3420results about "Special service for subscribers" patented technology

The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes identifying, in a contact center application of an agent, a start of an assisted leg of an interaction of a user with a system. A user interaction summary is retrieved that summarizes events that have previously occurred in the interaction. A generative large language model (LLM) artificial intelligence (AI) context prompt is extracted based on the user interaction summary. The context prompt is provided to a generative LLM AI engine to set a context for the generative LLM AI engine. Event information is received that includes a query for the generative LLM AI engine. The query is provided to and a query response is received from the generative LLM AI engine. The contact center application is updated in response to the query response.

Disclosed are systems and methods that ingest substantial volumes of data from a variety of sources associated with a computing system network to create integrated, intuitive, efficient, and coherent adaptive workspace display interfaces. The ingestion includes event data generated by event source software applications running on end user computing devices as well as call and end user attribute data that is used to determine state, presence, and performance data for the end user computing devices that is formatted for display on the adaptive workspace interfaces and used during performance monitoring and collaboration between system end users. The adaptive workspace interfaces provide functions that facilitate real time collaboration between end users that enhances shared experiences between system end users and customers.

The method and systems for automatically identified currently viewing website domain name or a uniform resource locator (URL), receiving, by the server, said automatically identified website domain name or the uniform resource locator (URL), a request or an invitation for initiating a communication including live chat, puts the request for initiating a communication including live chat from the first user and the second user in a communication including live chat queue of the website or the uniform resource locator (URL) associated account, for available or identified or relevant agents to pick up, routing request or invitation for initiating a communication including live chat from the first user and the second user according to a pre-set rules and start a communication including live chat by available agent by selecting particular request for initiating a communication including live chat from the queued communication including live chats.

The invention discloses a dynamic scheduling method and device for mass call requests and a medium, and relates to the technical field of information management, and the method comprises the steps: receiving mass call request information through an information sending device, carrying out the language processing to generate an analysis data set, and obtaining the request types through deep learning classification; performing multi-objective optimization on the analysis data set to generate a dynamic call queue; constructing a time sequence prediction model based on historical records, predicting a call peak, carrying out resource scheduling analysis, and formulating a scheduling strategy; and generating a feedback result after executing the scheduling strategy, synchronizing the feedback result to the call center, and updating the strategy according to response information to realize intelligent dynamic scheduling. The technical problem that in the prior art, the resource scheduling efficiency of the call center in the mass request processing process is low is solved, and the technical effect of optimizing the resource scheduling efficiency of the call center is achieved.

The invention relates to a distributed outbound system optimization method based on cloud computing. The method comprises the following steps: disassembling an outbound task into fine-grained subtasks including dialing, recording, interaction and recording; generating a unique identifier for each subtask; based on a distributed task scheduler; according to node real-time performance and network delay, dynamically distributing tasks, and introducing a task priority rule; monitoring a task execution state in real time through a distributed tracking system; recovering and redistributing abnormal and failed tasks; fragmenting the call-out data according to regions or customer groups, and distributing the call-out data to different nodes; processing the call record and the client feedback by using a stream processing framework comprising Apache Flink; adding a call task including Kafka into a distributed task queue; setting a dynamic priority queue, and adjusting the priority according to the task timeliness; setting an overtime threshold value for the call task, and monitoring a task state in real time; and when the task is overtime or failed, triggering a retry strategy including adjusting the call period and switching the node.

An emergency response assistant system may generate emergency response insights and provide the insights to an emergency responder application for an emergency communications center (ECC), an operations center, or a first responder. The emergency response assistant system may include a data retrieval system and an augmentation and generation system. The data retrieval system may be configured to transform emergency response procedures into vectors that are stored in a vector database. The augmentation and generation system may be configured to perform a vector search of the vector database with an AI model using context from call (e.g., a 911 call) characteristics and external data sources and may be configured to display the results on a user interface of an emergency management application to provide emergency response procedure suggestions to a user of an ECC computing system, an operations center computing system, or a first responder computing device.

The invention discloses an AI-based telephone answering system, and relates to the technical field of telephone answering. Comprising a real-time voice acquisition and preprocessing module, a voice speed detection and prediction module, a self-adaptive ASR dynamic regulation and control module, a refined voice slicing and decoding module, a keyword detection and voice recognition module, a semantic understanding and intention analysis module and an emergency response and scheduling module. And the real-time voice acquisition and preprocessing module is used for acquiring user voice data of the emergency call in real time, establishing a real-time audio stream transmission channel and quickly preprocessing the acquired audio signal. According to the method, the voice features of the user at the high speed are quickly and accurately captured, and the voice segmentation length of the ASR engine is dynamically adjusted, so that each voice segment is clearer and more accurate, cross-segmentation vocabulary segmentation and recognition errors caused by too high speed are avoided, and the user experience is improved. Therefore, omission and misjudgment risks of the emergency information of the user are effectively avoided in an emergency help-seeking scene.

The invention discloses a traffic domain service voice adaptive generation method and system based on a thinking chain fine-tuning large model, and the method comprises the steps: converting an input voice signal into a high-dimensional voice feature signal through a voice encoder, and generating a text output signal according to the high-dimensional voice feature signal through a text decoder and a pinyin decoder; according to the invention, a variational de-quantization joint data enhancement mechanism, a dual-channel multi-mode discriminator architecture and a layered composite loss function are adopted to carry out high-fidelity emotional speech generation and robustness synthesis in a complex noise environment, and semantic-driven dynamic rhythm optimization and professional term precise pronunciation are synchronously supported; in an application scene in the traffic field, a multi-task speech recognition method can be adopted to realize efficient linkage of character-level recognition, audio-to-pinyin conversion and sentence-level accent classification modules, so that complex accent, more noise and traffic term polyphone challenges are effectively handled, and the method is suitable for being widely popularized and used.

Systems and methods to cause an intent discovery system to identify new user intents without additional training. The system may comprise of two neural networks. The first neural network generates a prompt tailored to a particular domain (e.g., travel), and may include known intents pertinent to the domain selected examples from a training dataset to provide context to the prompt. The second neural network may use this prompt to identify intents from new utterances in the prompt. The identified intents that are not in the list of known intents are then used to update the database.

An automated support process implemented with a language model may be improved by allowing a human agent to provide input to the language model to fix mistakes or otherwise improve the automated support process. In some implementations, a language model prompt may be used to instruct a language model to be able to provide a response that includes requesting assistance from a human agent. The request for assistance may then be sent to the human agent and a response from the human agent may then be processed by the language model to allow the language model to provide better responses for the support process. In some implementations, a human agent may monitor automated support sessions and provide unrequested input into a support session where needed to improve the quality of the support session.

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

The system obtains network information by creating an index of network websites and obtains UE contextual information including: cell tower ID serving the UE, technical capability of the UE, UE performance information, updates to the UE, UE browsing history, and site deployment information. The system obtains from the UE a natural language query and provides the query, the network information, and the UE contextual information to an AI. The system obtains from the AI an answer to the natural language query, where the answer is a summary of a relevant website. Based on the UE contextual information, the system determines whether a user of the UE is technologically savvy or not technologically savvy. If the user is technologically savvy, the system presents the answer and a link to the relevant website; otherwise, the system offers to connect the user to an operator of the network.

An emergency management system (EMS) captures alarm reports that are called into an emergency call center (ECC) and converts the audio data of the alarm report into actionable alert data that may be displayed in and dispatched from an emergency response application that is operated at the ECC. The alert system includes a central station system, an ECC system, and an emergency management system (EMS). The ECC system has call handling equipment (CHE) to receive calls for an emergency call telephone number and an alarm reporting telephone number. The emergency response application can display a queue of alerts, and each of the alerts includes alert data that can be displayed. The EMS includes an automated interview system that can at least partially use an AI model to transform the audio data into alert data that is used to populate the queue of alerts.

Systems and processes are disclosed to leverage conversational AI across voice and digital channels by automating employee interactions with clients to deliver humanized experiences with speed and accuracy. Live phone conversation. Transcriptions between clients and agents are generated. AI assist provides automation based on client interactions. Prompts are provided to speak to customers. Real time updates and summaries are sent to client devices and streamed to customize agent sessions. Auto navigation of agent views and manipulations of the agent screen are optimized by AI. Instant client validations are enabled via audible and visual confirmations on client devices. Various other AI implementations for improved processing our similarly disclosed.

A call center data transfer system includes one or more processors that are configured to receive call data related to information provided by a caller during a first portion of a call with a first customer service representative. The one or more processors are also configured to assign an identifier to the call in response to receipt of an indication that the call should be transferred from the first customer service representative, associate the identifier with the call data in one or more databases, receive the identifier as an input during a second portion of the call with a second customer service representative, and use the identifier to retrieve the call data from the one or more databases. The one or more processors are further configured to display the call data via a display for visualization by the second customer service representative.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention relates to the technical field of voice dialogues, and discloses a robot dialogue intelligent early warning system based on voice outbound, which comprises a voice recognition module for receiving a voice signal in a real-time call initiated by an outbound robot and converting the voice signal into a dialogue text; the intention analysis module is used for extracting semantic features and emotional features from the dialogue text, inputting the semantic features and the emotional features into a pre-trained intention classification model and outputting a user intention label and confidence; the emergency degree evaluation module is used for generating a dialogue emergency level based on the user intention label, the confidence coefficient and a preset emergency keyword library; the early warning execution module is used for triggering real-time early warning operation when the conversation emergency level reaches a preset threshold value; the real-time early warning operation comprises at least one of dynamically adjusting a robot dialogue strategy, generating a manual seat transfer instruction and upgrading a dialogue priority. The method can solve the problems that an existing voice outbound robot cannot accurately judge the user intention, and the conversation emergency degree recognition is insufficient, and improves the user experience and the overall efficiency.

The invention belongs to the field of intelligent information system management, and particularly discloses an intelligent voice telephone robot system and method, voice and image multi-mode data are collected through a microphone and a camera, and after preprocessing, voice, emotion and semantic features are fused through an improved Transform architecture to achieve accurate recognition of user intentions; a double-layer decision network based on reinforcement learning is combined with a dynamic reward function to generate an optimal response strategy; and realizing rapid task migration and parameter optimization of the model by adopting a meta-learning mechanism. The system also has the functions of adaptive noise robustness, multi-language interaction, user portrait dynamic updating, man-machine collaboration and the like. Compared with a traditional scheme, the intention recognition accuracy, the task completion rate and the scene adaptability are remarkably improved, the interaction experience is effectively improved, and the method can be widely applied to the fields of customer service, intelligent marketing and the like.

A system for dynamic content generation for contact center interactions using artificial intelligence according to an embodiment includes determining, by a computing system and using an artificial intelligence model, an intent of an interaction between a contact center agent and a client based on a live transcript, identifying, by the computing system in real time, at least one image relevant to the determined intent in response to determining the intent of the live transcript, displaying, by the computing system in real time, the at least one image for the contact center agent on a dynamic interaction widget application executed by an agent device of the contact center agent, and sending, by the computing system, a selected image of the at least one image displayed on the dynamic interaction widget application to the client in response to selection of the selected image by the contact center agent.

Provided herein are techniques to facilitate synchronizing audio streams for a conference call involving multiple microphones utilized at a same location or proximity to one another. In one example, a method may include obtaining, by an aggregating node, each of an audio data stream from each of a plurality of participant devices that are proximate to each other within a conference space for a conference session in which each audio data stream obtained from each participant device comprises audio data and synchronization information, wherein the synchronization information is based on a synchronization sound broadcast during the conference session and received by each of the plurality of participant devices; and synchronizing, by the aggregating node, the audio data of each audio data stream based, at least in part, on the synchronization information included in each audio stream obtained from each of the plurality of participant devices.

A system provides notifications when a meeting participant interrupts another participant who has been selected as a speaker during a meeting. A notification is displayed when a first participant raises their hand, and prior to the first participant having a chance to speak, the system detects that a second participant has interrupted. The notification can be in the form of an animated gesture to gently remind the second participant that the first participant had requested to speak and that the second participant had taken the first participant's turn. The animation can include an icon that increases in size over time or the system performs other forms of a graphical transformation. The system can also control the volume of the interrupting user, which may be decreased after the interrupting user speaks for a period of time. The system can also utilize AI models to analyze the context of a conversation to determine if a participant is interrupting a designated speaker.

The invention relates to the technical field of voice recognition, in particular to a voice interaction system and method for customer service based on artificial intelligence, and the system comprises a voice input processing module, an intention classification and routing module, a context dynamic adjustment module, a user behavior learning module, a multi-level intention fusion module and a final result module. According to the method, a multi-dimensional feature system is constructed by extracting tone intensity, speech speed frequency and emotional fluctuation amplitude, intention categories, priority weights and confidence scores are generated to realize accurate acquisition of appeals, and dialogue history, context and emotional change dynamic reconstruction path nodes, switching rules and response time sequences are tracked during interaction. Historical behavior mining preference features, habit fusion intention relevance, emergency calculation of an optimal strategy, construction of service steps, resource allocation schemes and execution timelines, adjustment of an interactive interface, a service process and a feedback mechanism according to multi-dimensional analysis, guarantee of differentiated service experience, and improvement of response accuracy and user satisfaction.

The invention provides a method for improving the audio quality of a voice conference of multiple terminals at the same place. The method comprises the steps of time calibration, detection of equipment in the same room, audio quality calculation, delay detection, data collection and issuing, adjustment and playing of a dynamic buffer area, speaking button control and speaking sound processing. Time is unified through a time service server or a chip, equipment in the same room is detected, audio quality is calculated, delay is detected, equipment information is collected and issued, synchronous playing of a buffer area is adjusted, mutual exclusion of speaking buttons is controlled, speaking sound is processed to reduce howling and acoustic feedback, and use of the audio equipment is optimized. According to the invention, the audio synchronization and quality of the multi-terminal voice conference can be improved, howling and acoustic feedback are reduced, and the user experience is optimized.

In an example method, a system accesses first data including text transcripts of a plurality of voice calls, and generates, based on the first data, labeled representations of the voice calls using the one or more computerized LLMs. The system generates the labeled representations by determining a plurality of contextual categories associated with the voice calls, segmenting the text transcript into a plurality of transcript segments, and associating each of the transcript segments with a respective one of the contextual categories. Further, the system generates second data representing the labeled representations of the voice calls and stores the second data using the one or more hardware storage devices.

A data processing system for artificial intelligence-based setting of controls in an evaluation interface comprising a data store storing: a plurality of transactions; a plurality of completed evaluations, each completed evaluation including an indication of a transcript portion associated with an evaluation answer. The system determines a word or phrase common to a first set of transcript portions associated with the evaluation answer; creates a first set of auto answer parameters that includes the word or phrase; auto answers the question for a set of test transactions to generate an auto answer for each test transaction; and based on a determination that the first set of auto answer parameters auto answered the question with a threshold level of accuracy, configures an evaluation system to use the first set of auto answer parameters to preset an answer control in an evaluation operator interface.

The invention provides an outbound routing method and system based on multi-dimensional index coupling and large model decision, and the method comprises the steps: carrying out the preprocessing of the historical data of each line provider, predicting the future performance index through an LSTM neural network, and optimizing the prediction result through a Kalman filter, and eliminating the noise. In the route decision-making link, the system calculates the Q value of each line provider based on the deep Q learning network, comprehensively considers the call completing rate, average cost, negative feedback rate and other factors of each line provider, and intelligently selects the optimal line provider in combination with the user portrait, scene recognition and real-time load state. The system also constructs a complete feedback closed loop, continuously optimizes the model by collecting call result data, and automatically adjusts decision parameters. The call completing rate and the service quality of the outbound service are improved, and the communication cost is reduced.

A data processing system for artificial intelligence-based setting of controls in an evaluation interface comprising a data store storing: a plurality of transactions; a plurality of completed evaluations, each completed evaluation including an indication of a transcript portion associated with an evaluation answer. The system determines a word or phrase common to a first set of transcript portions associated with the evaluation answer; creates a first set of auto answer parameters that includes the word or phrase; auto answers the question for a set of test transactions to generate an auto answer for each test transaction; and based on a determination that the first set of auto answer parameters auto answered the question with a threshold level of accuracy, configures an evaluation system to use the first set of auto answer parameters to preset an answer control in an evaluation operator interface.

A contact center system. The contact center system comprises a node comprising a plurality of modules (e.g., microservices), each module comprising a shared memory module (e.g., shared memory library), wherein the shared memory module is configured to: obtain a shared memory key; obtain a shared memory segment identifier (smhid) using the shared memory key, the shared memory segment identifier identifying a shared memory segment; use the shared memory segment identifier to attach to the shared memory segment, wherein, for each of a plurality of memory block sizes, the shared memory segment stores information pertaining to a recycle list, and the plurality of memory block sizes comprises a first memory block size and a second memory block size that is greater than the first memory block size.

Communications between participants of a video conference are accentuated based on a participant's focus. A first participant of a video conference is determined to be focused on a user interface element associated with a second participant of the video conference. Responsive to the determining that the first participant is focused on the user interface element associated with the second participant, relative audio levels of communications are changed for the second participant such that a first audio level for communications from the first participant to the second participant is higher than a second audio level for other communications received by the second participant.