2400results about "Automatic exchanges" patented technology

Systems, apparatuses, methods, and computer program products are disclosed for predicting an emotion in real-time based on a multimodal input including at least (i) an amount of keystrokes over a period of time and (ii) text. An example method may include receiving, by a communications circuitry, a multimodal input from a user including at least (i) an amount of keystrokes over a period of time and (ii) text. The example method may further include generating, by a trained machine learning model of an emotion prediction circuitry and using the multimodal input, an EmotionPrint for the user. The example method may finally include determining, by the emotion prediction circuitry and using the EmotionPrint, a next action.

An apparatus comprises a processor and a memory storing instructions that, when executed by the processor, perform a method comprising receiving a voicemail message and generating a prompt for a generative artificial intelligence (AI) model. The prompt comprises a request for a classification into one of a plurality of classes and audio signal features of the voicemail message. The method also comprises determining whether to delay passing the prompt to the generative AI model, and in response to determining to delay, delaying passing the prompt to the generative AI model. The prompt is passed to the generative AI model and a classification from the generative AI model is received. An action is triggered on the voicemail message according to the received classification. In various examples the action facilitates security and / or scalability of a voicemail service.

A system for evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance may include a computing device; a memory; and a processor, the processor configured to: create a plurality of evaluation prompts for evaluating interaction data items of one or more interactions; generate evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning; create training recommendation prompts for the evaluation results; and generate training recommendations from training categories using the training recommendation prompts and machine learning.

Techniques are disclosed that relate to a computer system implementing a conversational artificial intelligence platform. The computer system may receive a request that includes text data specifying an utterance of a user. The computer system determines, form the utterance, an intent of the user. The computer system selects a first virtual agent to handle the request based on the determined intent. The computer system routes the text data and subsequent text data of the request to the first virtual agent for handling. The computer system receives, from the first virtual agent, a message that the determined intent is incorrect, the message including state information indicative of actions taken by the first virtual agent relative to the request. The computer system selects a second virtual agent to handle the request based on the state information. The computer system may receive input from an interactive voice response system or another channel.

A generative mediator engine can perform a requested interaction with a conversational agent of a target entity on behalf of a user. An internal conversational platform can identify intents for the requested interaction. An external artificial intelligence engine can perform intent discovery when an intent is not identified above a confidence threshold. A discovered intent unknown to the generative mediator engine can be received from the external artificial intelligence engine and used, with input requirements determined by the generative mediator for the requested interaction, by a dialog generator to generate a sample dialog for the requested interaction. User feedback can be received after review of action items and expected inputs identified from the sample dialog. The generative mediator engine can perform the requested interaction with the conversational agent on behalf of the user and without receiving user intervention during the requested interaction.

Described herein are systems, devices, methods, and media for an emergency response data exchange system. The system provides an operations center emergency management application with a textual and / or graphical notification of emergency communications initiated on a premises managed by the operations center. The system exchanges operations center data (e.g., floorplans, video feeds, point of contact information, building access controls) with an emergency communications center (ECC) and / or with a first responder device to facilitate reducing emergency response times. The system selectively establishes communications (e.g., messaging windows) between the operations center, ECC, and / or first responder device to facilitate real-time information sharing between relevant parties of an emergency.

The present invention describes a system and a method for monitoring and optimizing a user interaction session within an interactive voice response system during a human-computer interaction, the user interaction management system (100) for monitoring and optimizing the user interaction session comprising a conversation controller module (109), the conversation controller module (109) receives the audio features and processes outputs of ASR and NLU during the user interaction session to optimize the user interaction session duration. According to an embodiment of the present invention, the conversation controller may suggest the TTS to increase the rate of speech. According to yet another embodiment, the conversation controller may suggest the TTS to decrease the rate of speech.

This disclosure describes an invention that that mutes specific talkers using at least one beamforming microphone array 102 that is configured to generate N audio signals 108 where each audio signal is associated with a spatial pickup pattern 130, the microphone array(s) 102 are located in a room 200; a processor 104 and memory 105 operably coupled to the microphone array 102, the processor 104 configured to execute the following steps: (a) selectively mute or unmute an individual talker the room with a mute function 106 that controls whether to mute or unmute the individual talker T1-T7 that is picked up by one or more of the individual audio signals, the mute function 106 includes speech learning that learns to identify different talkers in real time to allow the mute function 106 to identify transitions from one talker to another talker in the room 200, (b) output an audio signal 110 based on the selective muting of the talkers T1-T7 in the room 200.

Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-turn dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

A method can include receiving, from a customer, a request for a support encounter. A method can include determining a customer intent indicative of an issue experienced by the customer. A method can include routing the customer to a support agent based on the intent. A method can include generating, using a first large language model, summaries of previous support encounters. A method can include providing the summaries to the support agent. A method can include determining, based on the intent and / or the summaries, one or more support documents related to the issue. A method can include generating, using a second large language model, a summary of each of the support documents, wherein an input to the second large language model comprises a support document. A method can include providing the generated summaries of the support documents to the support agent for use in mitigating the issue.

A server identifies, by parsing through code of an online page associated with a contact center system, information related to offerings associated with the online page. The server identifies a set of intents based on the information, at least a portion of the set of intents corresponding to at least one of headers or menu items of the online page. The server updates the set of intents based on supplemental information accessible via a hyperlink from the online page. The server transmits the updated set of intents to a first user device. The server receives an acceptance of a subset of the updated set of intents from the first user device. The server stores, in an intent data repository, the accepted subset of the updated set of intents.

A method of integrating an on-premises contact center with a cloud-based contact center according to an embodiment includes communicating resource data stored by the on-premises contact center to the cloud-based contact center, and synchronizing the resource data between the on-premises contact center and the cloud-based contact center to maintain consistency between the resource data stored by the on-premises contact center and the resource data stored by the cloud-based contact center.

A computing system displays, via a first display generation component, a first computer-generated environment and concurrently displays, via a second display generation component: a visual representation of a portion of a user of the computing system who is in a position to view the first computer-generated environment via the first display generation component, and one or more graphical elements that provide a visual indication of content in the first computer-generated environment. The computing system changes the visual representation of the portion of the user to represent changes in an appearance of the user over a respective period of time and changes the one or more graphical elements to represent changes in the first computer-generated environment over the respective period of time.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for announcing and detecting automated conversation are disclosed. One of the methods includes initiating, over a natural language communication channel, a conversation with a communication participant using a natural language communication method that includes a dialogue of natural language communications. The communication participant is determined to be automated using a pre-defined adaptive interactive protocol that specifies natural language linguistic transformations defined in a sequence. The conversation can be transitioned to a communication method that is different form the natural language communication method in response to determining that the communication participant is automated.

Techniques are disclosed that relate to a computer system implementing an interactive voice response (IVR) transcoder. The computer system may receive voice input from an interactive voice response (IVR) system, or other channel. The computer system converts the received input to a request having a common format supported by an artificial intelligence (AI) core including one or more virtual agents. The computer system routes the request to the AI core operable to handle requests specified in the common format. The computer system receives, by the interface module, response data including data (e.g. text data) responsive to the second request. The computer system may implement a conversational artificial intelligence (CAI) platform. The computer system may receive text input from a chat-based input source. The computer system may route another request based on the text input to the AI core, the request being specified in the common text format.

The present document describes techniques for providing a context-based user interface. These techniques include an electronic device having a user interface that dynamically adapts to a context of a visitor or a type of visitor. Characteristics associated with the visitor are detected, using sensors, and used to determine a context of the visitor. The user interface is then populated with curated, customized context-based options that correspond to the context of the visitor. The context-based options represent possible reasons for the visitor's visit and are estimated based on the detected characteristics. The visitor interacts with the user interface to select an appropriate context-based option to convey their intent for visiting an occupant of a building associated with the electronic device. Then, a notification associated with the selected context-based option is provided.

A computer-implemented method of providing content-independent detection of dropped customer service calls to an interactive platform, including receiving a batch of recorded calls for analysis, the recorded calls comprising recorded audio of customer service calls from a human user to the interactive platform, and call metadata for the recorded calls; featurizing the recorded calls into per-call feature vectors, comprising extracting features that are independent of content of the recorded calls; using a machine learning (ML) device to detect dropped calls based on the per-call feature vectors; providing the dropped calls to a human analyst; receiving, from the human analyst, a recommendation to improve the interactive platform based on the dropped calls; and implementing the recommendation on the interactive platform.

Systems and methods for generating speaker video and audio in multiple languages for videoconferencing are provided. For example, a computing device can access a speaker speech audio signal that includes a speaker speech in a first language, a video of the speaker and a translated speech audio signal of the speaker speech in a second language. The computing device generates, based on the translated speech audio signal, a converted translated speech audio signal that includes a speech in the second language having voice characteristics in the speaker speech. The computing device further generates a lip-synched speaker video based on the video of the speaker and the converted translated speech audio signal. Lip movements in the lip-synched speaker video correspond to the converted translated speech audio signal. The converted translated speech audio signal and the lip-synched speaker video are transmitted to a video conference provider configured to host the video conference.

The present teaching relates to conduct persona-adaptive communications with a customer at a geo-locale. Transcripts of a current and historic communications involving the customer are used to characterize the persona of the customer. Transcripts of historic communications with customers at the geo-locale are used to characterize the persona of the geo-locale. Current persona of the customer exhibited in the current communication is combined with the customer's persona and the geo-locale's persona to compute a response input vector, A language model generates, based on the response input vector, a persona-adaptive response, which is then sent to the customer a response.

Systems and methods for processing calls to determine if the call is potentially fraudulent or unwanted. The system extracts a speech signal from an audio signal associated with a call. The system identifies audio characteristics based on analysis of the audio signal. The system generates textual transcript of the audio signal based on automatic speech recognition of the speech signal, which is used to assign text categories for the call based on an automated multi-label textual classification of the textual transcript. The system assigns audio categories for the call based on automated multi-label acoustic classification of the speech signal. The system generates an output label for the call based on a combined analysis of the text categories, the audio categories, and the audio characteristics. The language spoken during the call may be detected and used to generate the textual transcript and to assign the text categories and the audio categories.

The invention discloses an intelligent customer service question and answer optimization method and system based on sentiment analysis, and relates to the technical field of language data processing, and the specific steps are as follows: reading voice data, and carrying out sentiment analysis and feature extraction on the voice data; judging the priority level of the voice data based on the extracted features; performing demand prediction on the voice data in sequence based on the priority level; and generating personalized reply content according to the extracted features, the prediction demand and the historical interaction record. By introducing emotion recognition, the emotion change of the user can be accurately captured, the reply tone and content are adjusted accordingly, more intimate service is provided, the negative emotion of the user can be relieved, and the trust feeling and satisfaction degree of the user to a power enterprise are increased.

A video conferencing method comprising a first and a second video conferencing device. In each video conferencing device, video images of a user are captured, transmitted to the other, remote video conferencing device, and displayed there by a display device. The invention further relates to a video conferencing system comprising a first video conferencing device having a first display device and a first image capture device, and comprising a second video conferencing device having a second display device and a second image capture device.

In an example embodiment, a method includes determining an attitudinal negativity score associated with a contact center agent, among a plurality of contact center agents, based on an interaction between the contact center agent and a user during a communication session, receiving data associated with an incoming user communication, determining a user ease score associated with the incoming user communication based on the data, and blocking routing of the incoming user communication to the contact center agent based on the attitudinal negativity score being above a first threshold score and the user ease score being below a second threshold score.

A method for processing data for training a predictive routing model. The method includes receiving interaction data from previous interactions that includes audio data capturing a conversation and transcript data of the conversation. The method continues by performing speech analytics by processing the audio data to determine scores for speech metrics that include a measure of how much the agent or customer speaks during the conversation. The method continues by performing sentiment analysis to determine scores associated with sentiment metrics, the sentiment metrics including a measure of a sentiment based on classifying utterances appearing in the transcript data as being positive or negative. The method continues by performing feature engineering to generate feature data and generating a training dataset therefrom. The method continues by applying a machine learning algorithm to the training dataset to train a predictive routing model.

A computing system displays, via a first display generation component, a computer-generated environment, and concurrently displays status information corresponding to the computing system via the second display generation component, including concurrently displaying: a visual representation of a portion of a user of the computing system who is in a position to view the computer-generated environment via the first display generation component, and one or more graphical elements that provide a visual indication of content in the computer-generated environment. The computing system detects a respective event; and in response, the computing system changes a level of immersion of the computer-generated environment displayed via the first display generation component, and changes the status information that is displayed via the second display generation component, including changing an appearance of the visual representation of the portion of the user of the computing system.

The document describes systems and techniques directed at three-dimensional, direction-dependent audio for multi-entity telecommunication. In aspects, a remote device receives multi-stream content, including at least one audio stream, from one or more audio-producing entities and obtains orientation information associated with the one or more audio-producing entities. The remote device can then, using the at least one audio stream and the orientation information, provide direction-dependent, three-dimensional audio sufficient to enable a multi-stereo audio output device to reproduce the spatial audio as if the at least one audio stream is originating from a direction, an elevation, and / or a proximity that corresponds to a physical location of the one or more audio-producing entities.

Systems and methods for providing one-to-one and audio and video calls or for providing multi-party audio or video conferences also provide language translation services. When language translation services are provided, a party to a call or conference hears both the audio of the speaker, and a translated version of the speaker's audio.

An automatic online voice translation system and method, comprising a virtual translator, a SIP gateway, and a storage, wherein the SIP gateway receives a call from a caller, when the virtual translator is not activated, the SIP gateway establishes a communication between the caller and the agent by streaming in real-time caller's voice directly to the agent and agent's voice directly to the caller, and when the virtual translator is activated, the SIP gateway creates audio files based on the received voice streams of both the caller and the agent, and sends the created audio files to the virtual translator for translating the caller's voice into a language understood by the agent, before transmitting the voice to the agent. In another embodiment, a contact center platform is used for applying features such as call recording, Interactive Voice Response (IVR) service, and conferencing service to the call.

A system for an automated real-time fraud detection based on predictive analytics of call data including a processor of a call analysis server (CAS) node configured to host a machine learning (ML) module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: capture user call data including a plurality of key elements from the at least one user-entity node; parse the user call data to extract a plurality of classifying features based on the plurality of key elements; query a local calls database to retrieve local historical calls-related data based on the plurality of classifying features; generate at least one feature vector based on the plurality of classifying features and the local historical calls-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of user call ranking parameters from a call predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate a fraud verdict for the user call based on the plurality of call ranking parameters.

An autonomous communication system includes a language model, a retrieval module, a caching mechanism, an autonomous agent, and a human interface for managing interactions and responses. A method and computer-readable medium for managing communication also include these components for processing, enhancing, summarizing, managing interactions, and allowing human intervention.