Abrégé

Disclosed herein are systems and method for generating animated avatars of users in real-time during a teleconference. The method includes training AI avatar generation models to create an avatar of a first user, deploying an AI avatar generation agent on a communication device, collecting sensor data from different sensors associated with the first user and sending the collected sensor data to the communication device of the second user, activating a data processing model and to identify the types of sensor data received from the communication device of the first user and activating the AI avatar generation agent of the second user to execute, on the communication device of the second user, the plurality of AI avatar generation models, and displaying, on the communication device of second user, the animated avatar of the first user synced with the real-time audio of the voice of the first user during the teleconference.

Classes IPC  ?

• G06T 13/20 - Animation tridimensionnelle [3D]
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G10L 13/047 - Architecture des synthétiseurs de parole
• G10L 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique
• G10L 25/63 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel

Abrégé

Disclosed herein are systems and method for a machine learning (ML) based method for providing driving instructions in a vehicle, including: acquiring parameters from a plurality of vehicle systems, sensors and other external sources of information; analyzing acquired parameters using a trained driving analysis ML model configured to generate: driving instructions for a driver of the vehicle, and corresponding emotional prosody parameters indicating a level of urgency and/or level of importance of the driving instructions; generating a voice audio recording of the driving instructions; applying to the voice audio recording a trained voice emotionalization ML model configured to modify emotional prosody of the voice audio recording of the driving instructions based on corresponding emotional prosody parameters to generate an emotionalized voice audio recording of the driving instructions; and providing the emotionalized voice audio recording of the driving instructions for audio playback to the driver via a speaker in the vehicle.

Classes IPC  ?

• B60K 35/26 - Dispositions de sortie, c.-à-d. du véhicule à l'utilisateur, associées aux fonctions du véhicule ou spécialement adaptées à celles-ci utilisant une sortie acoustique
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

Abrégé

Disclosed herein are systems and method for a snapshot based testing of software code. In one aspect, a method includes: receiving a first snapshot of an executed software code; receiving a second snapshot of the executed software code; comparing the first and second snapshots to identify static and/or dynamic parameters, wherein the static parameters have the same input and output values in both the first and second snapshots, and dynamic parameters have the same input values and different output values in each snapshot; analyzing the executed software code to determine relationships between the dynamic parameters and to identify related dynamic parameters; generating a test for testing the software code; receiving a modification to the software code; and applying the generated test to the executed modified software code.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel

Abrégé

Disclosed herein are systems and method for executing a text-to-speech machine learning model. A method includes: determining a first phoneme embedding from an input phoneme sequence; determining, using a text embedding model, a token-level embedding from an input word sequence, wherein the input phoneme sequence corresponds to the input word sequence; upsampling the token-level embedding into a second phoneme embedding; inputting both the first phoneme embedding and the second phoneme embedding in an encoder-decoder machine learning model configured to generate acoustic features for a vocoder model that produces a speech waveform; and executing the vocoder model to generate speech reciting the input word sequence.

Classes IPC  ?

• G10L 13/027 - Synthétiseurs de parole à partir de conceptsGénération de phrases naturelles à partir de concepts automatisés
• G10L 13/06 - Unités élémentaires de parole utilisées dans les synthétiseurs de paroleRègles de concaténation
• G10L 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique

Abrégé

Disclosed herein are systems and methods for a machine-learning based method for proctoring online examinations. In one aspect, an exemplary method includes monitoring a user taking an online examination. The method also includes detecting at least one suspicious cheating event from the user from a captured video stream or time-series telemetry data. The method further includes, in response to detecting a plurality of suspicious cheating events from the user, analyzing the detected suspicious cheating events using a trained AI proctoring model. The trained AI proctoring model is configured to: recognize a cheating pattern based on the detected plurality of suspicious cheating events, classify the cheating events based on the detected cheating pattern, and calculate a cheating risk score based on the recognized cheating patterns. The method further includes notifying a proctor of suspicious cheating by the user based on the calculated cheating risk score exceeding a predetermined threshold.

Classes IPC  ?

• G06F 3/08 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement à partir de, ou vers des supports d'enregistrement distincts, p. ex. carte perforée

Abrégé

Disclosed herein are systems and method for ML-based analysis of racing communications. In one aspect, the method includes: obtaining a plurality of audio message between a plurality of race team members, converting the messages into text format, determining roles of speakers, including at least one of: determining roles of some speakers based on analysis of specific words and/or phrases, and determining roles of other speakers based on analysis of background noise patterns in audio messages, recognizing topics of messages by applying a third neural network trained on racing data, identifying a list of predefined keywordsin the text messages, determining a level of importance of each message based on the role of the speaker, the topic of the message, the predefined keywords, and a relationship of the message with other messages, and displaying the plurality of text messages based on the level of importance in a user interface.

Classes IPC  ?

• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/103 - Mise en forme, c.-à-d. modification de l’apparence des documents
• G10L 25/78 - Détection de la présence ou de l’absence de signaux de voix

Abrégé

Disclosed herein are systems and method for generating a teaching avatar using machine learning. A method may include training, using a first machine learning algorithm, a teaching avatar to recite information using speech-based mannerisms and physical gestures of a real-life teacher, wherein the training is performed with a training dataset comprising videos and transcripts of real-life teachers administering courses. The method may include receiving a class attribute comprising information about at least one student of a course. The method may include setting a visual appearance and an audio configuration of the teaching avatar based on the class attribute. The method may including generating, using a second machine learning algorithm, a script based on the course. The method may include executing, on a computing device, the teaching avatar to recite the script with the speech-based mannerisms and physical gestures of the real-life teacher.

Classes IPC  ?

• G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]

Abrégé

Disclosed herein are systems and method for______. In one aspect,

Classes IPC  ?

• G06Q 30/0273 - Détermination des frais de publicité
• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo

Abrégé

Disclosed herein are systems and methods for generating realistic handwriting movements for a virtual avatar. An exemplary method includes: receiving an input comprising one of a drawing or text; assigning a coordinate and a timestamp to each respective point on the input; generating a curve including a plurality of coordinates assigned to points in the input; generating a weighted virtual object configured to trace the curve in an animation based on an order of a plurality of timestamps assigned to the points in the input, wherein the weighted virtual object has an inertial mass parameter that modifies the curve to represent different writing variations; configuring a hand of a virtual avatar to move along a modified version of the curve as traced by the weighted virtual object with the inertial mass parameter being set to a first value; and generating, for display, the avatar as hand writing the input.

Classes IPC  ?

• G06T 11/20 - Traçage à partir d'éléments de base, p. ex. de lignes ou de cercles

Abrégé

A system receives video data from a first device, audio data from a second device, and activity data indicative of events on a user device. The system detects at least one violation of user activity occurring during a time period by applying, on one of the video data, the audio data, and the activity data, at least one rule for controlling user interactions with critical data on the user device. The system stores, in the at least one memory, the at least one violation in association with time-synchronized video, audio, and activity events captured during the time period. The system terminates, on the user device, access to the critical data based on the at least one violation.

Classes IPC  ?

• G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
• G06F 21/32 - Authentification de l’utilisateur par données biométriques, p. ex. empreintes digitales, balayages de l’iris ou empreintes vocales
• H04L 67/50 - Services réseau

Abrégé

Disclosed herein are systems and method for training neural networks to identify and geolocate racers, comprising: obtaining a first dataset, a second dataset, and a map of the race course with unique geolocations; generating a first training dataset comprising the first dataset and geolocation labels identifying the unique geolocations; training a geolocation identification neural network to identify at least one unique geolocation in the images of the race course and to identify corresponding unique geolocations on the map of the race course; generating a second training dataset comprising the second dataset and racer labels identifying each racer in the images of racers; training a racer identification neural network to identify at least one racer in the images of racers based on identifying visual appearances of each racer; and using the trained geolocation identification neural network and the trained racer identification neural network to analyze racing videos to identify and geolocate positions of racers on the race course.

Classes IPC  ?

• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
• G06N 3/08 - Méthodes d'apprentissage
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 7/90 - Détermination de caractéristiques de couleur
• G06V 20/54 - Trafic, p. ex. de voitures sur la route, de trains ou de bateaux
• G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions

Abrégé

Disclosed herein are systems and method for synchronizing race telemetry, videos, and map data. In one aspect, a method includes: obtaining racing videos of racers on a race course and a map of the race course; identifying unique geolocations of the race course; executing a trained racer identification neural network to visually identify and track at least one racer at least at the identified unique geolocations in the racing videos; obtaining telemetry data associated with absolute race time for each identified racer; synchronizing the racing videos, the map, and the telemetry data for each identified racer based on the identified unique geolocations and the absolute race time; and generating a dynamic user interface (UI) for displaying time-synchronized videos comprising a visual identifier of each racer, the map including a visual identifier of the geolocation of each racer on the race course, and the telemetry data for each racer.

Classes IPC  ?

• G11B 27/34 - Aménagements indicateurs
• G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
• G06T 7/20 - Analyse du mouvement
• G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
• 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
• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
• G06V 40/10 - Corps d’êtres humains ou d’animaux, p. ex. occupants de véhicules automobiles ou piétonsParties du corps, p. ex. mains

Abrégé

A system generates a first dataset with input images of vehicles and corresponding output vectors identifying the vehicles. The system also creates a second dataset with images of damaged vehicles and output vectors detailing the damages. The system trains a machine learning model using the first dataset to detect vehicles in images, employing backbone and linear layers. The system then fine-tunes the model with the second dataset to identify damages on detected vehicles, updating the weights of the backbone layers during initial training and the first linear layer during fine-tuning. The system processes an input image of a vehicle through the trained model to detect and display any damages on a user interface, highlighting the vehicle and its damages.

Classes IPC  ?

• 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 9/451 - Dispositions d’exécution pour interfaces utilisateur

Abrégé

Systems and methods for analyzing images using deep learning and computer vision models. Automatic analysis of photographic images allows, for example, for the identification of important elements in these images, such as detection and measurement of vehicle details of interest to racing teams. Racing vehicles, typically cars that use standardized components, have specific geometry that can be detected and used for specific detection tasks.

Classes IPC  ?

• G06T 7/62 - Analyse des attributs géométriques de la superficie, du périmètre, du diamètre ou du volume
• 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

Abrégé

A system trains a video synthesis model using a video training dataset comprising video samples of one or more persons. The system receives a video sample of the target person. The system trains a video custom synthesis model based on the video sample. The system generates, using both the video synthesis model and the video custom synthesis model, a video avatar that mimics visuals of the target person, wherein generating the video avatar further comprises: generating a preliminary video of a head of the target person with controlled gestures based on recorded gestures from the video sample and a target gesture script; and adding lip synchronization to the preliminary video by matching a voice recording of the target person to a plurality of lip movements based on words spoken by the target person in the preliminary video.

Classes IPC  ?

• G06T 13/20 - Animation tridimensionnelle [3D]
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G10L 13/033 - Édition de voix, p. ex. transformation de la voix du synthétiseur
• G10L 13/047 - Architecture des synthétiseurs de parole
• G10L 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique

Abrégé

A system trains a voice synthesis model to convert text to speech, wherein the training is based on an audio training dataset comprising audio samples of one or more persons. The system receives at least one audio sample of the target person. The system trains a voice custom synthesis model to identify person-specific speech characteristics, wherein the training is based on the at least one audio sample. The system receives an input text. The system generates, using both the voice synthesis model and the voice custom synthesis model, an audio avatar that recites the input text in a voice of the target person. The system processes the audio avatar to be formatted by phrases and expressions.

Classes IPC  ?

• G06T 13/20 - Animation tridimensionnelle [3D]
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G10L 13/033 - Édition de voix, p. ex. transformation de la voix du synthétiseur
• G10L 13/047 - Architecture des synthétiseurs de parole
• G10L 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique

Abrégé

Systems and methods for checking the correctness of a computer program with at least one incorrect instruction inserted into at least one of a plurality of branches of the computer program. At least one prover generates a counterexample of computer program correctness in order to switch focus from a failed proof of the correctness of the computer program to a failed test of the correctness of the computer program.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel

Abrégé

Disclosed herein are systems and methods for generating realistic movements for a virtual avatar. An exemplary method includes: extracting, using a speech recognition algorithm, a plurality of words from an audio clip; inputting the plurality of words into a machine learning model configured to output a plurality of gestures to accompany the plurality of words, wherein the machine learning model is configured to: detect a group of words; identify a keyword in the group of words; and assign, to the group of words, a gesture corresponding to the keyword; and animating a virtual avatar to perform the outputted plurality of gestures while reciting the plurality of words, wherein the gesture is performed when reciting the group of words.

Classes IPC  ?

• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G06T 13/20 - Animation tridimensionnelle [3D]
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

disclosed herein are systems and method for generating custom courses using machine learning. a method may include: receiving, via a user interface (UI), a first user selection of a topic; retrieving content associated with the topic from a database of reference materials; generating, for display on the GUI, the content in a default organizational scheme; receiving, via the GUI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic; determining, by a hardware processor, an amount of time needed by a user to consume the content in the default organization scheme; and automatically updating the content displayed in the UI in accordance with the custom organizational scheme based on the preferred duration and the amount of time.

Classes IPC  ?

• G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
• G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
• G06F 16/9538 - Présentation des résultats des requêtes
• G06F 40/166 - Édition, p. ex. insertion ou suppression

Abrégé

Disclosed herein are systems and method for generating synthesized content using machine learning. A method may include: receiving, via a UI, a first user selection of a topic from a plurality of topics; identifying a first reference material and a second reference material from a plurality of reference materials related to the topic; determining a first complexity level and a first quality level of the first reference material; determining a second complexity level and a second quality level of the second reference material; calculating a weight distribution that is a combination of a ratio between the complexity levels and a ratio between the quality levels; executing a machine learning algorithm that generates content synthesized from both the first reference material and the second reference material based on the weight distribution; and outputting, for display, the content on the UI.

Classes IPC  ?

• G06Q 50/20 - Éducation
• G06N 20/20 - Techniques d’ensemble en apprentissage automatique
• G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits

Abrégé

Disclosed herein are systems and method for updating courses generated using machine learning. A method may include: receiving, via a user interface (UI), a user selection of a preferred duration for consuming course content associated with a topic; generating, using a machine learning algorithm at a first time, a course from reference materials describing a plurality of sub-topics associated with the topic, wherein the machine learning algorithm combines the reference materials in an organizational scheme such that a length of the course is not greater than the preferred duration; outputting the course on the GUI; detecting, at a second time, a new reference material describing a new sub-topic for inclusion in the course; modifying, using the machine learning algorithm, the course to include the new sub-topic in a manner such that the length of the course is not greater than the preferred duration; and outputting the modified course.

Classes IPC  ?

• G06Q 50/20 - Éducation
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
• G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p. ex. interaction avec des règles ou des cadrans

Abrégé

Disclosed herein are systems and method for integrating content into a sequence using machine learning. A method may include: receiving, via a user interface (UI), content describing a topic and a plurality of sub-topics associated with the topic; executing a first machine learning model configured to determine compatibility scores between the content and a plurality of curricula; identifying at least one curriculum with a compatibility score greater than a threshold compatibility score; executing at least one other machine learning model configured to generate a modified curriculum in which the content is inserted into an original sequence of courses associated with the at least one curriculum based on prerequisites of the content and available resources to provide access to the content; and outputting, on the UI, the modified curriculum.

Classes IPC  ?

• G06Q 50/20 - Éducation
• G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
• G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p. ex. interaction avec des règles ou des cadrans
• G06F 16/34 - NavigationVisualisation à cet effet

Abrégé

Disclosed herein are systems and methods for updating a graphical user interface displaying content related to a topic based on user performance. A method may include: receiving, via a user interface (UI), a user selection of a topic; generating, using a machine learning algorithm, a course from reference materials describing a plurality of sub-topics associated with the topic, wherein the machine learning algorithm includes, in the course, a plurality of assessments that test comprehension of the plurality of sub-topics; outputting the course on the UI; monitoring user interaction with a first subset of the assessments within the course on the UI; in response to determining, based on the monitoring, that the user interaction does not meet a comprehension criteria, modifying, using the machine learning algorithm, a first subset of the sub-topics corresponding to the first subset of the assessments; and outputting the modified course on the UI.

Classes IPC  ?

• G09B 7/08 - Dispositifs ou appareils d'enseignement à commande électrique procédant par questions et réponses du type à choix entre réponses multiples, c.-à-d. où pour une question donnée est fournie une série de réponses entre lesquelles un choix doit être fait caractérisés par une modification du programme d'enseignement à la suite d'une réponse erronée, p. ex. en répétant la question, en fournissant une information supplémentaire

Abrégé

A system determines whether to execute a first operation of a distributed machine learning model (MLM) on at least one server or on at least one client device. In response to determining that the first operation should be executed on the at least one server, the system: encrypts data associated with the first operation using a specific encryption scheme; and transmits the encrypted data to the at least one server for execution of the first operation on the encrypted data. In response to determining that the first operation should be executed on the at least one client device, the system performs the first operation on the data using the at least one client device without encrypting using the specific encryption scheme.

Classes IPC  ?

• H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité

Abrégé

Systems and methods for autonomous-vehicle navigation integrating path planning with a perception network. A Bird's Eye View costmap is generated at runtime using only onboard sensors. No external localization providers are used.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
• 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
• G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués

Abrégé

Disclosed herein are systems and methods for detecting a presence of a person in front of a display with a camera based on a reflection detection. In one aspect, an exemplary method includes obtaining, using a camera, a video stream of a user in front of the display. The method also includes changing a brightness and color characteristics of an object. The method further includes obtaining changes in a brightness and a color temperature of at least one surface on a face of the user from the video stream. The method further includes based on a determination that the obtained changes in brightness and color temperature of surfaces on the face of the user do not correspond to the brightness and color temperature of the object within the brightness threshold and the color temperature threshold, determining that the user is not in front of the display.

Classes IPC  ?

• G09G 3/20 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice
• G06V 40/10 - Corps d’êtres humains ou d’animaux, p. ex. occupants de véhicules automobiles ou piétonsParties du corps, p. ex. mains

Abrégé

The present invention provides a system and method for applying Siamese Neural Networks (“SNNs”) to model, characterize, and predict the effects of defects on material properties, specifically for 2-dimensional (“2D”) crystals such as transition metal dichalcogenides (“TMDCs”). The present invention provides a method for predicting physical properties with strong performance across both low and high-defect density scenarios.

Classes IPC  ?

• G06N 3/045 - Combinaisons de réseaux
• G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques

Abrégé

A system obtains, by a video evaluator, a video clip generated by a video generator of the avatar generator. The system obtains, by the video evaluator, video features of a target person that the avatar is representing. The system compares the video clip with the video features of the target person using a set of video metrics. The system generates a video evaluation score for the video clip based on a comparison of the video clip and the video features.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G10L 25/57 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour le traitement des signaux vidéo
• G10L 25/60 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour mesurer la qualité des signaux de voix
• G10L 25/84 - Détection de la présence ou de l’absence de signaux de voix pour différencier la parole du bruit
• G10L 25/90 - Détermination de la hauteur tonale des signaux de parole

Abrégé

Systems and methods for automated emotional text analysis and markup utilizing a sliding window mechanism. A method includes receiving input text data and employing a text preprocessing unit to parse the data into text segments. A contextual window control unit within a text markup unit applies a sliding window mechanism to each text segment, creating context windows for sentiment analysis. An emotional analysis model within the sentiment classification unit classifies the sentiment of the text segments within context windows. The emotional text markup unit associates classification results with the respective text segments, generating marked-up text that is used to produce media content with emotional expressions.

Classes IPC  ?

• G06F 40/30 - Analyse sémantique
• G06F 40/205 - Analyse syntaxique
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique

Abrégé

Methods and systems for enhancing a student's comprehension of visually narrated lectures by automatically augmenting narration of textual lectures with automatically generated textual scenarios inserted into the lecture, including by automatically selecting the locations of the insertion, contents, voice, and avatar characteristics for the scenarios.

Classes IPC  ?

• G09B 5/06 - Matériel à but éducatif à commande électrique avec présentation à la fois visuelle et sonore du sujet à étudier
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo

Abrégé

Systems and methods for verification of a result of execution of a computational system, including generating the result by executing elements of a computational system by a developer party, saving the result in a computer storage, preserving a current state of elements of the computational system by creating a meta-version label in a meta-version tracking system for the meta-version of the computational system and linking the elements of the computational system to the meta-version label, sending to the meta-version tracking system a request to verify the result of the computational system by a verifying party, recreating and deploying elements of the computational system by transferring information to an external device, generating a verification result, and comparing the verification result to the result previously generated.

Classes IPC  ?

• G06F 8/71 - Gestion de versions Gestion de configuration
• G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet

Abrégé

Methods and systems are disclosed for automatically obtaining a result of execution in a remote execution environment, such as high-performance computing cluster HPC, of elements of a version of a complex computational system relying on at least one data source. Configuration of the remote execution environment, as well as deployment of the version of the computational system and its configuration is performed automatically using instructions preserved within the version-tracking system. Intermediate statuses and the result of the execution are produced according to the instructions preserved within the version-tracking system and are preserved within the version-tracing system.

Classes IPC  ?

• G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation

Abrégé

A system obtains, by an audio evaluator, a speech generated by a text-to-speech module of the avatar generator. The system obtains, by the audio evaluator, audio features of a target person that the avatar is representing. The system compares the speech with the audio features of the target person using a set of audio metrics, and generating an audio evaluation score for the speech based on a comparison of the speech and the audio features, wherein generating the audio evaluation score comprises evaluating one or more of: m speech intelligibility using automatic-speech-recognition (ASR) based evaluation metrics, audio noise level using voice-activity-detection (VAD) based evaluation metrics, naturalness of speech intonation using pitch-based metrics, voice similarities using equal-error-rate (EER) and cosine (COS) metrics, and speech pronunciation statistics.

Classes IPC  ?

• G06T 7/00 - Analyse d'image
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G10L 25/57 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour le traitement des signaux vidéo
• G10L 25/60 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour mesurer la qualité des signaux de voix
• G10L 25/84 - Détection de la présence ou de l’absence de signaux de voix pour différencier la parole du bruit
• G10L 25/90 - Détermination de la hauteur tonale des signaux de parole

Abrégé

Systems and methods for generating a personalized three-dimensional avatar model are disclosed. A method includes generating pose data by identifying key points on a body figure mapped on user photographic images, loading a three-dimensional avatar model template based on persona characteristics and aligning the avatar model template with pose data to position the avatar in a corresponding posture. The personalized avatar model is generated using gradient descent optimization to adjust avatar model parameters based on a comparison of aligned avatar model with user images.

Classes IPC  ?

• G06T 19/20 - Édition d'images tridimensionnelles [3D], p. ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
• G06T 7/194 - DécoupageDétection de bords impliquant une segmentation premier plan-arrière-plan
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques

Abrégé

Systems and methods for analyzing images include an application that uses deep learning and computer vision models. Automatic analysis of photographic images allows, for example, for the identification of important elements in these images. For example, the application detects racing vehicles, vehicle numbers, vehicle details, and the orientation of these vehicles. These vehicles, typically cars, have specific attributes associated with a racing environment that can be detected with an application that comprises customized modules adapted to specific detection tasks.

Classes IPC  ?

• G06V 30/10 - Reconnaissance de caractères
• G06T 7/90 - Détermination de caractéristiques de couleur
• G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p. ex. de visages similaires sur les réseaux sociaux
• 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 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques

Abrégé

Systems and methods for automatically finding and fixing faults in software programs. The methodology is implemented by a tool that works solely on the basis of program text, using a prover. The tool verifies program code with a prover and if a fault is found, a set of counterexamples are generated that illustrate the causes of the proof failure. The tool uses the counterexample to infer invariants that characterize the circumstances under which the failure occurs. These invariants are used to generate candidate fixes, which are then validated by the prover. Correct fixes are applied to the program.

Classes IPC  ?

• G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel

Abrégé

A system determines whether a first operation performed by an MLM is compatible with a specific encryption scheme, wherein the MLM is distributed over at least one client device and at least one server. In response to determining that the first operation is compatible with the specific encryption scheme, the system encrypts data associated with the first operation using the specific encryption scheme, and transmits the encrypted data to the at least one server configured to apply the first operation. In response to determining that the first operation is incompatible with the specific encryption scheme, the system performs the first operation on the data using the at least one client device without encrypting using the specific encryption scheme.

Classes IPC  ?

• H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
• G06F 17/16 - Calcul de matrice ou de vecteur

Abrégé

A system generates an MLM comprising a plurality of layers. The system assigns a first encryption scheme for a first subset of layers in the plurality of layers. During a training phase of the MLM, the system determines whether a first input training vector comprises private data, in response to determining that the first input training vector does not comprise the private data, the system train the MLM such that, during backpropagation, an optimization algorithm is used to update any necessary weights in the plurality of layers; and in response to determining that the first input training vector comprises the private data, the system trains the MLM such that during the backpropagation, the optimization algorithm is used to update weights solely in the first subset of layers. The system executes the trained MLM on a user input vector to generate a user output value.

Classes IPC  ?

• G06N 20/00 - Apprentissage automatique
• H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
• H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système

Abrégé

A system parses an input training dataset by classifying public data and private data in the input training dataset. The system tokenizes the public data into standard tokens and the private data into secret tokens. The system trains an MLM using the standard tokens and the secret tokens to generate, for a given input prompt, a output response that does not reveal any values in the private data. The system receives a user prompt, and executes the trained MLM on the user prompt to generate a masked output response comprising at least one secret token. The system de-tokenizes, the at least one secret token, in the masked output response based on the tokens and user credentials of the user. The system outputs a version of the masked output response with the at least one secret token replaced with a corresponding value of the private data based on the user credentials.

Classes IPC  ?

• H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
• G06N 20/00 - Apprentissage automatique
• H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
• H04L 9/08 - Répartition de clés

Abrégé

A system receives a training dataset for the LLM that includes confidential data, wherein the LLM comprises preset parameters. The system trains the LLM using the training dataset, including: identifying one or more parameters changed during the training, and encrypting the changed parameters. The system receives an input query for the LLM from a user. The system determines if the user has access rights to the confidential data. In response to determining that the user has the access rights to the confidential data, the system decrypts the encrypted changed parameters of the LLM, and performs an LLM inference using the decrypted changed parameters. In response to determining that the user does not have the access rights to the confidential data, the system performs the LLM inference with the preset parameters without decrypting the encrypted changed parameters.

Classes IPC  ?

• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
• G06F 21/60 - Protection de données

Abrégé

A system determines whether a first operation performed by an MLM is compatible with one of a first encryption scheme and a second encryption scheme, wherein the MLM is distributed over at least one client device and at least one server. In response to determining that the first operation is compatible with the first encryption scheme, the system: encrypts data associated with the first operation using the first encryption scheme; and transmits the data encrypted by the first encryption scheme to the at least one server configured to apply the first operation. In response to determining that the first operation is incompatible with the first encryption scheme, the system: encrypts the data associated with the first operation using the second encryption scheme; and transmits the data encrypted by the second encryption scheme to the at least one server configured to apply the first operation.

Classes IPC  ?

• H04L 9/40 - Protocoles réseaux de sécurité
• H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
• H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes

Abrégé

A system outputs, on a first user computing system, a lesson material template on a session screen of a data sharing software, and on one or more task screens of the data sharing software, at least one task related to the lesson material template for a first user to perform on the first user computing system while the lesson material template is being presented on the session screen. The system modifies the lesson material template based on one or more first user modifications received via the one or more task screens. The system stores, in a first user session log, both the first user modifications to the lesson material template and first user activity related to performing the at least one task. The system receives and outputs, on the first user computing system, second user modifications to the lesson material template.

Classes IPC  ?

• G09B 5/02 - Matériel à but éducatif à commande électrique avec présentation visuelle du sujet à étudier, p. ex. en utilisant une bande filmée

Abrégé

A system receives a user query requesting a testable hypothesis about a scientific topic. The system classifies the user query into a first theoretical framework of a plurality of theoretical frameworks each comprising of terms and principles related to a particular scientific topic. The system generates the testable hypothesis by a first machine learning (ML) model that is configured to: receive as inputs: the user query, the first theoretical framework, and information from a graph document database comprising data associated with scientific documents, generate, as an output, the testable hypothesis that can be evaluated using the first theoretical framework and that does not reiterate a hypothesis or findings from the scientific documents in the graph document database. The system outputs the testable hypothesis via a user interface in response to the user query.

Classes IPC  ?

• G06F 16/332 - Formulation de requêtes
• G06F 16/338 - Présentation des résultats des requêtes
• G06F 16/353 - PartitionnementClassement dans des classes prédéfinies

Abrégé

A system receives a plurality of scientific documents for inclusion in an extended knowledge graph. For each respective scientific document of the plurality of scientific documents, the system classifies the respective scientific document with a theoretical framework of a plurality of theoretical frameworks each comprising terms and principles associated with a particular scientific topic, extracts metainformation of the respective scientific document, structures the metainformation in a document-specific ontology model that further comprises an indication of the theoretical framework, generates a plurality of text chunks from the respective scientific document of a given size, and generates, using a first ML model, one or more concepts from each of the plurality of text chunks. The system generates, using a second ML model, the extended knowledge graph using each of the plurality of text chunks, each concept, and the metainformation, and stores the extended knowledge graph in a graph document database.

Classes IPC  ?

• G06F 16/34 - NavigationVisualisation à cet effet
• G06F 16/35 - PartitionnementClassement
• G06F 16/36 - Création d’outils sémantiques, p. ex. ontologie ou thésaurus
• G06F 40/20 - Analyse du langage naturel

Abrégé

A system includes a performance optimization module of an autonomous vehicle configured to: (1) receive a first and second set of real-time parameter values of the autonomous vehicle; (2) identify instability of the autonomous vehicle in response to detecting one or more errors between a control command given by a controller unit to the autonomous vehicle and an execution of the control command by the autonomous vehicle based on the first set of real-time parameter values and the second set of real-time parameter values; (3) generate additional information associated with the autonomous vehicle and an environment in which the autonomous vehicle is driving based on the one or more errors; generate a corrective course of action for reducing a duration needed to drive a given route by the autonomous vehicle; and feed back the additional information and the corrective course of action to the controller unit for execution.

Classes IPC  ?

• B60W 30/02 - Commande de la stabilité dynamique du véhicule
• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes

Abrégé

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes

Abrégé

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• G01C 21/34 - Recherche d'itinéraireGuidage en matière d'itinéraire

Abrégé

Systems and methods for conducting a synchronized student-lecturer session on an e-learning server in a computer network. The method includes obtaining a lesson material template from the lecturer computing system; logging a lecturer and one or more students in the e-learning application by verifying respective credentials to access the lesson material template, presenting the lesson material template on a session screen of the e-learning application corresponding to the lecturer and the one or more student users; generating a lecturer session; generating a student session log; tracking the lecturer session log, wherein the lecturer activity as recorded in the lecturer session log is monitored by one or more student users, and tracking the student session log, wherein the student activity as recorded in the student session log is monitored by the lecturer user.

Classes IPC  ?

• G09B 5/02 - Matériel à but éducatif à commande électrique avec présentation visuelle du sujet à étudier, p. ex. en utilisant une bande filmée

Abrégé

Systems and methods for automatically enhancing the quality of images in the training set of a neural network NN. A method includes gaining access to a training set including a plurality of images. Using at least one image quality assessment method, at least one image is identified from a plurality of images in the training set, which matches a low-quality criterion as at least one low-quality image. At least one image enhancement method is used for enhancing the at least one low-quality image to obtain at least one enhanced image. The at least one low-quality image is replaced with the corresponding at least one enhanced image in the training set.

Classes IPC  ?

• G06T 5/60 - Amélioration ou restauration d'image utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
• G06V 10/98 - Détection ou correction d’erreurs, p. ex. en effectuant une deuxième exploration du motif ou par intervention humaineÉvaluation de la qualité des motifs acquis

Abrégé

Systems and methods for generating video highlights with a certain label and a specific duration (SD) using a trained ranking neural network (RankNet). The system obtains a request for highlight generation, specific duration, and a specific label. The video is split into a set of fragments of pre-defined duration forming a sequence. Digital representation in a form of 3D spatio-temporal embedding is generated for each fragment by a spatio-temporal encoder. Using the embedding value, a rank of each fragment is identified by a trained Ranking Neural Network. Ranks are recorded into a data structure. A minimum number of fragments are selected to cover the SD using a criteria comprising comparing ranks of different fragments. A video highlight is generated from concatenated selected fragments with a truncation, if necessary.

Classes IPC  ?

• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo

Abrégé

Systems and methods for automatic generation of highlights of video. The system includes a video processor to select one type of the video to be analyzed and split video clips from the video. The video processor recognizes positive clips, negative clips, and auxiliary clips. A spatio-temporal encoder is configured to select, from the recognized clips, a main positive clip, a main negative clip, and auxiliary positive and negative clips, and generate a three-dimensional (3D) embedding vector of each clip. The selected clips are processed by a ranking network having a self-attention layer. The self-attention layer, using a query head, a key head and the value head produces self-attention resultant vector on which an activation function is performed. A rank value is thus obtained for the selected clip. Based on the rank value, video highlights are generated.

Classes IPC  ?

• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
• G06F 40/40 - Traitement ou traduction du langage naturel
• 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 10/776 - ValidationÉvaluation des performances
• 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

Abrégé

A system and a method for performing brand detection and brand analysis in a video are disclosed herein. The method comprises receiving the video for performing the brand detection thereon; splitting the video for obtaining input video frames; performing an open set detection on the input video frames to compute instances of detecting brand media; determining an exact square region in which the brand media is occupied within the input video frame; resolving a scene understanding task in the input video frame; detecting crucial moments in the video; identifying an area on the input frame where a user's attention is focused to provide user focus index; generating heat maps using the detection of crucial moments and user focus index; and combining above inputs from the brand detection and the scene understanding into the heat maps for all the input video frames of the video for computing a brand advertising rate.

Classes IPC  ?

• G06Q 30/0273 - Détermination des frais de publicité
• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo

Abrégé

A system and method for performing brand detection in a video is disclosed herein. The method comprises receiving the video for performing the brand detection thereon; splitting the video for obtaining a plurality of video frames; performing an open set detection on each input video frame from the plurality of video frames, which comprises proposing one or more bounding boxes on the input video frames on regions of the video frame that potentially include brand media; cropping the one or more bounding boxes; providing the cropped bounding boxes to a classification module for obtaining embedding vectors corresponding to each of the cropped bounding boxes; and comparing the embedding vectors of the cropped bounding boxes with embedding vectors of one or more brand reference images provided by a user for computing instances of brand detection in each video frame of the plurality of video frames.

Classes IPC  ?

• G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
• 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 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
• G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques

Abrégé

The present disclosure relates to an avatar generator to generate an audio-visual avatar specific to an application, such as tutoring. The avatar generator includes a general synthesizer to receive a training dataset. The general synthesizer includes a voice synthesis module and a video synthesis module trained by the training dataset. The avatar generator includes a customized synthesizer consisting of a voice custom synthesis module and a video custom synthesis module trained on the audio-video samples of the target person. The avatar generator further includes a video generator to create an audio-visual avatar and is configured to synthesize a voice clone using an input text, process the voice clone, synthesize a video clone based on the video synthesis module and the video custom synthesis module, and apply the voice clone to the video clone.

Classes IPC  ?

• G06T 13/20 - Animation tridimensionnelle [3D]
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G10L 13/033 - Édition de voix, p. ex. transformation de la voix du synthétiseur
• G10L 13/047 - Architecture des synthétiseurs de parole
• G10L 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique

Abrégé

The present disclosure relates to a system to evaluate an avatar generated by an avatar generator. The system comprises an evaluation module including an audio evaluation module for evaluating audio features and a video evaluation module for evaluating video features. Evaluation of the avatar includes extracting audio and video features from the avatar and applying a set of evaluation metrics for generating audio and video evaluation scores. The scores are combined to generate a final score. For avatar generator evaluation, audio clip and video clip are provided to the audio evaluation module and video evaluation module, respectively. A set of evaluation metrics is applied for evaluation. Each metric can generate a score. All scores are combined to generate a final evaluation score.

Classes IPC  ?

• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
• G06T 7/00 - Analyse d'image
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
• G10L 25/57 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour le traitement des signaux vidéo
• G10L 25/60 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour mesurer la qualité des signaux de voix
• G10L 25/84 - Détection de la présence ou de l’absence de signaux de voix pour différencier la parole du bruit
• G10L 25/90 - Détermination de la hauteur tonale des signaux de parole

Abrégé

System and method for administration of remote user activities interacting with critical data are used to detect violations of user activities, generate hints in response to each violation or a group of violations, that includes video-based violations, audio-based violation, and display-control-based violations, and to display hints to the administrator in a prioritized manner helping the administrator to react to most critical violations in a proper way. The invention solves a problem of simultaneous monitoring of multiple user interactions with critical data by ranking each violation, each session of interaction and each user.

Classes IPC  ?

• G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
• G06F 21/32 - Authentification de l’utilisateur par données biométriques, p. ex. empreintes digitales, balayages de l’iris ou empreintes vocales
• H04L 67/50 - Services réseau

Abrégé

A system and method for regulating a motorsport racing event are disclosed. The system comprises sensor modules for sensing a plurality of race parameters to generate a plurality of input signals. A data analysis module receives the input signals for analysis. A decision-making module receives the analyzed signal and computes a recommendation or a decision corresponding to the level of violation. A penalty and recommendation module receives information associated with the recommendation or the decision and presents the recommendation or the decision.

Classes IPC  ?

• A63K 3/00 - Équipement ou accessoires pour les courses ou les sports équestres

Abrégé

A system and method for optimizing the performance of an autonomous race car in real-time during a race event are disclosed. An autonomous race car controller unit is pre-fed with a first set of initial parameter values and a second set of initial parameter values. A set of sensors is configured for measuring a first and a second set of real-time parameter values after the starting of the race event. A performance optimization module is configured to generate a corrective course by receiving the first and second sets of real-time parameters and detecting the presence of errors between a control command given by the controller unit and its execution.

Classes IPC  ?

• B60W 30/02 - Commande de la stabilité dynamique du véhicule
• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes

Abrégé

The invention relates to the field of computer engineering. The technical result consists in reducing the number of errors in the detection of breaches of remote examination regulations in automated proctoring systems. The technical result is achieved in that, if more than one breach is detected during an examination, a sum total of the weights of the detected breaches is determined and compared with at least one preset threshold value; a tip regarding the conduct of a student is returned, said tip indicating the extent to which said sum total of weights has reached the threshold value, wherein the weight of at least one breach is determined as the sum total of weights for said breach, detected in one or more modes from the following group: automatically detected, automatically detected and manually confirmed, and manually detected; wherein quantitatively differing weights are set for the same breach depending on which of the above-mentioned modes the breach was detected in.

Classes IPC  ?

• G06Q 50/20 - Éducation

Abrégé

A multi-layer path-planning system and method calculates trajectories for autonomous vehicles using a global planner, a fast local planner, and an optimizing local planner. The calculated trajectories are used to guide the autonomous vehicle along a bounded path between a starting point and a destination.

Classes IPC  ?

• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• B60W 30/18 - Propulsion du véhicule
• B60W 40/064 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés à l'état de la route degré d'adhérence
• H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p. ex. véhicule à nuage ou véhicule à domicile

Abrégé

A method for the computer-assisted learning of orthography, the method includes executing by a data processing system the steps of retrieving a main set of words from a data storage; retrieving an error data set associated with said main set of words from the data storage and repeatedly executing the steps of selecting a word to prompt the user with, by computing, for each word from the error data set, a statistic measure related to the probability of an error occurring in the word, and selecting the word which has the maximum value of the statistic measure; prompting the user with the word; accepting a user input specifying a sequence of symbols; comparing the user input with the word and updating and storing the error data set.

Classes IPC  ?

• G09B 1/00 - Matériel à but éducatif à commande manuelle ou mécanique utilisant des éléments formant ou comportant des symboles, des signes, des images ou similaires, qui sont agencés ou adaptés pour être disposés selon un ou plusieurs schémas particuliers

Abrégé

updating (17, 18) and storing the error data set.

Classes IPC  ?

• G09B 1/00 - Matériel à but éducatif à commande manuelle ou mécanique utilisant des éléments formant ou comportant des symboles, des signes, des images ou similaires, qui sont agencés ou adaptés pour être disposés selon un ou plusieurs schémas particuliers

Abrégé

A method encodes a word or words. A sequential input string of symbols representing a word or a plurality of words is parsed into segments. A graph is constructed having spatial levels, each level including nodes. The segments of the input string are mapped to the nodes according to the levels and attributes are assigned to the nodes according to the segments, where an entropy of the word or plurality of words is a constant times an entropy of the graph and of the nodal attributes.

Classes IPC  ?

• G09B 5/00 - Matériel à but éducatif à commande électrique