COMPUTERIZED-SYSTEM AND COMPUTERIZED-METHOD FOR GENERATIVE ARTIFICIAL INTELLIGENCE BASED DIGITAL CHANNEL SELECTION OF OUTBOUND CAMPAIGN COMMUNICATION, TO A PRODUCT OF A TENANT, IN A CLOUD-BASED CONTACT CENTER
A computerized-system for generative artificial intelligence based digital channel selection of outbound campaign communication, to a product of a tenant, in a cloud-based contact center. The computerized-system includes a contact center datastore; a product Customer Relationship Management (CRM) datastore; a memory to store the plurality of datastores; and processors. For each interaction of the outbound campaign the processors are configured to: (i) generate contextual word embeddings for contact center data of the tenant retrieved from the contact center datastore and data of the product of the tenant retrieved from the product CRM datastore; (ii) calculate a Channel Selection Score (CSS) of each digital channel in one or more digital channels of the tenant based on the generated contextual embeddings; (iii) select a digital channel having highest CSS from the one or more digital channels; and (iv) automatically redirect the outbound campaign communication via the selected digital channel.
SYSTEM AND METHOD FOR DYNAMICALLY IMPLEMENTING ADJUSTED FORECASTING ALGORITHMS IN AN APPLICATION PROVIDED AS A SERVICE TO TENANTS, IN A MULTI-TENANT CLOUD-BASED COMPUTING ENVIRONMENT
A computerized-system for dynamically implementing adjusted forecasting algorithms in an application, in a multi-tenant cloud-based computing environment. Processors in the computerized-system are configured to: (i) collect tenant-metadata and interactions data from each tenant and store it; (ii) operate tenant segmentation based on a calculated tenant-score and the stored interactions data to yield tenant-segments; (iii) for each tenant-segment: a. retrieve channel information and forecast parameters related to tenants in the tenant-segment; b. invoke a serverless compute service to initiate rule association generation with a data mining algorithm based on the retrieved interactions data to yield association rules; c. store the association rules; d. provide the stored association rules, the interaction data and the tenant-metadata to a LLM; e. receive from the LLM forecasting algorithms, channel preferences, and other parameters; and f. implement the received forecasting algorithms, channel preferences, and other parameters in the application for each tenant in the tenant-segment.
A system automatically display a summary of a customer-agent interaction before the end of the interaction. The system includes an agent computing device in communication with a contact center control system. Over a period of time, the system receives a group of interactions from a number of customers, and trains a machine learning model to assign an end-of-call probability weight to each phrase in the interactions. The system then receives a real-time interaction including a number of utterances, connects the interaction to the agent computing device, constructs a real-time transcript and, with the machine learning model, computes a cumulative end-of-call probability based on the utterances. When the end-of-call probability exceeds a threshold value the system constructs or updates a summary of the transcript, displays the summary, and performs at least one automated action based on the contents of the summary.
A system and method for automatic real-time identification of and compliance with worldwide data protection rules for online interactions is provided. An online interaction can be received, an applicable data protection rule based on a location of a sender of the online interaction can be determined, compliance rules can be created in real-time using machine learning, where the compliance rules can indicate access, security and/or retention policy for the interaction.
A system and method for an improved text to structured query language (SQL) for use with a large language model (LLM). Categories of possible questions for a database can be determined and a mapping between the data elements of the database and the categories can be determined. User questions can have a match level to the categories. Data elements from a data schema can be removed that do not match categories according to the match level, the modified data schema can be used for the database with the LLM.
A server is configured to, over a first period of time: receive a first set of deferrable contacts; automatically distribute the deferrable contacts to agents for resolution via the agent computing devices; measuring a respective time required for each resolution; and computing an average of the respective required times. The server is also configured to, continuously, in real time: receive a second set of deferrable contacts; based on the average of the respective required times, compute a total required time to resolve the second set of deferrable contacts; with the GUI: display the total required time; receive an approval; automatically generate a required staffing; automatically generate an agent schedule; and with the GUI, publish the agent schedule to the agents.
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
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
7.
SYSTEM AND METHOD FOR CUSTOMER INTERACTION INSIGHTS GENERATION
Method and system of customer interaction insights generation, and non-transitory computer readable media, include selecting, by a user, an interaction between a customer and a contact center agent; receiving input data comprising interaction information about the selected interaction, wherein the input data is generated based on analyzing a transcript of the selected interaction using a template associated with the user, and wherein the template comprises a set of interaction characteristics that may be of particular interest to the user, and a set of predetermined thresholds based on the set of interaction characteristics; analyzing the interaction information using the set of predetermined thresholds for the set of interaction characteristics; generating an initial prompt for a large language model to generate interaction insights for the selected interaction; and providing the interaction insights to the user for analysis.
A computerized-method for detection and implementation of litigation-hold and release for interactions recordings within a contact center. The computerized-method includes for each recording file of an interaction between a customer and an agent in the contact center: (i) retrieving interaction metadata and interaction analysis; (ii) calculating a Litigation Likelihood Score (LLS) for a recording file of the interaction based on the retrieved interaction metadata and interaction analysis; (iii) setting the recording file on litigation-hold status when the LLS is above a preconfigured threshold for a preconfigured period of time; (iv) checking if there was access to the recording file after the preconfigured period of time has elapsed; and (v) releasing the recording file from litigation-hold status after the preconfigured period of time when there is no access to the recording file.
A computerized-method for matching customer queries across diverse digital channels with a response in a multichannel contact center. The computerized-method includes: (i) continuously receiving queries from customers via digital communication channels; in each preconfigured time-window: (ii) converting each customer query in the received queries into a high-dimensional vector representation and scoring the customer query to yield a similarity score by operating a semantic embedder module; (iii) clustering each customer query based on the high-dimensional vector representation and the similarity score by operating a AIQA module to yield clusters of customer queries with semantic similarity; (iv) matching each cluster of customer queries with the response based on a preconfigured threshold by operating a CVSE; (v) automatically generating a notification for each customer query based on the matched response by operating a notification module; and (vi) automatically sending the notification to each customer that is associated with the customer query.
In general, systems and methods are provided for training a neural network based on sample screen recordings wherein the neural network learns to minimize a reconstruction error between an original image input to the VAE, and an image reconstructed by the VAE. The systems and methods can provide determining and displaying significant points in time of the frame based on a similarity between compressed frames of a screen recording based on the trained VAE.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/776 - ValidationÉvaluation des performances
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
11.
Methods to fine tune simple query language generation from natural language input
A real-time system for generating Structured Query Language (SQL) queries from natural language input includes a server in communication with a large language model (LLM). The server is configured to: receive a natural language input from a user; generate a prompt that includes a terse table name or column name from the database, and instructions for generating an SQL query for the database, based on the natural language input. The processor is further configured to: replace the terse table name or column name with a verbose table name or column name; send the prompt to the LLM; receive an SQL query from the LLM, including the at least one verbose table name or column name; replace the at least one verbose table name or column name with the at least one terse table name or column name; execute the SQL query on the database; and receive an SQL query result.
A customer issue resolution system and methods for resolving a customer issue include receiving an audio interaction between a customer and an agent; converting the audio interaction into text; identifying a customer issue in the text of the interaction; performing a semantic search in a vector database for the customer issue in past customer interactions of a plurality of customers; obtaining a plurality of transcripts of past customer interactions from the vector database that match the customer issue, where the customer issue was resolved, and that have a threshold customer satisfaction score; constructing a large language model (LLM) prompt based on the obtained plurality of transcripts and the customer issue; executing the LLM prompt to return a recommendation to resolve the customer issue; and displaying the recommendation to the agent in real-time.
A question processing system and methods are provided that are configured to intelligently generate structured data queries from natural language questions using a large language model (LLM). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which include receiving a natural language question for structured data including columns having discrete text values, generating, using an LLM, a first structured data query having a predicted text value, searching a data store for similar ones of the discrete text values, generating, using the LLM, a second structured data query that refines the first structured data query based on the similar discrete text values, and querying a structured database based on the first and/or second structured data query.
Systems and methods for evaluating and determining a root cause of a change in performance in a workforce management system are provided. Graphs can be created based on key performance indicators, behaviors and metrics to determined a root cause for agents who have a decrease in performance. The root cause can be transmitted to other computing systems which can select which interactions to record, selects a coaching program to run, or any combination.
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
15.
System and method for prioritizing migration sequence of data files of an application from a source-storage in a source-system to a target-storage in a target-system in a contact center
A computerized-method for prioritizing migration sequence of data files of an application from a source-storage in a source-system to a target-storage in a target-system in a contact center. The computerized-method includes a) for each data file with customers interactions data in the source-storage that has been marked for migration: (i) retrieving metadata of the file; (ii) retrieving agents details; (iii) identifying attributes related to customers interactions operations in the metadata of the file; (iv) receiving a configuration of weights and rules files. The configuration of weights and rules files includes attribute-type associated to each attribute, (v) normalizing value of each attribute based on a preconfigured formula that is related to the associated attribute-type; and (vi) operating a file-priority-score module to score priority of the file based on the normalized value of each attribute in the attributes; and b) migrating files to the target-storage based on their score of priority.
A computerized-method for handling emergency digital interactions in a contact center having agents handling concurrent interactions. The computerized-method includes for each agent in the contact center: (i) monitoring each real-time digital-interaction of an agent with a customer, that was routed by a ACD application, wherein the agent is concurrently handling two or more interactions via a User Interface (UI) that is associated to an application for handling concurrent interactions; (ii) operating an interaction-analyzer module on the monitored real-time digital-interaction to yield interaction-metadata; (iii) operating a prioritization module on the yielded interaction-metadata of the monitored real-time digital-interaction to set an emergency-mode thereto; The emergency-mode is one of: ‘on’ and ‘off’, and (iv) operating an interaction-handler module to prioritize the monitored real-time digital-interaction when the emergency-mode has been set to ‘on’ and move the agent into emergency-handling-mode.
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
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SaaS) services featuring software for
creating, deploying, and managing conversational artificial
intelligence agents, virtual assistants, chatbots, and
voicebots for automating customer service interactions;
development of software for communication systems; updating
of computer programs for third parties according to user
requirements; design, maintenance, rental and updating of
computer software; platform as a service featuring adaptive
software for speech and voice recognition, for converting
user's voice, for communicating with others, for updating
computer programs; platform as a service featuring speech
and voice recognition software; platform as a service
featuring adaptive software for speech analytics software;
platform as a service featuring software for processing
images, graphics and text.
18.
SYSTEM AND METHOD FOR ALTERING INTERACTION DISTRIBUTION SETTINGS
A system and method for managing interaction distribution, for example in a contact center, may, for a digital skill relating to interaction handling and impacted by a lack of resources during an interval, determine an agent having the digital skill; raise the identified agent's concurrency setting for the digital skill for the interval; and distribute interactions according to the concurrency setting. The concurrency setting may determine how many interactions relating to the digital skill the agent can handle concurrently.
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
H04L 41/5061 - Gestion des services réseau, p. ex. en assurant une bonne réalisation du service conformément aux accords caractérisée par l’interaction entre les fournisseurs de services et leurs clients réseau, p. ex. la gestion de la relation client
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
19.
SYSTEM AND METHOD FOR OPTIMIZING RULES USING A MACHINE LEARNING MODEL
A system and method for intelligent computerized task scheduling and execution, including: optimizing a time off rule including a quota of time off units for a time period—by changing the quota of time off units based on calculating a time off utilization indicator; updating a computerized task schedule based on the optimized time off rule; and executing tasks based on the updated schedule. In some embodiments, time off optimization may include identifying, by a machine learning model (such as, e.g., a generative artificial intelligence or large language model), rules matching a given time off rule, and deleting/merging rules based on similar rule names or activity codes. The machine learning model may generate rule names for merged rules. Optimized time off rules may be used to accept or reject time off requests transmitted and/or received, e.g. over a data or communication network.
A system and method for automatically identifying and masking information, including: identifying, using a machine learning model, one or more tokens in an image file using one or more data items extracted from the image file and including one or more strings, and masking one or more of the identified tokens in the image file. In some embodiments, masking includes coloring locations in the image file based on extracted location information—describing boxes or polygons bounding or surrounding of the strings in the image file. Tokens identified and/or masked by some embodiments may include personally identifiable information (PII) or data, and some embodiments may determine the type of PII data for tokens or texts identified as including PII. In some embodiments, rows of text extracted from the image file may be provided to the machine learning model in a sequential manner (e.g., one row after another).
A system is adapted to automatically manage schedule change requests. The system includes a workforce management server in electronic communication with a manager computing device, and agent computing devices. The server receives a number of SCRs. Continuously, in real time, the server: receives, a new SCR; fetches n SCRs from the SCR database whose deadlines are nearest; computes a respective net staffing impact for each of the n SCRs; and generates a prompt for an AI model. For each of the n SCRs, the AI model determines a respective priority, places the n SCRs in order of their respective priorities; and suggests a respective action to approve or decline the SCR. The server updates a dynamic GUI on the manager computing device to display, in the order of their respective priorities, the n SCRs and their suggested actions.
A method for automatedly determining hours of operation (HOO) is provided. A system for work allocation is also provided. A method for generating staffing forecasts is also provided. The method for automatedly determining hours of operation includes performing historical data analysis on historical data comprising past data for a plurality of skills, queue reports for the plurality of skills, or a combination thereof; identifying trends in skills demand for each of the plurality of skills based on the historical data analysis; performing analysis on forecasted reports for skills allocation to generate future demand patterns for each of the plurality of skills; performing HOO analysis for each of the plurality of skills based on the generated future demand patterns and identified skills demand trends; generating a report of the HOO analysis for the plurality of skills; and providing the analysis report to a user.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
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"
23.
SYSTEM AND METHOD TO ADDRESS COMPLIANCE COVERAGE FOR TRADE SURVEILLANCE
Systems adapted to provide trade surveillance and compliance coverage and methods, and non-transitory computer readable media, include providing to a machine learning model, trained to output an indication of whether suspicious activity has occurred, input data comprising market data for a unique financial instrument; generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a suspicious activity; identifying a set of analytical models that correspond with the suspicious activity identified by the shape detection metric, wherein the set of analytical models is enabled once identified; analyzing the input data using the identified set of analytical models to confirm the suspicious activity identified by the shape detection metric; and triggering, based on confirmation of the suspicious activity identified by the shape detection metric, a suspicious activity alert.
A computerized-method for identifying and addressing a real-time conversational conflict in a contact center. The computerized-method includes: (i) monitoring by one or more processors an interaction between an agent and a customer; (ii) continuously updating a conflict-score during the interaction when a conversational-cut is detected, by operating by the one or more processors a conflict-detector module. The conversational-cut is a point in the interaction that the agent speech is interrupted by the customer and the agent stops speaking, and (iii) addressing the real-time conversational conflict when the updated conflict-score is above a preconfigured conflict-threshold by routing the interaction to a queue of Subject Matter Experts (SME)s of an Automatic Calls Distributor (ACD) application.
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
H04L 65/65 - Protocoles de diffusion en flux de paquets multimédias, p. ex. protocole de transport en temps réel [RTP] ou protocole de commande en temps réel [RTCP]
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
25.
SYSTEM AND METHOD FOR PERSONALIZED COACHING RECOMMENDATION
A computerized-method for determining an agent personalized coaching. The computerized-method includes for each agent in an agents database: (i) retrieving by one or more processors historical data. The historical data includes at least one of: a) past feedback; b) KPIs; and c) coaching training sessions; (ii) cleaning and structuring the historical data by operating by the one or more processors a data processor; (iii) assessing a level of impact of a plurality of coaching-plans based on the structured historical data to predict an effective-score for each coaching-plan in the plurality of coaching-plans on the KPIs by operating a CATE estimator on the coaching-plan; (iv) normalizing the effective-score of each coaching-plan and storing the normalized effective-score of each coaching-plan in a data-storage; (v) automatically selecting the personalized coaching-plan by operating a recommendation model on effective-scores in the data-storage; and (vi) automatically scheduling the personalized coaching plan for the agent.
A computerized-method for operating intraday schedule optimization in real-time for a schedule having one or more time-intervals, in a contact-center. The computerized-method includes: (i) retrieving current-state data of the contact center for each time-interval in the schedule; (ii) generating a current-schedule-state node that includes the received current-state data of the contact center; (iii) generating a directed-graph of a plurality of updated-schedule-state nodes; (iv) applying a model to predict SLA-level for each updated-schedule-state node in the generated directed-graph; (v) applying a heuristic search graph algorithm on the generated directed-graph of the plurality of updated-schedule-state nodes based on the SLA level of each updated-schedule-state node in the generated directed-graph as a heuristic to yield a path in the directed-graph of updated-schedule-state nodes; (vi) updating the schedule in the contact center based on the change in agents activities of each edge in the yielded path in the updated-schedule-state nodes in the path.
A computerized-system for playing an audio-file of a pronunciation of a name of an inbound-caller via a computerized-device of a recipient. The computerized-system includes: an inbound-routing software, a CRM software, a name-pronunciation engine; and processors. The processors are configured to: (i) detect an inbound-interaction to the inbound-routing software; (ii) retrieve a name of the inbound-caller from the CRM software based on an ANI number of the inbound-caller; (iii) forward the retrieved name of the inbound-caller to a name-pronunciation engine to fetch an audio-file with pronunciation of the name of the inbound-caller; (iv) check an identity of a recipient of the inbound-interaction; (v) detect routing of the inbound-interaction to the recipient by the inbound-routing software; (vi) transmit the audio-file to the computerized-device of the recipient based on the identity thereof; and (vii) play the audio-file with the pronunciation of the name by a media-player before the recipient answers the inbound-interaction.
H04M 3/436 - Dispositions pour intercepter des appels entrants
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
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
28.
AUTOMATIC AVAILABILITY PREDICTION FOR A SUBJECT MATTER EXPERT
A system automatically identifies wait times for a subject matter expert (SME). The system includes a cloud server in communication with an agent computer, an SME computer, and a database for storing presence data associated with the SME computer. Over a first period of time, the processor receives the presence data associated with the SME computer; stores the presence data in the database; and trains a custom machine learning network. The processor receives an input from the agent computer requesting contact with the SME computer, and solicits a status from the SME computer. If the status is not “Available”, the processor, using the trained custom machine learning network, predicts a wait time after which the status will be “Available” and reports the predicted wait time to the agent computer. If the status is “Available”, the system establishes a communication link between the agent computer and the SME computer.
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
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) services featuring software for creating, deploying, and managing conversational artificial intelligence agents, virtual assistants, chatbots, and voicebots for automating customer service interactions; Development of software for communication systems; Updating of computer programs for third parties according to user requirements; Design, maintenance, rental and updating of computer software; Platform as a service featuring adaptive software for speech and voice recognition, for converting user's voice, for communicating with others, for updating computer programs; Platform as a service featuring speech and voice recognition software; Platform as a service featuring adaptive software for speech analytics software; Platform as a service featuring software for processing images, graphics and text
30.
SYSTEMS AND METHODS TO PRIORITIZE AGENT INBOX USING TRAFFIC SIGNAL PATTERN FOR DIGITAL CHANNELS
Interaction prioritization systems and methods, and non-transitory computer readable media, include receiving a transcript of a first customer interaction in an agent inbox; extracting, in real-time, keywords from the transcript; comparing, in real-time by an artificial intelligence (AI) model, the extracted keywords to keywords in a customized historical database; calculating, in real-time by the AI model, a priority score of the first customer interaction based on the comparison; assigning, in real-time by the AI model, a priority to the first customer interaction based on the calculated priority score; and applying, in real-time, a visual indicator on the first customer interaction in the agent inbox, wherein the visual indicator corresponds to the assigned priority of the first customer interaction.
A system and method are provided for evaluating data items using a machine learning model, including: producing, by a machine learning model, answers to questions applied to an input data item, where the questions and data item are input to the machine learning model; calculating a score for the input data item based on the produced answers; and transmitting one or more output data items to a remote computer over a communication network based on the calculated score. Some nonlimiting embodiments of the invention may relate to analyzing text data such as interaction transcripts in a contact center environment. In some embodiments questions may be included in a form and/or evaluation plan, and questions may include critical questions which are to be answered in the data item is to be assigned a non-zero scores. Questions may be organized in levels, and scores may be calculated based on the levels.
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 3/22 - Dispositions de supervision, de contrôle ou de test
32.
SYSTEMS AND METHODS TO IDENTIFY AND DISTRIBUTE DIVERSE EVALUATION ASSIGNMENTS
Interaction distribution systems and methods, and non-transitory computer readable media, include retrieving a diverse evaluation configuration including diverse evaluation configuration rules and diverse interaction category rules; retrieving historical evaluations for each evaluator based on the diverse evaluation configuration rules; retrieving diverse criteria prompt rules; retrieving an interaction transcript associated with each historical evaluation; constructing a large language model (LLM) prompt based on the diverse criteria prompt rules; executing the first LLM prompt on each interaction transcript to return a category of interaction for each interaction transcript; determining evaluation coverage for each returned category of interaction for each evaluator; determining that an evaluator has not evaluated a defined number of interactions for one or more of the diverse interaction category rules; and distributing one or more interactions that match the one or more diverse interaction category rules to the evaluator for evaluation.
A computerized-method for calculating an agent-burnout index and identifying root-cause factors. The computerized-method includes: (i) for each agent in an agents-database: a. calculating the agent-burnout index by operating a burnout-detection module and storing the agent-burnout index in the agents-database. The agent-burnout index indicates a level of stress and exhaustion of the agent; b. identifying root-cause factors of the calculated agent-burnout index by operating root-cause analyzer module and storing the identified root-cause factors in the agents-database; and (ii) automatically sending a push-notification to a user with details of agent-burnout index for each agent in the agents-database. The push-notification is displayed via a UI associated to a WFM application that is running on a computerized-device of the user.
A computerized-method for enabling true-to-interval analytics from an ACD-application. The computerized-method includes during a shift-schedule having time-intervals, (i) for each time-interval, a. every preconfigured time-period in the time-interval: i. polling data-feed from the ACD-application; and ii. obtaining true-to-interval parameters from the polled data-feed and storing the true-to-interval parameters with start-time of the preconfigured time-period. The true-to-interval parameters include for each contact: 1) a state of activity; 2) handle-time duration; and 3) hold-time duration. b. calculating number of contacts having the activity state, based on the true-to-interval parameters; c. calculating total interval-handle-time and total interval-hold-time for each contact; (ii) calculating total handle-time for all contacts during the time-interval; and (iii) retrieving the calculated total handle-time and total hold-time of each time-interval and a total handle-time of one or more shift-schedules from the tti-database and transmitting it to a WFM application, over a communication channel to enable the WFM application true-to-interval analytics.
A device, system and method is provided for monitoring a user's interactions with Internet-based programs or documents. Content may be extracted from Internet server traffic according to predefined rules. Extracted content may be associated with a user's Internet interaction. The user's Internet interaction may be stored and indexed. The user's Internet interaction may be analyzed to generate a recommendation provided to a contact center agent while the contact center agent is communicating with said user for guiding the user's interaction, for example, in real-time. Traffic other than Internet server traffic may also be used.
A computerized-method for detecting market sentiment manipulation that is related to financial-instruments trading. The computerized-method includes: (i) monitoring incoming alerts in an analytics-engine. (ii) retrieving data from each alert, by operating a context-extraction module; (iii) for each alert: a. collecting feeds from social-media servers based on the retrieved data by operating a social-media feeds-extraction module; b. for each feed, generating a summary by using Gen AI; and c. analyzing a social-media sentiment by providing the generated summary of each feed to the Gen-AI; (iv) for each financial instrument that has been traded in the preconfigured period, searching an anomaly between a sentiment from traditional news source and the analyzed social-media sentiment; (v) storing each anomaly in an anomalies database; and (vi) pausing each financial-transaction that has been processed and related to the financial-instrument in the anomalies database.
SYSTEM AND METHOD FOR DISTRIBUTING WORKLOAD OF A WORKING-SHIFT OF A SOURCE-LOCATION TO WORKING-SHIFTS IN TARGET-LOCATIONS IN A MULTIPLE-LOCATIONS CONTACT CENTER
A computerized-method for distributing workload of a working-shift of a source-location to working-shifts in target-locations, via a WFM-application in a multiple-locations contact center. The computerized-method comprising: (i) receiving a rebalancing-request and workload-information of the working-shift of the source-location; (ii) parsing the workload-information to extract affected-SUs, target-SUs and critical-skills; (iii) for each parallel time-interval in a parallel working-shift of each target-location and for each critical-skill: retrieving staffing-plans of the target-SUs, and marking the parallel time-interval as overstaffed for the critical-skill based on a net-staffing calculation; (iv) operating agents-distribution for each parallel time-interval and for each critical-skill based on the parallel time-intervals marked as overstaffed; and (v) configuring WFM-application to: update staffing-plans of parallel working-shift of each target-location, based on the operated agents-distribution; generate new-schedules for agents in the target-SUs based on the updated staffing plans of parallel working-shift of each target-location; and remove existing schedules of the affected-SUs of parallel working-shift.
Stress detection systems and methods, and non-transitory computer readable media, include building a library including previously identified stressful sentences and stressful phrases; receiving, by a trained neural network model, the library; receiving, by the trained neural network model, a text interaction between a customer and an agent; calculating, by the trained neural network model, a cosine similarity score between each stressful sentence or stressful phrase in the library and each sentence in the text interaction; determining, by the trained neural network model, a probability that the text interaction is stressful based on the calculated cosine similarity score; determining that a percentage of stressful interactions for the agent in a time interval is greater than a threshold percentage; providing a manager with recommended actions to decrease stress on the agent; receiving, from the manager, a selection of one or more recommended actions; and implementing the one or more recommended actions.
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
39.
System and method for re-skilling agents skills in an automatic call distributor (ACD) application due to a request of change to a scheduled-shift of an agent
A computerized-method for managing skills in an ACD-application due to a request of change to a scheduled-shift of an agent. The computerized-method includes: (i) receiving the request; (ii) retrieving all skills associated to the agent; (iii) for each skill, retrieving mapped ACD-skills and a corresponding SLA-threshold; for each ACD-skill: a. predicting an impact-level on the corresponding SLA-threshold; b. when the predicted impact-level of the ACD-skill is positive, selecting a different-agent that is assigned to the time-interval and activating the ACD-skill of the different-agent, and deactivating all other ACD-skills of the different-agent, to mitigate the ACD-skill corresponding SLA-threshold, and setting the predicted impact-level of the ACD-skill as negative, (iv) when the predicted impact-level of each ACD-skill is negative, granting the request and changing the scheduled-shift based on the request; and (v) configuring the ACD-application to route inbound-interactions to the different-agent based on the activated ACD-skill of each different-agent during the time-interval.
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
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
40.
SYSTEM AND METHOD FOR REAL-TIME IMPACT ASSESSMENT OF SOCIAL MEDIA POSTS WITH GENERATIVE ARTIFICIAL INTELIGENCE
A computerized-method for dynamically prioritizing social-media interactions in real-time, based on social-media posts in feeds, in a contact-center. The computerized-method includes: (i) monitoring by processors the social-media posts in the feeds of one or more social-media platforms which are integrated to the contact center. The social-media posts have been published during a preconfigured period, for each social-media post of a customer in each feed in the feeds: a. calculating a quality-score by operating an Artificial intelligence (AI) driven Content Quality Analysis (ACQA) module; and b. calculating a social-impact score based on the calculated quality score and one or more parameters; (ii) automatically prioritizing the social-media posts based on the calculated social-impact score to yield a priorities queue of social-media interactions. Each social-media post represents a social-media interaction, and (iii) automatically routing social-media interactions by a routine-engine to an available agent based on the yielded priorities queue of social-media interactions.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
41.
Accelerated security for real-time detection of suspicious transactions
A device, system and method for accelerated compliance testing for real-time detection of suspicious transactions in a transaction stream. A machine learning model may determine a global trend of cumulative transaction behavior based on all, a majority or a representative subset, of the stream of transactions. For each transaction in the stream, a skip rule engine may sort each security compliance test to be skipped or not skipped based on the test's relevance to the global trend of the cumulative transaction behavior. The not skipped security compliance tests may be executed in real-time for each transaction in the stream to generate real-time partial security assessments therefore comprising real-time suspicious transaction alerts. The remaining tests may be skipped in real-time and executed, at a time delay after transaction times, to generate time delayed supplemental security assessments therefore comprising time delayed suspicious transaction alerts to complete all compliance testing.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
42.
GENERATIVE ARTIFICIAL INTELLIGENCE-POWERED CALL INSIGHTS AND RESPONSE RECOMMENDATION SYSTEM
An artificial intelligence (AI)-based call response system and methods are provided that are configured to provide a context-based recommendation during a monitored conversation. The AI-based call response system includes a processor to perform conversation analysis operations, including determining transcribed words for the monitored conversation, analyzing the words using one or more machine learning (ML) models to produce a score associated with a model identifier (ID) identifying a ML model, comparing the score to a predefined threshold of the ML model, generating an alert when the score meets or exceeds the threshold, the alert including the model ID and a call identifier (ID) identifying the monitored conversation, creating one or more prompts with each prompt comprising an executable instruction that prompts, queries, or requests an output from a large language model for a response, retrieving the response for each of the prompts, and providing the response to a user.
Systems and methods for monitoring a performance of a chat bot include: analysing, by an artificial intelligence (AI) module, a transcript of at least one conversation in which the chat bot participates; identifying, by the AI module, one or more skills of the chat bot where performance falls below a pre-defined performance threshold; determining a chat bot effectiveness score; and determining whether to: automatically update a set of predefined responses of the chat bot based on at least one of: the one or more identified skills; and the chat bot effectiveness score; else, outputting an indication of the one or more identified skills and the chat bot effectiveness score.
A performance evaluation system and methods are provided that are configured to intelligently suggest answers to questions on performance evaluations using a generative artificial intelligence (AI) service. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which include receiving a selection of an interaction between a user and an agent for a performance evaluation, fetching evaluation form data for the performance evaluation, determining a transcript of the interaction a request object for one or more suggested answers to the one or more questions, requesting the one or more suggested answers from the generative AI service, updating the performance evaluation to include the one or more suggested answer, and outputting the updated performance evaluation in an interface.
A system is adapted to automatically create new automation routines. The system includes a processor configured to, over a period of time, store L actions taken by one or more customer support agents and, in real time, for values of n between a first loop value and a second loop value: with a window of length n, starting at the first position within the stored actions and ending at the L-nth position within the stored actions, repeatedly: scan the stored L actions; create an automation candidate from the actions that fall within the window; store the automation candidate; increment the position of the window; and increment the value of n. The processor is further configured to identify repeated automation candidates of the stored automation candidates; filter the repeated automation candidates to select final automation candidates; and create new automation routines from the final automation candidates.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing temporary use of online non-downloadable computer
software and non-downloadable computer applications for data
management, analysis, interpretation, identification, and
reporting for use in the fields of employee and customer
computer software incorporating omnichannel routing,
analytics, artificial intelligence, and automation to
perform functions for workforce optimization, customer
journey optimization, performance management; providing
temporary use of online, non-downloadable software in the
nature of application program interfaces for integrated
voice infrastructure and turnkey telephony equipment;
application service provider featuring software for queuing,
handling, logging, recording, monitoring, tracking,
supervising, managing, routing, disposition and distribution
of telephone calls, facsimile transmissions, emails, social
media, and web based messages to or from in office or at
home employees, contractors, subcontractors, parties,
callers or customers for use in the field of customer
service, customer support, inside sales, collections,
outside sales, and marketing; application service provider
featuring software for scheduling, surveying, monitoring,
supervising, rating, reviewing managing, performance
forecasting, call, video recording, analyzing and training
employees for use in the field of customer service, customer
support, inside sales, collections, outside sales, and
marketing; application service provider featuring an
application software development tool for use in customizing
telecommunication service applications, namely, software for
queuing, handling, logging, recording, monitoring, tracking,
supervising, managing, routing, disposition and distribution
of telephone calls, facsimile transmissions, emails, social
media, and web based messages to or from in office or at
home employees, contractors, subcontractors, parties,
callers or customers for use in the field of customer
service, customer support, inside sales, collections,
outside sales, and marketing; and technical support
services, namely, troubleshooting of computer software
problems; computer software consultation; providing
information relating to computer software maintenance, use
and development.
47.
EARNINGS INSIGHTS SYSTEM AND METHODS FOR EMPOWERING AGENTS WITH VISIBILITY INTO EXTRA HOURS IMPACT
Earnings visibility systems and methods, and non-transitory computer readable media, include receiving a request for extra time or time off, wherein the extra time or time off corresponds to a first time interval; displaying the first time interval via a graphical user interface (GUI); receiving a net staffing value for the first time interval; receiving a payroll rule that specifies a percentage increase or decrease in pay based on the net staffing value for the first time interval; assigning a color to a particular percentage increase or decrease in pay for the first time interval; determining the percentage increase or decrease in pay for the first time interval based on the net staffing value for the first time interval; and coloring the first time interval on the GUI with the assigned color based on the percentage increase or decrease in pay for the first time interval.
A system and method for automatic scheduling and routing of call data, including, e.g.: sending a scheduling calendar to a first device using a digital messaging channel, where the calendar comprises a plurality of time slots; scheduling an outbound call between the first device and a second device based on a response to the scheduling calendar, where the response comprises a selection of time slots; and transmitting data representing the outbound call using a voice communication channel and based on the scheduling of the outbound call. Some embodiments may include connecting or routing the call to an additional, third device—where the connecting may be based on skills or features required for the call. Time slots in the scheduling calendar may be highlighted based on an availability of resources associated with the relevant skill.
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
49.
SYSTEM AND METHOD FOR DISTRIBUTING INTERACTION DATA TO AGENTS
A system and method for distributing interaction data to agents may include a computing device; a memory; and a processor, the processor configured to: identify one or more interaction events from interaction metadata items located in one or more interactions assigned to an agent; generate a prediction prompt for estimating one or more future interaction events for said one or more interactions based on said identified interaction events; and apply said prediction prompt to a machine learning model to estimate said one or more future interaction events for said one or more interactions.
A computerized-method for resolving a query posted on an online-discussion-board of a social-media-platform and awaiting in a queue of a digital-communication-channel. The computerized-method includes: (i) monitoring the digital-communication-channel queue to select the query; (ii) operating an interaction-filtering module against the query to tag responses that were posted in a communication thread of the query in the online-discussion-board of the social-media, as valid; (iii) calculating a resolution-confidence score for each response of the responses that were tagged as valid, by operating a responder-recommended module; (iv) selecting a response from the responses that were tagged as valid that is above a preconfigured threshold and having a highest calculated resolution-confidence score; (v) automatically sending the selected response to the customer as a tryout-solution to the query; and (vi) upon receiving an indication from the customer that the query is resolved, removing the query from the queue of the digital-communication-channel.
G06F 16/3329 - Formulation de requêtes en langage naturel
G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
G06Q 30/015 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance
51.
SYSTEM AND METHOD FOR OPTIMIZING STAFFING OF A WORKING-SHIFT DURING A DATE RANGE BY PREDICTING ADHERENCE PARAMETER OF THE WORKING-SHIFT BASED ON A SCHEDULING UNIT
A computerized-method for optimizing staffing of working-shifts during a date-range by predicting adherence parameter of the working-shift based on an SU. The computerized-method includes: (i) configuring, a UI of a WFM application, to receive: a. date-range; b. SU; and c. activity code for the working-shifts, for the staffing. For each interval-time in each working-shift (ii) operating a forecast-adherence engine to yield the predicted adherence parameter; (iii) operating a coaching-aggregation engine to yield a coaching parameter; (iv) operating a time-off aggregation engine to yield a time-off parameter; (v) operating a shrinkage-calculator based on the predicted adherence parameter, the aggregated coaching parameter, and the aggregated time-off parameter, to yield a shrinkage parameter; (vi) configuring the WFM to automatically schedule staffing for the interval-time based on the yielded shrinkage parameter; (vii) storing the working-shift in a database and configuring the WFM application to automatically trigger a notification to each agent scheduled the working-shift.
Systems and methods for assessing the feasibility of a proposed contact center work plan are disclosed. The plan may include a proposed workload and a proposed performance metric and the method may include: generating, based on the proposed contact center work plan and data indicative of previous feasible contact center work plans, a feasibility for the proposed contact center work plan; and where the feasibility is below a threshold, generating, using a large language model, a modified contact center work plan for a user with a feasibility above the threshold, the modified contact center work plan comprising a modification to one or more aspects of the contact center work plan.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
Systems adapted for counter offering shift trades and methods, and non-transitory computer readable media, include monitoring a shift schedule for a set of agents using certain parameters; identifying a shift trade request from a source agent; identifying a set of tradeable shifts for the source agent; sending the shift trade request and the set of tradeable shifts to a set of target agents; receiving a counter shift trade request from a target agent, wherein the counter shift trade request comprises a tradeable shift within the set of tradeable shifts selected by the target agent; sending the counter shift trade request to the source agent; and wherein when the source agent accepts the counter shift trade request, the shift schedule is updated based on the counter shift trade request; and sending a schedule notification to the source agent and to the target agent regarding the updated shift schedule.
Dynamic call queue systems and methods, and non-transitory computer readable media, include training a generative artificial intelligence (AI) model to output a product recommendation; querying the generative AI model for the product recommendation for each of the plurality of customers; extracting keywords from the product recommendation; converting the keywords into a first numeric representation; receiving a description of a new product; transforming the description of the new product into a second numeric representation; calculating a cosine similarity score (CSS); generating a customer likelihood score (CLS); calculating a sentiment score; retrieving a customer category score (CCS); calculating a customer propensity score (CPS) based on the CSS, the CLS, the sentiment score, and the CCS for each of the plurality of customers; sorting the plurality of customers based on the CPS; generating a dynamic list of customers; and scheduling outbound interactions based on the dynamic list of customers.
A computerized-method for predicting a staffing-shrinkage percentage in a time-slot during a future staffing plan in a contact center, The computerized-method includes: (i) receiving via a User Interface that is associated to a Workforce Management (WFM) application, skills for the future staffing plan and the time-slot; (ii) calculating a historic-shrinkage percentage during a preconfigured period based on agents time-offs and understaffing-levels during the preconfigured period; (iii) determining the staffing-shrinkage percentage in the time-slot during the future staffing plan based on the calculated historic-shrinkage percentage; and (iv) configuring the WFM application to update staffing level in the future staffing plan, based on the determined staffing-shrinkage percentage.
Coaching simulator systems and methods, and non-transitory computer readable media, include receiving an interaction between a customer and an agent; scoring the interaction using an evaluation form; identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions; creating a prompt for a large language model (LLM) by populating a prompt template; providing a framework to invoke the LLM using the created prompt, a model and a plurality of hyperparameters; starting a first coaching simulation scenario by invoking the LLM to present a first question to the agent; receiving a first answer to the first question from the agent; querying the LLM to analyze the first answer to the first question; and querying the LLM to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question.
G09B 5/06 - Matériel à but éducatif à commande électrique avec présentation à la fois visuelle et sonore du sujet à étudier
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
57.
SYSTEM AND METHOD FOR REDUCING TIME TAKEN FOR EVALUATION OF AN INTERACTION THAT HAS BEEN RECORDED BY A RECORDING-PLAYER WEB-APPLICATION BY USING GENERATIVE AI WITH LARGE LANGUAGE MODELS
A computerized-method for reducing time of evaluation of an interaction by annotating a media-file of the interaction based on an evaluation-measurement. The computerized-method includes: (i) receiving a request from a user to playback the media-file of the interaction by operating a media-playback service of a recording-player web-application; (ii) configuring the media-playback service to: a. operate an interaction-insights module to generate point-in-time annotations of the media-file, based on parameters of the evaluation-measurement; and b. send the point-in-time annotations and a location of the media-file to the recording-player web-application; and (iii) configuring the recording-player web-application to playback the media-file and upon user-selection to present each point-in-time annotation of the one or more point-in-time annotations, via a UI that is associated to the recording-player web-application, on a timeline-bar as an annotation-marker. Each point-in-time annotation comprising a playhead position in the media-file and a text-annotation related to a parameter of the parameters of the evaluation-measurement.
A system and method for generating evaluation forms from interaction recordings may include a computing device; a memory; and a processor, the processor configured to: identify one or more interaction intents from an interaction transcript; generate one or more evaluation categories for the one or more interaction intents using machine learning; generate evaluation questions for the one or more evaluation categories using machine learning; and provide an evaluation form based on the evaluation questions.
System and method for detecting an active-screen in a plurality of screens of different monitors in a media-file of a recorded desktop activity, during playback of the media-file
A computerized-method for detecting an active-screen in screens of different-monitors in a media-file of a recorded desktop-activity, during playback of the media-file. The computerized-method may include: (i) configuring a recording-control service to create a stream-of-metadata of the desktop-activity; (ii) configuring a screen-recording-service to: a. receive a stream of video-data of the screens used during the desktop-activity; b. collect time-metadata of desktop-events from each device associated to the different-monitors; and c. store the video-data and the collected time-metadata, in a data-storage; (iii) configuring a media-playback application to: a. retrieve the collected time-metadata and the media-file; and b. operate a zoom-in module to detect the active-screen in the screens in each time-interval and to display the detected active-screen in each time-interval in an increased size in a central section of a UI, while each screen of all other screens is displayed side-by-side in a reduced-size in an upper-section of the UI.
A computerized-method for calculating a score of an outbound-marketing interaction. The computerized-method includes: (i) retrieving a transcription of the outbound-marketing interaction; (ii) identifying a product that is being marketed in the outbound-marketing interaction by executing an Artificial Intelligence (AI) Large Language Model (LLM) with a check-product-prompt having the transcription embedded therein; (iii) constructing a prompt based on: (a) the identified product; (b) one or more features of the identified product; (c) the transcription; and (d) one or more questions. Each question is related to a section in one or more preconfigured-sections; (iv) executing the AI LLM with the constructed prompt to yield an answer and a question-score to each question of the one or more questions; (v) calculating the score of the outbound-marketing interaction based on the question-score of each question; and (vi) sending the score to one or more applications for follow-on actions based on the score.
A system and method for managing interaction transcripts based on rules may include a computing device; a memory; and a processor, the processor configured to: identify one or more decision parameters in an interaction transcript; determine at least one decision category for a rule from the one or more decision parameters; calculate probabilities for the at least one decision category for the rule using the one or more decision parameters; and apply the rule by selecting one or more action categories for the interaction transcript based on the calculated probabilities for the at least one decision category.
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
62.
Method for identifying contact reason of a business process discovered by a desktop analytics tool
A computerized-method for identifying a contact-reason of a business-process that has been discovered by a desktop-analytics-tool. The computerized-method includes: (i) retrieving a random-sample of a preconfigured number of instances of a discovered-routine from the routines-datastore; (ii) for each instance in the random-sample of the preconfigured number of instances matching a related transcript-segment; (iii) identifying a contact-reason of each instance by operating a first-GEN-AI with LLM with a first-prompt-text including an embedded related transcript-segment; The first-GEN-AI with LLM has been trained to provide the contact-reason based on the transcript-segment embedded in the first-prompt-text, and (iv) identifying the contact-reason of the discovered-routine as the contact-reason of the business process by operating a second-GEN-AI with LLM with a second-prompt-text for aggregation of all the contact-reasons of all instances in the random-sample. The second-GEN-AI with LLM has been trained to provide a contact-reason based on the contact-reasons of all instances embedded in the second-prompt-text.
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
63.
SYSTEM AND METHOD FOR GENERATING A TEXT-SUMMARY OF A MULTIPLE-SECTIONS TEXT-DOCUMENT THAT WAS CREATED VIA AN APPLICATION THAT IS RUNNING IN A CLOUD-BASED CONTACT CENTER FOR A TENANT
A computerized-method for generating a text-summary of a multiple-sections text-document that was created via an application that is running in a cloud-based CC for a tenant. The computerized-method includes: (i) operating a prompt-generator module to yield a prompt-text, the prompt-generator module includes: a. retrieving a rule of configuration of the text-summary of the multiple-sections text-document; b. fetching data related to the multiple-sections text-document, the data related to the multiple-sections text-document includes sections, and each section of the sections comprising questions and each question of the questions has a corresponding answer, and c. generating the prompt-text based on the rule of configuration and the data related to the multiple-sections text-document and the multiple-sections text-document; (ii) generating the text-summary by operating a GenAI with LLMs service to execute the prompt-text; and (iii) storing the text-summary in a summary-database to be used to operate actions for the tenant.
Systems and methods for providing interaction recordings with removed personally identifiable information (PII) are disclosed, the systems and methods involving: extracting, from an interaction recording, using an automatic speech recognition engine, a timestamped recording transcript; identifying, using an artificial intelligence (AI) engine, time periods of the timestamped recording transcript which disclose PII; and removing, from the interaction recording, data present during the time periods which disclose PII, to produce a secure interaction recording.
A system and method for mitigating forgetting in machine learning models may include or involve augmenting an input batch of real data items with synthetic data items, and generating, by a first machine learning model, a prediction for data items in the augmented batch—where the first machine learning model may be trained using a dataset of past synthetic data items. Some embodiments of the invention may include generating, by a second machine learning model, synthetic data items based on the input batch, where the second machine learning model may be trained using a dataset of past real data items. In some embodiments, generating predictions by the first machine learning model and the generating synthetic data items by the second machine learning model may be performed simultaneously or concurrently. A plurality of additional operations and procedures may be included in different embodiments to adjust or optimize the models' performance.
A computerized-method for providing personalized-content for an interaction of an agent with a customer during an outbound conversational-marketing-campaign of a tenant operated via a cloud-based contact-center-platform. The computerized-method includes: (i) receiving details of the customer and details of the outbound conversational-marketing-campaign of the tenant; (ii) creating a prompt-text based on the received details of the customer and the details of the outbound conversational-marketing-campaign; (iii) generating the personalized content for the interaction with the customer in text-format by executing an LLM AI engine with a trained model with the created prompt-text; (iv) storing a text-file with the generated personalized content in text-format in a contents-database; (v) initiating the interaction of the agent by dialing to the customer; (vi) retrieving the text-file for the interaction from the contents-database; and (vii) sending the text-file to a computerized-device of the agent to be presented via a display-unit that is associated to the computerized-device.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Cloud-based computer software, namely, temporary use of
online, non-downloadable computer software and
non-downloadable computer applications for data management,
analysis, interpretation, identification, and reporting for
use in the fields of employee and customer computer software
incorporating omnichannel routing, analytics, artificial
intelligence, and automation to perform functions for
workforce optimization, customer journey optimization,
performance management; providing temporary use of online,
non-downloadable software in the nature of application
program interfaces for integrated voice infrastructure and
turnkey telephony equipment; application service provider
featuring software for queuing, handling, logging,
recording, monitoring, tracking, supervising, managing,
routing, disposition and distribution of telephone calls,
facsimile transmissions, emails, social media, and web based
messages to or from in office or at home employees,
contractors, subcontractors, parties, callers or customers
for use in the field of customer service, customer support,
inside sales, collections, outside sales, and marketing;
application service provider featuring software for
scheduling, surveying, monitoring, supervising, rating,
reviewing managing, performance forecasting, call, video
recording, analyzing and training employees for use in the
field of customer service, customer support, inside sales,
collections, outside sales, and marketing; application
service provider featuring an application software
development tool for use in customizing telecommunication
service applications, namely, software for queuing,
handling, logging, recording, monitoring, tracking,
supervising, managing, routing, disposition and distribution
of telephone calls, facsimile transmissions, emails, social
media, and web based messages to or from in office or at
home employees, contractors, subcontractors, parties,
callers or customers for use in the field of customer
service, customer support, inside sales, collections,
outside sales, and marketing; technical support services,
namely, troubleshooting of computer software problems;
computer software consultation; providing information
relating to computer software maintenance, use and
development; providing temporary use of online
non-downloadable software for collecting, processing,
integrating and displaying data from one or more sources for
risk management and detecting, monitoring, managing and
preventing fraud, financial crime, cybercrime, cyber threats
and cyber-attacks; providing temporary use of online
non-downloadable software for collecting, processing,
integrating and displaying data from one or more sources to
be used for managing legal, regulatory, risk and enterprise
compliance, capital market compliance, anti-money laundering
compliance, detecting breaches of compliance, third-party
due diligence, and monitoring of financial transactions,
financial trades, financial swaps and e-commerce
transactions; providing temporary use of online
non-downloadable software for collecting, processing,
integrating and displaying data from one or more sources to
be used for automating manual case management and workflow
processes, and to monitor, prioritize, enrich, distribute
and permit collaboration on case and workflow information;
providing temporary use of online non-downloadable software
for emergency communications incident data management,
recording, transcribing, evaluating, and analyzing real time
performance metrics for training and optimizing performance
of emergency telecommunicators; providing temporary use of
online non-downloadable software for collecting, analyzing
and sharing digital evidence in the nature of video content,
audio content, digital image files, digital text content,
Records Management System (RMS), Computer Aided Dispatch
(CAD) and Case Management System (CMS) data, and computer
log files.
68.
SYSTEM AND METHOD FOR ENHANCING EFFECTIVENESS OF A COACHING SESSION OF AN AGENT BY CREATING THE COACHING SESSION BASED ON A CALCULATED COACHING IMPACT SCORE OF COACHES, IN A CONTACT CENTER
A computerized-method for enhancing effectiveness of a coaching-session of an agent by creating the coaching-session based on a calculated coach-impact score of coaches, in a contact center. The computerized-method includes: (i) collecting coaching-feedback-data that has been received from the agent for a plurality of coaching sessions; (ii) filtering bias from the coaching-feedback-data by removing bias therefrom; (iii) operating a coach evaluation module based on the filtered coaching-feedback-data, to yield an effective-feedback score, an associated dynamic-weightage and a coaching-effectiveness score for each coach for the selected focus area and related behavior; (iv) calculating a coach-impact score for each coach in the plurality of coaches by operating a coaching impact score module on the yielded effective-feedback score, the associated dynamic-weightage and the coaching-effectiveness score of the coach; and (v) configuring a UI to selectively display a subset of the plurality of coaches based on the calculated coach-impact score of each coach.
A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and/or execute database queries and/or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.
A system for evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance may include a computing device; a memory; and a processor, the processor configured to: create a plurality of evaluation prompts for evaluating interaction data items of one or more interactions; generate evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning; create training recommendation prompts for the evaluation results; and generate training recommendations from training categories using the training recommendation prompts and machine learning.
A system and method for generating a list of tasks from an interaction recording may include a computing device; a memory; and a processor, the processor configured to: identify, for each sentence of one or more sentences of an interaction recording, whether the sentence comprises at least two nouns and at least one verb; generate a set of actionable items using a long short-term memory (LSTM) model by: when the sentence does not comprise at least two nouns and at least one verb, deleting the sentence; and when the sentence comprises at least two nouns and at least one verb, generating an actionable item; input the actionable items to a language model to generate a list of tasks. Computer systems may use a generated list of tasks to automatically allocate work to agents of a contact center or to automatically update a status of a completed task.
A data query system and methods are provided that are configured to intelligently generate structured data queries from natural language questions using large language models (LLMs). The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which include receiving a natural language question for structured data, converting the natural language question to embeddings, matching the embeddings to pre-generated questions from a user questions repository (UQR), determining an accuracy of the matching meets or exceeds a threshold similarity, determining, using an LLM and metadata corresponding to the pre-generated questions from the UQR, a structured data query for querying for the structured data, and querying the structured database system using the structured data query.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Cloud-based computer software, namely, temporary use of online, non-downloadable computer software and non-downloadable computer applications for data management, analysis, interpretation, identification, and reporting for use in the fields of employee and customer computer software incorporating omnichannel routing, analytics, artificial intelligence, and automation to perform functions for workforce optimization, customer journey optimization, performance management; providing temporary use of online, non-downloadable software in the nature of application program interfaces for integrated voice infrastructure and turnkey telephony equipment; application service provider featuring software for queuing, handling, logging, recording, monitoring, tracking, supervising, managing, routing, disposition and distribution of telephone calls, facsimile transmissions, emails, social media, and web based messages to or from in office or at home employees, contractors, subcontractors, parties, callers or customers for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; application service provider featuring software for scheduling, surveying, monitoring, supervising, rating, reviewing managing, performance forecasting, call, video recording, analyzing and training employees for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; application service provider featuring an application software development tool for use in customizing telecommunication service applications, namely, software for queuing, handling, logging, recording, monitoring, tracking, supervising, managing, routing, disposition and distribution of telephone calls, facsimile transmissions, emails, social media, and web based messages to or from in office or at home employees, contractors, subcontractors, parties, callers or customers for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; technical support services, namely, troubleshooting of computer software problems; computer software consultation; providing information relating to computer software maintenance, use and development; Providing temporary use of online non-downloadable software for collecting, processing, integrating and displaying data from one or more sources for risk management and detecting, monitoring, managing and preventing fraud, financial crime, cybercrime, cyber threats and cyber-attacks; providing temporary use of online non-downloadable software for collecting, processing, integrating and displaying data from one or more sources to be used for managing legal, regulatory, risk and enterprise compliance, capital market compliance, anti-money laundering compliance, detecting breaches of compliance, third-party due diligence, and monitoring of financial transactions, financial trades, financial swaps and e-commerce transactions; providing temporary use of online non-downloadable software for collecting, processing, integrating and displaying data from one or more sources to be used for automating manual case management and workflow processes, and to monitor, prioritize, enrich, distribute and permit collaboration on case and workflow information; providing temporary use of online non-downloadable software for emergency communications incident data management, recording, transcribing, evaluating, and analyzing real time performance metrics for training and optimizing performance of emergency telecommunicators; providing temporary use of online non-downloadable software for collecting, analyzing and sharing digital evidence in the nature of video content, audio content, digital image files, digital text content, Records Management System (RMS), Computer Aided Dispatch (CAD) and Case Management System (CMS) data, and computer log files
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Cloud-based computer software, namely, temporary use of online, non-downloadable computer software and non-downloadable computer applications for data management, analysis, interpretation, identification, and reporting for use in the fields of employee and customer computer software incorporating omnichannel routing, analytics, artificial intelligence, and automation to perform functions for workforce optimization, customer journey optimization, performance management; providing temporary use of online, non-downloadable software in the nature of application program interfaces for integrated voice infrastructure and turnkey telephony equipment; application service provider featuring software for queuing, handling, logging, recording, monitoring, tracking, supervising, managing, routing, disposition and distribution of telephone calls, facsimile transmissions, emails, social media, and web based messages to or from in office or at home employees, contractors, subcontractors, parties, callers or customers for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; application service provider featuring software for scheduling, surveying, monitoring, supervising, rating, reviewing managing, performance forecasting, call, video recording, analyzing and training employees for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; application service provider featuring an application software development tool for use in customizing telecommunication service applications, namely, software for queuing, handling, logging, recording, monitoring, tracking, supervising, managing, routing, disposition and distribution of telephone calls, facsimile transmissions, emails, social media, and web based messages to or from in office or at home employees, contractors, subcontractors, parties, callers or customers for use in the field of customer service, customer support, inside sales, collections, outside sales, and marketing; and technical support services, namely, troubleshooting of computer software problems; computer software consultation; providing information relating to computer software maintenance, use and development
75.
SYSTEM AND METHOD FOR AUTO-APPROVING PENDING REQUESTS OF AGENTS BASED IN A DYNAMIC ENVIRONMENT
Systems adapted to auto-approve pending requests of contact center agents based in a dynamic environment and methods, and non-transitory computer readable media, include monitoring operational parameters affecting staffing needs and automatic approval criteria; establishing that a new staffing situation is present based on the monitored operational parameters, automatic approval criteria, or both; determining that the new staffing situation meets a current staffing threshold; identifying a pending schedule change request submitted by a contact center agent; comparing the identified pending schedule change request with the automatic approval criteria; updating a schedule of the contact center agent based on the approved identified pending schedule change request or approved generated recommendation; and sending a schedule notification to the contact center agent.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
76.
SYSTEM AND METHOD FOR ADJUSTING AGENTS STAFFING LEVELS OF A SUCCESSIVE WORK-SHIFT OF A PREASSIGNED ONGOING WORK-SHIFT, DURING THE PREASSIGNED ONGOING WORK-SHIFT, IN A CONTACT CENTER
A computer-implemented method for adjusting agents staffing levels of a successive work-shift of a preassigned ongoing work-shift, during the preassigned ongoing work-shift, in a contact center. The computer-implemented method includes: (i) during the preassigned ongoing work-shift, determining a number of agents to handle inbound-interactions that require one or more skills for the successive work-shift; (ii) comparing the number of agents to handle inbound-interactions that require one or more skills to a threshold; (iii) when the number of agents to handle inbound-interactions that require one or more skills is above the threshold, calculating a number of extra-agents; and (iv) operating a shift-extension module to: (a) select the calculated number of extra-agents from agents having the one or more skills which are assigned to the preassigned ongoing work-shift; and (b) update corresponding schedules of the selected number of extra-agents, in a database of a Workforce Management (WFM) system.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
77.
System and method for providing an identical response to a similar issue that is received from different customers, via inbound-interaction in a digital multi-channel contact center
A computer-implemented method for providing an identical response to a similar issue that is received from different customers. The computer-implemented method includes when a contact-center occupancy-rate is above a first-preconfigured-threshold, for each inbound-interaction from a customer that is entering an interactions-queue: (i) creating a temporary-case for an issue raised in the inbound-interaction; (ii) operating a Similarity Detection Module on the created temporary-case and cases in a cases-queue to receive a similarity-score for the created temporary-case; (iii) when the received similarity-score is above a second-preconfigured-threshold, operating a category Qualifier Module on the temporary-case to provide an indication as to qualification of a category of the issue raised; (iv) when the provided indication as to qualification of the category of the issued raised is qualified, merging the temporary-case with cases in the cases-queue and retrieving a response of the cases in the cases-queue; and (v) sending the retrieved response to the customer.
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
A system is adapted to automatically optimize the routing of incoming calls. The system includes: a session controller; a first group of media servers; a second group of media servers; and a processor configured to perform operations. The operations include: regularly polling the first group of media servers to determine a first group of most-available servers, and regularly polling the second group of media servers to determine a second group of most-available servers. The operations also include, when the session controller receives an inbound call: selecting a server from the first group of most-available servers or the second group of most-available servers; and connecting the inbound call to the selected server.
H04L 65/1069 - Établissement ou terminaison d'une session
H04L 65/1104 - Protocole d'initiation de session [SIP]
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
79.
SYSTEM AND METHOD FOR OPTIMIZING A NUMBER OF SESSIONS OF A MULTI-SESSION MEETING BASED ON AGENTS SKILL REQUIREMENT DURING A TIME-RANGE OF SCHEDUELED WORK-SHIFTS IN A CLOUD-BASED CONTACT CENTER
A computer-implemented method for optimizing a number of sessions of a multi-session meeting based on agents skill requirement during a time-range of scheduled work-shifts, in a cloud-based contact center. The computer-implemented method includes receiving a time-range of scheduled work-shifts, a maximum number of agents in each session of the multi-session meeting, one or more skill-types and a buffer-level for each skill-type, operating a schedule manager MS to provide scheduled work-shifts of agents in the time-range of scheduled work-shifts that include open slots and net staffing data of each skill-type, determining a total number of agents, calculating a lower-bound of sessions and an upper-bound of sessions, iteratively determining a number of sessions of the multi-session meeting and allocating the total number of agents to the determined number of sessions of the multi-session meeting until an optimal number of sessions of the multi-session meeting is reached.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
80.
A COMPUTER-IMPLEMENTED METHOD FOR CONSISTENTLY IDENTIFYING AN AGENT FOR A COACHING SESSION, AND ASSESSING RELEVANT COACHING SUBJECT TO THE COACHING SESSION, IN A CONTACT CENTER
A computer-implemented method for consistently identifying an agent for a coaching-session and assessing relevant-coaching-subject to the coaching-session. The computer-implemented method includes: (i) receiving agents having KPIs below a threshold; for each agent: (ii) receiving focus-area and related behaviors for the KPIs; (iii) retrieving interactions and associated categories and behaviors; (iv) retrieving evaluations of the retrieved interactions that are below a threshold, and related interactions; (v) marking each category that is having an evaluation below the threshold; (vi) retrieving associated focus-area with behaviors for each category that is classified as negative; (vii) determining a number of categories for the coaching-session; (viii) identifying behaviors from interactions related to the categories based on the associated focus-area; (ix) determining a number of behaviors; (x) calculating a co-relation score for each behavior and associated focus-area; and (xi) selecting a number of behaviors and associated focus-area having highest co-relation score to schedule a coaching-session therewith.
Agent utilization systems and methods, and non-transitory computer readable media, include receiving net staffing data for an organization; identifying opportunities for agents by: calculating available agents per interval (AAI) information based on the net staffing data, calculating total addressable contacts (TAC) information based on the AAI information, and calculating opportunity information for a calling list opportunity, a coaching opportunity, a re-allocation opportunity, or a combination thereof, based on the TAC information; displaying the calling list opportunity, the coaching opportunity, the re-allocation opportunity, or a combination thereof, alongside the opportunity information; receiving a selection of one of the calling list opportunity, the coaching opportunity, the re-allocation opportunity, or a combination thereof; and in response to the selection, automatically implementing the selected calling list opportunity, the selected coaching opportunity, the selected re-allocation opportunity, or a combination thereof, in a workforce management system.
Methods and systems for digital scheduling include periodically determining, from a datastore of agent profiles, a set of agents which satisfy a predefined time-off criterion; and notifying, via a digital message, one or more agents of the determined set of agents of a recommended list of dates on which to take a time-off, wherein the recommended list of dates is generated in accordance with staffing prediction data for the dates.
G06Q 10/1093 - Ordonnancement basé sur un agenda pour des personnes ou des groupes
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"
83.
SYSTEMS AND METHODS FOR TRADING SHIFT OF UNAVAILABLE PREFERENCE USING TRADE QUEUES
Systems and methods for digital scheduling, include periodically checking a data storage for queued schedule trade requests; identifying two corresponding queued schedule trade requests; else, if two corresponding queued schedule trade requests are not identified: identifying a set of cyclic queued schedule trade requests; else, if a set of cyclic queued schedule trade requests are not identified: repeating the step of periodically checking; approving or rejecting the identified schedule trade requests based on one or more approval criteria; and if the identified schedule trade requests were approved, outputting one or more updated schedules reflecting the approved schedule trade requests.
A computerized system and method may calculate or update task or job execution schedules for a plurality of resources and perform automated actions based on the calculated or updated schedules. A computerized system including one or more processors, a memory, and a communication interface to communicate via a communication network with remote computing devices, may be used for forecasting or reforecasting task properties for relevant time intervals, where the properties may describe future tasks to be handled by a plurality of resources; calculating an allocation matrix for the time intervals based on the predicted properties; and calculating or updating a schedule based on the calculated allocation matrix—for example to automatically extend a break for one or more resources, where resources are not to handle tasks during the break. Some embodiments of the invention may perform, e.g., computer automated actions based on calculated or updated schedules.
Agent proficiency scoring systems and methods, and non-transitory computer readable media, include receiving performance details and behavior details for an agent; calculating a performance score and a behavior score for the agent based on the performance details and the behavior details; combining the performance score and the behavior score to yield a current proficiency score of the agent; calculating a proficiency deviation between the current proficiency score of the agent and a previous proficiency score of the agent; determining whether the proficiency deviation of the agent is within an acceptable range; automatically updating the previous proficiency score, or transmitting a proficiency score request to a supervisor of the agent for review; and implementing one or more actions based on the current proficiency score or the previous proficiency score of the agent.
A system and a method for managing interactions between agent devices and customer devices, the system comprising: a computing device; a local storage; a memory; and a processor, the processor configured to: identify interaction data in interactions between an agent device and a customer device that show a transition from a first connection type to a second connection type; and when a transition in connection types is identified, initiating one or more remedial measures.
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
An event prioritization system and methods are provided that are configured to dynamically compute call complexity for voice data calls using topics identified from call transcripts. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform call evaluation operations which include receiving a transcript of a voice data call, parsing the transcript for at least one topic, generating, based on the parsing, a list of topics, analyzing, by a call complexity evaluation engine, the list of topics for a complexity rating, determining, based on the complexity rating and a length of the voice data call, a deviation from an expectation, flagging the voice data call, and outputting the flagged voice data call via an interface.
Systems adapted to measure impact of supervisor actions and methods, and non-transitory computer readable media, include identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction; determining an impact score for each of a plurality of behavioral factors; aggregating the impact scores for the plurality of behavioral factors and determining an average of the impact scores to provide an overall impact score for the supervisor action; and performing an action automatically based on the overall impact score to improve contact center performance.
A computerized system and method may provide automated clustering procedures where each clustered entity or node may be included in a plurality of clusters (e.g., more than a single cluster). Clustering procedures provided by some embodiments of the invention may involve measuring and/or quantifying degrees of relevance and/or generality for a plurality of entities or nodes. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. A computerized system comprising a processor, and a memory, may be used for ranking a plurality of nodes; select nodes based on the ranking; cluster selected nodes into intermediate clusters; calculate distances between unselected nodes and intermediate clusters; and cluster unselected nodes and intermediate clusters into final clusters based on the calculated distances. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.
G06F 16/30 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données textuelles non structurées
G06F 16/3329 - Formulation de requêtes en langage naturel
G06F 16/355 - Création ou modification de classes ou de grappes
A system and a method of managing data transfer for a plurality of data centers may include identifying data transfer capacity and data transfer demand for each server of a plurality of data centers; calculating a data transfer distribution for the plurality of data centers that prioritizes data transfer between servers of a first data center of the plurality of data centers over data transfer between servers of the first data center and servers of the remaining data centers of the plurality of data centers based on the identified data transfer capacity and the data transfer demand; and allocating data transfers to the servers of the plurality of data centers.
H04L 12/24 - Dispositions pour la maintenance ou la gestion
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 67/101 - Sélection du serveur pour la répartition de charge basée sur les conditions du réseau
H04L 67/1023 - Sélection du serveur pour la répartition de charge basée sur un hachage appliqué aux adresses IP ou aux coûts
91.
System and method for intelligent and conditional cloud screen recording
A method of managing interaction recordings of an interaction between agent devices and customer devices, the method comprising: initiating an interaction recording of the interaction between an agent device and a customer device, wherein the interaction recording comprises metadata items; storing one or more parts of the interaction recording in a local storage; identifying parts of the interaction recording whose metadata items fulfill archiving criteria; and archiving parts of the interaction recording stored in the local storage whose metadata items fulfill the archiving criteria and deleting parts of the interaction recording from the local storage whose metadata items do not fulfill the archiving criteria.
Apparatus, systems, and methods for calibrating evaluator feedback relating to agent-customer interaction(s) based on corresponding customer feedback. An agent evaluation form is generated based on customer feedback provided via a customer questionnaire. By comparing evaluator feedback received via completion of the agent evaluation form by one or more user-selected evaluators to the customer feedback provided via the customer questionnaire, a customer feedback variance score is calculated for the one or more user-selected evaluators.
Embodiments are disclosed for securing internet-based transfer of voice call data. Embodiments may include: sending a primary-protocol-encoded communication, by a forwarding unit (FU), to a session border controller (SBC), when an indication is received at the FU that a secondary-protocol-encoded communication has been received at a gateway component, from a voice call data source, to propose transferring voice call data to the SBC; outputting by the SBC, in response to the primary-protocol-encoded communication an IP address encoded with the primary protocol; converting, by a gateway component, the IP address encoded with the primary protocol into an IP address encoded with the secondary protocol; and sending, by a gateway component, the IP address encoded with the secondary protocol to the voice call data source that is proposing to transfer the voice call data.
A computer-implemented method for unsupervised task segmentation. The computer-implemented method includes receiving a stream of data of desktop-actions. Each desktop-action relates to UI data-handling operations of applications, and labeled with an action-related integer id, operating an unsupervised task segmentation module on the stream of data of desktop-actions to identify sequences of desktop-actions. The unsupervised task segmentation module includes creating an integer sequence from the action related integer id, such that desktop-actions are consecutively concatenated, creating word embeddings of the UI data-handling operations of applications for each desktop-action based on the integer id thereof, to yield a vector of embeddings, and implementing unsupervised topic-segmentation NLP module on the created vector of embeddings to determine cutting-points in the integer sequence to yield segments such that semantic-similarity-level of embeddings in each yielded segment is maximized, and a number of non-complete business processes is reduced. Each cutting-point indicates an end of a segment.
Methods and systems for digital scheduling include, using a processor: identifying a set of target agents associated with a corresponding set of shift parameters that satisfy a set of preferences of a multi-day trade request initiated by a source agent; sending the multi-day trade request to a sub-set of the identified target agents selected by the source agent; and if a first target agent of the sub-set accepts the multi-day trade request, and if the multi-day trade request satisfies a pre-defined set of approval criteria, then automatically approving the multi-day trade request between the source agent and said first target agent, and automatically adjusting a schedule of the source agent and the first target agent to reflect the approved multi-day trade request; else: automatically rejecting the multi-day trade request.
A computer-implemented method for dynamically prioritizing inbound interactions in a digital multi-channel contact center. The computer-implemented method includes for each inbound interaction via a digital channel: (i) operating an interaction analyzer module to extract one or more metadata parameters from the inbound interaction; (ii) operating a prioritization module to calculate a Digital Interaction Priority Score (DIPS) of the inbound interaction based on the one or more metadata parameters; and (iii) forwarding the DIPS to an interaction distribution module to route the inbound interaction to an agent based on the DIPS. The DIPS is periodically updated until the interaction is assigned to the agent.
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
97.
System and method of efficient selection of evaluation form
Agent evaluation systems and methods, and non-transitory computer readable media, include receiving a recorded interaction between a customer and a contact center agent; retrieving or determining an interaction divergence range for each of a plurality of interaction parameters for the recorded interaction; calculating a form divergence determinant (FDD) score for each of a plurality of evaluation forms, wherein the lower the FDD score, the more suitable an evaluation form is for the recorded interaction; filtering out evaluation forms having an FDD score greater than a predefined threshold; ranking evaluation forms having an FDD score lower than the predefined threshold based on their FDD score; and providing a list of the ranked evaluation forms to a supervisor of the contact center agent.
A computer-implemented method for playing a personalized voice recording with a status update to follow-up customer on an inbound call in a contact center. The computer-implemented method includes marking one or more open tickets as predicted for follow-up inbound call and predicted date and time for the follow-up call in an inbound database; updating status of one or more open ticket marked as predicted for follow-up inbound call and predicted date and time; converting the updated status to a voice recording; and operating an inbound software to identify an inbound call as a follow-up inbound call of a customer received via a Voice over Internet Protocol (VoIP) network communicating with a customer's mobile device. The customer has an open ticket with an updated status and playing voice recording with the status update in the identified inbound call, thus reducing customers waiting time and agents' workload.
A computerized system and method may provide a robust, automated clustering procedure, including handling of outlier points, which may involve measuring and/or quantifying degrees of relevance and/or generality for a plurality of input entities. In some embodiments, a clustering procedure may be used, e.g., to generate a hierarchical, multi-tiered taxonomy of such entities. In some embodiments, a computerized system comprising a processor, and a memory, may be used for calculating a distance between nodes for each of a plurality of pairs of nodes, where the pairs may comprise a plurality of input entities and/or initial clusters; selecting one or more of the pairs based on the calculated distances; and merging one or more of the selected pairs, which may include a common node, into one or more final clusters. Some embodiments of the invention may allow routing interactions between remotely connected computer systems based on an automatically generated taxonomy.
Systems and methods are provided for configuring a set of one or more contact centers, the set of one or more contact centers associated with a total number of agents and a number of regions. The systems and methods may include predicting or selecting a number of scheduling units or an optimal number of skills for the set of one or more contact centers based on the total number of agents and the number of regions.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
