Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.
The present disclosure relates to methods, systems, and apparatuses for generating analytics from interaction data using language models. An application executing on a computing system receives a query associated with a topic of interest and identifies a subset of interaction data from a larger collection of stored interactions based on metadata and high-level characterization related to the topic. Each interaction in the subset is processed by invoking a language model with the query and data corresponding to that interaction to generate an analytic output. The analytic outputs are aggregated across the subset to produce a quantified result for the topic of interest. The quantified result is provided to a user together with references to portions of the interaction data that support the analytic outputs, enabling validation of the analytics presented.
A method for unsupervised auto-labeling of dialogue utterances with human-readable intent labels, the method including: receiving a series of text utterances; determining, using a pre-trained open intent discovery configuration prediction model, an open intent discovery configuration for the received series of text utterances based on a series of features of the series of text utterances; and using the determined open intent discovery configuration for performing the steps of: obtaining semantic representations of the series of received text utterances; generating clusters of intents based on the obtained semantic representations of the series of received text utterances; extracting candidate intent labels for the generated clusters; and labeling the generated clusters with a human-readable intent label selected from the extracted candidate intent labels.
Embodiments of the present disclosure facilitate viewing and monitoring a plurality of real-time interactions simultaneously. In some embodiments, interactions are combined into multiple real-time event streams. An example system may present, in a user interface, event notifications and labels, wherein the event notifications and labels are used to monitor or intervene in interactions between the entities.
Certain aspects of the disclosure provide methods and systems for providing resolution guidance to a help center agent are provided. One method generates a set of intent-resolution pairs based on a historical interactions, including: grouping similar intents extracted from the corpus of historical interactions in a same intent topic group of a plurality of intent topic groups, and grouping resolutions associated with the similar intents in a same resolution topic group of a plurality of resolution topic groups. Additionally, the method determines an intent of the current user utterance as a user intent. The method maps the user intent to one or more intent-resolution pairs from the set of intent-resolution pairs. Moreover, method provides, to the help center agent, one or more suggested resolutions corresponding to the one or more intent-resolution pairs. Also, the method provides, to the customer, a selected resolution from among the one or more suggested resolutions.
A method for providing automated agent performance ranking includes obtaining, for each agent of a plurality of agents, a performance score for each feature associated with a plurality of customer-agent interactions, wherein each feature is associated with a key performance indicator (KPI) metric and is under control of the agent; determining a task label for each of the plurality of customer-agent interactions; grouping the plurality of customer-agent interactions into one or more task groups based on the task label for each of the plurality of customer-agent interactions; determining, for each agent of the plurality of agents, a task-feature performance value, wherein the task-feature performance value is a combination of the performance score for each feature associated with the one or more task groups; and generating, for each feature, a report comprising a ranking for each agent of the plurality of agents based on the task-feature performance value.
A method for providing a predicted key performance indicator (KPI) value includes receiving a KPI metric and a target value for the KPI metric; receiving customer-agent interaction data; implementing a model corresponding to the KPI metric; predicting, with the model, the value for the KPI metric based on features that the model identifies and measures from the customer-agent interaction data; determining one or more features having a potential for improvement based on measured values of the features determined by the model; determining that improvement in a measured value of the one or more features maintains or increases the value of the KPI metric; and outputting a first indication of whether the value predicted for the KPI metric meets the target value and a second indication of the one or more features that maintains or increases the value of the KPI metric when the one or more features are improved.
A method for providing automated agent performance improvement includes obtaining a ranking for each agent of a plurality of agents; obtaining customer-agent interactions for each agent; determining a task label for each of the customer-agent interactions; selecting a first set of customer-agent interactions from the customer-agent interactions corresponding to a first task label; receiving application event streams corresponding to the first set of customer-agent interactions and comprising agent application usage data; analyzing the application event streams for statistically relevant differences for one or more activities performed by a first group of agents compared to a second group of agents; determining at least one activity of the one or more activities that is performed by the first group of agents differently than the second group of agents; generating guidance corresponding to the least one activity for the second group of agents; and deploying the guidance to the second group of agents.
Certain aspects of the present disclosure provide techniques for providing an issue resolution indication for an interaction. A method includes obtaining an interaction transcript; obtaining a resolution prompt; determining one or more issues presented to the first entity by the second entity, generating, with a first large language model based on the interaction transcript, the one or more issues, and the resolution prompt, one or more issue resolution indications; generating, for each of the one or more issue resolution indications indicating resolved status, a first narrative summarizing one or more actions implemented to resolve the one or more issues; generating, for each of the one or more issue resolution indications indicating unresolved status, a second narrative summarizing a reason the one or more issues are unresolved; and outputting the first narrative or the second narrative with each respective one of the one or more issue resolution indications.
A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
A method for providing an intent expressed in a conversational interaction in narrative form includes detecting a first intent, with a first large language model, from an input comprising at least an interaction transcript and an engineered prompt; generating, with the first large language model, a first narrative for the first intent and a first confidence score corresponding to a probability that the first intent is an intention in fact expressed in the interaction transcript; determining that the first confidence score is greater than or equal to a threshold; checking that the first narrative conforms to fluency and completeness rules, when the first confidence score is greater than or equal to the threshold; and outputting the first narrative as an output detected intent, when the first confidence score is greater than or equal to the threshold and when the first narrative conforms to the fluency and completeness rules.
Systems and methods for automating quality metrics are provided. Transcripts of interactions with agents and assigned quality metrics for a variety of questions are collected for an entity. The quality metrics may relate to questions such as “Did the agent greet the customer with a friendly greeting?” and “Did the agent answer the customer's question after returning from the hold?”. For each question used by the entity to generate quality metrics, a large language model or classifier is trained for the question using the transcripts and the assigned quality metrics for the question. The transcripts may be processed to include textual information corresponding to metadata associated with the communications such as time stamps for call holds, utterances, and silent periods. The trained large language model or classifiers may then be used later to automatically assign quality metrics to current interactions for their corresponding question.
Embodiments of the present disclosure provide systems and methods for generating a plurality of forecasts for a future time interval using a plurality of models and/or algorithms in order to assess the performance of each model. An example computer-implemented method can comprise generating a fitness function visualization corresponding with determined quantitative measures of forecast quality for each of the plurality of models and/or algorithms.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06F 17/18 - Complex mathematical operations for evaluating statistical data
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM) and customer experience (CX) automation; Providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM) software applications for allowing self-service and automated customer engagement with such applications via intelligent personal assistant devices; Computer software consulting for workforce forecasting and scheduling, knowledge and employee assistance, quality assurance, operational insights and analytics, and operational and performance management all in the fields of customer service and engagement, customer and employee support, employee and operations management, and compliance and security management; consulting services for computer software for self-service, namely, computer software consulting in the field of intelligent virtual assistants, web and mobile self-service, and social communities; consulting services for computer software for experience management, namely, computer software consulting in the field of software for capturing and correlating voice, video, email, text, chat, social digital, and survey interactions with customers for the purpose of improving customer service and experience
16.
INTELLIGENT VIRTUAL ASSISTANT TRAINING THROUGH PHASED OBSERVATIONAL LEARNING TASKS
Disclosed embodiments pertain to training an intelligent virtual assistant through phased observational learning tasks. A pre-trained language model can be updated offline to produce a second language model with self-supervised learning based on transcripts of historical interactions between one or more customers, one or more customer service agents, and one or more data stores. The second language model can be evaluated and determined to satisfy a predetermined performance threshold. Subsequently, the second language model can be updated online to produce a third language model with reinforcement learning based on received customer input and similarity between a response provided by a customer service agent and a predicted response generated by the second language model. The third language model can then be deployed with an intelligent virtual assistant to respond to received user input.
16 - Paper, cardboard and goods made from these materials
18 - Leather and imitations of leather
25 - Clothing; footwear; headgear
42 - Scientific, technological and industrial services, research and design
Goods & Services
Stickers; notepad. Tote bags; shoulder bags; all-purpose reusable carrying
bags. Clothing, namely, shirts, t-shirts, sweatshirts. Providing temporary use of on-line non-downloadable computer
software for use in customer relationship management (CRM);
providing temporary use of on-line non-downloadable computer
software for use in customer relationship management (CRM)
software applications that include self-service and
automated customer engagement via intelligent personal
assistant devices.
To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
A tagging system gathers all events (tagged and untagged) generated by remote sensors at a location or facility over time. Based on the gathered events the tagging system uses machine learning to train a model to learn the sensor layout of a facility or location and the timing between the triggering of sensors. Once trained, the model can predict the movement and location of individuals and objects throughout the facility based on a starting tagged event. Given a series of tagged and untagged events, the system can use the movement predictions of the model to tag the untagged events in the series with the identification of an individual or object that triggered the generation of the untagged event.
The present invention allows a CEC system to automatedly track the use, storage, access, and modification of sensitive information/data in the system through desktop monitoring. Further, through desktop, video, and audio monitoring of CSRs the system can automatedly determine the improper use, access, storage, and modification of sensitive information by implementing sensitive data use rules that allow a system to be specialized for the user. Finally, the system can automatedly determine and implement violation actions for the improper use, storage, access, and manipulation of sensitive information. This provides an intelligent system capable of locating all sensitive data in the system and regulating the use, access, and storage of sensitive data in the system.
G06F 21/74 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information operating in dual or compartmented mode, i.e. at least one secure mode
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 21/72 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in cryptographic circuits
G06V 20/40 - ScenesScene-specific elements in video content
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
Certain aspects of the present disclosure provide techniques for receiving audio data comprising a user voice command; determining a task to be completed by a remote service based on the user voice command; determining that a reference voice print associated with the user is stored in a user account; authenticating the user by determining that a sample voice print based on the user voice command matches the reference voice print associated with the user; storing authentication evidence associated with the task; and providing proof of user authentication to the remote service in order to initiate the task with the remote service.
Certain aspects of the present disclosure provide techniques for receiving, from a user, a command for a virtual assistant to perform a task on behalf of the user; determining a communication channel for the virtual assistant to communicate with a remote service in order to perform the task; registering a communication session with an identity provider service, wherein the communication session is associated with the communication channel; initiating the communication session with the remote service using the communication channel; receiving a communication session authentication query from the remote service; and determining, in response to the communication session authentication query, whether the user is authenticated.
Certain aspects of the present disclosure provide techniques for masking sensitive information in a data stream, comprising: receiving a request to record an image data stream associated with a graphical user interface; instantiating a display capturer configured to capture the image data stream associated with the graphical user interface; instantiating a virtual display configured to virtually display: an image layer comprising the image data stream associated with the graphical user interface; and a mask layer configured to mask sensitive information in the image data stream; and determining a mask layer state based on at least one of: a state of the graphical user interface; or a comparison of the mask layer at a first time and at a second time.
A method for deployment of cloud resources in one or more cloud environments includes receiving a request, through a user interface, to deploy a cloud resource, the request comprising an abstract resource definition and one or more target deployment locations; identifying and executing a first resource manager associated with a first target deployment location of the one or more target deployment locations; generating, with the first resource manager based on the abstract resource definition, a first manifest for deployment of the cloud resource at the first target deployment location; deploying, with the first resource manager, an instance of the cloud resource on a first cloud-computing infrastructure defined by the first target deployment location, wherein the instance is based on the first manifest; and returning, through the user interface, information corresponding to the instance of the cloud resource deployed on the first cloud-computing infrastructure.
A method for providing benchmark-plans to a customer based on benchmark information, comprising receiving a customer-defined service goal and a demand forecast for the customer; generating, with a planner, a plan for achieving the customer-defined service goal based on the demand forecast; determining a benchmark category from a plurality of benchmark categories that the customer belongs to based on at least an industry of the customer, wherein the benchmark category defines a plurality of other customer-defined service goals for other customers participating in at least the industry as the customer; determining benchmark service goals based on the determined benchmark category; executing the planner for each of the benchmark service goals thereby generating benchmark-plans for the demand forecast for the customer; and outputting, to the customer, the plan and the benchmark-plans, wherein the benchmark-plans are different from the plan.
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
Techniques for monitoring and improving emotional well-being of an employee are described. Stream of audio data corresponding to a call between an employee and a customer may be received. One or more acoustic features and/or audio feature data may be generated from the audio data. Word embedding data corresponding to the audio data may be generated. An employee experience score may be generated using a machine learning (ML) model, word embedding data, and the one or more acoustic features, where the score corresponds to an experience level of the first speaker during the call with the second speaker. Based on the score, an action may be caused to be performed. In some embodiments, one or more notifications may be generated based on data related to the audio data, where at least notification is configured to improve an experience level for the first speaker.
G10L 17/06 - Decision making techniquesPattern matching strategies
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
Techniques for monitoring and improving emotional well-being of an employee are described. Stream of audio data corresponding to a call between an employee and a customer may be received. One or more acoustic features and/or audio feature data may be generated from the audio data. Word embedding data corresponding to the audio data may be generated. An employee experience score may be generated using a machine learning (ML) model, word embedding data, and the one or more acoustic features, where the score corresponds to an experience level of the first speaker during the call with the second speaker. Based on the score, an action may be caused to be performed. In some embodiments, one or more notifications may be generated based on data related to the audio data, where at least notification is configured to improve an experience level for the first speaker.
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
16 - Paper, cardboard and goods made from these materials
18 - Leather and imitations of leather
25 - Clothing; footwear; headgear
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Stickers; notepad.
(2) Tote bags; shoulder bags; all-purpose reusable carrying bags.
(3) Clothing, namely, shirts, t-shirts, sweatshirts. (1) Providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM); providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM) software applications that include self-service and automated customer engagement via intelligent personal assistant devices.
29.
Automated validation of information exchanged during interactions
An automated interaction processing system is deployed to automatically process an interaction transcription or content to generate response data in a manner that does not require intensive human manual effort.
Techniques and architectures for implementing a team of virtual assistants are described herein. The team may include multiple virtual assistants that are configured with different characteristics, such as different functionality, base language models, levels of training, visual appearances, personalities, and so on. The characteristics of the virtual assistants may be configured by trainers, end-users, and/or a virtual assistant service. The virtual assistants may be presented to end-users in conversation user interfaces to perform different tasks for the users in a conversational manner. The different virtual assistants may adapt to different contexts. The virtual assistants may additionally, or alternatively, interact with each other to carry out tasks for the users, which may be illustrated in conversation user interfaces.
A system and method use a trained transformer model to generate summaries of audio interactions based on keywords. Training the transformer model includes obtaining a transcription of an audio interaction, obtain keywords for summarizing the audio interaction, training a transformer model to generate a summary of the audio interaction based on the keywords and the transcription, where the transcription is an input to the transformer model and the keywords are injected between an encoder and a decoder of the transformer model, and deploying the trained transformer model to be used for generating summaries of subsequent audio interactions.
G06F 16/683 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06F 40/40 - Processing or translation of natural language
33.
System and method for suggesting and generating a customer service template
The template generation system receives interaction data stored by the CEC from an interaction database and customer service templates (if any) from a template database. The template generation system processes interaction data and customer service templates to learn the domain language of CSR responses and the template responses within the CEC. The template generation system encodes the learned language and generates sentence vector embeddings for the CSR responses and template responses. Based on the learned language, the encoding, and the sentence vector embeddings, the template generation system processes CSR responses derived from the interaction data and customer service templates to predict the need for new customer service templates. Based on the predicted need for new customer service templates, the template generation system provides customer service template suggestions and may also auto-generate suggested customer service templates.
A system and method for updating computerized language models is provided that automatically adds or deletes terms from the language model to capture trending events or products, while maximizing computer efficiencies by deleting terms that are no longer trending and use of knowledge bases, machine learning model training and evaluation corpora, analysis tools and databases.
An interactive voice response (IVR) system including iterative speech recognition with semantic interpretation is deployed to determine when a user is finished speaking, thus saving them time and frustration. The IVR system can repeatedly receive an audio input representing a portion of human speech, transcribe the speech into text, and determine a semantic meaning of the text. If the semantic meaning corresponds to a valid input or response to the IVR system, then the IVR system can determine that the user input is complete and respond to the user after the user is silent for a predetermined time period. If the semantic meaning does not correspond to a valid input to the IVR system, the IVR system can determine that the user input is not complete and can wait for a second predetermined time period before determining that the user has finished speaking.
An interactive voice response (IVR) system including iterative speech recognition with semantic interpretation is deployed to process an audio input in a manner that optimizes and conserves computing resources and facilitates low-latency discovery of start-of-speech events that can be used to support external processes such as barge-in operations. The IVR system can repeatedly receive an audio input at a speech processing component and apply an input-aware recognition process to the audio input. In response to generating a start-of-speech event, the IVR system can apply an input-unaware recognition process to the remaining audio input and determine a semantic meaning in relation to the relevant portion of the audio input.
A system for detecting fraudulent activity using account analytics obtains an interaction record for an interaction between a remote device and a user account via an interaction channel, where the interaction is an attempt to access the user account, obtains historical data relating to the user account and the interaction channel that includes one or more historical interaction records relating to the user account and activity records relating to the interaction channel, calculates a threat score for the user account based on the interaction record and the one or more historical interaction records that indicates a likelihood that the user account is subject to fraudulent activity, generates a database record based on the interaction record that includes the threat score, and initiates corrective action if the threat score exceeds a predetermined threshold.
The present disclosure describes methods and systems for suggesting responses generated from an entity's own published information with links to the source of that generated response should provide a quality starting point that is already accurate and brand compliant or, if not, quickly editable to become so. The published information is ingested by the system, and a question/answer transformation process is applied against the ingested data using training language data that is tagged and categorized by intent to generate suggested responses. The suggested response may be presented in a user interface with a link to the URL which was used to construct the response. The suggested responses may be edited if needed.
An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.
An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a destination entity over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by another computer. Before the IVA contacts the destination entity, a specific user profile is injected into an IVA dialog state. The IVA contacts the destination entity and answers customer service agent (CSA) questions by using the specific user profile inserted. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out, or emailing a form.
An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.
The segment analysis system analyzes survey data to determine the influence each custom question/response combination (segment) has on a given aggregate scored survey metric for a given date/date range. The system removes from consideration all surveys that do not include a scored survey metric and date that matche the aggregate scored survey metric and given date/date range. The system further removes from consideration all surveys not pertaining received user-defined filtering. Once the system has eliminated all extraneous surveys from consideration, the system segments each question/response combinations across the pool of surveys to generate an influence score for each question/response combination. The system identifies which segment has the greatest positive and negative influence on the aggregate scored survey metric for the given date/date range. The system generates reports for the segment analysis and stores all segment analyses for further comparative analysis.
System and method for handling a transaction between a waiting party and queuing party include an independent communication system (ICS) managing calls between the waiting party and calling party for handling sensitive data as well as call-attached data. The ICS manages the transaction in different stages and with different levels of sensitivity. Either party is allowed to modify the call or call preferences during the transaction. The ICS works independently from the queuing party calling system.
Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.
G10L 17/00 - Speaker identification or verification techniques
G10L 17/02 - Preprocessing operations, e.g. segment selectionPattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal componentsFeature selection or extraction
45.
SELECTING FORECASTING ALGORITHMS USING MOTIFS AND CLASSES
Methods and systems for selecting a forecasting algorithm to use for a forecast for a time interval are provided. A class is a series of time intervals that is selected by an entity from time series data that relates to external data or is a series of time intervals from the time series data that corresponds to a motif. The time series data is processed by a computer to identify motifs, and classes are generated based on each identified motif. A user may further identify one or more classes in the time series data. For each class, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the class is determined. Later, when the entity desires to receive a forecast for a future time interval, the class associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined class is then used.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06N 5/00 - Computing arrangements using knowledge-based models
The present disclosure describes methods and systems for selecting the forecasting algorithm to use for a prediction based on motifs. A motif is a pattern of interval values that is found to repeat in time series data. Time series data that includes historical demand data (e.g., average communication volume) for an entity at various time intervals in the past is received. The time series data is processed to identify motifs. For each identified motif, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the motif is determined. Later, when the entity desires to receive a forecast for a future time interval, the motif associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined motif is then used to predict the demand for the future time interval.
The present invention allows text analysis and routing of an outgoing message. The system intercepts outgoing messages for analysis by a TAS software module. The module assigns an analytical score to the message, then compares the score to a threshold. If the score is below the threshold, the message is transmitted to its ultimate destination. If not, the message may be routed for correction by the message's composer or quality assurance staff. After such correction, the message new analytical score is generated and compared, and, if necessary, the process repeats again.
A realtime contextual event notification system that ingests events as streams from any authorized entity applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources and rules may be applied to provide realtime contextual information associated with the end-user.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
A real time contextual event notification system that ingests events as streams from any authorized entity applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources and rules may be applied to provide real time contextual information associated with the end-user.
A real-time contextual event notification system ingests events as streams from any authorized entity, applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources, and rules may be applied to provide real-time contextual information associated with the end user. One such event stream includes detected linguistic and/or acoustic events during a phone call between two or more persons.
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
An anomaly detection system using machine learning to generate predicted survey scores for a given duration and a given metric based on historic survey score data. The system compares a predicted survey score to the actual survey score and identifies anomalous actual survey scores. The anomaly detection system trains a plurality of survey score prediction models using historic survey score data. Each survey score prediction model is based on a specific survey score metric and a specific duration. The survey score prediction models generate expected survey score results for the given duration and the given metric. Based on the user-determined filtering and tolerances, the system determines if the actual survey score result is anomalous. The system generates reports for the detected anomalies and continually updates the survey score prediction models with newly obtained actual survey results, thereby improving the anomaly detection accuracy over time.
16 - Paper, cardboard and goods made from these materials
18 - Leather and imitations of leather
25 - Clothing; footwear; headgear
42 - Scientific, technological and industrial services, research and design
Goods & Services
Stickers; printed notepad Tote bags; shoulder bags; all-purpose reusable carrying bags Clothing, namely, shirts, t-shirts, sweatshirts Providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM); Providing temporary use of on-line non-downloadable computer software for use in customer relationship management (CRM) software applications for allowing self-service and automated customer engagement with such applications via intelligent personal assistant devices
In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.
A realtime contextual event notification system that ingests events as streams from any authorized entity applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources and rules may be applied to provide realtime contextual information associated with the end-user.
f is computed. A second mode M[D] computes, using the parameters just calibrated, demand-controlled service levels and an error metric e(M[D]) between the controlled service levels and actual levels. The user iteratively adjusts the shrinkage. When the user is satisfied that e(M[D]) is sufficiently small, calibration of the core parameters is complete.
Systems and methods are provided for dynamically adjusting a website of an entity using information that has been received, stored, gathered, and/or otherwise obtained about what people want to find on the entity's website. A website may be dynamically adjusted using trending information in response to determining that the usage of the monitored data source is greater than the baseline usage distribution or in response to determining that the usage of the monitored data source is not greater than the baseline usage distribution receiving NLP inputs of the user from the IVA and adjusting dynamic web content displayed on the website based on the NLP inputs.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
The present application includes a method and system for real-time predictive scheduling. The system receives information from at least one workload input and at least one personnel input, calculating an initial schedule based on the information and on analytics rules in a scheduling analytics engine. The system then allocates incoming workloads to customer service representatives according to the initial schedule, while monitoring adherence to the initial schedule by calculating deviation from schedule adherence. If the deviation from schedule adherence exceeds an acceptable deviation from schedule adherence within the analytics rules, the system calculates an updated schedule.
A system for generating wrap-up information is capable of learning how interactions are transformed into contact notes and outcome codes using natural language processing and can generate the contact notes and outcome codes for new incoming interactions by applying prediction models trained on interaction data, contact notes and outcome codes. The system for generating wrap-up information receives interaction data, including interaction audio data, interaction transcripts, associated contact notes and associated outcome codes. The interaction transcripts are generated from the previous interactions between agents and customers. The contact notes and outcome codes are generated by agents during the associated previous interactions. The system processes and uses the interaction data to train prediction models to analyze interaction audio data and interaction transcripts and predict appropriate contact notes and outcome codes for the interaction. Once trained the prediction model(s) can generate appropriate contact notes and outcome codes for new interactions.
A system for generating wrap-up information is capable of learning how interactions are transformed into contact notes and outcome codes using natural language processing and can generate the contact notes and outcome codes for new incoming interactions by applying prediction models trained on interaction data, contact notes and outcome codes. The system for generating wrap-up information receives interaction data, including interaction audio data, interaction transcripts, associated contact notes and associated outcome codes. The interaction transcripts are generated from the previous interactions between agents and customers. The contact notes and outcome codes are generated by agents during the associated previous interactions. The system processes and uses the interaction data to train prediction models to analyze interaction audio data and interaction transcripts and predict appropriate contact notes and outcome codes for the interaction. Once trained the prediction model(s) can generate appropriate contact notes and outcome codes for new interactions.
Provided is a system and method for adapting analysis to user profiles to reduce bias in customer or user generated content, specifically a system and method that discounts or adjusts bias in sentiment data based on the channel from which the content was received and/or the demographic of the user. The system includes a means to detect bias for any product, service, or company across multiple channels of customer data; a means to construct models to quantize bias by specific demographics and channels; and a means to adjust model output to reduce inflation by biased groups.
A system for data recording across a network includes a session border controller connecting incoming data from the network to an endpoint recorder. A load balancer is connected to the network between the session border controller and the endpoint and receives the incoming data from the session border controller, wherein the load balancer comprises computer memory and a processor configured to parse the incoming data into video data and audio data according to identification protocols accessible by the processor from the computer memory. A recording apparatus includes recording memory that receives the incoming data from the load balancer, stores a duplicate version of the incoming data in the recording memory, and connects the incoming data to the endpoint.
G06F 21/32 - User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
H04L 67/1036 - Load balancing of requests to servers for services different from user content provisioning, e.g. load balancing across domain name servers
H04L 67/568 - Storing data temporarily at an intermediate stage, e.g. caching
Disclosed are a framework and method for selecting an anomaly detection method for each of a plurality of class of time series based on characteristics a time series example that represents an expected form of data. The method provides classification of a given time series into one of known classes based on expected properties of the time series, filtering the set of possible detection methods based on the time series class, evaluating the remaining detection methods on the given time series using the specific evaluation metric and selecting and returning a recommended anomaly detection method based on the specific evaluation metric.
To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.
Certain aspects of the present disclosure provide techniques for generating multivariate time series data utilizing a variational auto-encoder (VAE) having an architecture for injecting custom temporal structures into the generated multivariate time series data. A method for generating multivariate time series data includes sampling a multivariate distribution forming a latent space vector, processing the latent space vector with an interpretable decoder of a variational auto-encoder, an architecture of the interpretable decoder comprising a plurality of blocks including one or more blocks configured to inject one or more temporal structures into multivariate time series data, and outputting, from the interpretable decoder, generated multivariate time series data comprising one or more temporal structures defined by the injected one or more temporal structures.
An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.
Integrating behavioral and lexical analysis of conversational audio signals with CRM (Customer Relationship Management) workflow analysis signals to provide real-time guidance to agents who are both speaking with a customer telephonically and interacting with the customer's information using a CRM system. This includes intaking audio and CRM analysis signals in real-time, extracting the behavioral and lexical signals from the audio. The CRM, behavioral, and lexical information are combined to produce guidance and scoring signals, which are output to the CRM in real-time to facilitate real-time guidance and scoring. The data can be stored for future reference.
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
Embodiments described herein provide systems and methods for sharing encoder output of video streams. In a particular embodiment, a method provides determining video profiles for each of a plurality of devices. The method further provides determining if two or more of the video profiles are similar by determining if parameters associated with each video profile differ by less than a threshold value. The method further provides transmitting a video stream encoded in a single format to the devices if they have similar profiles and transmitting video streams encoded in different formats to the devices if they do not have similar profiles.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 7/52 - Systems for transmission of a pulse code modulated with one or more other pulse code modulated signals, e.g. an audio signal or a synchronizing signal
H04N 7/24 - Systems for the transmission of television signals using pulse code modulation
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.
The present invention allows text analysis and routing of an outgoing message. The system intercepts outgoing messages for analysis by a TAS software module. The module assigns an analytical score to the message, then compares the score to a threshold. If the score is below the threshold, the message is transmitted to its ultimate destination. If not, the message may be routed for correction by the message's composer or quality assurance staff. After such correction, the message new analytical score is generated and compared, and, if necessary, the process repeats again.
Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.
This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
A system and method for integrating audio data collected, such as audio data and analytical data, to perform behavioral analysis on the audio data, using an application of acoustic signal processing and machine learning algorithms, by converting the audio data to text data and performing behavioral analysis on the text data. The behavioral analysis data from the audio application of acoustic signal processing is combined with machine learning algorithms and speech to text data to provide a call agent with feedback to assist in the next best action or insight into customer behaviors.
G10L 15/18 - Speech classification or search using natural language modelling
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
G10L 25/63 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for estimating an emotional state
A scalable system provides automated conversation review that can identify potential miscommunications. The system may provide suggested actions to fix errors in intelligent virtual assistant (IVA) understanding, may prioritize areas of language model repair, and may automate the review of conversations. By the use of an automated system for conversation review, problematic interactions can be surfaced without exposing the entire set of conversation logs to human reviewers, thereby minimizing privacy invasion. A scalable system processes conversations and autonomously marks the interactions where the IVA is misunderstanding the user.
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
Detecting fraudulent activity can be a complex, manual process. In this paper, we adapt statistical properties of count data in a novel algorithm to uncover records exhibiting high risk for fraud. Our method identifies shelves, partitioning data under the counts using a Student's t-distribution. We apply this methodology on a univariate dataset including cumulative results from phone calls to a customer service center. Additionally, we extend this technique to multivariate data, illustrating that the same method is applicable to both univariate and multivariate data.
Systems and methods for handling dual modality communication between at least one user device and at least one server. The modalities comprise audio modalities and mechanical motion modalities. The server may be simultaneously connected to the user device via a data network and a voice network and simultaneously receive audio-based input and mechanical motion-based input.
A system for data recording across a network includes a session border controller connecting incoming data from the network to an endpoint recorder. A load balancer is connected to the network between the session border controller and the endpoint and receives the incoming data from the session border controller, wherein the load balancer comprises computer memory and a processor configured to parse the incoming data into video data and audio data according to identification protocols accessible by the processor from the computer memory. A recording apparatus includes recording memory that receives the incoming data from the load balancer, stores a duplicate version of the incoming data in the recording memory, and connects the incoming data to the endpoint.
A system and method for updating computerized language models is provided that automatically adds or deletes terms from the language model to capture trending events or products, while maximizing computer efficiencies by deleting terms that are no longer trending and use of knowledge bases, machine learning model training and evaluation corpora, analysis tools and databases.
G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
A61K 31/198 - Alpha-amino acids, e.g. alanine or edetic acid [EDTA]
A61K 31/215 - Esters, e.g. nitroglycerine, selenocyanates of carboxylic acids
A61K 31/216 - Esters, e.g. nitroglycerine, selenocyanates of carboxylic acids of acids having aromatic rings, e.g. benactizyne, clofibrate
A61K 31/401 - ProlineDerivatives thereof, e.g. captopril
A61K 31/41 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with two or more ring hetero atoms, at least one of which is nitrogen, e.g. tetrazole
Systems and methods are disclosed for scheduling a workforce. In one embodiment, the method comprises receiving a shift activity template; receiving an association between the shift activity template and at least one worker; and scheduling a plurality of schedulable objects. The scheduling is performed in accordance with a workload forecast and schedule constraints. Each of the schedulable objects is based on the shift activity template. The shift activity template describes a worker activity performed during a shift. The template has range of start times and a variable length for the activity. The activity is associated with a queue.
The present invention allows a user to review the routing of various communications. The system receives incoming communications for analysis by a smart routing engine (SRE) software module. The SRE module analyzes the communication at various system routing points, which is used by SRE to route the communication to an appropriate party. The SRE updates a routing log at each point to ensure a record of the reasons for routing the communication in a certain way. The routing log passes with the communication. This ensures that the ultimate recipient of the communication understands why they have received the communication and reduces the time required for a communication to be acted upon.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
H04L 45/302 - Route determination based on requested QoS
H04L 41/5061 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the interaction between service providers and their network customers, e.g. customer relationship management
H04L 41/5022 - Ensuring fulfilment of SLA by giving priorities, e.g. assigning classes of service
Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. In implementations multiple logical queries are generated and are processed to remove a duplicate logical query. A logical query can be used to extract data from a database associated with the personalized analytics system based on a modified scope.
As described herein, a system for expanding contractions in electronically stored text includes expanding contractions having only on expanded form. For remaining contractions, a grammar check is performed for all possible expanded forms to determine if an expanded form can be selected based on context and grammar rules. If an expanded form is not evident from the first two steps, all possible expanded forms of the remaining contractions are converted to a vector representation along with the original text. A Word Movers Distance (WMD) for each possible expansion is calculated using the vectors for each possible expansion and the original text. An expanded form is chosen without human intervention based on the grammar score alone or the WMD and the grammar score.
An artificial intelligence (AI) application uses an external machine learning component from a different computing environment to develop context data for use by the AI application. The context data includes raw data outputs from the external machine learning component. An active machine learning component is executed with the context data and provides a suggested next step to a computer to implement as an automated output. A feedback loop adds the suggested next step from the active machine learning component to the context data and forms an augmented data set for providing context to the AI application. A context component selects a rule from a rules engine that corresponds to the augmented data set. The computer implements an automated output according to the rule that was selected.
System and method for calibration of WFM system modeling parameters. A first mode M[D,S] of a modeler computes demand-shrinkage controlled service levels and an error metric e(M[D,S]) between the controlled and actual service levels. A user device iteratively adjusts each core parameter. When the user is satisfied that e(M[D,S]) is sufficiently small, calibration of the core parameters is complete. The same is done for calibrating the modeling factor, and then a final e(M[D,S])f is computed. A second mode M[D] computes, using the parameters just calibrated, demand-controlled service levels and an error metric e(M[D]) between the controlled service levels and actual levels. The user iteratively adjusts the shrinkage. When the user is satisfied that e(M[D]) is sufficiently small, calibration of the core parameters is complete.
A system and method for attributing the performance of an organization employee or team to events in the employees' career record and predicting future performance. The system acquires historical career record data comprising data of an employee or team of employees, including key performance indexes (KPIs) of the employee/team; finds at least one signpost—an individual data point or group of data points in the career record data having a comparatively high correlation with one of the KPIs of the employee/team or with increases/decreases of the KPI; monitors the career record for new occurrences of the signposts; predicts the KPI or whether the KPI will increase/decrease as a function of the occurrence of the signpost, and transmits the predicted KPI or increase/decrease thereof and its attribution to the occurrence of the signpost to a data consumer. In some embodiments, the system provides prescriptive measures for improving future performance.
A non-ontological hierarchy for language models is based on established psycholinguistic and neuro-linguistic evidences. By using non-ontological hierarchies, a more natural understanding of user's inputs and intents improve toward a better potential for producing intelligent responses in a conversational situation.
An IVR and chatbot, or other system, employing a language model, the language model resulting from a method and computer product encoding the method is available for preparing a domain or subdomain specific glossary. The method included using probabilities, word context, common terminology and different terminology to identify domain and subdomain specific language and a related glossary updated according to the method.
Various embodiments are described for searching and retrieving documents based on a natural language input. A computer-implemented natural language processor electronically receives a natural language input phrase from an interface device. The natural language processor attributes a concept to the phrase with the natural language processor. The natural language processor searches a database for a set of documents to identify one or more documents associated with the attributed concept to be included in a response to the natural language input phrase. The natural language processor maintains the concepts during an interactive session with the natural language processor. The natural language processor resolves ambiguous input patterns in the natural language input phrase with the natural language processor. The natural language processor includes a processor, a memory and/or storage component, and an input/output device.
An architecture for assessing and identifying fraudulent contact with client contact systems, such as IVR, includes threshold and machine learning scoring and filtering of calls based on these criteria. The criteria may include behavioral, situational and reputational scoring.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware and downloaded computer software for use
in the fields of customer service and engagement, customer
and employee support, employee and operations management,
and compliance and security management incorporating
workforce engagement software, namely, software for
workforce forecasting and scheduling, knowledge and employee
assistance, quality assurance, operational insights and
analytics, and operational and performance management;
downloadable computer software for self-service and
automated customer engagement, namely, intelligent virtual
assistants, web and mobile self-service, and social
communities; downloadable computer software for experience
management, namely, software for capturing and correlating
voice, video, email, text, chat, social digital, and survey
interactions with customers for the purpose of improving
customer service and experience; downloadable computer
software for enterprise recording to enhance regulatory
compliance and minimize fraud, namely, omnichannel recording
of voice, text, screen, and video, compliance recording,
voice biometrics and authentication, and real-time analysis
of caller behavior and related call parameters to detect
potential fraud. Consulting services in the fields of business technology
software, design and development of computer hardware, and
computer software and cloud deployment, namely, self-service
and automation, telecommunications, digital security and
surveillance, computer and telecommunication networks and
multimedia; providing temporary use of on-line
non-downloadable computer software for use in the fields of
customer service and engagement, customer and employee
support, employee and operations management, and compliance
and security management incorporating workforce engagement
software, namely, software for workforce forecasting and
scheduling, knowledge and employee assistance, quality
assurance, operational insights and analytics, and
operational and performance management; providing temporary
use of on-line non-downloadable computer software for
self-service and automated customer engagement, namely,
intelligent virtual assistants, web and mobile self-service,
and social communities; providing temporary use of on-line
non-downloadable computer software for enterprise recording
to enhance regulatory compliance and minimize fraud, namely,
omnichannel recording of voice, text, screen, and video,
compliance recording, voice biometrics and authentication,
and real-time analysis of caller behavior and related call
parameters to detect potential fraud.
90.
System and computer-implemented method for in-page reporting of user feedback on a website or mobile app
Computer-implemented techniques are disclosed for presenting an in-page console on a website for reviewing interaction data captured during user interaction with one or more web pages of the website. The web browser activates the in-page console via an activation procedure. One or more of the web pages of the website are selected after activation of the in-page console. A feedback badge on the website can be replaced with a reporting badge upon activation of the in-page console and with the reporting badge displaying an indicator of interaction data captured for the selected web page. The in-page console is overlaid one or more of the selected web pages. The in-page console displays the interaction data, or recordings of user interaction, captured during user interaction with the selected web page to enable review of the captured interaction data for the selected web page overlaid on the selected web page.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware and downloaded computer software for use
in the fields of customer service and engagement, customer
and employee support, employee and operations management,
and compliance and security management incorporating
workforce engagement software, namely, software for
workforce forecasting and scheduling, knowledge and employee
assistance, quality assurance, operational insights and
analytics, and operational and performance management;
downloadable computer software for self-service and
automated customer engagement, namely, intelligent virtual
assistants, web and mobile self-service, and social
communities; downloadable computer software for experience
management, namely, software for capturing and correlating
voice, video, email, text, chat, social digital, and survey
interactions with customers for the purpose of improving
customer service and experience; downloadable computer
software for enterprise recording to enhance regulatory
compliance and minimize fraud, namely, omnichannel recording
of voice, text, screen, and video, compliance recording,
voice biometrics and authentication, and real-time analysis
of caller behavior and related call parameters to detect
potential fraud. Consulting services in the fields of business technology
software, design and development of computer hardware, and
computer software and cloud deployment, namely, self-service
and automation, telecommunications, digital security and
surveillance, computer and telecommunication networks and
multimedia; providing temporary use of on-line
non-downloadable computer software for use in the fields of
customer service and engagement, customer and employee
support, employee and operations management, and compliance
and security management incorporating workforce engagement
software, namely, software for workforce forecasting and
scheduling, knowledge and employee assistance, quality
assurance, operational insights and analytics, and
operational and performance management; providing temporary
use of on-line non-downloadable computer software for
self-service and automated customer engagement, namely,
intelligent virtual assistants, web and mobile self-service,
and social communities; providing temporary use of on-line
non-downloadable computer software for enterprise recording
to enhance regulatory compliance and minimize fraud, namely,
omnichannel recording of voice, text, screen, and video,
compliance recording, voice biometrics and authentication,
and real-time analysis of caller behavior and related call
parameters to detect potential fraud.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware and downloaded computer software for use
in the fields of customer service and engagement, customer
and employee support, employee and operations management,
and compliance and security management incorporating
workforce engagement software, namely, software for
workforce forecasting and scheduling, knowledge and employee
assistance, quality assurance, operational insights and
analytics, and operational and performance management;
downloadable computer software for self-service and
automated customer engagement, namely, intelligent virtual
assistants, web and mobile self-service, and social
communities; downloadable computer software for experience
management, namely, software for capturing and correlating
voice, video, email, text, chat, social digital, and survey
interactions with customers for the purpose of improving
customer service and experience; downloadable computer
software for enterprise recording to enhance regulatory
compliance and minimize fraud, namely, omnichannel recording
of voice, text, screen, and video, compliance recording,
voice biometrics and authentication, and real-time analysis
of caller behavior and related call parameters to detect
potential fraud. Consulting services in the fields of business technology
software, design and development of computer hardware, and
computer software and cloud deployment, namely, self-service
and automation, telecommunications, digital security and
surveillance, computer and telecommunication networks and
multimedia; providing temporary use of on-line
non-downloadable computer software for use in the fields of
customer service and engagement, customer and employee
support, employee and operations management, and compliance
and security management incorporating workforce engagement
software, namely, software for workforce forecasting and
scheduling, knowledge and employee assistance, quality
assurance, operational insights and analytics, and
operational and performance management; providing temporary
use of on-line non-downloadable computer software for
self-service and automated customer engagement, namely,
intelligent virtual assistants, web and mobile self-service,
and social communities; providing temporary use of on-line
non-downloadable computer software for enterprise recording
to enhance regulatory compliance and minimize fraud, namely,
omnichannel recording of voice, text, screen, and video,
compliance recording, voice biometrics and authentication,
and real-time analysis of caller behavior and related call
parameters to detect potential fraud.
93.
System and method for omnichannel user engagement and response
A telephone subnet crawler is used to access automated telephone response systems and index the information, contents and structure contained therein. A database of the information, contents and structure of a plurality of automated telephone response systems is created by the telephone subnet crawler. A user interface provides a waiting party with direct access to the information, contents and structure of the automated telephone response systems contained in the database. Where an automated telephone response system requires user input, the user interface calls the automated telephone response system and navigates to the node requiring user input, provides the user input and displays the results to the user. Where an automated telephone response system connects to an operator, the user interface calls the automated telephone response system, navigates to the node for an operator, and when an operator is detected, calls the user at a user provided callback number.
G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
H04M 7/00 - Arrangements for interconnection between switching centres
H04M 11/10 - Telephonic communication systems specially adapted for combination with other electrical systems with dictation recording and playback systems
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
In the fields of customer engagement and security, computer
hardware and downloaded computer software for use in the
fields of video and data being shared with both internal and
external persons, the sharing being a confidential and
secure solution by encrypting shared data: computer hardware
and software for storing multiple video files. In the fields of customer engagement and security, providing
temporary use of on-line non-downloadable computer software
to provide the ability to share video and data with both
internal and external persons in the cloud, the sharing
being confidential and secure by encrypting shared data;
storing multiple video files online.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
In the fields of customer engagement and security, computer
hardware and downloaded computer software for use in the
fields of video and data being shared with both internal and
external persons, the sharing being a confidential and
secure solution by encrypting shared data; computer hardware
and software for storing multiple video files. In the fields of customer engagement and security, providing
temporary use of on-line non-downloadable computer software
to provide the ability to share video and data with both
internal and external persons in the cloud, the sharing
being confidential and secure by encrypting shared data;
storing multiple video files online.
96.
Distributed sensing and video capture system and apparatus
Systems and apparatus for sensing and video capture include at least one camera with an optical sensor that captures video image data of a first sampling rate. An auxiliary sensor captures auxiliary data at a second sample rate. A processor is communicatively connected to the optical sensor and auxiliary sensor. The processor transmits video image data captured at the first sample rate auxiliary sensor data captured at the second sampling rate across a data connection to a centralized computer that receives the video image data and the auxiliary sensor data and operate to present the video image data and the auxiliary sensor data on a graphical display.
H04L 41/06 - Management of faults, events, alarms or notifications
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
97.
SYSTEM, METHOD AND APPARATUS FOR CONVERSATIONAL GUIDANCE
The present disclosure provides real-time, contextually appropriate behavioral guidance by utilizing machine learning models applied in real-time to call audio data. The systems and methods disclosed herein use a combination of acoustic signal processing and automatic speech recognition to convert raw call audio into features that are utilized in the machine learning models to create usable outputs to provide a user with behavioral guidance within a given context of a call or interaction with a customer in real-time.
The present invention allows a CEC system to automatedly, and without human intervention, identify interactions that are likely in need of supervisor intervention. The system reviews all incoming and outgoing interactions for analysis by a metadata analytics service (MAS) software module. The MAS analyzes the interactions to generate interaction metadata, which is used by an interaction analysis engine (IAE) to score the quality of the interaction. If the quality of the interaction is not sufficient, the system marks the interaction as being a problem interaction and notifies a supervisor of the interaction. This ensures the intelligent and dynamic determination of interactions that require additional assistance and assures notification to a supervisor.
G06F 16/908 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
99.
System and method of automated routing and guidance based on continuous customer and customer service representative feedback
The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.
In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.
