Abrégé

A method of applying a confidence classifier for intent classification in association with an automated chat hot according to an embodiment includes processing, by a computing system, an utterance with an intent classifier to determine a probability distribution of possible intents associated with the utterance, generating, by the computing system, a plurality of measures of peakedness of the probability distribution, and applying, by the computing system, a trained confidence classifier to determine a single normalized probability of a most likely intent associated with the utterance based on the plurality of measures of peakedness of the probability distribution.

Classes IPC  ?

• G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
• G06F 17/27 - Analyse automatique, p.ex. analyse grammaticale, correction orthographique
• G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
• G06N 3/00 - Agencements informatiques fondés sur des modèles biologiques
• G06N 5/02 - Représentation de la connaissance; Représentation symbolique

Abrégé

A method comprising: receiving a first text corpus comprising punctuated and capitalized text; annotating words in said first text corpus with a set of labels indicating a punctuation and a capitalization of each word; at an initial training stage, training a machine learning model on a first training set comprising: (i) said annotated words in said first text corpus, and (ii) said labels; receiving a second text corpus representing conversational speech; annotating words in said second text corpus with said set of labels; at a re-training stage, re-training said machine learning model on a second training set comprising: (iii) said annotated words in said second text corpus, and (iv) said labels; and at an inference stage, applying said trained machine learning model to a target set of words representing conversational speech, to predict a punctuation and capitalization of each word in said target set.

Classes IPC  ?

• G06F 40/232 - Correction orthographique, p.ex. vérificateurs d’orthographe ou insertion des voyelles
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06N 20/00 - Apprentissage automatique
• G10L 15/00 - Reconnaissance de la parole

Abrégé

A method for conducting an automated dialogue between an inbound automated voice resource and an outbound automated voice resource during a voice communication session according to one embodiment includes receiving at the inbound automated voice resource an initiation of the voice communication session from the outbound automated voice resource; transmitting, by the inbound automated voice resource, a speech communication to the outbound automated voice resource during the voice communication session, wherein a digital watermark is embedded in the speech communication; identifying, by the outbound automated voice resource, the digital watermark in response to analyzing the speech communication; converting, by the outbound automated voice resource, an outbound automated voice resource communication language from speech to machine language in response to determining that the inbound automated voice resource interprets machine language based on the digital watermark; transmitting, by the outbound automated voice resource, a machine language communication to the inbound automated voice resource; converting, by the inbound automated voice resource, an inbound automated voice resource communication language from speech to machine language in response to determining that the outbound automated voice resource interprets machine language based on the machine language communication; and completing the automated dialogue between the inbound automated voice resource and the outbound automated voice resource using machine language.

Classes IPC  ?

• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/279 - Reconnaissance d’entités textuelles
• H04W 4/70 - Services pour la communication de machine à machine ou la communication de type machine

Abrégé

A first text corpus comprising punctuated and capitalized text is received. The words in the first text corpus are then annotated with a set of labels indicating a punctuation and a capitalization of each word. At an initial training stage, a machine learning model is trained on a first training set using the annotated words from the first text corpus and the labels. A second text corpus is received representing conversational speech. The words in the second text corpus are then annotated with the set of labels. In a re-training stage, the machine learning model is re-trained on a second training set comprising the annotated words from the second text corpus, and the labels. At an inference stage, the trained machine learning model is applied to a target set of words representing conversational speech to predict a punctuation and capitalization of each word in the target set.

Classes IPC  ?

• G06F 40/232 - Correction orthographique, p.ex. vérificateurs d’orthographe ou insertion des voyelles
• G06N 20/00 - Apprentissage automatique
• G06F 40/279 - Reconnaissance d’entités textuelles
• G06F 40/169 - Annotation, p.ex. données de commentaires ou notes de bas de page
• G10L 15/04 - Segmentation; Détection des limites de mots
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G10L 15/197 - Grammaires probabilistes, p.ex. n-grammes de mots
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 

Abrégé

A system and method for multimedia contact center interactions via an audiovisual asynchronous messaging channel is provided. In an embodiment, an organization might operate a website comprising information and for providing an entry point of communication with a contact center. A communications channel may be integrated directly with the organization's website, which includes an editable audiovisual interface. A user may interact with the editable interface through a variety of means, including sketching on a webpage and recording to create a video form of audiovisual asynchronous query over the web page. The interaction is analyzed and routed to an agent for handling. The agent may then be connected to the user through the asynchronous channel.

Classes IPC  ?

• G06F 16/00 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet
• G06F 16/438 - Présentation des résultats des requêtes
• G06F 16/432 - Formulation de requêtes

Abrégé

A method for transforming a first data display into a second data display according to one embodiment includes presenting the first data display on a graphical user interface element of a user device; modifying the graphical user interface element of the user device in response to receiving a user input; analyzing data display criteria comprising at least one of user device parameters, administrator parameters, user parameters, and data parameters; transforming the first data display into the second data display based on the data display criteria and the modified graphical user interface element of the user device, wherein the first data display is different from the second data display; and presenting the second data display on the modified graphical user interface element of the user device.

Classes IPC  ?

• 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

Abrégé

A system for detecting anomalies in metric data provided by one or more customers according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive metric data indicative of a plurality of time-series based observations for a particular customer metric, to define, based on the metric data, a plurality of parameters to characterize one or more spheres each configured to capture a number of time- series based observations for the particular customer metric, and to generate, based on the plurality of parameters, the one or more spheres to determine coverage of the metric data within the one or more spheres and detect one or more anomalies in the metric data.

Classes IPC  ?

• G06F 11/00 - Détection d'erreurs; Correction d'erreurs; Contrôle de fonctionnement
• G06F 16/215 - Amélioration de la qualité des données; Nettoyage des données, p.ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
• G06F 16/28 - Bases de données caractérisées par leurs modèles, p.ex. des modèles relationnels ou objet
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06N 20/00 - Apprentissage automatique
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds

Abrégé

A system for estimating a wait time to connect a customer to an agent according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive incoming interaction data from the customer indicative of a request to estimate the wait time, retrieve historical data indicative of interaction information for one or more interactions that have already been serviced in response to receipt of the request, and generate a wait time estimation based on the historical data.

Classes IPC  ?

• H04M 3/50 - Dispositions centralisées pour répondre aux appels; Dispositions centralisées pour enregistrer des messages pour abonnés absents ou occupés

Abrégé

A system including a mobile application on a user's mobile device for connecting to a plurality of contact centers and providing personalized context to the user. The system includes: the mobile application; a connection from the mobile application to a central API service, wherein the central API service provides authentication tokens to the mobile application for facilitating interaction therebetween; a plurality of vendors which the user has associated with their profile and have been integrated with the central API service, wherein the vendors are operably connected with the central API service for information sharing; and a plurality of media channels connecting the plurality of vendors with the central API service, allowing the user to: view personalized context from past interactions between the user and the plurality of contact centers through the mobile application; and connect with the contact centers.

Classes IPC  ?

• H04L 67/53 - Services réseau en utilisant des fournisseurs tiers de services
• H04L 67/50 - Services réseau
• H04L 67/562 - Courtage des services de mandataires
• H04L 67/306 - Profils des utilisateurs
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur

Abrégé

A system including a mobile application on a user's mobile device for connecting to a plurality of contact centers and providing personalized context to the user. The system includes: the mobile application; a connection from the mobile application to a central API service, wherein the central API service provides authentication tokens to the mobile application for facilitating interaction therebetween; a plurality of vendors which the user has associated with their profile and have been integrated with the central API service, wherein the vendors are operably connected with the central API service for information sharing; and a plurality of media channels connecting the plurality of vendors with the central API service, allowing the user to: view personalized context from past interactions between the user and the plurality of contact centers through the mobile application; and connect with the contact centers.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• H04M 1/72436 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité avec des moyens interactifs de gestion interne des messages pour la messagerie textuelle, p.ex. SMS ou courriel
• H04M 1/72454 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en tenant compte des contraintes imposées par le contexte ou par l’environnement
• 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
• H04W 12/06 - Authentification

Abrégé

A method for optimizing a workflow of deferrable work interactions in a contact center that includes: providing a NLP models and a priority model; using text derived from received deferrable working interactions as inputs to the NLP models to generate the NLP scores; using the LP scores as inputs to the priority model to generate the priority score; identifying candidate agents of the agents for handling the deferrable work interactions; receiving an inbound work forecast; receiving agent work schedule data; using an optimization process to generate an optimized workflow for the deferrable work interactions, where he optimized workflow includes assignments in which an agent from the candidate agents is selected to handle the deferrable work interaction and a target timeframe for handling is scheduled; and routing the deferrable work interactions in accordance with the assignments of the optimized workflow.

Classes IPC  ?

• G06F 40/20 - Analyse du langage naturel
• G06F 40/30 - Analyse sémantique
• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Classes IPC  ?

• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06F 16/2458 - Types spéciaux de requêtes, p.ex. requêtes statistiques, requêtes floues ou requêtes distribuées
• G06N 5/02 - Représentation de la connaissance; Représentation symbolique
• 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
• G10L 15/19 - Contexte grammatical, p.ex. désambiguïsation des hypothèses de reconnaissance par application des règles de séquence de mots
• G06F 16/35 - Groupement; Classement
• G06F 16/332 - Formulation de requêtes
• G06F 40/30 - Analyse sémantique
• G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06F 40/205 - Analyse syntaxique
• G06F 40/216 - Analyse syntaxique utilisant des méthodes statistiques
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/295 - Reconnaissance de noms propres

Abrégé

A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.

Classes IPC  ?

• G06F 40/30 - Analyse sémantique
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement

Abrégé

A method for optimizing a workflow of deferrable work interactions in a contact center that includes: providing a NLP models and a priority model; using text derived from received deferrable working interactions as inputs to the NLP models to generate the NLP scores; using the LP scores as inputs to the priority model to generate the priority score; identifying candidate agents of the agents for handling the deferrable work interactions; receiving an inbound work forecast; receiving agent work schedule data; using an optimization process to generate an optimized workflow for the deferrable work interactions, where the optimized workflow includes assignments in which an agent from the candidate agents is selected to handle the deferrable work interaction and a target timeframe for handling is scheduled; and routing the deferrable work interactions in accordance with the assignments of the optimized workflow.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• 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
• G06F 40/40 - Traitement ou traduction du langage naturel

Abrégé

A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.

Classes IPC  ?

• G06F 40/30 - Analyse sémantique
• G10L 15/08 - Classement ou recherche de la parole
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06F 16/2458 - Types spéciaux de requêtes, p.ex. requêtes statistiques, requêtes floues ou requêtes distribuées
• G06N 5/02 - Représentation de la connaissance; Représentation symbolique
• G06Q 30/00 - Commerce
• G10L 15/19 - Contexte grammatical, p.ex. désambiguïsation des hypothèses de reconnaissance par application des règles de séquence de mots
• G06F 16/35 - Groupement; Classement
• G06F 16/332 - Formulation de requêtes
• G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06F 40/205 - Analyse syntaxique
• G06F 40/216 - Analyse syntaxique utilisant des méthodes statistiques
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/295 - Reconnaissance de noms propres

Abrégé

A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Classes IPC  ?

• G06F 40/30 - Analyse sémantique
• G06F 40/35 - Représentation du discours ou du dialogue
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06F 40/289 - Analyse syntagmatique, p.ex. techniques d’états finis ou regroupement

Abrégé

A method related to predicting agent attrition rate that includes: providing an attrition model; measuring and recording agent journey data of a first agent of a contact center, the agent journey data describing aspects related to an employment of the first agent and having data types that corresponds in kind to data types of inputs of the attrition model; determining that prediction of an attrition rate of the first agent is required; using the attrition model to predict the attrition rate by providing values for the inputs from applicable values taken from the agent journey data of the first agent, and calculating the attrition rate as the output of the attrition model; determining if the calculated current attrition rate indicates a high risk of attrition; and transmitting an alert communication to a supervisor of the first agent.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 40/40 - Traitement ou traduction du langage naturel
• G06N 20/00 - Apprentissage automatique

Abrégé

A method facilitating a customer exercising post-purchase rights associated with a purchase made by the customer of a product. The method includes: receiving first documentation related to the purchase of the product; extracting purchase data from the first documentation, where the extracted purchase data describes the purchase including a vendor identify, purchase date, and product type; conducting search using terms derived from the extracted purchase data to connect to a vendor web site; conducting a search of the vendor web site using terms derived from the extracted purchase data to locate a first vendor policy of the vendor that defines a first post-purchase right of the customer; determining a milestone date associated with the first post-purchase right of the customer; and sending a notification to the customer appraising the customer of the milestone date and the material change.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 10/00 - Administration; Gestion
• G06F 16/953 - Requêtes, p.ex. en utilisant des moteurs de recherche du Web
• G06N 20/00 - Apprentissage automatique
• H04L 12/58 - Systèmes de commutation de messages
• H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c. à d. systèmes dans lesquels le signal vidéo n'est pas diffusé

Abrégé

A method for resolving customer requests that includes: providing a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer and producing a transcript thereof; determining an intent based on the transcript and customer information relating to the determined intent; transmitting an initial set of data to the asynchronous resolution facilitator and assembling therefrom a resolution package that includes an agent interface showing information required to expeditiously resolve the customer request including one or more recommended business processes; displaying the agent interface on a screen of the agent device; receiving input from the agent device that indicates the agent has completed preparing a resolution for the customer request; and providing notification to the first customer of the achieved resolution via the personal device of the first customer.

Classes IPC  ?

• 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

Abrégé

A method for resolving customer requests that includes: providing a personal bot assistant and an asynchronous resolution facilitator; receiving a customer request from a first customer and producing a transcript thereof; determining an intent based on the transcript and customer information relating to the determined intent; transmitting an initial set of data to the asynchronous resolution facilitator and assembling therefrom a resolution package that includes an agent interface showing information required to expeditiously resolve the customer request including one or more recommended business processes; displaying the agent interface on a screen of the agent device; receiving input from the agent device that indicates the agent has completed preparing a resolution for the customer request; and providing notification to the first customer of the achieved resolution via the personal device of the first customer.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 40/30 - Analyse sémantique
• 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
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole

Abrégé

A method facilitating a customer exercising post-purchase rights associated with a purchase made by the customer of a product. The method includes: receiving first documentation related to the purchase of the product; extracting purchase data from the first documentation, where the extracted purchase data describes the purchase including a vendor identify, purchase date, and product type; conducting search using terms derived from the extracted purchase data to connect to a vendor website; conducting a search of the vendor website using terms derived from the extracted purchase data to locate a first vendor policy of the vendor that defines a first post-purchase right of the customer; determining a milestone date associated with the first post-purchase right of the customer; and sending a notification to the customer appraising the customer of the milestone date and the material change.

Classes IPC  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Bureautique; Gestion du temps

Abrégé

A system for performing contact center agent scheduling according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to generate a workload forecast indicative of a demand that will be introduced into the contact center in a future planning period based on a workload forecast model and time series data, generate a staffing requirement forecast indicative of a number of agents required to handle the workload forecast based on the workload forecast, one or more service goals, and a staffing requirement model, and perform schedule optimization using column generation to generate an optimized contact center agent shift schedule for a plurality of agents based on the staffing requirement forecast and one or more constraints.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 10/10 - Bureautique; Gestion du temps
• G06Q 50/30 - Transport; Communications

Abrégé

A contact center system for performing call progress analysis including tone and speech classification according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the contact center system to determine a cumulative average of an entropy of an audio signal received by the contact center system, determine a cumulative average power spectral amplitude and a cumulative average spectral entropy of the audio signal, calculate a difference measure of the audio signal as a difference between the cumulative average of the entropy and the cumulative average spectral entropy, distinguish tones from speech of the audio signal based on the difference measure, and process one or more tones of the audio signal in response to identification of the one or more tones in the audio signal.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• G10L 25/84 - Détection de la présence ou de l’absence de signaux de voix pour différencier la parole du bruit

Abrégé

A system and method are presented for creating bots for automating first party touchpoints with contact centers. Software bots created using Robotic Process Automation (RPA) are integrated with a contact center's different customer touch points (e.g. chatbots, emails and SMS, websites, IVR application, mobile applications, agent workstation). Artificial intelligence behind the touch points finds the intent of an interaction and the respective RPAs to be triggered using a fulfillment engine that finds and executes the appropriate fulfillment for each interaction intent. A REST API Request is sent to an RPA execution server, which runs the selected RPA and sends the result to the respective touch point that triggered it.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
• G06F 9/54 - Communication interprogramme
• G06F 40/30 - Analyse sémantique

Abrégé

A method for selectively transcribing voice communications that includes: receiving keywords; receiving an audio stream of audio data of speech; searching the audio stream to detect keywords or keyword detections and recording parameter data for each that includes a location of the keyword within the audio stream; generating one or more cumulative datasets for one or more portions of the audio stream that each includes parameter data for the keyword detections occurring therein; for each of the one or more portions of the audio stream, calculating a transcription favorableness score via inputting the corresponding one of the one or more cumulative datasets into an algorithm; and determining whether to transcribe each of the one or more portions of the audio stream by comparing the corresponding transcription favorableness score against a predetermined threshold.

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

A system and method are presented relating to emotion-based action recommendations. The present invention may include a recommender engine that analyzes several pieces of information and using artificial intelligence derived models, provides a course of action for a contact center agent and the probability and estimations behind the recommendation as a justification. System inputs include cost of the item or service being troubleshooted, estimated costs for different resolutions, cost of each escalation level of the interaction, history of previous interactions, agent costs, history of the current interaction, acoustic events and sentiment or primary detected emotions of the customer's transcript. The recommended course of action, rationale, and authorization can be provided as output. The system may record whether the agent followed the recommendation to improve further recommendations.

Classes IPC  ?

• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G06Q 10/10 - Bureautique; Gestion du temps
• G06N 3/08 - Méthodes d'apprentissage
• G06F 40/295 - Reconnaissance de noms propres
• G06F 18/2113 - Sélection du sous-ensemble de caractéristiques le plus significatif en classant ou en filtrant l'ensemble des caractéristiques, p.ex. en utilisant une mesure de la variance ou de la corrélation croisée des caractéristiques

Abrégé

A method related to customers orchestrating engagements with service providers. The method includes providing, by a facilitator entity, a customer journey orchestration service that includes: service providers affiliated with the customer journey orchestration service; a virtual datacenter in which cloud-based applications and databases are operably linked to communication devices of the customers and to the affiliated services providers; customer interface portals for enabling the customers to interact with the virtual datacenter, each customer interface portal being associated with a specific customer; and a customer profile database that stores customer profiles corresponding to respective ones of the customers. The method further includes configuring, by the facilitator entity, an account of the customer for participation in the customer journey orchestration service. The method further includes orchestrating, by the facilitator entity, a current engagement relating to a plurality of the preferred service providers.

Classes IPC  ?

• G06Q 30/00 - Commerce

Abrégé

A method for conducting an automated interview session between a candidate and an automated chat resource includes analyzing a candidate resume to identify a first personality trait; generating a first resume-based question based on the first personality trait; presenting, by the automated chat resource, the first resume-based question to the candidate during the automated interview session based on the identified first personality trait; generating a first trait score based on a first response received from the candidate, wherein the first trait score is indicative of a relevance of the identified first personality trait; generating a first question-answer score based on the first resume-based question and the first response, wherein the first question-answer score is indicative of a first question-answer relevance and is used to determine whether to present a second resume-based question to the candidate; analyzing a communication transcript to identify a second personality trait; generating a first transcript-based question based on the second personality trait; presenting, by the automated chat resource, the first transcript- based question to the candidate during the automated interview session based on the identified second personality trait; analyzing a second response from the candidate to determine whether the candidate possesses the identified second personality trait; generating a second trait score based on the second response in response to determining whether the candidate possesses the identified second personality trait, wherein the second trait score is indicative of a relevance of the identified second personality trait; and generating a second question-answer score based on the first transcript-based question and the second response, wherein the second question-answer score is indicative of a second question-answer relevance and is used to determine whether to present a second transcript-based question to the candidate.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 10/10 - Bureautique; Gestion du temps
• G06Q 10/00 - Administration; Gestion

Abrégé

A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program code, the program code executable by the at least one hardware processor to: receive, by a processor, data associated with an interaction between a customer and an agent in a contact center, automatically analyze, by the processor, said data to identify one or more topics associated with a content of said interaction, automatically associate, by the processor, one or more questions from a dataset of questions with each of said identified topics, automatically construct, by the processor, a questionnaire comprising said associated questions, and output said questionnaire to said customer.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation

Abrégé

A method for personalizing a delivery of services to a first customer including: providing a customer profile; updating the customer profile via performing a first process to collect interaction data, the first process including the steps of: monitoring activity on the communication device and, therefrom, detecting the first interaction with the first contact center; identifying data relating to the first interaction for collecting as the interaction data; and updating the customer profile to include the interaction data identified from the first interaction; generating an interaction predictor, the interaction predictor comprising knowledge about the first customer derived, at least in part, from the data stored within the customer profile, the knowledge comprising a behavioral factor attributable to the first customer given a first type of interaction; and augmenting the customer profile by storing therein the interaction predictor.

Classes IPC  ?

• G06Q 30/00 - Commerce
• 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
• G06N 3/08 - Méthodes d'apprentissage
• G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation

Abrégé

A computer-implemented method for automating actions for a customer in relation to an interaction between the customer and an agent of a contact center, the interaction including an exchange of statements made by the customer and agent. The method includes the steps of: receiving a transcript of the interaction; via a first analysis, analyzing the transcript; from results of the first analysis, identifying: a pending action, wherein the pending action is an action promised by the customer or agent that will be resolved after the interaction; and a target timeframe for resolving the pending action; given the pending action, determining a follow-up workflow that includes one or more follow-up actions, each of the one or more follow-up actions comprising an action intended to assist the customer to resolve the pending action; and automatically executing the one or more follow-up actions.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06Q 10/10 - Bureautique; Gestion du temps
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• H04L 12/58 - Systèmes de commutation de messages
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06Q 30/00 - Commerce
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06N 3/08 - Méthodes d'apprentissage
• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels

Abrégé

A computer-implemented method related to routing incoming interactions of contact centers. The method may include: receiving initial data identifying a first incoming interaction that includes information disclosing at least an intent of the first incoming interaction; and performing a first subprocess to generate a personalized routing profile tailored to facilitate routing the first incoming interaction in accordance with preferences of a first customer. The first subprocess may include: accessing data from a database, the database including at least a first customer profile storing data relating to the first customer; based on the accessed data and the intent of the first incoming interaction, determining preferred agent characteristics data of the first customer for the first incoming interaction; and generating the personalized routing profile so to include the preferred agent characteristics data of the first customer.

Classes IPC  ?

• 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

Abrégé

A method for implementing an enterprise's outbound campaign in which an offer is communicated to target customers in a manner personalized for each. The method includes: providing a customer profile database; providing a personalization platform; updating the customer profile of a first target customer according to data received from a personal bot running on a device of the first target customer; deriving interaction predictors for the first target customer relating to behavioral tendency for a interaction type; receiving an enterprise campaign dataset from the enterprise related to the outbound campaign, the enterprise campaign dataset including: information describing the offer, context; and list of the target customers; augmenting, at the personalization platform, the enterprise campaign dataset with the interaction predictors related to the first target customer to produce an enriched campaign dataset; and transmitting the enriched campaign dataset to the enterprise.

Classes IPC  ?

• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06N 20/00 - Apprentissage automatique

Abrégé

A method for configuring a selected application of a contact center to facilitate handling of incoming interactions. The method may include: collecting data; generating individual customer models and aggregated customer models, wherein the aggregated customer models each comprises an aggregation of a grouping of the individual customer models; generating individual agent models and aggregated agent models, wherein the aggregated agent models each comprises an aggregation of a grouping of the individual agent models; from the customer models, generating a customer predictor configured to predict customer behavior; from the agent models, generating an agent predictor configured to predict agent behavior; using the customer predictor to make a customer prediction; using the agent predictor to make an agent prediction; and modifying an allocation of a contact center resource based on the customer and the agent predictions.

Classes IPC  ?

• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06Q 30/00 - Commerce

Abrégé

A method comprising: receiving a plurality of audio segments comprising a speech signal, wherein said audio segments represent a plurality of verbal interactions; receiving labels associated with an emotional state expressed in each of said audio segments; dividing each of said audio segments into a plurality of frames, based on a specified frame duration; extracting a plurality of acoustic features from each of said frames; computing statistics over said acoustic features with respect to sequences of frames representing phoneme boundaries in said audio segments; at a training stage, training a machine learning model on a training set comprising: said statistics associated with said audio segments, and said labels; and at an inference stage, applying said trained model to one or more target audio segments comprising a speech signal, to detect an emotional state expressed in said target audio segments.

Classes IPC  ?

• G10L 25/63 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel
• 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
• G06F 40/30 - Analyse sémantique
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la parole; Sélection d'unités de reconnaissance 
• G10L 15/04 - Segmentation; Détection des limites de mots
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur

Abrégé

A system and method for predicting performance for a contact center via machine learning includes invoking, by a processor, an interaction between a contact center resource and an end user, and recording, by the processor, the interaction. The processor automatically analyzes the recorded interaction for identifying attributes associated with the interaction. The processor provides the identified attributes to a machine learning model which predicts a performance score based on the identified attributes. The performance score is compared against a threshold score, and a recommendation is output by the processor based on the comparing. The end user may be a candidate contact center agent considered for hiring, and the recommendation may be to hire the candidate or advance the candidate to a next step of an interview process.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

A root behavior tree is stored with a flow control node and a plurality of child nodes. The flow control node may determine a sequence of execution of child nodes. A first-level behavior tree is stored and executed to accomplish a task with associated intent. The first-level tree comprises a child node of the root tree and nodes defining actions. A dialog between contact center and user is hosted over a communication channel and a processor receives inputs from the dialog and executes the root tree and the first-level tree as a child node of the root tree in response. Upon detecting a match between an input and a data pattern, the execution sequence within the root tree or a further tree is altered. Responsive to the altered sequence, an output is provided to cause an action.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 40/30 - Analyse sémantique
• G06F 40/35 - Représentation du discours ou du dialogue
• 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

Abrégé

A computer-implemented method for managing a dialog between a contact center system and a user thereof, comprising the steps of: hosting a dialog over a communication channel between an automated dialog engine of said contact center and said user thereof, said dialog comprising messages sent between said automated dialog engine and said user in both directions; said automated dialog engine receiving input messages from said user, and determining response messages in response to said inputs; detecting from said messages of said dialog a trigger event matching a rule; in response to detection of said trigger event: providing said agent station with a summary of said dialog; and providing control of said automated dialog engine to said agent station.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 40/30 - Analyse sémantique
• G06F 40/35 - Représentation du discours ou du dialogue
• 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

Abrégé

A processor receives inputs from a dialog between an agent and a user performed over a communication channel A knowledge base is stored, comprising entries with a subset of said entries defined as higher priority. A match is detected between an input from said dialog and a plurality of said knowledge base entries. At least one of said plurality of entries is retrieved corresponding to said match. The at least one of said plurality of entries is pushed as an output to one or more of a device operated by said user and at least one device operated by one of said user and said agent. A further match is detected between an input from said dialog and a higher priority entry in said knowledge base and pushed to said at least one device while the dialog is ongoing.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 40/35 - Représentation du discours ou du dialogue
• 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
• G06F 40/30 - Analyse sémantique

Abrégé

A method for protecting personal data pursuant to behavioral factors unique to a first customer. The method includes: storing a customer profile of the first customer that includes transaction data from transactions with entities; providing a personal assistant that is configured to access the customer profile pursuant to engagement rules to conduct the transactions; updating the customer profile pursuant to a first new transaction; generating a predictor from the updated customer profile, the predictor including knowledge about the first customer derived from the updated customer profile including a behavioral factors attributable to the first customer given a characteristic related to the first new transaction; augmenting the customer profile by storing therein the predictor, wherein the predictor: modifies at least one of the rules of the engagement rules and links the behavioral factor to the characteristic of the first transaction.

Classes IPC  ?

• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
• G06F 16/23 - Mise à jour
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
• G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
• G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
• G06Q 50/26 - Services gouvernementaux ou services publics
• G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
• G06N 20/00 - Apprentissage automatique
• G06F 40/40 - Traitement ou traduction du langage naturel

Abrégé

A method for delivering messages from customers to bots that includes providing a bot gateway and, pursuant to a process, formatting and sending the messages. The bot gateway includes bot schemas that each defines a data field arrangement for sending requests to a particular bot. The process includes receiving a first message and determining therefrom a first customer, a first tenant, a first bot, and a text message from the first customer to the first bot. The process includes: providing a bot configuration data set; selecting a first bot schema pertaining to the first bot; creating a formatted request via mapping the text message and the data values defined in the bot configuration data set to corresponding data fields defined within the data field arrangement; and sending the formatted request to the first bot.

Classes IPC  ?

• H04L 12/58 - Systèmes de commutation de messages
• G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail

Produits et services

ONLINE NON-DOWNLOADABLE CLOUD COMPUTING SOFTWARE FOR EMPLOYEE ENGAGEMENT FOR CONTACT CENTERS; SOFTWARE AS A SERVICE (SAAS) FEATURING SOFTWARE FOR EMPLOYEE ENGAGEMENT FOR CONTACT CENTERS, INCLUDING GAME MECHANICS AND RECOGNITION AND SOCIAL MEDIA CONCEPTS

Produits et services

PROVIDING ONLINE NON-DOWNLOADABLE CLOUD COMPUTING SOFTWARE FOR EMPLOYEE ENGAGEMENT FOR CONTACT CENTERS; PROVIDING SOFTWARE AS A SERVICE (SAAS) FEATURING SOFTWARE FOR EMPLOYEE ENGAGEMENT FOR CONTACT CENTERS, INCLUDING GAME MECHANICS AND RECOGNITION AND SOCIAL MEDIA CONCEPTS

Abrégé

Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.

Classes IPC  ?

• G06F 40/20 - Analyse du langage naturel
• G06F 40/35 - Représentation du discours ou du dialogue
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

Abrégé

Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.

Classes IPC  ?

• G06F 40/20 - Analyse du langage naturel
• G06F 40/35 - Représentation du discours ou du dialogue
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

Abrégé

A system and method are presented for the addition of content to contact center interactions. An interaction is routed to an agent in the contact center environment. The interaction is routed through one or more providers of embedded client applications. Providers may be a first party, a DSL provider, or a programmatic provider. The provider triggers a user interface which displays one or more specified embedded client applications to the agent and when the agent selects one of the applications, it is asynchronously loaded through an inline frame.

Classes IPC  ?

• G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
• G06F 9/54 - Communication interprogramme
• H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• 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
• G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation

Abrégé

Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.

Classes IPC  ?

• G06F 40/30 - Analyse sémantique
• G06F 40/35 - Représentation du discours ou du dialogue
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

Abrégé

A system and method for multimedia contact center interactions via an audiovisual asynchronous messaging channel is provided. In an embodiment, an organization might operate a website comprising information and for providing an entry point of communication with a contact center. A communications channel may be integrated directly with the organization's website, which includes an editable audiovisual interface. A user may interact with the editable interface through a variety of means, including sketching on a webpage and recording to create a video form of audiovisual asynchronous query over the web page. The interaction is analyzed and routed to an agent for handling. The agent may then be connected to the user through the asynchronous channel.

Classes IPC  ?

• G06F 16/00 - Recherche d’informations; Structures de bases de données à cet effet; Structures de systèmes de fichiers à cet effet
• G06F 16/438 - Présentation des résultats des requêtes
• G06F 16/432 - Formulation de requêtes

Abrégé

A method for automating interactions with enterprises includes: receiving, through a user interface, a natural language request from a customer; determining a customer intent from the natural language request; conducting an automated interaction by: determining, by a personal bot controller circuit, an enterprise to service the customer intent; loading an enterprise library for interacting with an application programming interface (API) of the enterprise, the enterprise library including one or more library functions; identifying a library function for servicing the customer intent from among the one or more library functions; generating a request based on the identified library function and the customer intent; transmitting the generated request to the enterprise to initiate an interaction; receiving a response from the enterprise; and notifying the customer of a result of the interaction.

Classes IPC  ?

• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• G06Q 30/00 - Commerce

Produits et services

Recorded and downloadable computer software for debt collection and recovery and management of related workflow, namely, for importing, exporting, and sharing of account data, for managing data flow and the entire debt-purchasing life cycle, including contractual relationships and account placements with outsourced agencies, for providing visual flowcharts and tools to assist in the planning, analysis, and automation of collection processes and account treatment strategies, and for providing interfaces with third-party offerings regarding debt collection Providing temporary use of on-line non-downloadable software and applications for debt collection and recovery and management of related workflow, namely, for importing, exporting, and sharing of account data, for managing data flow and the entire debt-purchasing life cycle, including contractual relationships and account placements with outsourced agencies, for providing visual flowcharts and tools to assist in the planning, analysis, and automation of collection processes and account treatment strategies, and for providing interfaces with third-party offerings regarding debt collection

Abrégé

A system and method are presented for providing a contextual summary of an interaction between a first party and a second party over a media channel in an interaction transfer to a third party. A request is received to transfer an interaction to the third party, the request being triggered from the interaction. A text transcript of the interaction is obtained. The text transcript is processed to obtain the contextual summary and metadata, which is provided to the third party for handling the interaction with the interaction transfer. The contextual summary is provided by a contextual summarizer, which comprises a custom plurality of application programming interfaces (APIs). The plurality of APIs comprises at least one of: a text summarizer API, a churn predictor API, a sentiment analysis API, a next best action API, and an interaction reference detector API.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• H04M 5/00 - Centraux manuels
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds
• H04L 51/00 - 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
• 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
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G06F 40/10 - Traitement de texte
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur

Abrégé

A method for managing automated resources that includes: calculating a channel RIQ score for a first bot in relation to a first communication channel and using it to modify a deployment of the first bot. The calculating the channel RIQ score for the first bot may include: identifying a first engagement scenario that the first bot is able to perform via the first communication channel; identifying target bot capabilities for performing the tasks of the first engagement scenario via the first communication channel; collecting performance data related to the first bot performing the first engagement scenario via the first communication channel; calculating the channel RIQ score by comparing: the performance data related to the first bot performing the first engagement scenario; and the target bot capabilities for performing the first engagement scenario via the first communication channel.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• 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
• G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur

Abrégé

A system and method are presented for delivering modular tools through a cloud-based remote computing environment. Users may create and customize extensions to the modular tools for integration with the product system without requiring change to product. Using a script tag added to a website, a user is able to install the modular tool to a website. The modular tool extensions are able to directly communicate with handlers and websites and are able to become directly integrated with a premises-based product. This may be done through a multiplexed API exposed to the modular tool extensions that directly couples them to the premises-based product. The modular tools are able to be configured in the premises-based product, however they are hosted in the cloud and as such, the premises-based product is able to receive continuous integration and deployment from the cloud.

Classes IPC  ?

• G06F 9/54 - Communication interprogramme
• G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
• G06F 40/14 - Documents en configuration arborescente
• G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
• G06Q 30/00 - Commerce
• H04L 41/12 - Découverte ou gestion des topologies de réseau
• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
• H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
• H04L 67/02 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP]
• H04L 41/0859 - Récupération de la configuration du réseau; Suivi de l’historique de configuration du réseau en conservant l'historique des différentes générations de configuration ou en revenant aux versions de configuration précédentes
• H04L 41/5041 - Gestion des services réseau, p.ex. en assurant une bonne réalisation du service conformément aux accords caractérisée par la relation temporelle entre la création et le déploiement d’un service
• H04L 41/0869 - Validation de la configuration au sein d'un élément de réseau
• G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p.ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
• H04L 41/50 - Gestion des services réseau, p.ex. en assurant une bonne réalisation du service conformément aux accords
• H04L 41/0233 - Techniques orientées objet, pour la représentation des données de gestion de réseau, p.ex. l’architecture commune de répartition des requêtes d’objets [CORBA]

Abrégé

A system and method are presented for analyzing network performance of a web application. A user interface in a cloud-based collaboration platform provides a notification when the user's location is having network connectivity issues to the collaboration platform. Connectivity issues may be determined through aggregation of measurements of network quality and performance (e.g. XMLHttpRequest requests, Websockets, and WebRTC) and alerting the user when issues arise. Data may be pushed back to an analytics ingestion system for aggregate-of-aggregate analytics and alerting as well as viewing network performance across a plurality of users and organizations.

Classes IPC  ?

• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison

Abrégé

A system and method are presented for the correction of packet loss in audio in automatic speech recognition (ASR) systems. Packet loss correction, as presented herein, occurs at the recognition stage without modifying any of the acoustic models generated during training. The behavior of the ASR engine in the absence of packet loss is thus not altered. To accomplish this, the actual input signal may be rectified, the recognition scores may be normalized to account for signal errors, and a best-estimate method using information from previous frames and acoustic models may be used to replace the noisy signal.

Classes IPC  ?

• G10L 19/005 - Correction d’erreurs induites par le canal de transmission, lorsqu’elles sont liées à l’algorithme de codage
• G10L 15/08 - Classement ou recherche de la parole
• G10L 15/14 - Classement ou recherche de la parole utilisant des modèles statistiques, p.ex. des modèles de Markov cachés [HMM]
• G10L 15/20 - Techniques de reconnaissance de la parole spécialement adaptées de par leur robustesse contre les perturbations environnantes, p.ex. en milieu bruyant ou reconnaissance de la parole émise dans une situation de stress
• G10L 19/00 - Techniques d'analyse ou de synthèse de la parole ou des signaux audio pour la réduction de la redondance, p.ex. dans les vocodeurs; Codage ou décodage de la parole ou des signaux audio utilisant les modèles source-filtre ou l’analyse psychoacoustique
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la parole; Sélection d'unités de reconnaissance 
• G10L 25/18 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits les paramètres extraits étant l’information spectrale de chaque sous-bande
• G10L 25/21 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits les paramètres extraits étant l’information sur la puissance

Produits et services

DOWNLOADABLE COMPUTER SOFTWARE FEATURING A DOWNLOADABLE MOBILE DEVICE APPLICATION FOR FACILITATING STAFFING INTERACTIONS WITH CONTACT CENTER WORKFORCE AND WORKFORCE MANAGEMENT, NAMELY, FOR USE IN SCHEDULING, TRACKING, AND STATUS MONITORING OF CONTACT CENTER WORKFORCE PROVIDING TEMPORARY USE OF WEB-BASED SOFTWARE APPLICATIONS FOR FACILITATING STAFFING INTERACTIONS WITH CONTACT CENTER WORKFORCE AND WORKFORCE MANAGEMENT, NAMELY, FOR USE IN SCHEDULING, TRACKING, AND STATUS MONITORING OF CONTACT CENTER WORKFORCE

Produits et services

INTERNET-BASED TELEPHONY SERVICES, NAMELY, VOICE OVER INTERNET PROTOCOL (VOIP) SERVICES, PROVIDING PUBLIC TELEPHONY ACCESS TO CLOUD SUBSCRIPTION SERVICES PROVIDING TEMPORARY USE OF NON-DOWNLOADABLE SOFTWARE FOR USE IN AN INTERNET-BASED TELEPHONY SERVICE; PROVIDING TEMPORARY USE OF ONLINE NON-DOWNLOADABLE CLOUD COMPUTING SOFTWARE FOR INTERNET-BASED TELEPHONY SERVICES; SOFTWARE AS A SERVICE (SAAS) SERVICES FEATURING SOFTWARE FOR INTERNET-BASED TELEPHONY SERVICES; PROVIDING TEMPORARY USE OF ON-LINE NON-DOWNLOADABLE SOFTWARE AND APPLICATIONS USED FOR INTEGRATING, OPERATING AND MANAGING AN ORGANIZATION'S INTERNET-BASED TELEPHONY SERVICE, NAMELY, FOR USE IN LOCAL CALLING, LONG DISTANCE CALLING, INTERNATIONAL CALLING, EMERGENCY SERVICES, TOLL-FREE SERVICES, NUMBER PURCHASING, AND NUMBER PORTING

Abrégé

A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.

Classes IPC  ?

• G06Q 20/00 - Architectures, schémas ou protocoles de paiement
• G06N 20/00 - Apprentissage automatique
• G06F 21/60 - Protection de données
• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail

Abrégé

A method, system, and computer program product for unsupervised automated generation of lexicons in a specified target domain, comprising tokens having domain-specific sentiment orientation, by selecting a seed set of tokens from a source lexicon; generating a candidate set of tokens from a text corpus in the target domain based on a similarity parameter with the seed set; calculating a sentiment score for each of the tokens in the candidate set; and automatically updating the source lexicon based on the candidate list.

Classes IPC  ?

• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence
• G06F 40/49 - Traduction appuyée sur des données utilisant de très grands corpus, p.ex. le Web
• G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G10L 25/63 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour estimer un état émotionnel

Abrégé

A system is presented for load balancing across media server instances. In an embodiment, media is broken out into a multi-tenanted service allowing the media to be scaled independently of the number of organizations supported on a cloud-based collaboration platform. Scaling may occur in a scaling-out or a scaling-in operation. States for a media service may comprise in-service, quiescing, quiesced, failed, etc. The states may be used to monitor sessions associated with an instance and determine which media instances to terminate during a scaling-in operation. In an embodiment, new instances may be added to a collection of media instances in response to an increased workload in a scaling-out operation.

Classes IPC  ?

• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• H04L 67/1031 - Commande du fonctionnement des serveurs par un répartiteur de charge, p.ex. en ajoutant ou en supprimant de serveurs qui servent des requêtes
• H04L 67/1029 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour accéder à un serveur parmi une pluralité de serveurs répliqués en utilisant des données liées à l'état des serveurs par un répartiteur de charge
• H04L 67/1008 - Sélection du serveur pour la répartition de charge basée sur les paramètres des serveurs, p.ex. la mémoire disponible ou la charge de travail
• H04L 47/70 - Contrôle d'admission; Allocation des ressources
• H04L 67/60 - 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
• H04L 67/14 - Gestion de session
• H04L 65/61 - Diffusion en flux de paquets multimédias pour la prise en charge des services de diffusion par flux unidirectionnel, p.ex. radio sur Internet
• H04L 47/125 - Prévention de la congestion; Récupération de la congestion en équilibrant la charge, p.ex. par ingénierie de trafic
• H04L 65/401 - Prise en charge des services ou des applications dans laquelle les services impliquent une session principale en temps réel et une ou plusieurs sessions parallèles additionnelles en temps réel ou sensibles au temps, p.ex. accès partagé à un tableau blanc ou mise en place d’une sous-conférence
• H04L 65/1083 - Procédures en session
• H04L 69/14 - Protocoles multicanaux ou multi-liaisons
• H04L 67/53 - Services réseau en utilisant des fournisseurs tiers de services

Abrégé

A system and method are presented for invoking integration actions in a unified collaboration system. A client communicates with a bridging web server through a ReST. The bridging web server comprises a cloud service which facilitates communication with integration servers, which may be located on-premises. In an embodiment, the integration server(s) host a number of plugins which are capable of implementing integration actions. The bridging web server decides which action implementation is the best one to service a request. The implementation may be based on prior configuration. Routing decisions on the bridging web server may be automatically selected based on prior configuration.

Classes IPC  ?

• G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
• H04L 67/01 - Protocoles
• H04L 67/02 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP]
• H04L 67/53 - Services réseau en utilisant des fournisseurs tiers de services
• 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

Abrégé

A method for routing interactions arriving at a contact center. The method may include: providing a cost schedule for the interactions that includes a classifier in which categories of interaction types are defined and a cost value corresponding to each of the interaction types; providing a capacity value for agents; and routing the interactions in accordance with a routing process. The routing process may include: receiving the first interaction; pursuant to the classifier, classifying the first interaction as being a first interaction type; determining the cost value of the first interaction as being the cost value corresponding to the first interaction type in the cost schedule; calculating for each of the agents an available capacity; and routing the first interaction to a first agent based on the available capacity of the first agent being sufficient given the cost value.

Classes IPC  ?

• H04M 3/00 - Centraux automatiques ou semi-automatiques
• 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 3/56 - Dispositions pour connecter plusieurs abonnés à un circuit commun, c. à d. pour permettre la transmission de conférences
• H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur

Abrégé

A system and method are presented for adaptive skill level assignments of agents in contact center environments. A client and a service collaborate to automatically determine the effectiveness of an agent handling an interaction that has been routed using skills-based routing. Evaluation operations may be performed including emotion detection, transcription of audio to text, keyword analysis, and sentiment analysis. The results of the evaluation are aggregated with other information such as the interaction's duration, agent skills and agent skill levels, and call requirement skills and skill levels, to update the agent's profile which is then used for subsequent routing operations.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06N 3/08 - Méthodes d'apprentissage
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/04 - Segmentation; Détection des limites de mots
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

A method for engaging in an automated dialog with a user that includes: retrieving a preset dialog flow that includes various blocks directing the dialog with the user; providing a prompt to the user based on a current block of the dialog flow; receiving an action from the user in response to the prompt; and retrieving a classification tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow representing user intents. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is identified based on the selected node, and a response is output in response to the identified target block.

Classes IPC  ?

• H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
• G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance
• G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 16/332 - Formulation de requêtes
• G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques

Abrégé

A system and method for handling interactions of a contact center. A processor of the system monitors a real-time interaction between a contact center agent and a user. The monitoring may include analyzing a first input by the user, and a first response by the contact center agent to the first input. The processor identifies an intent of the user based on monitoring the real-time interaction, and in response to identifying the intent, monitors for a first trigger condition. In response to identifying the first trigger condition, the processor invokes an automated agent for outputting a second response to a second input provided by the user. The first trigger condition may be a command to invoke the automated agent, or deviance by the agent from dialog in a dialog script associated with the identified intent.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• 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 comport
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G10L 15/30 - Reconnaissance distribuée, p.ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06F 40/30 - Analyse sémantique
• G06F 40/279 - Reconnaissance d’entités textuelles
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

A system and method are presented for unsupervised discovery of similar audio events collected from an automated dialing campaign. Locality Sensitive Hashing (LSH) is used to search for similar audio clips within a large dataset of audio recordings. A database is queried for possible matches between an unknown audio clip and any reference carrier audio message present in the database. The database is updated when new, or changed, carrier audio messages are detected.

Classes IPC  ?

• G06F 16/65 - Groupement; Classement
• G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
• G06F 16/61 - Indexation; Structures de données à cet effet; Structures de stockage

Abrégé

A system and method are presented for delivering modular tools through a cloud-based remote computing environment. Users may create and customize extensions to the modular tools for integration with the product system without requiring change to product. Using a script tag added to a website, a user is able to install the modular tool to a website. The modular tool extensions are able to directly communicate with handlers and websites and are able to become directly integrated with a premises-based product. This may be done through a multiplexed API exposed to the modular tool extensions that directly couples them to the premises-based product. The modular tools are able to be configured in the premises-based product, however they are hosted in the cloud and as such, the premises-based product is able to receive continuous integration and deployment from the cloud.

Classes IPC  ?

• H04L 12/24 - Dispositions pour la maintenance ou la gestion
• G06F 8/77 - Métriques logicielles
• G06F 21/10 - Protection de programmes ou contenus distribués, p.ex. vente ou concession de licence de matériel soumis à droit de reproduction
• G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
• G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
• G06F 9/54 - Communication interprogramme
• G06F 40/14 - Documents en configuration arborescente
• G06Q 30/00 - Commerce
• G06F 21/62 - Protection de l’accès à des données via une plate-forme, p.ex. par clés ou règles de contrôle de l’accès
• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison

Abrégé

A system and method are presented for delivering modular tools through a cloud-based remote computing environment. Users may create and customize extensions to the modular tools for integration with the product system without requiring change to product. Using a script tag added to a website, a user is able to install the modular tool to a website. The modular tool extensions are able to directly communicate with handlers and websites and are able to become directly integrated with a premises-based product. This may be done through a multiplexed API exposed to the modular tool extensions that directly couples them to the premises-based product. The modular tools are able to be configured in the premises-based product, however they are hosted in the cloud and as such, the premises-based product is able to receive continuous integration and deployment from the cloud.

Classes IPC  ?

• 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 comport
• G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
• G06F 1/16 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES - Détails non couverts par les groupes et - Détails ou dispositions de structure
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur

Abrégé

Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.

Classes IPC  ?

• G06F 7/00 - Procédés ou dispositions pour le traitement de données en agissant sur l'ordre ou le contenu des données maniées
• G06F 17/27 - Analyse automatique, p.ex. analyse grammaticale, correction orthographique
• G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet

Abrégé

Methods, systems, and computer program product for automatically performing sentiment analysis on texts, such as telephone call transcripts and electronic written communications. Disclosed techniques include, inter alia, lexicon training, handling of negations and shifters, pruning of lexicons, confidence calculation for token orientation, supervised customization, lexicon mixing, and adaptive segmentation.

Classes IPC  ?

• G06F 40/00 - Maniement de données en langage naturel
• G06F 40/35 - Représentation du discours ou du dialogue
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

Abrégé

A system and method are presented for model derivation for entity prediction. An LSTM with 100 memory cells is used in the system architecture. Sentences are truncated and provided with feature information to a named-entity recognition model. A forward and a backward pass of the LSTM are performed, and each pass is concatenated. The concatenated bi-directional LSTM encodings are obtained for the various features for each word. A fully connected set of neurons shared across all encoded words is obtained and the final encoded outputs with dimensions equal to the number of entities is determined.

Classes IPC  ?

• G06F 40/295 - Reconnaissance de noms propres
• 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 40/253 - Analyse grammaticale; Corrigé du style
• G06N 3/044 - Réseaux récurrents, p.ex. réseaux de Hopfield
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

A method for orchestrating a chat interaction with a customer and multiple sources that includes providing an animation library that includes conversational states, graphic stills corresponding to respective ones of the conversational states, animation templates, and source-based modifications corresponding to respective ones of the multiple sources. The method may include generating a source-informative animation by: determining a source of a chat message; determining a conversational state for the chat message; selecting a graphic still for the message; selecting an animation template for the chat message based on the selected graphic still; selecting a source-based modification that corresponds to the source; and modifying the animation template pursuant to the source-based modification so to generate the source-informative animation. The method may include sending a signal to the customer configured to generate thereon a chat interface having the chat message and source-informative animation.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06T 13/80 - Animation bidimensionnelle [2D], p.ex. utilisant des motifs graphiques programmables
• H04L 12/58 - Systèmes de commutation de messages
• G06T 11/40 - Remplissage d'une surface plane par addition d'attributs de surface, p.ex. de couleur ou de texture
• G06T 11/20 - Traçage à partir d'éléments de base, p.ex. de lignes ou de cercles
• G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image

Abrégé

A method of anticipating user action by a contact center system based on machine learning includes receiving, by a processor of the contact center system, a first signal from an end user device in communication with the contact center system, the first signal being indicative of an event associated with a user journey, the user journey including a plurality of events, identifying, by the processor, a subset of the plurality of events of the user journey as a plurality of input events, predicting, by the processor, a next event associated with the user journey based on the plurality of input events; and generating, by the processor, a second signal for transmission to the end user device, the second signal including data corresponding to the next event.

Classes IPC  ?

• G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
• G06Q 30/00 - Commerce
• G06N 3/08 - Méthodes d'apprentissage

Abrégé

A system and method are presented for predicting workload demand in a customer journey application. Using historical information from journey analytics, journey moments can be aggregated through various stages. Probability-distribution-vectors can be approximated for various paths connected the stages. Stability of such probability distribution can be determined through statistical methods. Predictions for future volumes progressing through the stages can be determined through recursive algorithms after applying a time-series forecasting algorithm at the originating stage(s). Once future volumes have been forecasted at every stage, future workload can be estimated to better capacity planning and scheduling of resources to handle such demand to achieve performance metrics along the cost function.

Classes IPC  ?

• G06F 9/46 - Dispositions pour la multiprogrammation
• G06Q 10/00 - Administration; Gestion
• G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
• H04M 3/50 - Dispositions centralisées pour répondre aux appels; Dispositions centralisées pour enregistrer des messages pour abonnés absents ou occupés
• 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

Abrégé

A system and method are presented for predicting workload demand in a customer journey application. Using historical information from journey analytics, journey moments can be aggregated through various stages. Probability-distribution-vectors can be approximated for various paths connected the stages. Stability of such probability distribution can be determined through statistical methods. Predictions for future volumes progressing through the stages can be determined through recursive algorithms after applying a time-series forecasting algorithm at the originating stage(s). Once future volumes have been forecasted at every stage, future workload can be estimated to better capacity planning and scheduling of resources to handle such demand to achieve performance metrics along the cost function.

Classes IPC  ?

• G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projets; Planification d’entreprise ou d’organisation; Modélisation d’entreprise ou d’organisation
• G06Q 30/00 - Commerce

Abrégé

A system and method are presented for implementing cross-origin sessions in collaboration platforms. A webchat iframe may store data with an expiration and a list of domains that are able to access the iframe. Functionalities such as co-browse and webchat are enabled to work across navigation from one friendly domain to another friendly domain as a result. Browser restrictions may be circumnavigated that forbid webpages housed on different domains to share data. In an embodiment, a whitelist may be stored of potential accessors and a time to live with each datum. In an embodiment, data may be retrieved from the same origin or it may be retrieved from another origin. In another embodiment, data may be protected from unauthorized accessors.

Classes IPC  ?

• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole

Abrégé

A system and method are presented for configuring topic-specific chatbots. Clustering interaction transcripts between customers and agents of a contact center is performed to generated a plurality of interaction clusters. The clusters corresponding a topic. Topic-specific dialogue trees are extracted for each cluster. The trees comprise nodes connected by edges. The topic-specific dialogue tree is modified to generate a deterministic dialogue tree. The deterministic dialogue tree is used to configure a topic-specific chatbot to generate and automatically respond to messages regarding the topic.

Classes IPC  ?

• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G06F 40/35 - Représentation du discours ou du dialogue
• H04M 3/527 - Dispositions centralisées de réponse aux appels ne demandant pas l'intervention d'un opérateur
• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06F 16/332 - Formulation de requêtes
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G10L 25/30 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux

Abrégé

A system and method are presented for dialogue tree generation. The dialogue tree may be used for generating a chatbot. Similar phrases from phrases comprising the interactions between a first party and a second party are group together from the first party of a cluster. For each group of similar phrases, percentages are determined and compared against a threshold occurrence rate. Anchors are generated and used in alignment in the determination of dialogue flows. Topic-specific dialogue trees may be determined from the dialogue flows. The topic-specific dialogue trees may be modified to generate a deterministic dialogue tree.

Classes IPC  ?

• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• H04M 3/527 - Dispositions centralisées de réponse aux appels ne demandant pas l'intervention d'un opérateur
• G06F 16/332 - Formulation de requêtes
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G10L 25/30 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux

Abrégé

A system and method are presented for omni-channel notification and selection. A first party selects a first channel from a plurality of channels based on an estimated wait time for interaction with another party in a contact center or enterprise setting. The plurality of channels may comprise any means of interaction, such as chat, phone call, SMS, video, etc. While waiting in queue for the preferred first channel, the first party is presented with estimated wait times, which are dynamically updated, for other channels. Based on the first party's preferences and the presented estimated wait times for the other channels, the first party may elect to change interaction channels from the first channel to a second channel. The first party may then be moved to a different queue corresponding to the second interaction channel.

Classes IPC  ?

• 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
• H04M 1/725 - Téléphones sans fil

Abrégé

A method for generating a dialogue tree for an automated self-help system of a contact center from a plurality of recorded interactions between customers and agents of the contact center includes: computing, by a processor, a plurality of feature vectors, each feature vector corresponding to one of the recorded interactions; computing, by the processor, similarities between pairs of the feature vectors; grouping, by the processor, similar feature vectors based on the computed similarities into groups of interactions; rating, by the processor, feature vectors within each group of interactions based on one or more criteria, wherein the criteria include at least one of interaction time, success rate, and customer satisfaction; and outputting, by the processor, a dialogue tree in accordance with the rated feature vectors for configuring the automated self-help system.

Classes IPC  ?

• H04M 11/00 - Systèmes de communication téléphonique spécialement adaptés pour être combinés avec d'autres systèmes électriques
• H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• G06F 16/332 - Formulation de requêtes
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• H04M 7/00 - Dispositions d'interconnexion entre centres de commutation 

Abrégé

A system and method are presented for a replication protocol in a real-time statistical engine. A real-time statistical engine monitors external data sources and uses received data to derive on-demand statistics. The engine may be comprised of a plurality of instances. In an embodiment with two instances of the engine, the instances are labeled as a primary instance and a backup instance. In case of primary instance failure, client servers of the engine are able to connect to the backup instance and obtain the same statistics with minimal data loss. The communication system between the primary instance and the backup instance may be used to exchange information about requested statistics, to clean unused statistics and to unconditionally delete statistics for which computation has become impossible.

Classes IPC  ?

• G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement
• G06F 9/448 - Paradigmes d’exécution, p.ex. implémentation de paradigmes de programmation
• H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole

Abrégé

A method for automating interactions with enterprises includes: receiving, through a user interface, a natural language request from a customer; determining a customer intent from the natural language request; conducting an automated interaction by: determining, by a personal bot controller circuit, an enterprise to service the customer intent; loading an enterprise library for interacting with an application programming interface (API) of the enterprise, the enterprise library including one or more library functions; identifying a library function for servicing the customer intent from among the one or more library functions; generating a request based on the identified library function and the customer intent; transmitting the generated request to the enterprise to initiate an interaction; receiving a response from the enterprise; and notifying the customer of a result of the interaction.

Classes IPC  ?

• H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
• H04M 3/50 - Dispositions centralisées pour répondre aux appels; Dispositions centralisées pour enregistrer des messages pour abonnés absents ou occupés
• 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

Abrégé

A system and method are presented for F0 transfer learning for improving F0 prediction with deep neural network models. Larger models are trained using long short-term memory (LSTM) and multi-layer perceptron (MLP) feed-forward hidden layer modeling. The fundamental frequency values for voiced and unvoiced segments are identified and extracted from the larger models. The values for voiced regions are transferred and applied to training a smaller model and the smaller model is applied in the text to speech system for real-time speech synthesis output.

Classes IPC  ?

• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
• G10L 13/04 - Procédés d'élaboration de parole synthétique; Synthétiseurs de parole - Détails des systèmes de synthèse de la parole, p.ex. structure du synthétiseur ou gestion de la mémoire
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel

Abrégé

A method for automating interactions with enterprises includes: receiving, by a user interface, a natural language request from a customer; determining a customer intent from the natural language request; conducting an automated interaction by: determining, by a personal bot controller circuit, an enterprise to service the customer intent; loading an enterprise library for interacting with a chatbot of the enterprise, the enterprise library comprising one or more library functions; identifying a library function for servicing the customer intent from among the one or more library functions; generating a text string request based on the identified library function and the customer intent; transmitting the generated text string request to the chatbot of the enterprise; receiving a text response from the chatbot of the enterprise; and notifying the customer of a result of the interaction.

Classes IPC  ?

• H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
• H04M 3/50 - Dispositions centralisées pour répondre aux appels; Dispositions centralisées pour enregistrer des messages pour abonnés absents ou occupés
• 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

Abrégé

According to one embodiment, a method for automating an interaction between a user and a contact center includes: receiving, by a processor, a natural language inquiry from the user; identifying, by the processor, a user intent from the natural language inquiry using a natural language processing module; loading, by the processor, a script corresponding to the user intent, the script comprising a plurality of fields of information associated with the user intent; filling at least one of the fields of information of the script based on a stored user profile; and supplying the filled fields of information to the contact center in accordance with the script. Some embodiments of the present invention relate to systems and methods for augmenting interactions between the user and the contact center.

Classes IPC  ?

• 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
• G10L 13/04 - Procédés d'élaboration de parole synthétique; Synthétiseurs de parole - Détails des systèmes de synthèse de la parole, p.ex. structure du synthétiseur ou gestion de la mémoire
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel

Abrégé

A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 15/01 - Estimation ou évaluation des systèmes de reconnaissance de la parole
• G10L 15/08 - Classement ou recherche de la parole
• G10L 15/10 - Classement ou recherche de la parole utilisant des mesures de distance ou de distorsion entre la parole inconnue et les gabarits de référence
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole

Abrégé

A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 15/10 - Classement ou recherche de la parole utilisant des mesures de distance ou de distorsion entre la parole inconnue et les gabarits de référence
• G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
• G06N 20/00 - Apprentissage automatique
• G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
• G10L 15/14 - Classement ou recherche de la parole utilisant des modèles statistiques, p.ex. des modèles de Markov cachés [HMM]

Abrégé

In a system for managing blended agent and automated chat in a contact center setting, the system includes: a chat orchestration server invoking natural language processing on a received communication, selecting a standardized text communication responsive to the natural-language-processed communication, and transmitting the standardized text communication to an agent device; and a knowledge management server for determining confidence values and populating ranked lists of responsive standardized text communications.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• H04L 12/58 - Systèmes de commutation de messages
• G06F 17/27 - Analyse automatique, p.ex. analyse grammaticale, correction orthographique

Abrégé

A system is provided for routing transactions to individual ones of a group of agents, comprising, a queue wherein transactions to be routed are placed, a transmission facility sending invitations to interact with a transaction from the queue to agents, and a router routing transactions from the queue to individual agents who accept the invitation.

Classes IPC  ?

• 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

Abrégé

In a method for managing a communication system, the method includes: identifying, by a processor, initiation of a chat communication session with a user device operated by a user; establishing, by the processor, a connection to the chat communication session by an automated chat resource; routing, by the processor, the chat communication session to a first agent device operated by a first agent; detecting, by the processor, a disconnection of the user device from the chat communication session; maintaining, by the processor, the connection to the chat communication session by the automated chat resource during a timeout period after the disconnection of the user device is detected; detecting, by the processor, the user device reconnecting to the chat communication session; and routing, by the processor, the chat communication session to a contact center resource.

Classes IPC  ?

• G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation

Abrégé

In a method for managing a communication system, the method includes: identifying, by a processor, initiation of a chat communication session with a user device operated by a user; establishing, by the processor, a connection to the chat communication session by an automated chat resource; routing, by the processor, the chat communication session to a first agent device operated by a first agent; detecting, by the processor, a disconnection of the user device from the chat communication session; maintaining, by the processor, the connection to the chat communication session by the automated chat resource during a timeout period after the disconnection of the user device is detected; detecting, by the processor, the user device reconnecting to the chat communication session; and routing, by the processor, the chat communication session to a contact center resource.

Classes IPC  ?

• G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes
• H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
• H04L 12/18 - Dispositions pour la fourniture de services particuliers aux abonnés pour la diffusion ou les conférences
• H04L 12/58 - Systèmes de commutation de messages

Abrégé

In a method for managing a communication system, the method includes: identifying, by a processor, initiation of a chat communication session with a user device operated by a user; establishing, by the processor, a connection to the chat communication session by an automated chat resource; routing, by the processor, the chat communication session to a first agent device operated by a first agent; detecting, by the processor, a disconnection of the user device from the chat communication session; maintaining, by the processor, the connection to the chat communication session by the automated chat resource during a timeout period after the disconnection of the user device is detected; detecting, by the processor, the user device reconnecting to the chat communication session; and routing, by the processor, the chat communication session to a contact center resource.

Classes IPC  ?

• G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
• 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 comport
• H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
• G06Q 10/10 - Bureautique; Gestion du temps
• G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds

Abrégé

A method for automated quality management of agents of a contact center includes: selecting, by a processor, a topic for interacting with a human agent of the contact center; identifying, by the processor, a dialog tree associated with the selected topic; and engaging, by the processor, in an automated communication session with the human agent based on the identified dialog tree, wherein the engaging of the automated communication session includes: receiving, by the processor, an agent input; identifying, by the processor, a current node of the dialog tree associated with the agent input; selecting, by the processor, an automated phrase to be output in response to identifying the current node; and outputting, by the processor, the automated phrase.

Classes IPC  ?

• H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
• H04L 12/58 - Systèmes de commutation de messages
• G06F 17/27 - Analyse automatique, p.ex. analyse grammaticale, correction orthographique
• H04M 7/00 - Dispositions d'interconnexion entre centres de commutation 

Abrégé

A system and method are presented for the automatic filtering of test utterance mismatches in automatic speech recognition (ASR) systems. Test data are evaluated for match between audio and text in a language-independent manner. Utterances having mismatch are identified and isolated for either removal or manual verification to prevent incorrect measurements of the ASR system performance. In an embodiment, contiguous stretches of low probabilities in every utterance are searched for and removed. Such segments may be intra-word or cross-word. In another embodiment, scores may be determined using log DNN probability for every word in each utterance. Words may be sorted in the order of the scores and those utterances containing the least word scores are removed.

Classes IPC  ?

• G10L 15/00 - Reconnaissance de la parole
• G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la parole; Sélection d'unités de reconnaissance 
• G10L 15/197 - Grammaires probabilistes, p.ex. n-grammes de mots
• G10L 15/14 - Classement ou recherche de la parole utilisant des modèles statistiques, p.ex. des modèles de Markov cachés [HMM]

Abrégé

A system and method are presented for optimization of audio fingerprint search. In an embodiment, the audio fingerprints are organized into a recursive tree with different branches containing fingerprint sets that are dissimilar to each other. The tree is constructed using a clustering algorithm based on a similarity measure. The similarity measure may comprise a Hamming distance for a binary fingerprint or a Euclidean distance for continuous valued fingerprints. In another embodiment, each fingerprint is stored at a plurality of resolutions and clustering is performed hierarchically. The recognition of an incoming fingerprint begins from the root of the tree and proceeds down its branches until a match or mismatch is declared. In yet another embodiment, a fingerprint definition is generalized to include more detailed audio information than in the previous definition.

Classes IPC  ?

• G06F 16/683 - Recherche de données caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
• G10L 25/51 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation

Abrégé

A system and method that pre-configure a telecommunication path between two users across a network. A server communicates an Application Programming Interface (API) to a website of an entity which, when accessed by a first user at a remote terminal, loads the API into the user terminal, captures data representative of the interaction of the first user with the website in real time, and communicates the data across the network to the server. The server compares the data with stored attributes of second users to identify a matching second user. The data is analyzed and used to predict a successful outcome between the two users. The server selects a network address of the matched second user, and, upon initiation of a telecommunication with the entity by the first user, routes the telecommunication to the network address and communicates the data to a second terminal of the matched second user.

Classes IPC  ?

• H04W 4/21 - Signalisation de services; Signalisation de données auxiliaires, c. à d. transmission de données par un canal non destiné au trafic pour applications de réseaux sociaux
• H04L 67/02 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP]
• G06N 20/00 - Apprentissage automatique

Abrégé

Event data that includes an identification of a registered device and a state of the registered device may be received by a contact center, where the registered device is different from the user device. A resource requirement for a communications session between a user device and an agent device may be determined by the contact center based on the event data. Routing information that enables the user device to establish a communications session with the agent device without a need for a user of the user device to provide the identification of the registered device to an agent of the agent device may be generated by the contact center based on the resource requirement.

Classes IPC  ?

• H04L 12/24 - Dispositions pour la maintenance ou la gestion

Abrégé

A method for configuring a topic-specific chatbot comprising: clustering a plurality of transcripts of interactions between customers and agents of a contact center of an enterprise to generate a plurality of clusters of interactions, each cluster of interactions corresponding to a topic, each of the interactions including agent phrases and customer phrases; for each cluster of the plurality of clusters of interactions: extracting a topic-specific dialogue tree for the cluster; pruning the topic-specific dialogue tree to generate a deterministic dialogue tree; and configuring a topic-specific chatbot in accordance with the deterministic dialogue tree; and outputting the one or more topic-specific chatbots, each of the topic-specific chatbots being configured to generate, automatically, responses to messages regarding the topic of the topic-specific chatbot from a customer in an interaction between the customer and the enterprise.

Classes IPC  ?

• G06F 17/00 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques

Abrégé

A method for configuring a topic-specific chatbot: clustering, by a processor, a plurality of transcripts of interactions between customers and human agents of a contact center of an enterprise to generate a plurality of clusters of interactions, each cluster of interactions corresponding to a topic, each of the interactions including agent phrases and customer phrases; for each cluster of the plurality of clusters of interactions: extracting, by the processor, a topic-specific dialogue tree for the cluster; pruning, by the processor, the topic-specific dialogue tree to generate a deterministic dialogue tree; and configuring, by the processor, a topic-specific chatbot in accordance with the deterministic dialogue tree; and outputting, by the processor, the one or more topic-specific chatbots, each of the topic-specific chatbots being configured to generate, automatically, responses to messages regarding the topic of the topic-specific chatbot from a customer in an interaction between the customer and the enterprise.

Classes IPC  ?

• G06F 17/27 - Analyse automatique, p.ex. analyse grammaticale, correction orthographique
• 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
• H04M 3/527 - Dispositions centralisées de réponse aux appels ne demandant pas l'intervention d'un opérateur
• G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
• G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
• G06F 16/332 - Formulation de requêtes