents, published by Original Equipment Manufacturers (OEMs), to repair information provided in a repair estimate record. A baseline set of repair estimate records specifying one or more parts of a baseline vehicle and an associated set of repair documents specifying instructions for repairing the one or more parts of the baseline vehicle may be saved using a data categorization model in a mapping dataset. The repair documents associated with baseline set of repair estimate records which have been saved in the mapping dataset may then be used to automatically determine associations between another set of repair estimate records and corresponding repair documents.
Systems and methods for managing predictions for vehicle repair estimates are provided. A method includes providing one or more images of a damaged vehicle as input to a machine learning model, wherein the machine learning model has been trained with images of other damaged vehicles and corresponding vehicle operations, wherein each of the vehicle operations represents the repair or replacement of a vehicle component; receiving output of the machine learning model responsive to the input, wherein the output comprises a plurality of values each corresponding to one of a plurality of the vehicle operations; determining a confidence metric based on the values; making a comparison between the confidence metric and a confidence threshold value; and selecting the one of the plurality of the vehicle operations corresponding to the highest value as a predicted operation based on the comparison.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
G06F 18/40 - Dispositions logicielles spécialement adaptées à la reconnaissance des formes, p.ex. interfaces utilisateur ou boîtes à outils à cet effet
G06Q 10/0875 - Gestion d’inventaires ou de stocks, p.ex. exécution des commandes, approvisionnement ou régularisation par rapport aux commandes Énumération ou classification des pièces, des fournitures ou des services, p.ex. nomenclatures
26.
VEHICLE REPAIR ESTIMATING TOOL WITH NEAR-REAL-TIME COMPLIANCE
A vehicle repair estimating tool with near-real-time compliance is provided, In general, one aspect disclosed features a method comprising: providing a near-real-time view of a repair estimate record to a client device, the repair estimate record having one or more fields, receiving, from the client device, a data entry input to be applied to one of the fields of the repair estimate record, selecting, in near real time, one or more compliance rules related to the one of the fields responsive to receiving the data entry input, determining, in near real time, whether the data entry input is valid based on the selected one or more compliance rules, and when the data entry input is determined not to be valid, notifying the client device, in near real time, that the data entry input is not valid for the one of the fields.
In general, one aspect disclosed features a computer-implemented method comprising: obtaining an image related to a damaged vehicle; determining an image type of the image, wherein the image type describes an item contained in the image; extracting one or more images of text from the image; extracting one or more text strings from each image of text; identifying a type of each text string based on the determined image type; obtaining a record, and for each text string: selecting a field of the record based on the identified type of the text string, and populating the selected field with the text string; and determining an identity of the damaged vehicle based on the populated record.
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 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
28.
METHODS FOR MORE ACCURATELY MANAGING PROCESSING OF MEDICAL BILL DATA AND DEVICES THEREOF
Methods, non-transitory computer readable media, and computing apparatus that assist with more accurately managing processing of medical bill data includes identifying previously submitted bill data associated with received medical bill data from a client based on one or more service data parameters in the received medical bill data and the identified previously submitted bill data. The received medical bill data is determined to be erroneous medical bill data based on the identified previously submitted bill data and a time period between the previously submitted bill data and the received medical bill data. The received medical bill data is classified as a follow-up procedure bill data when the received medical bill data is determined to be an erroneous medical bill data. Compensation data is restricted for the received medical bill data classified as the follow-up procedure bill data.
A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify the part of the vehicle that has sustained damage. Damage data on an extent of the damage in the identified part of the vehicle is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified generic part of the vehicle is used to obtain a corresponding Part ID Code by using a parts dictionary. Part Qualifier information obtained using VIN information is then used in conjunction with the Part ID to obtain the OEM-specific part and then generate one or more repair lines for the repair estimate.
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a Workflow Module supports more accurate and timely decisions on vehicle repair. An advantage of this new automotive collision repair technology is that it enables proper and proven repairs, which in turns increases operation safety of repaired vehicle and people safety.
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
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
A method, non-transitory computer readable medium, and apparatus that improves automated damage appraisal includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified damaged part and may be used to determine one or more adjacent parts based on the vehicle information and the repair operation type.
A computer-implemented method comprising: obtaining repair estimate data that describes a repair estimate for a damaged vehicle; extracting, from an OEM repair procedure database according to the received repair estimate data, repair procedure data that describes repair procedures for the damaged vehicle; transmitting the extracted repair procedure data to at least one technician computing device; obtaining repair log data generated by the at least one technician computing device, wherein the repair log data describes one or more repairs performed upon the damaged vehicle; determining whether the repair procedures for the damaged vehicle have been satisfactorily performed based on the received repair log data and the extracted repair procedure data; sending an electronic approval message responsive to determining the repair procedures for the damaged vehicle have been satisfactorily performed; and sending an electronic disapproval message responsive to determining the repair procedures for the damaged vehicle have not been satisfactorily performed.
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
G06F 16/90 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet - Détails des fonctions des bases de données indépendantes des types de données cherchés
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
33.
Automated vehicle repair estimation by aggregate ensembling of multiple artificial intelligence functions
Automated vehicle repair estimation by aggregate ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets for a damaged vehicle, wherein each of the vehicle repair recommendation sets identifies at least one recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; aggregating a plurality of the recommended vehicle repair operations; generating a composite vehicle repair recommendation set that identifies the aggregated recommended vehicle repair operations; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.
Automated vehicle repair estimation by random ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a vehicle damage object that includes a plurality of metadata objects of the damaged vehicle; fragmenting the object into fragments each including at least one of the metadata objects; providing each of the fragments to a respective artificial intelligence function; receiving a respective vehicle repair recommendation set from each of the artificial intelligence functions, wherein each of the vehicle repair recommendation sets identifies a recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; selecting a plurality of the recommended vehicle repair operations; generating a composite vehicle repair recommendation set that identifies the selected recommended vehicle repair operations; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.
Automated vehicle repair estimation by voting ensembling of multiple artificial intelligence functions is provided. A method comprises receiving a plurality of vehicle repair recommendation sets, each identifying (i) at least one component of a damaged vehicle, (ii) a recommended vehicle repair operation for each identified component, and (iii) a score and/or confidence percentage for each operation; when a plurality of the sets identify recommended operations for one of the components, selecting the operation having the highest score, and unselecting the other operations for the component; generating a composite vehicle repair recommendation set, wherein the composite vehicle repair recommendation set identifies the selected recommended vehicle repair operation, and wherein the composite vehicle repair recommendation set does not identify the unselected recommended vehicle repair operation; and providing the composite vehicle repair recommendation set to one or more claims management systems.
Automated vehicle repair estimation by preferential ensembling of multiple artificial intelligence functions is provided. A method comprises receiving, from each source of a plurality of the sources, a respective vehicle repair recommendation set for a damaged vehicle, wherein each vehicle repair recommendation set identifies a recommended vehicle repair operation of a plurality of the vehicle repair operations for the damaged vehicle; determining a respective source rank of each source from a plurality of the source ranks; generating a composite vehicle repair recommendation set that identifies the recommended vehicle repair operations in an order determined according to the source ranks of the respective sources; and providing the composite vehicle repair recommendation set to one or more vehicle repair insurance claims management systems.
Automated vehicle repair estimation by adaptive ensembling of multiple artificial intelligence functions is provided. A method comprises receiving, from a plurality of sources, vehicle repair recommendation sets identifying recommended vehicle repair operations for damaged vehicle components; selecting, by a trained artificial intelligence function, one of the operations for each component based on a plurality of learned states; generating a composite vehicle repair recommendation set identifying the selected operation; providing the composite vehicle repair recommendation set to one or more claims management systems; and repeatedly retraining the trained artificial intelligence function by adjusting the learned states according to the vehicle damage objects received, and the corresponding composite vehicle repair recommendation generated, since the last retraining of the trained artificial intelligence function.
Systems and methods are provided for automating the process of generating image metadata related to a vehicle and damage sustained by the vehicle during a collision event by using image analysis tools employing machine learning algorithms. The image with collision metadata renders the image capable of being analyzed using content-based searching.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
39.
Systems and methods for determining a provider-specific likelihood of payment acceptance
Embodiments of the application are directed toward a method comprising: receiving, by a transaction management computing apparatus, an electronic payment estimate via a secure communication channel for services rendered by a provider; receiving determining a likelihood of payment acceptance by generating, by the transaction management computing apparatus, an payment amount acceptable to the provider computing device based on data associated with the provider computing device, a type of service provided, or a number of days to make the payment; determining, by the transaction management computing apparatus, whether the generated exact amount is acceptable to the provider computing device; and completing, by the transaction management computing apparatus, a payment transaction with the generated exact amount when the generated exact amount is determined to be acceptable to the provider computing device.
Systems and methods are provided for automating the process of automatically determining appropriateness of surgical team services performed during a medical procedure. Clinical resource data with known surgical guideline indicator (SGIs) may be us used to generate and store a mapping of medical procedure codes to indications of the appropriateness of surgical team services corresponding to the medical procedure codes. A medical bill associated with an insurance claim may be analyzed to extract a medical procedure code corresponding to a surgical procedure, and an automated adjudication recommendation for the medical bill may be made based on the mapping between medical procedure codes and SGIs.
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p.ex. pour la gestion du personnel hospitalier ou de salles d’opération
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p.ex. pour analyser les cas antérieurs d’autres patients
G06Q 20/14 - Architectures de paiement spécialement adaptées aux systèmes de facturation
G16H 70/20 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des pratiques ou des directives
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a workflow module supports more accurate and timely decisions on vehicle repair. An advantage of this new automotive collision repair technology is that it enables a determination of relatedness likelihood of individual DTC, which in turn decreases costs and increases savings.
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
B60R 21/0136 - Circuits électriques pour déclencher le fonctionnement des dispositions de sécurité en cas d'accident, ou d'accident imminent, de véhicule comportant des moyens pour détecter les collisions, les collisions imminentes ou un renversement réagissant à un contact effectif avec un obstacle
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
42.
Methods for estimating injury recovery time data and devices thereof
Disclosed technology includes extracting claimant medical data including a current claimant's age, gender, and at least one injury from an electronic claims document. Estimated injury recovery time data is determined by correlating demographic medical data comprising prior estimated injury recovery time data associated with different prior claimant's ages, genders, and injuries based on programmed estimation rules configured to identify statistical correspondence between different combinations of the ages, the genders, and the injuries in the demographic medical data and the claimant medical data comprising at least the current claimant's age, gender, and at least one injury. The determined estimated injury recovery time data is updated based on at least identified and obtained medical treatment data and prescription medication data associated with the current claimant's at least one injury. The updated estimated injury recovery time data is provided via a graphical user interface to a claim management device.
G06Q 40/00 - Finance; Assurance; Stratégies fiscales; Traitement des impôts sur les sociétés ou sur le revenu
G16H 50/50 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicales; TIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour la simulation ou la modélisation des troubles médicaux
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p.ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p.ex. pour s’assurer de l’administration correcte aux patients
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p.ex. pour des dossiers électroniques de patients
43.
Methods for evaluating and optimizing preferred provider organization (PPO) network stacks and devices thereof
Methods, non-transitory machine readable media, and network stack analysis devices that generate optimized preferred provider organization (PPO) network stacks are disclosed. With this technology, electronic transactions are applied to each of a first plurality of network stacks to determine a cost reduction value for each of the first network stacks. Each of the first network stacks includes an ordered subset of networks. The first network stacks are resampled based on the determined cost reduction values. A determination is made when one or more convergence criteria are met by the resampled first network stacks. When the determination indicates that the convergence criteria are not met by the resampled first network stacks, one or more of the first network stacks are modified based on genetic crossover or mutation operation(s) to generate a second plurality of network stacks. The application, resampling, and determination are then repeated for the second network stacks.
A method, non-transitory computer readable medium, and computing apparatus that identifies with automated image analysis two or more different types of content in image data for an electronic image associated with one or more of a plurality of types of claims. The image data associated with each of the identified two or more different types of content is converted by a different one of a plurality of automated content conversion techniques based on the association with the one or more types of claims and on the identified one of the plurality of types of content. Modified image data for the electronic image is generated based on the converted image data associated with each of the identified two or more different types of content. The modified image data for the electronic image with the converted image data for each of the identified two or more different types of content is provided.
A method, non-transitory computer readable medium, and an apparatus for automated estimation of repair data includes applying a first generated artificial intelligence model on a received vehicle damage image associated with an electronic claim to identify damaged component(s) on a vehicle without using any metadata. A heat map analysis is performed on the received actual vehicle damage image to identify a damage severity value associated with at least one of the identified damaged component(s). A second generated artificial intelligence model is applied on the received actual vehicle damage image and the damage severity value associated with the identified damaged component(s) to determine repair data and a repair-or-replace designation. The determined repair data and the determined repair-or-replace designation for at least one of the identified one or more damaged components is provided in response to the received actual vehicle damage image associated with the electronic claim.
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
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
46.
Methods for improved delta velocity determination using machine learning and devices thereof
Methods, non-transitory computer readable media, and insurance claim analysis devices are disclosed that provide an improved, automated delta velocity determination. With this technology, one or more images of a damaged motor vehicle and contextual data, associated with an electronic insurance claim and a motor vehicle accident involving the damaged motor vehicle, are obtained. The obtained images and one or more portions of the contextual data are compared to historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values. A delta velocity value is calculated based on the comparison. The calculated delta velocity value is provided to verify damage severity during automated processing of the electronic insurance claim.
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
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
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
47.
Systems and methods for use of diagnostic scan tool in automotive collision repair
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a Workflow Module supports more accurate and timely decision on vehicle repair. An advantage of this new automotive collision repair technology is that it enables proper and proven repairs, which in turn increases operation safety of repaired vehicle and people safety.
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
Methods, non-transitory computer readable media, and apparatuses for automated processing of hybrid electronic invoice data include identifying at least a first type of charge data from one or more other types of charge data in received hybrid electronic invoice data based on one or more parsing techniques. The first type of charge data is disassembled from the received hybrid electronic invoice data based on the identification. The disassembled first type of charge data is adjudicated based on execution of one of a plurality of sets of adjudication procedures identified to correspond to the disassembled first type of charge data. The received hybrid electronic invoice data is transformed with the adjudicated first type of charge data. The transformed electronic invoice data is provided for additional processing.
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santé; TIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p.ex. pour la gestion du personnel hospitalier ou de salles d’opération
G06F 40/117 - Mise en forme, c. à d. modification de l’apparence des documents Étiquetage; Annotation ; Désignation de bloc; Choix des attributs
Methods and systems for performing the methods are disclosed. The methods include splitting a large execution load/execution component link generation job, into a number of smaller execution load/execution component link generation jobs, and conditionally subsplitting one or more of the smaller execution load/execution component link generation jobs. The methods also include solving each of the smaller execution load/execution component link generation jobs to generate links between data packets corresponding with execution loads of each smaller execution load/execution component link generation job and data packets corresponding with execution components of each smaller execution load/execution component link generation job.
The disclosed systems and methods generate links for candidate execution load/execution component pairings, each candidate pairing identifying one of the data packets corresponding with the execution loads and one of the data packets corresponding with the execution components. Ranks are generated for the candidate pairings, and candidate pairings are selected for potential linkage based on the ranks. If the data packet corresponding with an execution load of a candidate pairing is linkable to the data packet corresponding with an execution component of the candidate pairing, the data packet corresponding with the execution load is linked to the data packet corresponding with the execution component. If the data packet corresponding with the execution load of the candidate pairing is not linkable to the data packet corresponding with the execution component of the pairing, a next candidate pairing is selected.
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a Workflow Module supports more accurate and timely decision on vehicle repair. An advantage of this new automotive collision repair technology is that it enables proper and proven repairs, which in turn increases operation safety of repaired vehicle and people safety.
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
Systems and methods of allocating execution loads to execution components are disclosed. The systems and methods select one of the execution components and one of the execution loads based on selection criteria. The systems and methods then determine whether the selected execution load may be allocated to the selected execution component. If the selected execution load may be allocated to the selected execution component, the systems and methods allocate the execution load accordingly. If the selected execution load may not be allocated to the selected execution component, the systems and methods select another one of the execution components and another one of the execution loads for attempted allocation.
A method, non-transitory computer readable medium and apparatus for vehicle valuation includes integrating with an insurance claim application executed by an agent device in response to an electronic request for a claim for a vehicle. Automated valuation of the claim vehicle in the insurance claim application executed by an agent computing device is managed. Corresponding build sheet data from a build sheet data server device for the comparable vehicles and the vehicle based on a corresponding vehicle identifier for each is obtained. A comparable base value of each of the comparable vehicles to the claim vehicle is adjusted based on differences between the obtained build sheet data for each. A claim base value for the claim vehicle is determined based on the adjusted comparable base values for each of the identified comparable vehicles. The determined claim base value is set in the insurance claim application executed by the agent device.
A new automotive collision repair technology is provided, including system and data flow architectures that are designed to provide enhanced data and enhanced data flow in the context of vehicle diagnosis and repair, particularly when repairs are necessary due to collisions. In some examples, the data flow through the network is streamlined, to avoid network congestion, to use fewer computer and network resources and/or to enable the utilization of smaller databases. In other examples, enhanced access to data in real-time and near real-time enabled by a Workflow Module supports more accurate and timely decisions on vehicle repair. An advantage of this new automotive collision repair technology is that it enables proper and proven repairs, which in turns increases operation safety of repaired vehicle and people safety.
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
This technology optimizes management of third party insurance claim processing includes retrieving third party claim data associated with a selected third party claim and from an automatic integration with at least one additional insurance claim application initiated based on the selection of the third party claim. One or more customized claim management operations are executed based on the selected third party claim and at least a portion of the retrieved third party claim data. At least a portion of a customized hierarchical claim management graphical user interface system is generated based on at least a portion of the retrieved third party claim data and the executed one or more customized claim management operations. The customized hierarchical claim management graphical user interface system includes an initial dashboard graphical user interface comprising a plurality of interactive panels and a plurality of specialized third-party claim management graphical user interfaces nested beneath the initial dashboard graphical user interface.
A collaboration message is received at a computer system network node of a computer network that operates in a decentralized arrangement such that network nodes comprise work process sources and destinations, and the collaboration messages convey process state updates among the collaborators. There is no central authority though which all process messages and state updates must pass and which thereby may create a system bottleneck and limit system growth. The computer system is scalable and system operation remains efficient with increasing numbers of network nodes.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
An estimate compliance tool specifying estimate compliance criteria is presented. The estimate compliance can be accessed via hardware, software or a combination thereof. The tool can be configured to include functions facilitating creation of rules that outline criteria for estimate creation or invoicing. A user can enter vehicle and parts identification via a user interface to which a rule will apply. The user can also enter a rule requirement or suggestion. The compliance tool can include multiple modules configured to accept user input regarding vehicle or parts criteria or configured to create an estimate compliance rule based on the vehicle or parts criteria. The compliance tool can further include a storage medium configured to store multiple estimate compliance rules and a profile module. The profile module creates a rules profile comprising multiple estimate compliance rules. Additionally, the tool provides a compatibility module that checks compliance rules for conflicting rule criteria.
A self-insured or self funded medical benefit plan is provided by an employer wherein the self-insured medical benefit plan is governed by ERISA, and wherein the employer who is providing the medical benefit plan is 100 percent responsible for payment for medical services provided to an employee, receiving the benefit of the medical benefit plan, to a medical service provider for covered medical services and products. The medical benefit plan and method for providing the medical benefit plan determines a reasonable value for the medical services provided by a medical service provider to a participant of the plan, reprices a bill or claim from the medical service provider, and protects the participant/employee under ERISA from attempted collections of additional moneys that a medical service provider may believe is owed for the medical services but were not paid by the exemplary self insured medical benefit plan.
An estimate compliance tool to specify estimate compliance criteria is presented. In one embodiment, the tool can be provided using hardware, software or a combination thereof, and can be configured to include functionality to facilitate creation of rules to outline criteria for estimate creation or invoicing. In one embodiment, a user interface is provided to a user to provide the opportunity to enter vehicle and parts identification to which a rule will apply. The user can also be prompted to enter a rule requirement or suggestion such as, for example, the type of parts required. The system can include a first module configured to accept user input specifying vehicle criteria; a second module configured to accept user input specifying parts criteria; and a third module configured to create an estimate compliance rule based on the entered vehicle selection criteria and parts criteria. The system can further include a storage medium configured to store a plurality of created estimate compliance rules and a profile module configured to create a rules profile comprised of a plurality of estimate compliance rules. Additionally, a compatibility module configured to check a plurality of created rules for conflicting rule criteria can be provided.
A method and business technique for reviewing medical service provider bills, recalculating and providing payment recommendation to a paying party for the bills. The method includes analyzing medical bills and determining erroneous and inappropriate charges on bills. The method provides a payment recommendation using multiple databases and sophisticated mathematical modeling that includes one or more of the following: a medical service provider's actual cost of delivering the medical services provided; the average profit-margin of that provider, an average profit margin of comparable medical providers in an area, other industry-specific profit-margin benchmarks; an average acceptable payment by medical service providers in the area for comparable services; payment rates negotiated by large health insurers and managed care organizations; and other industry benchmarks for reasonable payment for comparable services.
G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
G06Q 40/00 - Finance; Assurance; Stratégies fiscales; Traitement des impôts sur les sociétés ou sur le revenu
brevets
