There is provided a system of training a deep learning model for support of a device in a remote technical support system, the system comprising a processing circuitry configured to: receive a plurality of images of a device to be supported; provide a user interface of annotating each image of the plurality of images, the annotating being data indicative of one or more of: one or more image identification labels, and one or more image subcomponent label tuples; and utilize, at least, the plurality of annotated images, to train a deep learning model to classify and/or segment an image to data indicative of at least one of: one or more image identification labels, and one or more image subcomponent label tuples, thereby providing a platform enabling automated creation of a remote support service.
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
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”
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
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
2.
ARTIFICIAL INTELLIGENCE ASSISTED SPEECH AND IMAGE ANALYSIS IN SUPPORT OPERATIONS
Artificial-intelligence-based technical support operations and remote artificial intelligence-assisted electronic warranty verification operations are disclosed. The technical support operations may include receiving audio signals and image signals associated with a technical support session from a mobile device, analyzing the signals using artificial intelligence, accessing a data structure to identify an image capture instruction and presenting the same to the mobile device, receiving and analyzing second image signals using artificial intelligence, and determining a support resolution status. The electronic warranty verification operations may include performing product image analysis to identify a product-distinguishing characteristic, performing receipt image analysis to identify product purchase information, using the product-distinguishing characteristic and product purchase information to identify in a universal data structure the specific product, accessing a link to a supplier's warranty data structure to lookup the specific product, receiving a warranty coverage indication, and transmitting an indication of warranty coverage.
A non-transitory computer readable medium includes instructions that, when executed by at least one processor, cause the at least one processor to perform artificial-intelligence-based technical support operations. The operations may include receiving over at least one network first audio signals including speech data associated with a technical support session and first image signals including image data associated with a product for which support is sought from a mobile communications device, analyzing the first audio signals and the first image signals using artificial intelligence, aggregating the analysis thereof, accessing at least one data structure to identify an image capture instruction, presenting the image capture instruction including a direction to alter and capture second image signals of a structure identified in the first image signals to the mobile communications device, receiving from the mobile communications device second image signals, analyzing the same using artificial intelligence, and determining a technical support resolution status.
G06V 30/242 - Division des suites de caractères en groupes avant la reconnaissanceSélection des dictionnaires
G10L 13/02 - Procédés d'élaboration de parole synthétiqueSynthétiseurs de parole
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G06F 40/35 - Représentation du discours ou du dialogue
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
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 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
A system for performing remote artificial intelligence-assisted electronic warranty verification including at least one processor configured to transmit an instruction to capture at least one product image of a specific product, receive and perform product image analysis on the product image to identify at least one product-distinguishing characteristic, transmit an instruction to capture an image of a purchase receipt, receive and perform receipt image analysis on the purchase receipt image to identify product purchase information, access a universal data structure containing data on products offered by suppliers, use the at least one product-distinguishing characteristic and product purchase information to identify in the universal data structure the specific product, identify in the universal data structure a link to a warranty data structure of the supplier, access the link to lookup the specific product and receive a warranty coverage indication from the supplier data structure, and transmit an indication of warranty coverage.
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
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
Techniques for conducting a support session and determine suitable instructions for resolving a certain technical mal-function in a device/equipment of a user. Imagery data associated the technical mal-function is received from a user's device and used for determining at least one improperly setup property associated with the mal-function in the mal-functioning device/equipment based on a comparison of the received imagery data with reference data. Instructions comprising augmented imagery for resolving the mal-function can be then generated, or fetched form a database, based on the determined at least one improperly setup property. A new database record can be generated comprising the augmented imagery data for use in future support sessions associated with the mal-function.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
H04L 65/401 - Prise en charge des services ou des applications dans laquelle les services impliquent une session principale en temps réel et une ou plusieurs sessions parallèles additionnelles en temps réel ou sensibles au temps, p. ex. accès partagé à un tableau blanc ou mise en place d’une sous-conférence
An image processing system including at least one processor configured to receive at least one first image of an inoperative product captured by a mobile device of a user; perform image analysis on the first image to identify a status of a functional element associated with the inoperative product; access memory to determine why the product is inoperative; cause visual guidance to be displayed by the mobile device, wherein the visual guidance is associated with a plurality of sequential actions for causing the inoperative product to become operative; receive at least one second image of the product, the second image being indicative of a completion of the plurality of sequential actions; perform image analysis on the at least one second image to determine that the completion of the plurality of sequential actions caused the inoperative product to become operative; and notify the user that the problem has been resolved.
An image processing system including at least one processor configured to receive image data captured by a mobile device image sensor. The image data includes images of an inoperative appliance. The processor is further configured to perform image recognition on the image data to identify the inoperative appliance and determine a likely cause of inoperability; retrieve a plurality of sequential instructions for enabling a user to remedy the inoperability; cause the mobile device to sequentially display the plurality of sequential instructions; detect that the inoperative appliance is outside a field of view of the image sensor, based on the image data; suspend display of additional sequential instructions when the inoperative appliance is outside of the field of view; detect when the inoperative appliance returns to the field of view; and resume display of the additional sequential instructions after the inoperative appliance returns to the field of view.
An image processing system including at least one processor configured to receive at least one first image of an inoperative product captured by a mobile device of a user; perform image analysis on the first image to identify a status of a functional element associated with the inoperative product; access memory to determine why the product is inoperative; cause visual guidance to be displayed by the mobile device, wherein the visual guidance is associated with a plurality of sequential actions for causing the inoperative product to become operative; receive at least one second image of the product, the second image being indicative of a completion of the plurality of sequential actions; perform image analysis on the at least one second image to determine that the completion of the plurality of sequential actions caused the inoperative product to become operative; and notify the user that the problem has been resolved.
An image processing system for visually augmenting a real-time video stream including at least one processor configured to receive the real-time video stream captured by an image sensor. The real-time video stream includes images of at least one cable and an electronic appliance. The processor is further configured to analyze the video stream to identify a plurality of ports in the electronic appliance; analyze the video stream to identify a cable for association with a specific port of the plurality of ports; cause a movable augmented indicator to display on the video stream, wherein the movable augmented indicator is configured to guide a user's connection of the specific cable to the specific port; monitor changing locations of the specific port as the image sensor moves relative to the electronic appliance; and adjust positions of the movable augmented indicator to account for the changing locations of the specific port.
An image processing system including at least one processor configured to receive real-time image data captured by an image sensor of a mobile device. The real-time image data includes at least one image of an inoperative product. The processor is further configured to perform image recognition on the real-time image data to identify a likely source product inoperability; cause the mobile device to display sequential instructions for mitigating the inoperability; determine that an error was made when a particular one of the instructions is not complied with; cause the mobile device to display an error notification when the particular instruction is not complied with and before a subsequent instruction is displayed; determine that the particular instruction was subsequently complied with based on real-time image data captured following the notification; and cause the mobile device to display the subsequent instruction after the particular instruction is determined to have been complied with.
Techniques for conducting a support session and determine suitable instructions for resolving a certain technical mal-function in a device/equipment of a user. Imagery data associated the technical mal-function is received from a user's device and used for determining at least one improperly setup property associated with the mal-function in the mal-functioning device/equipment based on a comparison of the received imagery data with reference data. Instructions comprising augmented imagery for resolving the mal-function can be then generated, or fetched form a database, based on the determined at least one improperly setup property. A new database record can be generated comprising the augmented imagery data for use in future support sessions associated with the mal-function.
Techniques for conducting a support session and determine suitable instructions for resolving a certain technical mal-function in a device/equipment of a user. Imagery data associated the technical mal-function is received from a user's device and used for determining at least one improperly setup property associated with the mal-function in the mal-functioning device/equipment based on a comparison of the received imagery data with reference data. Instructions comprising augmented imagery for resolving the mal-function can be then generated, or fetched form a database, based on the determined at least one improperly setup property. A new database record can be generated comprising the augmented imagery data for use in future support sessions associated with the mal-function.
Techniques for conducting a support session and determine suitable instructions for resolving a certain technical mal-function in a device/equipment of a user. Imagery data associated the technical mal-function is received from a user's device and used for determining at least one improperly setup property associated with the mal-function in the mal-functioning device/equipment based on a comparison of the received imagery data with reference data. Instructions comprising augmented imagery for resolving the mal-function can be then generated, or fetched form a database, based on the determined at least one improperly setup property. A new database record can be generated comprising the augmented imagery data for use in future support sessions associated with the mal-function.
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