711results about "Customer relationship" patented technology

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the technical field of customer service quality inspection, in particular to an online customer service intelligent quality inspection system and method based on artificial intelligence. And automatically extracting a user demand keyword, an emotion expression keyword and a potential violation term keyword, and generating a structured keyword sequence. Performing emotion analysis on each dialogue round through a Transform model, and accurately outputting a customer emotion classification and an intensity score; and semantic correlation of the context is carried out through a neural network model. And automatically identifying the content of each round of dialogue and counting illegal verbal skills. Furthermore, the customer emotion value, the context coherence score and the occurrence frequency of violation verbal skills are input into a quality inspection scoring model, a comprehensive quality inspection score is automatically calculated, and whether the service is qualified or not is judged according to the comprehensive quality inspection score, so that the dynamic evaluation of the service quality is realized, the quality inspection efficiency is improved, and the customer experience is truly reflected.

The invention relates to the technical field of intelligent customer service, and provides an AI intelligent customer service system based on a large model, and the system is characterized in that an unstructured text processed by a multi-source knowledge fusion subsystem is reconstructed into structured knowledge entries with a multi-dimensional label system, intention classification and context association, and the structured knowledge entries are dynamically integrated into an enterprise knowledge graph; the response generation subsystem is used for receiving the content queried by the user, analyzing the intention through a deep semantic understanding model in combination with an enterprise knowledge graph, and dynamically maintaining the context in combination with a multi-round dialogue state tracking technology; meanwhile, emotion dimension analysis is carried out on the content, and a personalized response is generated by fusing user service feedback; and the autonomous optimization subsystem dynamically adjusts the weight distribution of structured knowledge entries in the enterprise knowledge graph and the priority of process nodes in combination with an attribution analysis result, and triggers a knowledge updating and API process reconstruction mechanism. The method has self-learning and continuous optimization capabilities, and continuously improves the service quality and the user experience.

Novel techniques are disclosed for providing vPLC-specific metadata service including customized vPLC-specific metadata. In certain embodiments, each vPLC may generate a customized metadata using its corresponding vPLC-specific customization instructions. In some embodiments, a vPLC-specific metadata service may be performed using pre-generated customized vPLC-specific metadata, on-the-fly customized metadata, pre-generated CSP-format metadata, or combinations thereof.

Techniques for facilitating connectivity to vPLCs created in a CSP-provided infrastructure in a region. Within the CSP-provided infrastructure in a region, when the destination of a packet is determined to be an endpoint associated with a particular vPLC, the packet is tagged with information related to the particular vPLC. The vPLC-related information for the particular vPLC can include, for example, a vPLC identifier identifying the particular vPLC, an identifier identifying a customer associated with the endpoint, a virtual cloud network identifier identifying a virtual cloud network (VCN) belonging to the particular vPLC and where the endpoint is part of the VCN, and other vPLC-related information. The packet is then routed or communicated within the CSP-provided infrastructure in a region along with the tagged vPLC-related information. The vPLC-related information is used as part of the connectivity and for routing of packets within the CSP-provided infrastructure in a region.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

The invention provides an intelligent customer service dynamic intention recognition system based on a semantic analysis model, and the system collects the multi-dimensional data of a user through a data collection module, constructs a user portrait through a feature extraction module, extracts the portrait features, carries out the semantic analysis of multiple rounds of historical dialogue data, and extracts preference features. And constructing a dynamic user-entity association graph to extract GNN node features. Multi-modal data of a user is analyzed through an emotion recognition module, emotion features are recognized, portrait features, preference features and emotion features are fused and analyzed based on an MLP model to obtain a final emotion state, and the portrait features, the preference features, GNN node features and the emotion features are fused through an intention recognition module to obtain a final emotion state. According to the method, dynamic intention analysis is carried out on the basis of the Transform sequence-to-sequence model, the current intention of the user is recognized, the recognition accuracy is high, the dynamically changing intention is adjusted in real time, and more accurate and targeted answers or services are provided for the user.

The invention discloses an intelligent customer acquisition and user behavior analysis system based on AI full ecology, and relates to the technical field of artificial intelligence and big data marketing, and the system comprises a multi-source data collection module which collects internal and external multi-channel data of an enterprise; the data cleaning module processes abnormal and missing values and unifies data formats; the user portrait construction module extracts multi-dimensional labels to describe user features; the intelligent customer obtaining module uses transfer learning to screen high-potential customers, and the user behavior analysis module mines behavior logic by means of Transform; the intelligent recommendation engine is fused with multiple algorithms to realize personalized recommendation, and the real-time decision module is combined with rules and reinforcement learning to optimize marketing. According to the method, multi-source data integration and deep analysis are realized, clients are accurately identified, behavior requirements are mined, the client obtaining efficiency and the conversion rate are improved, the marketing effect is optimized through personalized recommendation and intelligent decision, and a whole-process intelligent marketing solution from data acquisition to decision execution is provided for enterprises.

Computerized methods and systems analyze, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a first party and a second party to extract from the at least one source of data, information that is descriptive of at least part of the interaction. The computerized methods and systems upload information derived from the extracted information to a data management system that manages data associated with the first party.

The invention discloses a customer influence propagation prediction method and system based on a graph neural network, and the method comprises the steps: receiving customer historical interaction data and social attribute data, and constructing a dynamic heterogeneous customer graph; performing multi-scale space-time diagram convolution processing on the dynamic heterogeneous customer graph, and explicitly separating a structure embedding vector and a semantic embedding vector of an output node; identifying influence transition points in the dynamic heterogeneous customer graph by using a structure embedding vector and a semantic embedding vector; taking the influence transition point as a key anchor point, executing bidirectional probabilistic propagation path prediction, and generating a candidate influence propagation path set with an activation probability; and performing path feature fusion and dynamic recombination optimization on the candidate influence propagation path set to generate a final influence propagation prediction result. According to the embodiment of the invention, the prediction accuracy of customer influence propagation can be improved.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, IoT, and technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics.

Systems, methods and user interfaces are provided for generating real-time and / or diagnostic omnichannel interaction insights. The method may include obtaining transcripts corresponding to digital service channels. The method may also include generating and inputting channel-specific prompts to machine learning models to obtain insights. The method may also include generating and / or displaying analytical insights. The method may also include obtaining a natural language question, via a conversational interface, directed to a benefits database. The method may also include parsing the question. The method may also include ranking benefits using a recommendation algorithm. The method may also include generating a context by applying a language template. The method may also include inputting the context to a large language model. The method may also include providing a response to an agent to cause the agent to perform one or more actions. The method may also include generating and displaying a dashboard.

A system and method are disclosed for tracking and verifying authenticity, loss, fraud, or unauthorized use of assets within a supply chain. The method includes scanning a multimodal tag (e.g., QR code and NFC tag) associated with an asset to generate scan data comprising a hash code, asset identifier, and verification URL. The scan data is transmitted to an application server of a parent organization, where a controller retrieves associated location and organization identifiers. The controller determines authorization status, compares the hash code with reference values in a verification database, and performs authenticity checks to classify the asset as genuine, lost, duplicated, or pirated. The system integrates with a CRM platform to register new child organizations using data synchronization objects comprising payloads, callout instructions, and error handling. AI-based analysis may detect route deviations, fraudulent insertions, or asset misuse patterns based on route history and scanning behavior.

A flux system includes a memory and a processor in communication with the memory and a sensing device, the memory storing a plurality of capabilities and a plurality of semantic fluxes associated with the plurality of capabilities. The computing system is configured to infer a semantic based on received inputs and to infer an activity interest semantic based on an input, and to assign at least one augmentation servicing agent to service an activity interest based on semantic matching.

The invention discloses a customer complaint work order automatic circulation system and method based on composite intention disassembly. The system receives and preprocesses the multi-mode customer complaint data and initializes a service circulation context; business intention identification and business key slot position extraction are carried out; disassembling the composite business intention, and constructing an ordered work order execution queue of a directed acyclic graph structure through cyclic dependence verification; evaluating the work order circulation action with the maximum expected cumulative income by using a reinforcement learning model; when business knowledge query is involved, compliance evidence fragments are retrieved, sorted and output; and generating a candidate reply and a business operation instruction by the fine-tuned large language model, outputting the candidate reply after business compliance verification, and calling an API (Application Program Interface) of an underlying business system to execute entity business handling operation in a cross-system manner. The problems of data interaction and state synchronization among multi-source heterogeneous systems are solved, the automatic execution efficiency of the composite work order at the bottom layer node is improved, and the system unauthorized and dirty data risks caused by an uncertain instruction are effectively avoided.

The invention provides a supplier relation management method and system combined with big data analysis, and the method comprises the steps: firstly constructing a session text semantic association network based on historical session text big data, and then mining a supplier interaction intention set based on the session text semantic association network; calling a natural language processing model to jointly analyze the intention set and the network, predicting a supplier relation state, positioning an optimized and improved node according to a prediction result, and finally generating a supplier relation dynamic management and control mechanism based on the optimized and improved node and the supplier interaction intention set and applying the mechanism to a cooperation process. Therefore, the continuous maintenance and adjustment of the intelligent manufacturing production line robot related field supplier relationship can be realized.

The invention discloses a CNN and Informer fused high-risk user loss early warning and retention method and system, and belongs to the technical field of big data, and the method comprises the steps: obtaining the multi-source data of each user, and constructing the static features, daily dynamic features, and a time sequence feature matrix of a set time period of the user through the multi-source data; constructing a CNN and Informer fused deep learning model, and predicting the loss probability of the user by using the trained deep learning model based on the static features and daily dynamic features of the user and the time sequence feature matrix of the time period set by the user; constructing an RFL model, and obtaining a user value score by using the RFL model and the multi-source data; and in combination with the value scores and loss probabilities of all the users, grouping all the users by using a Kmenas model, outputting user grouping results and grouping portraits, and performing early warning and retention. According to the method, potential high-risk lost users can be accurately identified, and the retention cost is reduced.

A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

The invention belongs to the technical field of intelligent electric charge questions and answers, and particularly relates to a hierarchical knowledge network construction and retrieval method for intelligent electric charge questions and answers. The invention provides an iterative adaptive enhanced RAG method, based on a hierarchical knowledge network, information retrieved by a first round of problems is used as a knowledge carrier, newly retrieved information is continuously updated into initialized information in subsequent multi-round iterations, information is adaptively collected from the perspective of knowledge increase, and the information retrieval efficiency is improved. New knowledge and collected knowledge are flexibly integrated, and the problem that information interaction among different retrieval steps is insufficient is solved; in addition, a self-adaptive exploration stopping strategy is formulated, uncertain active retrieval is replaced, time deviation of active retrieval prediction is effectively avoided, and continuous knowledge increase is ensured.

A method is provided. The method is executed by an extraction engine implemented as a computer program within a computing environment. The extraction engine executes action and task mining on unstructured data. The method includes receiving a communication including unstructured data defining an action and automatically processing the communication by utilizing at least one generative artificial intelligence (AI) model to extract details of the action being performed in the unstructured data. The method includes automatically converting the action into an activity or a task of a process associated with the communication.

The invention discloses an intelligent customer obtaining method and system based on multi-modal data, and relates to the technical field of multi-modal data. Corresponding customer demand information is determined according to detection of a demand space; according to the content of the customer demand information, the information weight and the multi-modal data, multiple pieces of target data in the multi-modal data are determined, and the final customer obtaining path is determined based on the multiple pieces of target data, the enterprise database and the customer demand information, so that the accuracy of the customer obtaining path is improved. Therefore, the corresponding customer information is determined according to the detection of the customer obtaining node of the customer obtaining path, and the corresponding customer obtaining mode is matched based on the customer information and the customer demand information; and determining a corresponding customer obtaining number according to the execution of the customer obtaining mode, and triggering autonomous optimization of the customer obtaining mode according to the customer obtaining number and the execution condition of the customer obtaining mode until the customer obtaining number is greater than a preset customer obtaining number threshold value, so as to improve the intelligent customer obtaining effect.

The invention discloses a dynamic context window customer service quality inspection method based on a large model, and the method comprises the following steps: S1, generating a dynamic window: scoring the dialogue round of a customer service and a user, dynamically updating and maintaining a context window according to a scoring result, and carrying out the context reconstruction of a dialogue in the window; s2, multi-dimensional quality inspection: inputting the dialogue text in the dynamic window into a large model, and outputting a preset field through a customized prompt trigger model; s3, clustering attribution: carrying out semantic clustering on related contents output by the large model by adopting a kmeans algorithm and BERT vectorization, and then generating a general description and an operable suggestion for each clustering result through prompt; and S4, result output and application: generating a structured json result containing a multi-dimensional quality inspection result and a clustering result, wherein the structured json result is used for api calling or visual platform display. The method has the advantages that efficient, accurate and multi-dimensional customer service quality inspection can be achieved, and the service quality can be improved and overcome.

Embodiments described herein relate to systems and methods for providing a recommendation panel for a graphical user interface of an issue tracking platform. The system and methods can include causing display of the recommendation panel in the issue-view graphical user interface. The recommendation panel can include a first section including a set of one or more selectable link objects, where each selectable link object associated with a respective content item identified for the request type; a second section including a link to a user profile of a subject matter expert user, where the subject matter expert user selected based on a subject matter determined using the issue data; and a third section including suggested action narrative. The suggested action narrative can be determined using a generative response received from the generative output engine in response to the prompt.

The invention discloses a multi-mode intelligent customer service adaptive dialogue interaction system based on generative AI, and relates to the technical field of intelligent customer service. The working process of the system comprises the following steps: collecting multi-modal data, carrying out sentiment analysis, OCR (Optical Character Recognition) and semantic analysis, extracting potential problem targets, and associating historical dialogue features; constructing a user problem risk field model, quantifying a current theme and related theme risks, dynamically adjusting parameters through historical data, and generating a risk perception index; fitting a user satisfaction function based on nonlinear regression, and setting a four-level response threshold value; historical dialogue resource consumption and progress data are extracted, the trend model is fitted in a segmented mode, and the current dialogue resource surplus and the solving progress are predicted; and monitoring resource abnormality and progress abnormality in real time, and triggering knowledge expansion, AI takeover or manual intervention. According to the system, the service efficiency and the user experience in a complex scene are improved through multi-modal perception, risk-driven decision and an active intervention mechanism.

Disclosed is a conference monitoring system that classifies conversations and performs automated actions based on different context detected within the conversations. The system receives conversations that result in an unsuccessful engagement, classifies different segments of the conversations with contextual trackers that identify different context within each segment, and determines a recurring pattern of a common set of contextual trackers in different segments of the conversations that contribute to the unsuccessful engagement. The system monitors a particular conversation, tags one or more segments of the particular conversation with the common set of contextual trackers, and performs an automated action that contributes to a successful engagement in response to tagging the one or more segments with the common set of contextual trackers and the common set of contextual trackers contributing to the unsuccessful engagement.

The invention provides an intelligent customer interaction method and system based on cloud computing and Al, and the method comprises the steps: carrying out the elastic shunting of user request data in a high-concurrency scene in a cloud computing architecture, and obtaining a classified request data set; performing AI model-driven semantic analysis and emotion feature extraction on a user input text in the request data set to obtain a user core intention tag and an emotion tendency coefficient; performing fusion analysis on the user core intention label, the emotional tendency coefficient and the user historical interaction behavior data to obtain a user personalized demand portrait; and based on the personalized demand portrait of the user, performing adaptive optimization on the preset UI interaction scheme and the service response template to obtain a scenarized optimal interaction scheme. According to the method and the device, the service stability is improved, the core demand in the text input by the user can be deeply understood, and the defect that the personalized interaction demand of different users is difficult to meet at present is overcome.

The invention discloses an intelligent customer opinion processing system based on multiple agents. A receiving module receives complaint information of a user and standardizes the complaint information. And the task distribution module realizes splitting of customer complaint tasks and intelligent agent adaptation for standardized customer complaint data through a multi-intelligent agent iterative learning framework. And the subtask execution module executes the received customer complaint task. And the feedback optimization module collects full-link indexes in real time, detects abnormal events by adopting a sliding time window, and optimizes the construction of a decision tree in the sub-task execution module. And the content filtering module is used for carrying out post-processing on output texts of the consultation agent and the risk identification agent by using a text filtering and optimizing framework based on an off-line large model. And the result storage module adopts a multi-level storage strategy to record a full-link operation log. According to the method, the problems of low efficiency, insufficient accuracy, weak dynamic adaptive capacity and the like of a traditional customer complaint processing method can be effectively solved.

A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.

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.