Disclosed embodiments provide a framework for implementing automated bots configured to automatically and in real-time process messages exchanged with a user to determine whether to present an opt-in offer for supplemental communications. An agent bot processes ongoing messages exchanged in real-time during a first communications session as these messages are exchanged to determine whether to present an opt-in authorization request for supplemental communications. If the user approves the request, contact information associated with the user is used to facilitate a second communications session through which the user is prompted to provide an opt-in confirmation. The opt-in confirmation and the approval of the opt-in authorization request is provided to allow for transmission of the supplemental communications to the user.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 40/35 - Représentation du discours ou du dialogue
Systems and methods may provide techniques for detecting hallucination in machine-generated responses. A computer-implemented method can include accessing text data. The text data can include one or more machine-generated responses that are supplemented by outputs generated by a retrieval-augmentation generation (RAG) system. In some instances, the outputs are associated with the prompt associated with a user. The computer-implemented method can also include applying one or more hallucination-detection models to the text data to generate a set of classification labels. A classification label can indicate whether a corresponding machine-generated response of the one or more machine-generated responses contradicts at least part of the knowledge base accessed by the RAG system. The computer-implemented method can also include generating annotated text data that includes the one or more machine-generated responses annotated with corresponding classification labels of the set of classification labels. The computer-implemented method can also include outputting the annotated text data.
A device may receive, at a server system, a free-form query. The device may select two-way communication history records from a database associated with the free-form query to identify selected history records. The device may generate individual summaries of individual history records of the selected history records by processing a set of corresponding individual history records using a chunking algorithm, constructing language responses from outputs of the chunking algorithm using a large language model, and aggregating the language responses. The device may process the language responses using the large language model to generate an individual summary for corresponding individual history records.
A device may receive, at a server system, a free-form query. The device may select two-way communication history records from a database associated with the free-form query to identify selected history records. The device may generate individual summaries of individual history records of the selected history records by processing a set of corresponding individual history records using a chunking algorithm, constructing language responses from outputs of the chunking algorithm using a large language model, and aggregating the language responses. The device may process the language responses using the large language model to generate an individual summary for corresponding individual history records.
Disclosed embodiments provide a framework for dynamically processing messages exchanged through communications sessions in real-time using machine learning algorithms and artificial intelligence to identify user intents and to seamlessly integrate automated bots associated with these identified intents into these communications sessions. The messages are processed in real-time to detect a present intent and determine whether an automated bot engaged in the communications session is associated with the intent. If the automated bot is not associated with the intent, the system can automatically identify another automated bot within the bot group that is associated with the intent. When the communications session is transferred to the identified automated bot, any contextual information garnered through the communications session is automatically provided to the automated bot to prevent repetitious queries during the communications session.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 40/35 - Représentation du discours ou du dialogue
6.
Systems, media, and methods for automated response to queries made by interactive electronic chat
Systems, media, and methods for automated response to social queries comprising: monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; applying a first machine learning algorithm to the monitored communications to identify a query susceptible to response automation; applying a second machine learning algorithm to the query susceptible to response automation to identify one or more responses likely to resolve the query; and either i) notifying a human to respond to the query susceptible to response automation with the one or more responses likely to resolve the query, or ii) instantiating an autonomous software agent configured to respond to the query susceptible to response automation with the one or more responses likely to resolve the query.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
Systems and method are provided for identifying a source of communications for improved communications. A computing device may receive a communication from a first device over a first time interval and extract a set of features from the communication. The features may correspond to characteristics indicative of how the communication was generated over the first time interval. The computing device may execute a machine-learning model using the set of features to generate a predicted identity associated with a source of the communication. A response can be generated based on the predicted identify and transmitted to the first device.
Systems and method are provided for identifying a source of communications for improved communications. A computing device may receive a communication from a first device over a first time interval and extract a set of features from the communication. The features may correspond to characteristics indicative of how the communication was generated over the first time interval. The computing device may execute a machine-learning model using the set of features to generate a predicted identity associated with a source of the communication. A response can be generated based on the predicted identify and transmitted to the first device.
Methods and systems for transcript-based bot creation are provided. Information may be stored in memory regarding different automation templates associated with different statement types. A transcript may be imported that includes statements, which may be analyzed and classified as one or more of the different statement types. The imported transcript may be displayed in a graphic user interface with its statements displayed in accordance with the automation templates associated with the respective statement type. User input may be received, including modification input that modifies at least one automation template associated with at least one statement of the displayed transcript designated as an integration point. A custom bot may thereafter be generated based on the modification input and configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the modified automation template.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
10.
Dynamic response prediction for improved bot task processing
Systems and methods can be provided for predicting responses during communication sessions with network devices. In some implementations, systems and methods can facilitate predicting responses using machine learning techniques. Messages received through a platform can be stored in a repository. A machine learning model may be trained using the stored messages. When a terminal device is communicating with a network device in a communication session, the messages exchanged in the communication session and the machine learning model can be used to predict future responses in real-time. The predicted future responses can be presented at the terminal device. A predicted response can be selected at the terminal device. Upon selection, the selected predicted response is transmitted to the network device during the communication session.
G06Q 10/107 - Gestion informatisée du courrier électronique
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
H04L 67/63 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en acheminant une demande de service en fonction du contenu ou du contexte de la demande
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
11.
Function-as-a-service for two-way communication systems
The present disclosure relates generally to systems and methods for facilitating two-way communication sessions using serverless cloud-based functions configured in a function-as-a-service (FaaS) system. One example includes accessing a template configured to execute a response based on an event, facilitating a two-way communication session with a user device, and processing data of the two-way communication session to identify an event trigger corresponding to the template. Execution of a serverless cloud-based function associated with the event trigger is requested, and one or more outputs of the serverless cloud-based function associated with the event trigger are integrated into the two-way communication session.
The present disclosure relates generally to systems and methods for analyzing intent. Intents may be analyzed to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication.
Disclosed embodiments provide a framework for automatically establishing recording parameters according to specified recording restrictions and generating analytics corresponding to communications recorded subject to the recording restrictions. During a communications session between a user and an agent, a system can identify any recording restrictions corresponding to user communications exchanged during the communications session. The system automatically processes, in real-time, communications exchanged during the communications session as these communications are exchanged to identify the user communications and agent communications. The system generates a transcript that includes the agent communications but selectively records and transcribes the user communications according to the recording restrictions. A machine learning algorithm is trained to generate a set of inferences corresponding to a user sentiment based on historic recordings and transcripts of historic communications sessions between users and agents, as well as corresponding feedback. From the set of inferences, the system generates agent analytics.
H04M 3/00 - Centraux automatiques ou semi-automatiques
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
14.
Methods and systems for dynamic agent resource allocation
A load-balancing system can provide dynamic allocation of distributed resources to one or more communication interfaces. The load-balancing system may receive an indication that a value of a resource is less than a threshold. The value may represent a quantity of resources associated with a communication network. The load-balancing system may transmit a request for resources to a set of computing device. The load-balancing system may receive an identification of a subset of the set of computing devices that approve the request for the resources and facilitate allocation of the resources associated with the subset of the set of computing devices to the communication network by causing a connection to be established between the subset of the set of computing devices and the communication network.
A load-balancing system can provide dynamic allocation of distributed resources to one or more communication interfaces. The load-balancing system may receive an indication that a value of a resource is less than a threshold. The value may represent a quantity of resources associated with a communication network. The load-balancing system may transmit a request for resources to a set of computing device. The load-balancing system may receive an identification of a subset of the set of computing devices that approve the request for the resources and facilitate allocation of the resources associated with the subset of the set of computing devices to the communication network by causing a connection to be established between the subset of the set of computing devices and the communication network.
A redirection and messaging system receives telephony information identifying a caller and call context from a telephony system. The system selects one of a plurality of messaging operators based on the call context, optionally sends an introductory message to the caller via a messaging service, and generates a message interface for the selected message operator. The message interface includes the caller and call context and any messages sent between the caller and the selected message operator, with an input interface allowing the selected message operator to input and send messages to the caller.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
17.
SYSTEMS AND METHODS FOR INTENT DISCOVERY AND PROCESS EXECUTION
Disclosed embodiments provide a framework for intent discovery based on user input and execution of processes based on the discovered intents. An intent processing system provides, via an interface, a graphical representation of different intent clusters corresponding to different intents. An intent cluster includes a set of intent terms and/or phrases that can be used to submit a request or issue that is associated with an intent. As a user selects intent terms and/or phrases from an intent cluster via the interface, the intent processing system can identify actions that can be performed to address the user's request or issue.
G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
18.
SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE ASSISTANCE IN VIDEO COMMUNICATIONS
A communication assist service may extract one or more video frames during a communication session between a user device and a terminal device. The video frames may include a representation of an object associated with an issue for which the communication session was established. The communication assist service may generate a feature vector from the video frames and execute a trained neural network configured to generate predictions associated with a resolution to the issue. The neural network may output predicted actions that if executed may resolve the issue or provide additional information that will improve a likelihood of resolving the issue. The communication assist service may then transmit the predicted actions to the terminal device in real time.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/82 - 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
19.
SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE ASSISTANCE IN VIDEO COMMUNICATIONS WITH COMMUNICATION-IMPAIRED USERS
A communication assist service may extract one or more video frames during a communication session between a user device and a terminal device. The video frames may include a representation of a gesture provided by a user of the user device. The communication assist service may generate a feature vector from the video frames and execute a trained neural network configured to generate classify the semantic meaning of gesture. The neural network may output the semantic meaning of the gesture, which may be used to generate an appropriate communication response to the gesture. The communication assist service may facilitate transmission of the communication response to a device of the new communication session in real time.
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
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 10/774 - 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 10/82 - 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
G09B 21/00 - Moyens d'enseignement ou de communication destinés aux aveugles, sourds ou muets
20.
Alternate communication options during communication delay
Systems and techniques are described herein for providing alternate options to a voice caller to record a message rather than be placed on or remain on hold. For example, a process may include: receiving a voice call; determining that no appropriate agent device of a plurality of agent devices are currently available to service the voice call; determining that a trigger condition is met, wherein the trigger condition determines whether an option to record a message is offered; providing a record message option, wherein the recorded message option is provided when the trigger condition is met; receiving a recorded message in response to providing the record message option; analyzing the recorded message to determine an intent; and providing the recorded message to an agent based on the intent, wherein the agent performs a response action after assessing the recorded message.
H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
H04M 3/428 - Dispositions pour placer des appels entrants en attente
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
Systems and techniques are described herein for providing alternate options to a voice caller to record a message rather than be placed on or remain on hold. For example, a process may include: receiving a voice call; determining that no appropriate agent device of a plurality of agent devices are currently available to service the voice call; determining that a trigger condition is met, wherein the trigger condition determines whether an option to record a message is offered; providing a record message option, wherein the recorded message option is provided when the trigger condition is met; receiving a recorded message in response to providing the record message option; analyzing the recorded message to determine an intent; and providing the recorded message to an agent based on the intent, wherein the agent performs a response action after assessing the recorded message.
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.
Systems and method are provided for artificial-intelligence-based formatting of data into interface-specific representations. A computing device may receive datasets including information structured for presentation through various interfaces. The computing device may train a machine-learning model using feature vectors defined from the datasets. The machine-learning model may be trained to generate interface-specific representations of data. The computing device may then receive a query through a first type of interface and execute the trained machine-learning model using the query and an identification of the first type of interface. The machine-learning model may generate a response to the query that includes a structure tailored for interfaces that correspond to the first type of interface. The computing device may then facilitate a transmission of the response to the query through an interface that corresponds to the first type of interface.
Systems and method are provided for artificial-intelligence-based formatting of data into interface-specific representations. A computing device may receive datasets including information structured for presentation through various interfaces. The computing device may train a machine-learning model using feature vectors defined from the datasets. The machine-learning model may be trained to generate interface-specific representations of data. The computing device may then receive a query through a first type of interface and execute the trained machine-learning model using the query and an identification of the first type of interface. The machine-learning model may generate a response to the query that includes a structure tailored for interfaces that correspond to the first type of interface. The computing device may then facilitate a transmission of the response to the query through an interface that corresponds to the first type of interface.
A computing device of a communication network may generate a first connection context that can manage a first voice connection between a first device and a second device. A signal may be received over the first connection context indicative of a request by the first device. In response, the computing device may connect the first device to a second connection context that can manage a second voice connection between the first device and an automated service such as a communication bot. The first device can transmit natural language requests to the automated service. The automated service facilitates execution of the natural language requests by the computing device.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04M 7/12 - Dispositions d'interconnexion entre centres de commutation pour l'exploitation entre centraux comportant différents types d'équipement de commutation, p. ex. à entraînement mécanique et pas à pas ou décimal et non décimal
H04W 76/14 - Établissement de la connexion en mode direct
26.
AUTOMATED SYSTEMS FOR COMMUNICATIONS ANALYSIS ACCORDING TO RECORDING RESTRICTIONS
Disclosed embodiments provide a framework for automatically establishing recording parameters according to specified recording restrictions and generating analytics corresponding to communications recorded subject to the recording restrictions. During a communications session between a user and an agent, a system can identify any recording restrictions corresponding to user communications exchanged during the communications session. The system automatically processes, in real-time, communications exchanged during the communications session as these communications are exchanged to identify the user communications and agent communications. The system generates a transcript that includes the agent communications but selectively records and transcribes the user communications according to the recording restrictions. A machine learning algorithm is trained to generate a set of inferences corresponding to a user sentiment based on historic recordings and transcripts of historic communications sessions between users and agents, as well as corresponding feedback. From the set of inferences, the system generates agent analytics.
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/20 - Administration de la réparation ou de la maintenance des produits
G06Q 30/01 - Services de relation avec la clientèle
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
27.
METHODS AND SYSTEMS FOR COMBINED VOICE AND SIGNALING
A computing device of a communication network may generate a first connection context that can manage a first voice connection between a first device and a second device. A signal may be received over the first connection context indicative of a request by the first device. In response, the computing device may connect the first device to a second connection context that can manage a second voice connection between the first device and an automated service such as a communication bot. The first device can transmit natural language requests to the automated service. The automated service facilitates execution of the natural language requests by the computing device.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/58 - Dispositions pour transférer les appels reçus d'un abonné à un autreDispositions pour permettre des conversations intérimaires entre le demandeur ou le demandé et une tierce personne
28.
Automated systems for communications analysis according to recording restrictions
Disclosed embodiments provide a framework for automatically establishing recording parameters according to specified recording restrictions and generating analytics corresponding to communications recorded subject to the recording restrictions. During a communications session between a user and an agent, a system can identify any recording restrictions corresponding to user communications exchanged during the communications session. The system automatically processes, in real-time, communications exchanged during the communications session as these communications are exchanged to identify the user communications and agent communications. The system generates a transcript that includes the agent communications but selectively records and transcribes the user communications according to the recording restrictions. A machine learning algorithm is trained to generate a set of inferences corresponding to a user sentiment based on historic recordings and transcripts of historic communications sessions between users and agents, as well as corresponding feedback. From the set of inferences, the system generates agent analytics.
H04M 3/00 - Centraux automatiques ou semi-automatiques
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
29.
METHODS AND SYSTEMS FOR IMPLEMENTING DYNAMIC-ACTION SYSTEMS IN REAL-TIME DATA STREAMS
Systems and method are provided for implementing dynamic-action systems in real-time data streams. A computing device may generate a feature vector representing a portion of a real-time data stream. The computing device may execute a set of machine-learning models using the feature vector to generate a set of characteristics associated with the data stream. The computing device may determine that a condition of a trigger of a set of triggers is satisfied based on the set of characteristics. In response, execute a function associated with the trigger that is configured modify data that is to be transmitted over the data stream. The computing device may use another machine-learning model to monitor activation triggers and dynamically modify triggers to adjust the set of characteristics.
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p. ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
30.
METHODS AND SYSTEMS FOR IMPLEMENTING DYNAMIC-ACTION SYSTEMS IN REAL-TIME DATA STREAMS
Systems and method are provided for implementing dynamic-action systems in real-time data streams. A computing device may generate a feature vector representing a portion of a real-time data stream. The computing device may execute a set of machine-learning models using the feature vector to generate a set of characteristics associated with the data stream. The computing device may determine that a condition of a trigger of a set of triggers is satisfied based on the set of characteristics. In response, execute a function associated with the trigger that is configured modify data that is to be transmitted over the data stream. The computing device may use another machine-learning model to monitor activation triggers and dynamically modify triggers to adjust the set of characteristics.
Disclosed embodiments provide a framework for implementing automated bots configured to automatically and in real-time process messages exchanged with a user to determine whether to present an opt-in offer for supplemental communications. An agent bot processes ongoing messages exchanged in real-time during a first communications session as these messages are exchanged to determine whether to present an opt-in authorization request for supplemental communications. If the user approves the request, contact information associated with the user is used to facilitate a second communications session through which the user is prompted to provide an opt-in confirmation. The opt-in confirmation and the approval of the opt-in authorization request is provided to allow for transmission of the supplemental communications to the user.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]
Disclosed embodiments provide a framework for implementing automated bots configured to automatically and in real-time process messages exchanged with a user to determine whether to present an opt-in offer for supplemental communications. An agent bot processes ongoing messages exchanged in real-time during a first communications session as these messages are exchanged to determine whether to present an opt-in authorization request for supplemental communications. If the user approves the request, contact information associated with the user is used to facilitate a second communications session through which the user is prompted to provide an opt-in confirmation. The opt-in confirmation and the approval of the opt-in authorization request is provided to allow for transmission of the supplemental communications to the user.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 40/35 - Représentation du discours ou du dialogue
Disclosed embodiments provide a framework to enable rich messaging via offline channels to obtain payments for completion of business transactions. In response to a request to obtain information associated with a customer to support a transaction performed via a communications session, a messaging platform transmits a request to a computing device associated with the customer to obtain authorization for the information. In response to obtaining the authorization for the information from the computing device associated with the customer, the messaging platform uses the authorization at a processing service to obtain the information subject to the authorization. The information is provided to fulfill the initial request.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Systems, media, and methods for automated response to social queries comprising: monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; applying a first machine learning algorithm to the monitored communications to identify a query susceptible to response automation; applying a second machine learning algorithm to the query susceptible to response automation to identify one or more responses likely to resolve the query; and either i) notifying a human to respond to the query susceptible to response automation with the one or more responses likely to resolve the query, or ii) instantiating an autonomous software agent configured to respond to the query susceptible to response automation with the one or more responses likely to resolve the query.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
Described are computer-based methods and apparatuses, including computer program products, comprising the steps of, or structure for, storing a plurality of expert profiles in a database, each of the plurality of expert profiles comprising information associated with a person having knowledge in a particular category, subject or topic; receiving search criteria over a network from a query source; selecting at least one of the plurality of expert profiles comprising information that satisfy the search criteria; and transmitting expert profile data for each of the selected expert profiles to the remote search engine, the expert profile data comprising data that defines a displayable representation of a corresponding expert profile, the expert profile data further comprising data that facilitates a client-initiated, real-time communication session over the network with a person associated with the corresponding expert profile.
The present disclosure relates generally to facilitating routing of communications. More specifically, techniques are provided to dynamically transfer messaging between a network device and a terminal device to a type of bot based on intents identified from the messaging. Further, techniques are provided to track performance of the selected type of bot during automation.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable mobile and web applications featuring software for enabling businesses to provide and obtain customer support, to assist in providing customer engagement tools, marketing, customer care, online commerce, and messaging to customers, and for facilitating interaction, connections and digital messaging and chat conversations; Downloadable mobile and web applications featuring software for facilitating interaction, connections, and conversations between users in the digital messaging, digital chat, and customer engagement fields; Downloadable mobile and web applications featuring customer engagement software for businesses; Downloadable mobile and web applications featuring software for use in facilitating interaction, connections, and conversations between users in the fields of artificial intelligence, customer care, digital messaging, customer engagement, and contact center solutions
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing business information and analysis of business data to improve customer engagement; Providing business information and analysis of business data through artificial intelligence to improve customer support Software as a service (SAAS) services featuring software for enabling businesses to provide and obtain customer support, to assist in providing customer engagement tools, marketing, customer care, online commerce, and messaging to customers, and for facilitating interaction, connections and digital messaging and chat conversations; Software as a service (SAAS) services featuring software for facilitating interaction, connections, and conversations between users in the digital messaging, digital chat, and customer engagement fields; Software as a service (SAAS) services featuring customer engagement software for businesses; Software as a service (SAAS) services featuring software for use in facilitating interaction, connections, and conversations between users in the fields of artificial intelligence, customer care, digital messaging, customer engagement, and contact center solutions
39.
SYSTEMS AND METHODS FOR AUGMENTED COMMUNICATIONS USING MACHINE-READABLE LABELS
Disclosed embodiments provide a framework for generating machine-readable labels for augmenting existing communications sessions between users and automated bot agents. During a communications session facilitated between a user and an automated bot through a point-of-sale terminal, the automated bot may automatically, and in real-time, process any communications as these communications are exchanged to determine whether to facilitate an alternative communications channel. If so, the automated bot generates a machine-readable label that can be presented through the point-of-sale terminal. When the machine-readable label is scanned using a computing device, the automated bot facilitates an alternative communications session between the user and the automated bot through the computing device while the original communications session is ongoing.
Disclosed embodiments provide a framework for generating machine-readable labels for augmenting existing communications sessions between users and automated bot agents. During a communications session facilitated between a user and an automated bot through a point-of-sale terminal, the automated bot may automatically, and in real-time, process any communications as these communications are exchanged to determine whether to facilitate an alternative communications channel. If so, the automated bot generates a machine-readable label that can be presented through the point-of-sale terminal. When the machine-readable label is scanned using a computing device, the automated bot facilitates an alternative communications session between the user and the automated bot through the computing device while the original communications session is ongoing.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
A redirection and messaging system receives telephony information identifying a caller and call context from a telephony system. The system selects one of a plurality of messaging operators based on the call context, optionally sends an introductory message to the caller via a messaging service, and generates a message interface for the selected message operator. The message interface includes the caller and call context and any messages sent between the caller and the selected message operator, with an input interface allowing the selected message operator to input and send messages to the caller.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
42.
Targeted generative AI from merged communication transcripts
The present disclosure relates generally to systems, methods, instructions, and other aspects describing automated transcription and associated script generation. In one aspect, a method includes facilitating a voice bot segment of a two-way communication session, where the voice bot segment is between a customer device and a non-human bot agent, and transfer of the session to a human agent device as part of a human voice segment of the two-way communication session, wherein the transfer occurs following a failure of the non-human bot agent to resolve a customer issue. Accessing survey data describing the two-way communication session, wherein the survey data is associated with successful resolution of the customer issue and automatically processing transcript data from the two-way communication with the survey data to identify language data from the transcript associated with resolution of the customer issue. The non-human bot agent is then dynamically updated using the language data.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/58 - Dispositions pour transférer les appels reçus d'un abonné à un autreDispositions pour permettre des conversations intérimaires entre le demandeur ou le demandé et une tierce personne
43.
TARGETED GENERATIVE AI FROM MERGED COMMUNICATION TRANSCRIPTS
The present disclosure relates generally to systems, methods, instructions, and other aspects describing automated transcription and associated script generation. In one aspect, a method includes facilitating a voice bot segment of a two-way communication session, where the voice bot segment is between a customer device and a non-human bot agent, and transfer of the session to a human agent device as part of a human voice segment of the two-way communication session, wherein the transfer occurs following a failure of the non-human bot agent to resolve a customer issue. Accessing survey data describing the two-way communication session, wherein the survey data is associated with successful resolution of the customer issue and automatically processing transcript data from the two-way communication with the survey data to identify language data from the transcript associated with resolution of the customer issue. The non-human bot agent is then dynamically updated using the language data.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 12/18 - Dispositions pour la fourniture de services particuliers aux abonnés pour la diffusion ou les conférences
H04L 51/214 - Surveillance ou traitement des messages en utilisant le transfert sélectif
44.
Systems and methods for external system integration
The present disclosure relates generally to facilitating routing of communications across external systems. More specifically, techniques are provided to dynamically route issue tracking tickets to disparate endpoints based on the content of the ticket.
H04L 41/5074 - Traitement des plaintes des utilisateurs ou des tickets d’incident
H04L 41/0631 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse des causes profondesGestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse de la corrélation entre les notifications, les alarmes ou les événements en fonction de critères de décision, p. ex. la hiérarchie ou l’analyse temporelle ou arborescente
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
An intent confusion evaluation engine receives conversation data corresponding to conversations between customers and agents. The engine evaluates annotations in the conversation data corresponding to intents identified from messages exchanged between customers and agents to determine levels of confusion amongst different intents. Based on these levels of confusion, the engine creates a graphical representation that illustrates the various intents and the level of confusion between different pairings of intents for the set of conversations. If an update is provided to the annotations, the graphical representation is updated dynamically and in real-time to provide updated levels of confusion amongst the various intents in accordance with the update.
G06F 40/35 - Représentation du discours ou du dialogue
G06F 40/169 - Annotation, p. ex. données de commentaires ou notes de bas de page
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06T 11/20 - Traçage à partir d'éléments de base, p. ex. de lignes ou de cercles
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]
The present disclosure relates generally to facilitating two-way communications. One example involves receiving a hold indication associated with a hold status for the two-way voice communication session, and executing a hold bot for the communication session. Hold bot functionality is communicated as part of the two-way voice communication session, and when the hold bot functionality message is received by the user computer device, the user computing device generates a voice message. The system receives and processes processing the voice message using a voice-to-text system of the hold bot to generate a voice-to-text message, and transmits the voice-to-text message during the hold status so that the agent device receives the voice-to-text message during the hold status.
The present disclosure relates generally to facilitating two-way communications. One example involves receiving a hold indication associated with a hold status for the two-way voice communication session, and executing a hold bot for the communication session. Hold bot functionality is communicated as part of the two-way voice communication session, and when the hold bot functionality message is received by the user computer device, the user computing device generates a voice message. The system receives and processes processing the voice message using a voice-to-text system of the hold bot to generate a voice-to-text message, and transmits the voice-to-text message during the hold status so that the agent device receives the voice-to-text message during the hold status.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/066 - Adaptation de format, p. ex. conversion de format ou compression
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]
H04L 51/214 - Surveillance ou traitement des messages en utilisant le transfert sélectif
48.
Bot response generation with dynamically-changing website or native application
Systems and methods provide a conversational website or native application. The conversational website or native application includes an interface that enables a network device to exchange one or more messages with a bot or a terminal device (operated by a live agent) during a communication session. The interface may include a communication area (e.g., a portion of the screen) and a dynamic content area (e.g., another portion of the screen). The content of a message received from the network device may be used to select dynamic content to display in the dynamic content area. Dynamic updating of content displayed in the dynamic content area occurs continuously as messages are exchanged during the communication session.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 3/0483 - Interaction avec des environnements structurés en pages, p. ex. métaphore livresque
Disclosed embodiments provide a framework to enable automatic identification and authentication of users to allow for multichannel communications in an authenticated state. In response to an authentication request from an end agent engaged in a communications session with a user, a current authentication state associated with the user is determined. Based on the current authentication state and a set of authentication rules associated with the end agent, a set of authentication challenges are identified and executed by an application implemented on the user's computing device. Data corresponding to completion of these authentication challenges is used to determine a new authentication state, which can be used to update the communications session.
Systems and method are provided for generating personal virtual agents. A computing device may receive communication request from a first device associated with a particular domain. In response, the computing device may facilitate a connection between the first device and a personal virtual agent that was generated for a user corresponding of, wherein the first device. The computing device may execute the personal virtual agent using a subsequent communication from the first device to generate a response. The computing device may generate an accuracy metric corresponding to the response generated by the personal virtual agent. The personal virtual agent may be trained using the accuracy metric to improve subsequent response generated by the personal virtual agent with respect to the user of the first device.
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
Systems and method are provided for generating personal virtual agents. A computing device may receive communication request from a first device associated with a particular domain. In response, the computing device may facilitate a connection between the first device and a personal virtual agent that was generated for a user corresponding of, wherein the first device. The computing device may execute the personal virtual agent using a subsequent communication from the first device to generate a response. The computing device may generate an accuracy metric corresponding to the response generated by the personal virtual agent. The personal virtual agent may be trained using the accuracy metric to improve subsequent response generated by the personal virtual agent with respect to the user of the first device.
G10L 13/033 - Édition de voix, p. ex. transformation de la voix du synthétiseur
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
52.
SYSTEMS AND METHODS FOR ACCOUNT SYNCHRONIZATION AND AUTHENTICATION IN MULTICHANNEL COMMUNICATIONS
Disclosed embodiments provide a framework to enable automatic identification and authentication of users to allow for multichannel communications in an authenticated state. In response to an authentication request from an end agent engaged in a communications session with a user, a current authentication state associated with the user is determined. Based on the current authentication state and a set of authentication rules associated with the end agent, a set of authentication challenges are identified and executed by an application implemented on the user's computing device. Data corresponding to completion of these authentication challenges is used to determine a new authentication state, which can be used to update the communications session.
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
Systems and methods for determining predictive model types are provided. A method may include generating a predictive model for a web page of a web site, wherein the web page includes a configuration defining one or more objects presented with the web page, and wherein each object is associated with a predictive model. The method may include determining one or more predictive model types that are associated with the predictive model, determining one or more performance indicators that correspond to each determined predictive model type, wherein performance indicators represent one or more benefits to a website, selecting a predictive model type of the predictive model out of the one or more predictive model types, wherein the predictive model type is selected based on a performance indicator corresponding to the selected predictive model type, and determining a configuration of the web page using the selected predictive model type of the predictive model.
A computer-implemented method of providing specialized media on a website is disclosed. The method includes producing campaign media which is delivered to a user through a third-party provider. After the user has received the campaign media, and upon indication of a request from the user to access a website controlled by a content provider, specialized media on a website controlled by the content provider may be provided. Providing the specialized media may be done based on the campaign media previously delivered to the user through the third-party provider.
Described are computer-based methods and apparatuses, including computer program products, for dynamically enabling customized web content and applications. One or more rules are stored in a database. Default tag code is transmitted to a browser in response to a request from the browser, the default tag code including data that causes the browser to generate a tag module. An update is received from the tag module comprising data indicative of a visitor's interaction with web page content displayed through the browser. A condition associated with a rule from the one or more rules is determined to be satisfied based on the update. An action associated with the rule is performed, comprising transmitting custom targeted tag code to the tag module, wherein the custom targeted tag code includes data that causes the browser, upon execution of the custom targeted tag code by the tag module, to perform an action.
A network device (e.g., a user's mobile phone) may be used to make a telephone call to a landline telephone associated with a client device (e.g., a business's device). If the telephone call is terminated, either the network device or the client device may generate a text message to the other and establish a communication session. The client device may present a variety of options to the network device of service requests that may be completed by text message. The network device may transmit the service request and the service request may be fulfilled by the client.
H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
H04W 4/14 - Services d'envoi de messages courts, p. ex. SMS ou données peu structurées de services supplémentaires [USSD]
57.
Dynamic text message processing implementing endpoint communication channel selection
The present disclosure relates generally to providing a concierge service to handle a wide variety of topics and user intents via a text messaging interface. The concierge service can be part of a connection management system that can dynamically manage and facilitate natural language conversations between a user making a request or providing an instruction and one or more endpoints for the purposes of fulfilling the request or instruction.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Electronic messaging services; Instant messaging services; Interactive online communication services, namely, providing on-line facilities for real-time interaction between businesses and consumers for on-line customer service and customer support via bots, artificial intelligence, automations and live support Downloadable computer software for facilitating interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Downloadable software in the nature of a mobile application for facilitating interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Downloadable computer software using artificial intelligence for natural language processing, generation, understanding and analysis; Downloadable software in the nature of a mobile application using artificial intelligence for natural language processing, generation, understanding and analysis; Downloadable computer software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Downloadable software in the nature of a mobile application using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Downloadable computer software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Downloadable software in the nature of a mobile application for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Downloadable computer software using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets; Downloadable software in the nature of a mobile application using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets Software as a Service (SaaS) services featuring software to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Platform as a Service (PaaS) services featuring computer software platforms to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Providing online, non- downloadable software to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Application Service Provider (ASP) featuring software to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Software as a Service (SaaS) services featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Platform as a Service (PaaS) services featuring computer software platforms using artificial intelligence for natural language processing, generation, understanding and analysis; Providing online, non-downloadable software using artificial intelligence for natural language processing, generation, understanding and analysis; Application Service Provider (ASP) featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Software as a Service (SaaS) services featuring software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Platform as a Service (PaaS) services featuring computer software platforms using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Providing online, non-downloadable software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Application Service Provider (ASP) featuring software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Software as a Service (SaaS) services featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Platform as a Service (PaaS) services featuring computer software platforms for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Providing online, non-downloadable software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Application Service Provider (ASP) featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Software as a Service (SaaS) services featuring software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets; Platform as a Service (PaaS) services featuring computer software platforms for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets; Providing online, non- downloadable software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets; Application Service Provider (ASP) featuring software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights, metrics and parameters from such datasets
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a Service (SaaS) services featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Providing online, non-downloadable software using artificial intelligence for natural language processing, generation, understanding and analysis; Application Service Provider (ASP) featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Software as a Service (SaaS) services featuring software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Providing online, non-downloadable software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Application Service Provider (ASP) featuring software using artificial intelligence for enabling businesses to provide customer support and to engage with their customers, including through messaging and chat; Software as a Service (SaaS) services featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Providing online, non-downloadable software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Application Service Provider (ASP) featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Software as a Service (SaaS) services featuring software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights from such datasets; Providing online, non-downloadable software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights from such datasets; Application Service Provider (ASP) featuring software for using artificial intelligence to enable businesses to navigate and analyze datasets and extract insights from such datasets
60.
Methods and systems for dynamic load balancing of processing resources in distributed environments
Systems and method are provided for load balancing in distributed environments. A computing device may instantiate a first quantity of partitions within a processing node. The first set of partitions can be configured to support a service accessible by multiple user devices. The computing device may determine that a load value corresponding to the first quantity of partitions is greater than a threshold and in response, cause an autoscaler to instantiate a second quantity of partitions. The quantity of petitions in the second quantity of partitions may be determined based on the first quantity of partitions. The computing device may then modify the autoscaler based on an indication that the second quantity of partitions has been instantiated. Modifying the autoscaler can include adjusting the threshold to reduce a likelihood that a subsequent load value is greater than the threshold.
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
61.
Methods and systems for AI-based load balancing of processing resources in distributed environments
Systems and method are provided for load balancing in distributed networks. A computing device uses historical resource allocation data associated with a service deployed within a distributed network to train a machine-learning model configured to generate a threshold processing load usable to determine when processing resources allocated to a service within a particular distributed network are to be increased. An autoscaler of the computing device may instantiate a first quantity of partitions within a processing node of the particular distributed network. The computing device may execute the machine-learning model using a load value associated with the first quantity of partitions to generate a threshold processing load. Upon receiving an updated load value that is greater than the threshold process load, the autoscaler may instantiate a second quantity of partitions and, in response, modify the autoscaler based on the updated load value.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
Disclosed embodiments provide a framework for processing sets of credentials in real-time using machine learning models to identify requisitions that can be recommended to users. In response to receiving a set of credentials from a user, a system assigns a classification to the set of credentials based on a set of characteristics associated with the set of credentials. A machine learning model is used to assign tags to the text of the set of credentials. These tags correspond to the set of characteristics. Using these tags, a set of open requisitions are identified and provided to the user.
Systems and method are provided for load balancing in distributed environments. A computing device may instantiate a first quantity of partitions within a processing node. The first set of partitions can be configured to support a service accessible by multiple user devices. The computing device may determine that a load value corresponding to the first quantity of partitions is greater than a threshold and in response, cause an autoscaler to instantiate a second quantity of partitions. The quantity of petitions in the second quantity of partitions may be determined based on the first quantity of partitions. The computing device may then modify the autoscaler based on an indication that the second quantity of partitions has been instantiated. Modifying the autoscaler can include adjusting the threshold to reduce a likelihood that a subsequent load value is greater than the threshold.
Systems and method are provided for load balancing in distributed networks. A computing device uses historical resource allocation data associated with a service deployed within a distributed network to train a machine-learning model configured to generate a threshold processing load usable to determine when processing resources allocated to a service within a particular distributed network are to be increased. An autoscaler of the computing device may instantiate a first quantity of partitions within a processing node of the particular distributed network. The computing device may execute the machine-learning model using a load value associated with the first quantity of partitions to generate a threshold processing load. Upon receiving an updated load value that is greater than the threshold process load, the autoscaler may instantiate a second quantity of partitions and, in response, modify the autoscaler based on the updated load value.
Disclosed embodiments provide a framework for processing sets of credentials in real-time using machine learning models to identify requisitions that can be recommended to users. In response to receiving a set of credentials from a user, a system assigns a classification to the set of credentials based on a set of characteristics associated with the set of credentials. A machine learning model is used to assign tags to the text of the set of credentials. These tags correspond to the set of characteristics. Using these tags, a set of open requisitions are identified and provided to the user.
A system for automated account interaction has access to information about asset accounts, and receives transaction indications indicating transactions made by those accounts. The system automatically selects a first transaction indication for a first transaction made using a first asset account, for instance based on location, time, asset quantity, randomization, and/or a trained machine learning model. The system automatically selects a second asset account associated with a second user, for instance based on the second user's location, account balance, randomization, and/or a trained machine learning model. The system automatically transfers an asset quantity from the second asset account to the first asset account, and communicates an indicator indicating transfer completion. The asset quantity can be the quantity corresponding to the first transaction, or a portion thereof. The confirmation indicator can be sent to device(s) associated with either or both account(s) and/or can be published to a feed, graph, and/or index.
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p. ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 30/01 - Services de relation avec la clientèle
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Electronic messaging services; Instant messaging services; Interactive online communication services, namely, providing on-line facilities for real-time interaction between businesses and consumers for on-line customer service and customer support via bots, artificial intelligence, automations and live support Software as a service (SAAS) services featuring software for use in facilitating interactions in the digital engagement, conversational commerce, bots and automation, artificial intelligence, and customer engagement fields; Software as a service (SAAS) services featuring software to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Software as a service (SAAS) services featuring software to build bots for interaction and conversations with customers; Software as a Service (SaaS) services featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Software as a service (SAAS) services featuring software using artificial intelligence for enabling businesses to obtain customer support and to assist in providing customer engagement tools and messaging to their customers; Software as a service (SAAS) services featuring software using artificial intelligence for providing automated questions and answer tool within bots to help agents communicate and interact with consumers; Software as a service (SAAS) services featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software for use in facilitating interactions in the digital engagement, conversational commerce, bots and automation, artificial intelligence, and customer engagement fields; Downloadable computer software for facilitating interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Downloadable computer software for building bots for interaction and conversations with customers; Downloadable computer software that uses artificial intelligence for natural language processing, generation, understanding and analysis; Downloadable computer software that uses artificial intelligence for enabling businesses to obtain customer support and to assist in providing customer engagement tools and messaging to their customers; Downloadable computer software that uses artificial intelligence for providing automated questions and answer tool within bots to help agents communicate and interact with consumers; Downloadable computer software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields; Downloadable software in the nature of a mobile application for use in facilitating interactions in the digital engagement, conversational commerce, bots and automation, artificial intelligence, and customer engagement fields; Downloadable software in the nature of a mobile application for facilitating interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Downloadable software in the nature of a mobile application for building bots for interaction and conversations with customers; Downloadable software in the nature of a mobile application that uses artificial intelligence for natural language processing, generation, understanding and analysis; Downloadable software in the nature of a mobile application that uses artificial intelligence for enabling businesses to obtain customer support and to assist in providing customer engagement tools and messaging to their customers; Downloadable software in the nature of a mobile application that uses artificial intelligence for providing automated questions and answer tool within bots to help agents communicate and interact with consumers; Downloadable software in the nature of a mobile application for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields Electronic messaging services; Instant messaging services; Interactive online communication services, namely, providing on-line facilities for real-time interaction between businesses and consumers for on-line customer service and customer support via bots, artificial intelligence, automations and live support Software as a service (SAAS) services featuring software for use in facilitating interactions in the digital engagement, conversational commerce, bots and automation, artificial intelligence, and customer engagement fields; Software as a service (SAAS) services featuring software to facilitate interactions and conversations with customers in the digital engagement, conversational commerce, bots and automation, artificial intelligence and customer engagement fields; Software as a service (SAAS) services featuring software to build bots for interaction and conversations with customers; Software as a Service (SaaS) services featuring software using artificial intelligence for natural language processing, generation, understanding and analysis; Software as a service (SAAS) services featuring software using artificial intelligence for enabling businesses to obtain customer support and to assist in providing customer engagement tools and messaging to their customers; Software as a service (SAAS) services featuring software using artificial intelligence for providing automated questions and answer tool within bots to help agents communicate and interact with consumers; Software as a service (SAAS) services featuring software for identifying, flagging, correcting and reconciling errors, inconsistencies, or failures in interactions, connections and conversations via automated chat, between brands and users in the digital engagement, conversational commerce, messaging, artificial intelligence, bots and automation, and customer engagement fields
69.
Domain adaptation of AI NLP encoders with knowledge distillation
Systems, methods, devices, instructions, and other examples are described for natural language processing. One example includes accessing natural language processing general encoder data, where the encoder data is generated from a general-domain dataset that is not domain specific. A domain specific dataset is accessed and filtered encoder data using a subset of the encoder data is generated. The filtered encoder data is trained using the domain specific dataset to generate distilled encoder data, and tuning values for the distilled encoder data are generated to configure task outputs associated with the domain specific dataset.
Systems and methods for dynamic communication routing based on consistency weighting and routing rules are disclosed. A computing device can receive a communication including content data. The communication can be stored in a queue position of a primary queue. For example, the primary queue can include a plurality of queue positions for storing communications. The communication can be retrieved from the queue position of the primary queue and analyzed. In some instances, analyzing can include parsing the content data for a keyword. A keyword can correspond to a secondary queue. When the keyword is identified in the communication, the communication can be stored in the secondary queue that corresponds to the keyword. A terminal device associated with the secondary queue can be identified. A retrieval request to access the communication from the secondary queue can be received, and the communication can be routed to the terminal device.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for use by businesses to build bots for interaction and conversations with employees and customers; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to facilitate interactions in the digital engagement, conversational commerce, bots and automation, and customer engagement fields; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to provide automated concierge services in nature of customer support; Software as a service (SAAS) services featuring software using artificial intelligence for the artificial production of human speech and text
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for use by businesses to build bots for interaction and conversations with employees and customers; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to facilitate interactions in the digital engagement, conversational commerce, bots and automation, and customer engagement fields; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to provide automated concierge services in nature of customer support; Software as a service (SAAS) services featuring software using artificial intelligence for the artificial production of human speech and text
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for use by businesses to build bots for interaction and conversations with employees and customers; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to facilitate interactions in the digital engagement, conversational commerce, bots and automation, and customer engagement fields; Software as a service (SAAS) services featuring software using artificial intelligence for use by businesses to provide automated concierge services in the nature of customer support; Software as a service (SAAS) services featuring software using artificial intelligence for the artificial production of human speech and text
74.
SYSTEMS AND METHODS FOR INTENT RESPONSE SOLICITATION AND PROCESSING
Disclosed embodiments provide a framework to solicit and evaluate responses from different systems and other users to the intents communicated by requesting users. In response to obtaining an intent, an intent messaging service provides the intent to a selected system to solicit a response to the intent. In response to obtaining an intent response, the intent messaging service prohibits the system from generating further intent responses and provides the obtained intent response to the requesting user. The intent messaging service establishes a communications session between the requesting user and the system in response to another request corresponding to the intent. This allows for additional responses to be provided to the requesting user by the system identified by the intent messaging service.
The present disclosure relates generally to facilitating routing of communications. More specifically, techniques are provided to dynamically routing messages having multiple intents between bots and terminal devices during communication sessions configured with multi-channel capabilities.
G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
A system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies a transaction involving the account. The system uses one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models. The system outputs the recommended transaction and receives a confirmation regarding the recommended transaction.
Systems and methods for performing dynamic code management, such as dynamic management of JavaScript tags in webpages or code segments in native applications, are disclosed. A user device loading a web or native application can access a factor, such as a user device-specific attribute or a piece of content of the webpage or native application being loaded. That factor can be applied to a rule that is evaluated (e.g., by the user device or a code server) to select one or more desired segments of code (e.g., JavaScript tags or native application code) to be executed by the user device from a pool of available code (e.g., pre-embedded code or dynamically injected code).
A system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies a transaction involving the account. The system uses one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models. The system outputs the recommended transaction and receives a confirmation regarding the recommended transaction.
The present disclosure relates generally to facilitating two-way communications. One example involves receiving input data as part of a two-way communication session associated with a plurality of bots, accessing confidence scores from the bots. Mapped scores are then generated for the plurality of bots from the confidence scores using a bot score mapper. A selected bot is identified using the mapped scores, and the two-way communication session is facilitated using the selected bot. Further, techniques are provided to track performance of the selected bot and dynamically updated mapping adjustments in the bot score mapper using feedback and machine learning systems.
H04L 41/5051 - Service à la demande, p. ex. définition et déploiement des services en temps réel
H04L 41/50 - Gestion des services réseau, p. ex. en assurant une bonne réalisation du service conformément aux accords
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/23 - Contrôles de fiabilité, p. ex. acquittements ou signalement de fautes
80.
Systems and methods for external system integration
The present disclosure relates generally to facilitating routing of communications across external systems. More specifically, techniques are provided to dynamically route issue tracking tickets to disparate endpoints based on the content of the ticket.
H04L 41/5074 - Traitement des plaintes des utilisateurs ou des tickets d’incident
H04L 41/0631 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse des causes profondesGestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse de la corrélation entre les notifications, les alarmes ou les événements en fonction de critères de décision, p. ex. la hiérarchie ou l’analyse temporelle ou arborescente
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
81.
Intent analysis for call center response generation
A system obtains conversation data corresponding to conversations between users and agents of a client. The system identifies a set of intents from the conversations and identifies a set of contexts, explicit elements, and implied elements of these intents. The system identifies actions that can be performed to recognize new explicit and implied elements from new conversations and to address intents in these new conversations. Based on these actions, the system generates a set of recommendations that can be provided to the client. As agents communicate with users, the system monitors adherence to the set of recommendations.
Disclosed embodiments provide a framework to assist bot managers and builders in identifying particular friction points between bots and customers to allow for real-time identification of bot conversation issues and to train bots to improve conversation flows. Conversation data is processed using machine learning models to detect bot states within conversations and calculate a Meaningful Automated Connection Score (MACS) for these conversations. The MACS for a conversation is provided to bot builders to allow the bot builders to identify friction points and update bots accordingly.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
The present disclosure relates generally to systems and methods for analyzing intent. Intents may be analyzed to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication.
In some embodiments, a set of user groups can be defined, with each group relating to a different webpage experience, user action, etc. Requests are assigned to one of the groups based on actual webpage presentation features and/or user actions. A group-specific model is generated for each group and translates user information to a preliminary result (e.g., a purchasing probability). A model combination includes a weighted combination of a set of available group-specific models. User information is processed using the model combination to generate a model result. The model result is evaluated to determine whether a requested webpage is to be customized in a particular manner and/or an opportunity is to be offered.
A redirection and messaging system receives telephony information identifying a caller and call context from a telephony system. The system selects one of a plurality of messaging operators based on the call context, optionally sends an introductory message to the caller via a messaging service, and generates a message interface for the selected message operator. The message interface includes the caller and call context and any messages sent between the caller and the selected message operator, with an input interface allowing the selected message operator to input and send messages to the caller.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
86.
Application customization using a customization file
Techniques and systems for receiving and using a customization file are provided, including a computing device, a method, or a computer-program product. For example, a method may include receiving a customization file that includes customized content for customizing a communication interface overlay. The method may further include accessing native application code and executing the native application code to run a native application. The method may further include accessing a compiled set of code that is separate from the native application code. The compiled set of code is accessible by the native application code. The method may further include executing the compiled set of code, wherein a default file of the compiled set of code provides a native communication interface overlay, wherein the native communication interface overlay is overlaid over a graphical interface of the native application, and wherein the native communication interface overlay allows communication with a resource of a third-party. The method may further include executing the received customization file, wherein executing the received customization file customizes the compiled set of code, and wherein the customized compiled set of code customizes the native communication interface overlay to provide a customized communication interface overlay.
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 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
H04L 67/00 - Dispositions ou protocoles de réseau pour la prise en charge de services ou d'applications réseau
H04L 67/06 - Protocoles spécialement adaptés au transfert de fichiers, p. ex. protocole de transfert de fichier [FTP]
H04M 1/724 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles
H04M 1/72469 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles pour faire fonctionner le dispositif en sélectionnant des fonctions à partir de plusieurs éléments affichés, p. ex. des menus ou des icônes
H04M 1/00 - Équipement de sous-station, p. ex. pour utilisation par l'abonné
A context-aware account management system is described. The system receives a partial request and parses the partial request to identify one or more characters in the partial request. The system determines a plurality of valid requests that include the one or more characters, for instance in response to querying one or more data structures and/or generating the valid requests using a trained machine learning model. The system generates a suggested request by selecting one of the plurality of valid requests, and in some cases, by identifying valid values for variables within the selected one of the plurality of valid requests by querying one or more data structures and/or generating the valid values using a trained machine learning model. The system outputs the suggested request and receives a confirmation in response. The system performs an action indicated by the suggested request using a user account indicated by the suggested request.
A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.
Systems, methods, and computer program products include smart capacity workload routing with workload modeling. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is processed and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model. An agent with capacity that is available to handle the requested user communication is identified. A communication slot for the identified agent is activated and defined by the identified workload measurement, and the request is routed to the identified agent and updating available workload capacity in the system.
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
90.
System and method for interactive application preview
An interactive demonstration application can be used to test and experience the use of overlay elements (e.g., application add-ons, such as an interactive chat overlay) on a graphical interface (e.g., of a website or a computer application) before fully integrating the overlay elements into the graphical interface. The interactive demonstration application can also be used to demonstrate and update the live settings of a live overlay element used on a live webpage.
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
G06F 40/143 - Balisage, p. ex. utilisation du langage SGML ou de définitions de type de document
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p. ex. interaction avec des règles ou des cadrans
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
91.
Dynamic response prediction for improved bot task processing
Systems and methods can be provided for predicting responses during communication sessions with network devices. In some implementations, systems and methods can facilitate predicting responses using machine learning techniques. Messages received through a platform can be stored in a repository. A machine learning model may be trained using the stored messages. When a terminal device is communicating with a network device in a communication session, the messages exchanged in the communication session and the machine learning model can be used to predict future responses in real-time. The predicted future responses can be presented at the terminal device. A predicted response can be selected at the terminal device. Upon selection, the selected predicted response is transmitted to the network device during the communication session.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
G06Q 10/107 - Gestion informatisée du courrier électronique
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
H04L 67/63 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en acheminant une demande de service en fonction du contenu ou du contexte de la demande
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06N 20/10 - Apprentissage automatique utilisant des méthodes à noyaux, p. ex. séparateurs à vaste marge [SVM]
92.
System and methods for searching and communication
Described are computer-based methods and apparatuses, including computer program products, comprising the steps of, or structure for, storing a plurality of expert profiles in a database, each of the plurality of expert profiles comprising information associated with a person having knowledge in a particular category, subject or topic; receiving search criteria over a network from a query source; selecting at least one of the plurality of expert profiles comprising information that satisfy the search criteria; and transmitting expert profile data for each of the selected expert profiles to the remote search engine, the expert profile data comprising data that defines a displayable representation of a corresponding expert profile, the expert profile data further comprising data that facilitates a client-initiated, real-time communication session over the network with a person associated with the corresponding expert profile.
An intent confusion evaluation engine receives conversation data corresponding to conversations between customers and agents. The engine evaluates annotations in the conversation data corresponding to intents identified from messages exchanged between customers and agents to determine levels of confusion amongst different intents. Based on these levels of confusion, the engine creates a graphical representation that illustrates the various intents and the level of confusion between different pairings of intents for the set of conversations. If an update is provided to the annotations, the graphical representation is updated dynamically and in real-time to provide updated levels of confusion amongst the various intents in accordance with the update.
Disclosed embodiments provide a framework for intent discovery based on user input and execution of processes based on the discovered intents. An intent processing system provides, via an interface, a graphical representation of different intent clusters corresponding to different intents. An intent cluster includes a set of intent terms and/or phrases that can be used to submit a request or issue that is associated with an intent. As a user selects intent terms and/or phrases from an intent cluster via the interface, the intent processing system can identify actions that can be performed to address the user's request or issue.
Disclosed embodiments provide a framework for intent discovery based on user input and execution of processes based on the discovered intents. An intent processing system provides, via an interface, a graphical representation of different intent clusters corresponding to different intents. An intent cluster includes a set of intent terms and/or phrases that can be used to submit a request or issue that is associated with an intent. As a user selects intent terms and/or phrases from an intent cluster via the interface, the intent processing system can identify actions that can be performed to address the user's request or issue.
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
An intent confusion evaluation engine receives conversation data corresponding to conversations between customers and agents. The engine evaluates annotations in the conversation data corresponding to intents identified from messages exchanged between customers and agents to determine levels of confusion amongst different intents. Based on these levels of confusion, the engine creates a graphical representation that illustrates the various intents and the level of confusion between different pairings of intents for the set of conversations. If an update is provided to the annotations, the graphical representation is updated dynamically and in real-time to provide updated levels of confusion amongst the various intents in accordance with the update.
G06F 40/35 - Représentation du discours ou du dialogue
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06F 40/169 - Annotation, p. ex. données de commentaires ou notes de bas de page
G06T 11/20 - Traçage à partir d'éléments de base, p. ex. de lignes ou de cercles
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Business consultation services; business advice and commercial information; business consultation and information in the field of targeted online advertising; business organization advice; providing commercial information and advice for consumers in the choice of products and services
(2) Instant messaging services; Interactive online communication services, namely, providing on-line facilities for real-time interaction between businesses and their customers and potential customers
(3) Application service provider (ASP) featuring software for use by businesses to enable real-time communications with online customers through the use of coupons, ads, offers, video and other types of online content based on business rules and analytics about the visitors' online behavior; computer services, namely, hosting on-line web facilities for others for organizing, serving and conducting online interactive promotions, namely, coupons, ads, offers, video and other types of online content; Software as a Services (SAAS) services featuring software for providing interactive message services for customers and potential customers with businesses; · Software as a service (SAAS) services featuring software for enabling businesses to provide and obtain customer support, to assist in providing customer engagement tools, marketing, customer care, online commerce, and messaging to customers, and for facilitating interaction, connections and digital messaging and chat conversations; Software as a service (SAAS) services featuring software for facilitating interaction, connections, and conversations between users in the digital messaging, digital chat, and customer engagement fields; Software as a Service (SaaS) services featuring software for social media marketing and social media strategy and marketing consultancy; Software as a Service (SaaS) services featuring software for online social networking; Application service provider (ASP) featuring software for use by businesses for sales and customer service communications via on-line chats, communication, and business analytics services; computer services, namely, hosting on-line web facilities for others for organizing and conducting online and interactive discussions; computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Business consultation services; business advice and commercial information; business consultation and information in the field of targeted online advertising; business organization advice; providing commercial information and advice for consumers in the choice of products and services
(2) Instant messaging services; Interactive online communication services, namely, providing on-line facilities for real-time interaction between businesses and their customers and potential customers
(3) Application service provider (ASP) featuring software for use by businesses to enable real-time communications with online customers through the use of coupons, ads, offers, video and other types of online content based on business rules and analytics about the visitors' online behavior; computer services, namely, hosting on-line web facilities for others for organizing, serving and conducting online interactive promotions, namely, coupons, ads, offers, video and other types of online content; Software as a Services (SAAS) services featuring software for providing interactive message services for customers and potential customers with businesses; · Software as a service (SAAS) services featuring software for enabling businesses to provide and obtain customer support, to assist in providing customer engagement tools, marketing, customer care, online commerce, and messaging to customers, and for facilitating interaction, connections and digital messaging and chat conversations; Software as a service (SAAS) services featuring software for facilitating interaction, connections, and conversations between users in the digital messaging, digital chat, and customer engagement fields; · Software as a Service (SaaS) services featuring software for social media marketing and social media strategy and marketing consultancy; Software as a Service (SaaS) services featuring software for online social networking; Application service provider (ASP) featuring software for use by businesses for sales and customer service communications via on-line chats, communication, and business analytics services; computer services, namely, hosting on-line web facilities for others for organizing and conducting online and interactive discussions; computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking
99.
Systems and methods for real-time remote control of mobile applications
Systems and methods for real-time, remote-control of mobile applications are provided. A communication session between a network device and a terminal device can be established. The network device can be configured to execute a mobile application. For example, the mobile application can include an input element at an initial state. The mobile application can be remotely controlled by the terminal device. Further, a data stream including content data can be received and transmitted during the communication session. For example, the content data can include an object presented by the mobile application. The content data can be displayed on the terminal device. An input event associated with the content data can be detected. Control data can be generated based on the detected input event. The control data can be received at the network device and the initial state of the input element can be modified.
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
H04M 1/72403 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité
100.
Dynamic communications routing to disparate endpoints
The present disclosure relates generally to facilitating routing of communications. More specifically, techniques are provided to dynamically route messages having certain intents between bots and user devices during communication sessions configured with multi-channel capabilities.
H04L 51/56 - Messagerie unifiée, p. ex. interactions entre courriel, messagerie instantanée ou messagerie IP convergente [CPM]
G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/06 - Adaptation des messages aux exigences du terminal ou du réseau
H04L 51/214 - Surveillance ou traitement des messages en utilisant le transfert sélectif
