404 results about "Call centre" patented technology

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.

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.

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 provides an intelligent call center resource optimization method and system based on predictive analysis, and relates to the technical field of call center management, and the method comprises the steps: obtaining to-be-mediated customer data, mediator data and external economic indicators; processing according to the to-be-conciliated customer data to obtain a customer emotion feature matrix; performing feature selection and quantitative evaluation processing according to the mediator data to obtain a mediator capability feature matrix; performing scene simulation according to the external economic indexes, the customer emotion feature matrix and the mediator ability feature matrix to obtain a mediation effect score list; performing time sequence analysis and behavior pattern recognition according to the historical mediation information to obtain a prediction result; and performing optimization according to the mediation effect score list and the prediction result to obtain a final mediator scheduling scheme. According to the method, emotion analysis, behavior pattern recognition and scheduling optimization technologies are integrated, the processing efficiency of the mediation request is improved by using data analysis and a prediction model, the waiting time of a customer is shortened, and resource allocation is optimized.

A disclosed method may include (i) transforming an original corpus of support call transcriptions for support calls received at a telecommunication provider at least in part by prompting a large language model to summarize each support call transcript in the original corpus of support call transcripts for the support calls received at the telecommunication provider to output a summary corpus of large language model generated summaries of support call transcriptions, (ii) extracting from the summary corpus of large language model generated summaries of support call transcriptions a ranked ordering of client support topics for clusters within the summary corpus of large language model generated summaries of support call transcriptions, and (iii) resolving, by the telecommunication provider, the client support topics in an actual order that is determined at least in part based on the ranked ordering of client support topics for clusters within the summary corpus.

Systems and methods include using a clustering engine to determine topics in a communication. An example system includes a user interface module that receives user input relating to a criteria to define a set of communications, wherein the criteria is at least one of a category, a score, a sentiment, an agent, an agent grouping, a speaker, a location, an event attribute, a call center, a time of communication, or a date of communication, an acoustic analysis module that analyzes the set of communications to determine one or more acoustic characteristics of one or more communications in the set of communications, and a clustering engine that analyzes words and phrases in the set of communications and the one or more acoustic characteristics, and to determine a topic of the set of communications based on at least one commonality in words, phrases, or the one or more acoustic characteristics.

The invention relates to the technical field of data processing, and discloses a scheduling method and system for realizing cloud terminal call center data based on edge computing, and the method comprises the steps: carrying out the feature extraction of a data scheduling request, obtaining a feature descriptor, and calling request association data from a database of a cloud terminal call center; calculating the adaptation degree between the multimode integrated semantics and the edge computing nodes, and screening out data execution nodes corresponding to the request associated data from the edge computing nodes; calculating a channel noise ratio between the data scheduling equipment and the node server, and calculating a communication throughput rate between the data scheduling equipment and the node server; calculating the total load of task data when the request associated data is transmitted, and constructing a data transmission path between the data scheduling equipment and the node server; and executing scheduling processing on the request associated data by using the data scheduling equipment and the node server to obtain a scheduling result. The scheduling efficiency of the cloud terminal call center data can be improved.

The invention provides a call center verbal skill recommendation method and device, and relates to the technical field of verbal skill generation, and the method comprises the steps: obtaining historical recommendation data and to-be-recommended user information through a call center; constructing a user behavior graph through the historical recommendation data and the to-be-recommended user information, and generating a portrait vector of the to-be-recommended user based on the user behavior graph; performing multi-modal sentiment analysis according to the portrait vector to obtain sentiment features; constructing a verbal skill recommendation model according to the historical recommendation data, and inputting the portrait vector and the emotion feature into the verbal skill recommendation model to generate a preliminary recommendation verbal skill; and performing auditing optimization on the preliminary recommendation verbal skill based on a preset word bank to obtain an optimized recommendation verbal skill, and adjusting the optimized recommendation verbal skill through a mediator to obtain a final recommendation verbal skill. The problems that the prior art is difficult to adapt to complex dispute scenes, cannot accurately understand the deep emotion of the user and does not have compliance detection capability are solved.

Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and / or continuously, in some cases in addition to actively and / or statically, enrolling speakers as the speakers speak into or around an edge device (e.g., car, television, radio, phone). The system identifies users on the fly without requiring a new speaker to mirror prompted utterances for reconfiguring operations. The system manages speaker profiles as speakers provide utterances to the system. Machine-learning architectures implement a passive and continuous voice biometrics system, possibly without knowledge of speaker identities. The system creates identities in an unsupervised manner, sometimes passively enrolling and recognizing known or unknown speakers. The system offers personalization and security across a wide range of applications, including media content for over-the-top services and IoT devices (e.g., personal assistants, vehicles), and call centers.

Embodiments described herein provide for passive caller verification and / or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and / or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

An automated system and corresponding method is configured to predict a call duration of a customer service interaction between a caller and a customer-service agent of a call center, based at least in part on information provided orally by the caller to the automated system. The automated system transcribes the orally provided information, preprocesses the transcribed data, adds feature enrichment data to supplement the transcribed data, and executes a machine-learning model to predict the caller's intent. If the predicted caller's intent does not have an adequate confidence score associated therewith, the system requests additional data from the caller, and supplements the original data with newly provided data, and again determines a predicted call intent. This process may iterate until the confidence score satisfies applicable confidence criteria prior to utilizing two additional machine-learning models to predict a call duration of the interaction between the caller and a customer-service agent.

In some embodiments, the present disclosure provides an exemplary method that may include steps of monitoring, a conversation script between a call center agent and a customer; utilizing, a speech-to-text deep machine learning model to transcribe the audio call to text; utilizing, a natural language processing deep machine learning model to map intent mappings of the audio call; utilizing, a similarity measurement model to determine a semantic similarity between predefined intent mappings and the intent mappings of the call audio text; determining, an error based on the semantic similarity in the intent mapping call audio text; determining a training session based on the error.

Systems and methods for facilitating inter-network handover of a communication with a call center are provided. In one example, a computer-implemented method includes: receiving, at a first network from a second network, information indicating a location of a user device, detecting, at the first network, presence or absence of an active connection between the user device and a call center designated to the first network, obtaining, at the first network, the location of the user device from the received information, in response to the detection of the presence of the active connection, and transmitting the information indicating the location to a location database in communication to the call center.

The invention relates to the technical field of call center control, and discloses an unmanned vehicle formation control and obstacle avoidance reconstruction system based on complex road conditions. The system call data acquisition module is used for acquiring multi-source interaction data in real time; the dynamic feature extraction module is used for performing semantic intention hierarchical analysis on the voice stream text conversion data, performing event fragment segmentation on the user behavior time sequence data, and generating an operation track feature vector; the scene clustering engine module is used for performing space-time alignment on the semantic intention hierarchical analysis result and the event fragment segmentation result to generate a multi-dimensional scene clustering tag; the strategy matching core module is used for retrieving an interaction strategy set matched with the current scene from a distributed strategy library according to the multi-dimensional scene clustering label and generating a dynamic control instruction sequence; and the instruction distribution execution module is used for distributing the dynamic control instruction sequences to corresponding seat terminals and voice routing equipment after sorting the dynamic control instruction sequences according to priorities.

The present disclosure relates to systems and methods for enhancing call center features using generative AI. There is provided a server computer system, comprising: a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores a playbook of a call center and instructions that, when executed, configure the processor to monitor a call in real-time during the call with a caller, identify, from the call, a caller issue in real-time using a trained machine learning model, obtain a response to the caller issue based on execution of a generative artificial intelligence (GenAI) model and the playbook stored in the memory, and implement the response during the call.

Embodiments described herein provide for automatically authenticating telephone calls to an enterprise call center. The system disclosed herein builds on the trust of a data channel for the telephony channel. Certain types of authentication information can be received through the telephony channel, as well. But the mobile application associated with the call center system may provide additional or alternative forms of data through the data channel. The system may send requests to a mobile application of a device to provide information that can reliably be assumed to be coming from that particular device, such as a state of the device and / or a user's response to push notifications. In some cases, the authentication processes may be based on quantity and quality of matches between certain metadata or attributes expected to be received from a given device as compared to the metadata or attributes received.

The invention discloses an intelligent call dynamic response method and system fusing ASR (automatic voice recognition) and emotion recognition, and is applied to an interaction scene of a voice robot and a call center. The robot response strategy is dynamically adjusted by synchronously analyzing the text content (ASR) and emotional characteristics (such as intonation, speech speed and energy) of the user voice in real time. When the negative emotion is detected, a manual seat is automatically triggered to switch over or switch the pacifying verbal skill; and for the positive emotion of the high-value customer, a precision marketing module is started. The method solves the problems that an existing call center is single in response mode and cannot sense the emotion of a user, and the customer satisfaction and the service conversion rate are remarkably improved.

The embodiment of the present specification provides a kind of gas resource scheduling method and Internet of Things system based on wisdom gas call center, the method is realized by the gas resource scheduling Internet of Things system based on wisdom gas call center, comprising: obtaining the gas use data of different types of gas users, and determining gas use characteristics;Obtain gas demand data;Based on gas use characteristics, gas demand data and the gas maintenance data of wisdom gas call center, predict whether the gas supply of multiple second times meets the gas demand;In response to the gas demand that the gas supply of at least one second time in multiple second times cannot meet, adjust gas allocation scheme.

The embodiment of the specification provides a gas operation method and an Internet of Things system based on a smart gas call center. The method comprises the following steps: acquiring gas user call data of the call center; predicting demand information of different types of users based on the gas user call data, wherein the demand information at least comprises gas product demand and gas service demand; determining and pushing gas operation push features based on the demand information of different types of users, wherein the gas operation push features comprise push type features and push content features.

The invention relates to the technical field of calling in financial and medical scenes, and discloses a call center unified API implementation method, device and equipment and a storage medium, and the method comprises the steps: dividing the type of an API into a first type of interface developed based on the capability of a product and a second type of interface customized and developed by depending on a service system; the method comprises the following steps: receiving a calling request initiated by a heterogeneous service system through a protocol analysis engine, extracting a protocol feature identifier in the calling request to obtain a heterogeneous protocol, uniformly converting the heterogeneous protocol into a standard API protocol format according to a preset protocol conversion rule base, and analyzing the type of an API interface in the conversion process; and executing differentiated routing according to the type of the API interface. In the invention, aiming at the call center of financial and medical scenes, a core framework of a unified API (Application Program Interface) of the call center is constructed, and the interface standardization of a call center system is realized.

The invention relates to a vehicle active safety early warning device and early warning method based on a call center, and the early warning device comprises a vehicle-mounted equipment module, a data analysis server, and a call center module. The vehicle-mounted equipment module is composed of an MCU control unit, a GNSS positioning module, a driver visual recognition module, a lateral blind area early warning module, a forward collision early warning module and a 4G communication module. A vehicle positioning database, a vehicle early warning database, a data analysis engine and an Internet of Vehicles data gateway are installed in the data analysis server; the call center module is composed of a telephone traffic center server SIP switch and a mobile terminal. The vehicle-mounted equipment module is connected with a vehicle networking data gateway of the data analysis server through the 4G communication module, and the data analysis server is connected with a telephone traffic center server of the call center module through an intranet communication network cable. The system has the advantages that the real-time monitoring and active early warning of the vehicle safety state are realized, the motorcade management efficiency is improved, and the driving safety is guaranteed.

The system provides a real-time video and audio communication with a 911 call center to perform effective CPR on a patient experiencing OHCA with real-time monitoring and feedback. The system includes a Mobile App that communicates with a smart phone with camera, a wireless remote-CPR equipment stand and emergency CPR kit. The mobile app will automatically send a link to a caregiver's cell phone responsive to a 911 call placed by the caregiver, to establish a secured video call via which the 911 center can guide the caregiver in performance of CPR until an ambulance arrives. This integrated description provides a comprehensive overview of the AI-driven CPR guidance system, detailing its core components, operational workflow, training processes, and scalability. By combining real-time coaching, advanced diagnostics, and EMS coordination, the system offers a groundbreaking approach to improving OHCA outcomes.

Provided are an adaptive network address translation (NAT) method and apparatus for VoIP. The NAT method includes: obtaining a first public network address of a STUN server, and connecting to the STUN server through a first port; obtaining a second public network address and a second port based on a connection status with the STUN server, where the second public network address and the second port are generated by the STUN server or a registration server; and initiating registration with the registration server based on the second public network address and the second port, whereby a public network address and a port of a phone in the registration server are respectively updated to the second public network address and the second port, and then a call center establishes a connection to the phone through the second public network address and the second port.

Systems and methods are disclosed for transitioning multi-channel call center customer experience via user interfaces. An example method includes obtaining, by a communications server, contact information of a contact. The example method includes establishing, by the communications server, a first connection between a contact and an agent terminal over a first communication channel. The example method includes establishing, by the communications server, a second connection between the contact and the agent terminal over a second communication channel. The example method includes sending a first communication to the contact from the agent terminal across the second communication channel. The example method includes receiving, at the agent terminal, a second communication from the contact across the second communication channel.

A system, device, and method to reconnect a terminated communication to the original queue and provide queue status over a different communication channel. The method includes receiving a first communication over a first communication channel and placing the first communication in a contact center queue. The method also includes providing a user with an option to terminate the first communication over the first communication channel and receive queue status information over a second communication channel, and in response to the user selecting the option: terminating the first communication while maintaining a queue status in the contact center queue for the first communication; providing the queue status information; in response to the queue status of the first communication being within a threshold, notifying the user to initiate a second communication over the first communication channel; and replacing the first communication in the contact center queue with the second communication.

Disclosed are systems and methods including computing-processes, which may include layers of machine-learning architectures, for assessing risk for calls directed to call center systems using carrier signaling metadata. A computer evaluates carrier signaling metadata to perform various new risk-scoring techniques to determine riskiness of calls and authenticate calls. When determining a risk score for an incoming call is received at a call center system, the computer may obtain certain metadata values from inbound metadata, prior call metadata, or from third-party telecommunications services and executes processes for determining the risk score for the call. The risk score operations include several scoring components, including appliance print scoring, carrier detection scoring, ANI location detection scoring, location similarity scoring, and JIP-ANI location similarity scoring, among others.

The invention discloses a call center work order intelligent distribution system and method supporting multi-channel access, and belongs to the field of call center service systems.The system comprises a multi-channel access engine, an intelligent processing engine, a dynamic distribution engine, a load monitoring module and a knowledge base module; the method comprises the following steps: S1, receiving work order information from multiple channels through a multi-channel access engine; s2, identifying a channel of the obtained work order information through an intelligent processing engine, determining a channel type, carrying out format unification processing and key information extraction, and generating a standard work order; and S3, the dynamic distribution engine distributes the work order to the corresponding customer service staff according to a preset intelligent distribution strategy in combination with the real-time load condition and the historical processing efficiency of the customer service staff. The method has the advantages that multi-channel unified access and automatic information processing are realized, so that the efficiency and the accuracy are greatly improved; the strategy is dynamically and intelligently distributed, and accurate matching is achieved; and real-time load monitoring and knowledge base dynamic updating support strategy continuous optimization.

The invention discloses a FreeSwitch real-time voice monitoring and call control method and a FreeSwitch real-time voice monitoring and call control system based on WebSocket. After the voice monitoring module is dynamically loaded in the FreeSwitch, the real-time voice flow of the call channel is pushed to the voice gateway through the WebSocket according to a uidaudio command; and the gateway completes ASR identification and quality inspection rule matching, and generates a FreeSwitch executable command in real time according to a violation event or an agent manual instruction, thereby realizing closed-loop control such as millisecond-level hang-up, switching and the like. The system does not need an MRCP server, supports multi-channel concurrent monitoring, hot plugging and log tracing, is simple in deployment, low in delay and high in expansibility, and can be widely applied to call center real-time quality inspection and seat auxiliary scenes.

A method of setting up and / or maintaining an emergency call may include a voice connection and / or data connection between a vehicle and an emergency control center. The vehicle executes at least one first protocol to transmit connection set-up data according to the at least one first protocol between the vehicle and at least one server of a server infrastructure outside the vehicle provided for the emergency call, so that the voice connection and / or data connection is set up between the vehicle and the server infrastructure. The voice connection and / or the data connection is continued by the server infrastructure by execution of at least one second protocol via a public telephone network to the emergency control center, so that voice data and / or information data are exchanged between the vehicle and the emergency call center in each case via the emergency call.