Abstract

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

IPC Classes  ?

• G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
• G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction
• G06F 17/30 - Information retrieval; Database structures therefor
• G06N 3/00 - Computing arrangements based on biological models
• G06N 5/02 - Knowledge representation; Symbolic representation

Abstract

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

IPC Classes  ?

• G06F 40/232 - Orthographic correction, e.g. spell checking or vowelisation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06N 20/00 - Machine learning
• G10L 15/00 - Speech recognition

Abstract

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

IPC Classes  ?

• G06F 40/35 - Discourse or dialogue representation
• G06F 40/279 - Recognition of textual entities
• H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

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

IPC Classes  ?

• G06F 40/232 - Orthographic correction, e.g. spell checking or vowelisation
• G06N 20/00 - Machine learning
• G06F 40/279 - Recognition of textual entities
• G06F 40/169 - Annotation, e.g. comment data or footnotes
• G10L 15/04 - Segmentation; Word boundary detection
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G10L 15/197 - Probabilistic grammars, e.g. word n-grams
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog

Abstract

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

IPC Classes  ?

• G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
• G06F 16/438 - Presentation of query results
• G06F 16/432 - Query formulation

Abstract

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

IPC Classes  ?

• 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

Abstract

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

IPC Classes  ?

• G06F 11/00 - Error detection; Error correction; Monitoring
• G06F 16/215 - Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 20/00 - Machine learning
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

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

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising

Abstract

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

IPC Classes  ?

• H04M 3/50 - Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers

Abstract

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

IPC Classes  ?

• H04L 67/53 - Network services using third party service providers
• H04L 67/50 - Network services
• H04L 67/562 - Brokering proxy services
• H04L 67/306 - User profiles
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 1/72436 - User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages for text messaging, e.g. SMS or e-mail
• H04M 1/72454 - User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
• H04M 3/42 - Systems providing special services or facilities to subscribers
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• H04W 12/06 - Authentication

Abstract

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

IPC Classes  ?

• G06F 40/20 - Natural language analysis
• G06F 40/30 - Semantic analysis
• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

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

IPC Classes  ?

• G10L 15/18 - Speech classification or search using natural language modelling
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06N 5/02 - Knowledge representation; Symbolic representation
• G06Q 30/016 - After-sales
• G10L 15/19 - Grammatical context, e.g. disambiguation of recognition hypotheses based on word sequence rules
• G06F 16/35 - Clustering; Classification
• G06F 16/332 - Query formulation
• G06F 40/30 - Semantic analysis
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/205 - Parsing
• G06F 40/216 - Parsing using statistical methods
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/295 - Named entity recognition

Abstract

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

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• G06F 40/40 - Processing or translation of natural language

Abstract

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

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G10L 15/08 - Speech classification or search
• G10L 15/18 - Speech classification or search using natural language modelling
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06N 5/02 - Knowledge representation; Symbolic representation
• G06Q 30/00 - Commerce
• G10L 15/19 - Grammatical context, e.g. disambiguation of recognition hypotheses based on word sequence rules
• G06F 16/35 - Clustering; Classification
• G06F 16/332 - Query formulation
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/205 - Parsing
• G06F 40/216 - Parsing using statistical methods
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/295 - Named entity recognition

Abstract

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

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 40/35 - Discourse or dialogue representation
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 40/40 - Processing or translation of natural language
• G06N 20/00 - Machine learning

Abstract

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

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 10/00 - Administration; Management
• G06F 16/953 - Querying, e.g. by the use of web search engines
• G06N 20/00 - Machine learning
• H04L 12/58 - Message switching systems
• H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 40/30 - Semantic analysis
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• G10L 15/26 - Speech to text systems

Abstract

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

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 10/10 - Office automation; Time management

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 10/10 - Office automation; Time management
• G06Q 50/30 - Transportation; Communications

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• G10L 25/84 - Detection of presence or absence of voice signals for discriminating voice from noise

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 16/22 - Indexing; Data structures therefor; Storage structures
• G06F 9/54 - Interprogram communication
• G06F 40/30 - Semantic analysis

Abstract

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

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 15/26 - Speech to text systems
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G10L 15/08 - Speech classification or search

Abstract

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

IPC Classes  ?

• G10L 15/26 - Speech to text systems
• G06Q 10/10 - Office automation; Time management
• G06N 3/08 - Learning methods
• G06F 40/295 - Named entity recognition
• G06F 18/2113 - Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation

Abstract

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

IPC Classes  ?

• G06Q 30/00 - Commerce

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 10/10 - Office automation; Time management
• G06Q 10/00 - Administration; Management

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

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

IPC Classes  ?

• G06Q 30/00 - Commerce
• H04M 3/42 - Systems providing special services or facilities to subscribers
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06N 3/08 - Learning methods
• G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G10L 15/18 - Speech classification or search using natural language modelling
• G06Q 10/10 - Office automation; Time management
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04L 12/58 - Message switching systems
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06Q 30/00 - Commerce
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06N 3/08 - Learning methods
• G10L 15/16 - Speech classification or search using artificial neural networks

Abstract

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

IPC Classes  ?

• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06N 20/00 - Machine learning

Abstract

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

IPC Classes  ?

• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06Q 30/00 - Commerce

Abstract

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

IPC Classes  ?

• 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 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/26 - Speech to text systems
• G06F 40/30 - Semantic analysis
• G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
• G10L 15/04 - Segmentation; Word boundary detection
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G10L 15/08 - Speech classification or search

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 40/30 - Semantic analysis
• G06F 40/35 - Discourse or dialogue representation
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 40/30 - Semantic analysis
• G06F 40/35 - Discourse or dialogue representation
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 40/35 - Discourse or dialogue representation
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• G06F 40/30 - Semantic analysis

Abstract

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

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 16/23 - Updating
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 30/06 - Buying, selling or leasing transactions
• G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
• G06Q 20/38 - Payment architectures, schemes or protocols - Details thereof
• G06Q 50/26 - Government or public services
• G06Q 50/00 - Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
• G06N 20/00 - Machine learning
• G06F 40/40 - Processing or translation of natural language

Abstract

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

IPC Classes  ?

• H04L 12/58 - Message switching systems
• G06Q 30/06 - Buying, selling or leasing transactions

Goods & Services

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

Goods & Services

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

Abstract

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

IPC Classes  ?

• G06F 40/20 - Natural language analysis
• G06F 40/35 - Discourse or dialogue representation
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

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

IPC Classes  ?

• G06F 40/20 - Natural language analysis
• G06F 40/35 - Discourse or dialogue representation
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

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

IPC Classes  ?

• 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
• G06F 9/54 - Interprogram communication
• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines

Abstract

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

IPC Classes  ?

• G06F 40/30 - Semantic analysis
• G06F 40/35 - Discourse or dialogue representation
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

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

IPC Classes  ?

• G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
• G06F 16/438 - Presentation of query results
• G06F 16/432 - Query formulation

Abstract

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

IPC Classes  ?

• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• G06Q 30/00 - Commerce

Goods & Services

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

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04M 5/00 - Manual exchanges
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising
• H04L 51/00 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
• 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
• G10L 15/26 - Speech to text systems
• G06F 40/10 - Text processing
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

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

IPC Classes  ?

• G06F 9/54 - Interprogram communication
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• 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
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 40/14 - Tree-structured documents
• G06F 9/455 - Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G06Q 30/00 - Commerce
• H04L 41/12 - Discovery or management of network topologies
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
• H04L 41/0859 - Retrieval of network configuration; Tracking network configuration history by keeping history of different configuration generations or by rolling back to previous configuration versions
• H04L 41/5041 - Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
• H04L 41/0869 - Validating the configuration within one network element
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• H04L 41/50 - Network service management, e.g. ensuring proper service fulfilment according to agreements
• H04L 41/0233 - Object-oriented techniques, for representation of network management data, e.g. common object request broker architecture [CORBA]

Abstract

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

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure

Abstract

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

IPC Classes  ?

• G10L 19/005 - Correction of errors induced by the transmission channel, if related to the coding algorithm
• G10L 15/08 - Speech classification or search
• G10L 15/14 - Speech classification or search using statistical models, e.g. Hidden Markov Models [HMM]
• G10L 15/20 - Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise or of stress induced speech
• G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
• G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
• G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
• G10L 25/21 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being power information

Goods & Services

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

Goods & Services

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

Abstract

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

IPC Classes  ?

• G06Q 20/00 - Payment architectures, schemes or protocols
• G06N 20/00 - Machine learning
• G06F 21/60 - Protecting data
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06Q 30/06 - Buying, selling or leasing transactions

Abstract

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

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/49 - Data-driven translation using very large corpora, e.g. the web
• G06F 17/18 - Complex mathematical operations for evaluating statistical data
• G10L 15/26 - Speech to text systems
• 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

Abstract

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

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 67/1031 - Controlling of the operation of servers by a load balancer, e.g. adding or removing servers that serve requests
• H04L 67/1029 - Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers using data related to the state of servers by a load balancer
• H04L 67/1008 - Server selection for load balancing based on parameters of servers, e.g. available memory or workload
• H04L 47/70 - Admission control; Resource allocation
• H04L 67/60 - Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
• H04L 67/14 - Session management
• H04L 65/61 - Network streaming of media packets for supporting one-way streaming services, e.g. Internet radio
• H04L 47/125 - Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
• H04L 65/401 - Support for services or applications wherein the services involve a main real-time session and one or more additional parallel real-time or time sensitive sessions, e.g. white board sharing or spawning of a subconference
• H04L 65/1083 - In-session procedures
• H04L 69/14 - Multichannel or multilink protocols
• H04L 67/53 - Network services using third party service providers

Abstract

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

IPC Classes  ?

• 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 67/01 - Protocols
• H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
• H04L 67/53 - Network services using third party service providers
• H04L 67/63 - Routing a service request depending on the request content or context

Abstract

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

IPC Classes  ?

• H04M 3/00 - Automatic or semi-automatic exchanges
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• H04M 3/56 - Arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities
• H04M 3/493 - Interactive information services, e.g. directory enquiries
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06N 3/08 - Learning methods
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/04 - Segmentation; Word boundary detection
• G10L 15/08 - Speech classification or search

Abstract

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

IPC Classes  ?

• H04M 3/493 - Interactive information services, e.g. directory enquiries
• G06N 5/00 - Computing arrangements using knowledge-based models
• G06N 7/00 - Computing arrangements based on specific mathematical models
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 16/332 - Query formulation
• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 3/0481 - 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
• 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 15/18 - Speech classification or search using natural language modelling
• G06F 40/30 - Semantic analysis
• G06F 40/279 - Recognition of textual entities
• G10L 15/08 - Speech classification or search

Abstract

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

IPC Classes  ?

• G06F 16/65 - Clustering; Classification
• G06F 16/22 - Indexing; Data structures therefor; Storage structures
• G06F 16/61 - Indexing; Data structures therefor; Storage structures

Abstract

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

IPC Classes  ?

• H04L 12/24 - Arrangements for maintenance or administration
• G06F 8/77 - Software metrics
• G06F 21/10 - Protecting distributed programs or content, e.g. vending or licensing of copyrighted material
• 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
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/54 - Interprogram communication
• G06F 40/14 - Tree-structured documents
• G06Q 30/00 - Commerce
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure

Abstract

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

IPC Classes  ?

• G06F 3/0481 - 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
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 1/16 - Constructional details or arrangements
• G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer

Abstract

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

IPC Classes  ?

• G06F 7/00 - Methods or arrangements for processing data by operating upon the order or content of the data handled
• G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction
• G06F 17/30 - Information retrieval; Database structures therefor

Abstract

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

IPC Classes  ?

• G06F 40/00 - Handling natural language data
• G06F 40/35 - Discourse or dialogue representation
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

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

IPC Classes  ?

• G06F 40/295 - Named entity recognition
• 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
• G06F 40/253 - Grammatical analysis; Style critique
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06N 3/08 - Learning methods

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06T 13/80 - 2D animation, e.g. using sprites
• H04L 12/58 - Message switching systems
• G06T 11/40 - Filling a planar surface by adding surface attributes, e.g. colour or texture
• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06T 3/00 - Geometric image transformation in the plane of the image

Abstract

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

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G06Q 30/00 - Commerce
• G06N 3/08 - Learning methods

Abstract

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

IPC Classes  ?

• G06F 9/46 - Multiprogramming arrangements
• G06Q 10/00 - Administration; Management
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• H04M 3/50 - Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• G06Q 30/00 - Commerce

Abstract

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

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 29/06 - Communication control; Communication processing characterised by a protocol

Abstract

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

IPC Classes  ?

• G10L 15/26 - Speech to text systems
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G06F 40/35 - Discourse or dialogue representation
• H04M 3/527 - Centralised call answering arrangements not requiring operator intervention
• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/18 - Speech classification or search using natural language modelling
• G06F 16/332 - Query formulation
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

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

IPC Classes  ?

• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/18 - Speech classification or search using natural language modelling
• G10L 15/26 - Speech to text systems
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• H04M 3/527 - Centralised call answering arrangements not requiring operator intervention
• G06F 16/332 - Query formulation
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• H04M 1/725 - Cordless telephones

Abstract

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

IPC Classes  ?

• H04M 11/00 - Telephonic communication systems specially adapted for combination with other electrical systems
• H04M 3/493 - Interactive information services, e.g. directory enquiries
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G06F 16/332 - Query formulation
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• H04M 7/00 - Arrangements for interconnection between switching centres

Abstract

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

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers
• G06F 9/448 - Execution paradigms, e.g. implementations of programming paradigms
• H04L 29/06 - Communication control; Communication processing characterised by a protocol

Abstract

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

IPC Classes  ?

• H04M 3/493 - Interactive information services, e.g. directory enquiries
• H04M 3/50 - Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G10L 13/04 - Methods for producing synthetic speech; Speech synthesisers - Details of speech synthesis systems, e.g. synthesiser structure or memory management
• G10L 15/18 - Speech classification or search using natural language modelling

Abstract

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

IPC Classes  ?

• H04M 3/493 - Interactive information services, e.g. directory enquiries
• H04M 3/50 - Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing
• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• G10L 13/04 - Methods for producing synthetic speech; Speech synthesisers - Details of speech synthesis systems, e.g. synthesiser structure or memory management
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/18 - Speech classification or search using natural language modelling

Abstract

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

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 15/01 - Assessment or evaluation of speech recognition systems
• G10L 15/08 - Speech classification or search
• G10L 15/10 - Speech classification or search using distance or distortion measures between unknown speech and reference templates
• G10L 15/26 - Speech to text systems

Abstract

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

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 15/10 - Speech classification or search using distance or distortion measures between unknown speech and reference templates
• G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
• G06N 20/00 - Machine learning
• G10L 15/26 - Speech to text systems
• G10L 15/14 - Speech classification or search using statistical models, e.g. Hidden Markov Models [HMM]

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04L 12/58 - Message switching systems
• G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04M 3/523 - Centralised call answering arrangements requiring operator intervention with call distribution or queuing

Abstract

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

IPC Classes  ?

• G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
• H04W 4/029 - Location-based management or tracking services

Abstract

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

IPC Classes  ?

• 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 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference
• H04L 12/58 - Message switching systems

Abstract

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

IPC Classes  ?

• G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
• G06F 3/0481 - 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
• H04W 4/029 - Location-based management or tracking services
• G06Q 10/10 - Office automation; Time management
• G06Q 30/02 - Marketing; Price estimation or determination; Fundraising

Abstract

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

IPC Classes  ?

• H04M 3/51 - Centralised call answering arrangements requiring operator intervention
• H04L 12/58 - Message switching systems
• G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction
• H04M 7/00 - Arrangements for interconnection between switching centres

Abstract

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

IPC Classes  ?

• G10L 15/00 - Speech recognition
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
• G10L 15/197 - Probabilistic grammars, e.g. word n-grams
• G10L 15/14 - Speech classification or search using statistical models, e.g. Hidden Markov Models [HMM]

Abstract

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

IPC Classes  ?

• 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
• 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

Abstract

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

IPC Classes  ?

• H04W 4/21 - Services signalling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel for social networking applications
• H04L 67/02 - Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
• G06N 20/00 - Machine learning

Abstract

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

IPC Classes  ?

• H04L 12/24 - Arrangements for maintenance or administration

Abstract

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

IPC Classes  ?

• G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions

Abstract

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

IPC Classes  ?

• G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction
• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/26 - Speech to text systems
• H04M 3/527 - Centralised call answering arrangements not requiring operator intervention
• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/18 - Speech classification or search using natural language modelling
• G06F 16/332 - Query formulation