1936results about "Customer relationship" patented technology

The present invention provides a vertiport system that can be implemented at a VTOL hotel for managing unmanned vehicles in delivering goods or passengers from a source location to a destination location smartly. The system includes smart aiml vertiport and vtol hotel, smart kitchen and casino autonomous intelligent infrastructure.

Systems and methods for generating a message that is to be sent electronically to a user include the use of an artificial intelligence or large language model (AI / LLM) service to provide recommendations about how a message could be changed to improve customer engagement with the message. The systems and methods can present an individual generating the message with options for making changes to a draft of the message, and the individual can then select the changes that are to be made to create a final version of the message. In some instances, the systems and methods include analyzing the change recommendations provided by a AI / LLM service to ensure they will actually improve customer engagement and not clash with or impair the original purpose of the message.

The present disclosure provides a multi-agent system-based information processing method and a multi-agent system. The multi-agent system comprises multiple agents. The method is applied to a target agent, and the target agent is any agent among the multiple agents. The method comprises: determining information to be processed corresponding to a target agent; on the basis of an information processing model, when said information does not match a function corresponding to the target agent, determining an agent to which a function matching said information corresponds; and sending said information to the matching agent for processing. In the present disclosure, information to be processed can be quickly and accurately transferred between agents by utilizing the analytical capability of an information processing model, and is processed by a suitable agent, thereby effectively improving the processing effect of tasks.

The invention discloses an automatic large language model selection method based on multi-dimensional evaluation and dynamic weight adjustment, and aims to solve the problems of low text generation quality, inaccurate dialogue questions and answers, low online assistant task completion rate and the like in an existing large language model by introducing a multi-dimensional evaluation mechanism. According to the method, a plurality of evaluation dimensions such as ROUGE index evaluation, text quality evaluation, semantic similarity evaluation, safety compliance evaluation, front suitability evaluation, question and answer evaluation, dialogue evaluation, online customer service evaluation and online assistant evaluation are comprehensively considered, the performance of the large language model is comprehensively evaluated, and through analysis of operation data and user feedback, the evaluation accuracy of the large language model is improved. And dynamically adjusting the weight of each evaluation dimension to adapt to continuously changing application requirements and user expectations. And finally, according to the comprehensive score, an optimal large language model is automatically selected, and high quality and user satisfaction of Web services are ensured.

A framework provides an approach for utilizing contextualized content from a user's designed set of documents and private data sets to generate customized, contextualized, private, and domain-specific outputs of agents within an artificial intelligence computing environment and a supporting architecture. The agents and artificial intelligence computing environment include augmenting a language model with the contextualized content, and prompting the language model to generate defined, domain-specific outputs. Such agents enable computing systems to execute specific actions identified by a user that are external to the supporting architecture from the defined, domain-specific outputs.

The invention discloses an intelligent customer service method for adaptively adjusting an interaction strategy by using reinforcement learning, and the method comprises the following steps: S1, obtaining a natural language text inputted by a user, and generating a multi-dimensional state vector; s2, grouping the users by using a real-time dynamic clustering algorithm, and updating a clustering result; s3, generating an action space in a specific state, and enriching the action space in combination with a rule-based strategy and a learning-based strategy; s4, aggregating neighbor node features through a graph convolutional network, and extracting local features; s5, weighting adjacent node features by using a graph attention network, and dynamically allocating attention weights; s6, carrying out interaction strategy optimization by adopting a spiral ladder type deep exploration network, and optimizing action selection; and S7, combining online learning with an experience playback mechanism, continuously updating an interaction strategy, and making a quick response to dynamically changing user demands. According to the method, reinforcement learning, the graph neural network and the sentiment analysis technology are combined, and intelligent customer service interaction strategy self-adaptive adjustment and optimization are achieved.

Computerized systems and methods are described for converting traditional technology products into an “As a Service” (AaS) model, facilitating the transition from capital expenses (CapEx) to operational expenses (OpEx). Methods include receiving user inputs for technology product selections and accessing a Real-Time Data Mesh (RTDM) to retrieve data. An Advanced Analytics and Machine Learning (AAML) Module analyzes user inputs and market data, optimizing the conversion into subscription-based services. Process results are displayed to the user through a Single Pane of Glass User Interface (SPOG UI). An AaS Conversion Module performs transition of products into customizable subscription packages. This method emphasizes dynamic pricing based on usage, flexibility, and / or scalability of services. Methods are provided for real-time reporting, subscription management, and vendor system integration, enabling a comprehensive AaS conversion process suitable for modern technology products and services.

The invention discloses a customer relationship intelligent management and analysis system based on CRM, and relates to the technical field of intelligent analysis, a business logic layer comprises a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit, the customer information management unit is used for extracting basic attributes, consumption habits and preference characteristics of customers from customer data by using a data analysis technology so as to construct a comprehensive and three-dimensional customer portrait; and the customer information management unit further divides the constructed customer portraits into subdivided groups with common demands, preferences and behavior patterns through in-depth market research. According to the method, the wide customer groups are divided into the subdivided groups with common requirements, preferences and behavior patterns, so that an enterprise can more accurately understand each user group, a more targeted marketing strategy is formulated, resource allocation can be optimized to a certain extent, and the market response speed is increased.

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.

A method for efficiently generating a plurality of personalized email strategies and corresponding personalized emails for campaign recipients is disclosed. Multi-dimensional data comprising account data, recipient data, and seller data is received from one or more data sources. The received multi-dimensional data is processed to extract relevant features. A dynamic feature hierarchy is generated using the extracted features. A pre-trained machine learning model is fine-tuned using the dynamic feature hierarchy to generate email strategies, wherein model parameters are adjusted based on the hierarchy during fine-tuning. A plurality of email strategies is generated for each recipient by applying the fine-tuned model's recommendations. An email strategy is selected from the plurality of strategies based on one or more factors. A personalized email corresponding to the selected email strategy is generated. The personalized email and the selected email strategy used to generate it are displayed to a user.

The invention provides a customer service method and device and computer equipment, and relates to the technical field of communication, and the customer service method comprises the steps: obtaining corresponding service requests submitted by a plurality of users in a plurality of different communication channels, and analyzing the corresponding intention information in the service requests based on a natural language processing technology; analyzing preset emotion information of each vocabulary or statement in the intention information; determining a service type required by the user according to the intention information, and matching a corresponding service resource based on the service type; determining a priority order of the service request according to the emotion information and the service resources, and allocating a customer service team or customer service staff corresponding to the service resources according to the priority order; after the service request is distributed, continuous monitoring and data analysis are performed to determine the service quality of the customer service team or the customer service staff for processing the service request, and the evaluation and guidance result is fed back to the corresponding customer service team or the corresponding customer service staff based on the service quality, so that the efficiency and the accuracy of the customer service can be improved.

An industrial technical support system acts as an interactive assistant that leverages generative artificial intelligence (AI) techniques to suggest solutions to industrial alarm conditions or other performance problems based on earlier documented solutions, thereby expediting the process of finding alarm resolutions. The system enhances a user's prompt with relevant contextual data retrieved from stored documentation as well as relevant past chat histories to assist the system's generative AI model in recommending accurate resolutions to alarm conditions or performance issues described by the user's prompt.

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.

A system comprising a capture device and a database. The capture device may be configured to capture video, perform video analysis on the captured video to identify characteristics about objects in the captured video and determine an absolute location of the objects, store metadata separate from the captured video in response to the video analysis and communicate with a wireless communication device. The database may be configured to communicate with the wireless communication device, store the metadata received from the wireless communication device, re-create a scene in response to the characteristics and absolute location of the objects in the metadata and generate an animation based on the scene using the metadata received over time. The metadata may be communicated without the captured video. The animation may provide a visualization similar to the captured video using only the metadata.

The invention discloses a personalized customer service method and system based on distributed intelligent knowledge management, and relates to the technical field of customer services, and the method comprises the steps: collecting customer information, verifying the identity of a customer, receiving customer request data, and carrying out the preprocessing of the customer request data, and generating a customer portrait; and performing multi-modal sentiment analysis and problem classification based on the preprocessed client request data, constructing a dynamic knowledge graph based on a classification result, and performing knowledge graph query. Through combination of a HowNet emotion dictionary, a capsule network dynamic routing mechanism and a Skip-gram model, customer emotions can be accurately captured, optimized emotion features can be generated, customer request features are modeled in combination with a graph neural network, knowledge graph nodes are dynamically updated and expanded, support is provided for quick response of complex problems, and the method has the advantages of being high in practicability and easy to popularize. The personalized service scheme is generated through the Seq2Seq model, the service content is dynamically adjusted according to the emotional state and the historical behavior habit of the customer, and the customer satisfaction and the service efficiency are remarkably improved.

The invention discloses a digital marketing system based on big data. The digital marketing system comprises a data acquisition module, a data analysis module, a data planning module, a customer relationship management module and an effect evaluation module. The data acquisition uses advanced technology to collect multi-source data, the analysis module deeply mines values, the planning module customizes personalized strategies, the customer relationship management operates efficiently, and the effect evaluation module monitors in real time and adjusts the strategies according to results. All the modules work cooperatively, precision and intelligence of digital marketing are achieved, the marketing effect and efficiency are improved, and a corresponding innovative marketing method and related technologies are applied to all the steps to ensure efficient operation of the system; according to the digital marketing system, comprehensive and accurate data collection and integration, deep intelligent analysis and mining of data values, personalized marketing planning execution, efficient customer relationship management and real-time dynamic effect evaluation and optimization are realized, the marketing effect and efficiency are improved, resource waste is reduced, customer loyalty is enhanced, and innovative development of enterprise marketing is promoted.

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.

Systems and methods for cloud-based computer-aided dispatch (CAD) of emergency medical services (EMS) include receiving real-time EMS vehicle location tracking information from and sending an EMS vehicle assignment to a vehicle of an EMS agency, and communicatively coupling to a computing system associated with the EMS agency to provide services including request intake configured to receive a request for a patient's EMS trip, a dispatch GUI hosted at the EMS agency, and a dispatch code service for determining dispatch code(s) for the EMS trip based on the request. The dispatch GUI may include an interactive graphical map, and a data entry window for capturing trip information. Based on a user-selected position on the map, a portion of the trip information may automatically populate in the data entry window. The vehicle may be assigned using the trip information, the real-time vehicle location tracking information, the request, and the dispatch code(s).

A robotic post includes a memory and a processor in communication with the memory, an optical sensor, a support holder and a securable hook. The robotic post is configured to secure an item container by coupling it to the securable hook and further adjusting the support holder. An optional robotic arm manipulates items in or out the item container based on particular semantics of interest inferred based on inputs from the optical sensor.

Computerized systems and methods are described for generating and optimizing vendor product roadmaps using predictive insights. Leveraging a Real-Time Data Mesh (RTDM) module, data from diverse sources including market trends, customer feedback, and technological advancements is aggregated and standardized. An Analytics and Machine-Learning (AAML) module analyzes this data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, with communication facilitated through a Single Pane of Glass (SPoG) user interface (UI). Scenario analysis, decision-support systems, and continuous monitoring improve strategic decision-making.

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.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention provides an intelligent voice recognition method and system, computer equipment and a storage medium, and belongs to the technical field of voice recognition, and the method comprises the steps: S101, obtaining historical customer call recording data, carrying out the historical ID marking, and building a purchase intention keyword vector; s102, extracting a call audio signal, obtaining a customer intonation frequency vector, a customer volume intensity vector and a customer speech speed, and obtaining comprehensive emotion feature data; s103, generating a keyword-emotion model, and outputting an emotion credible probability and a keyword matching probability; s104, obtaining voice data of a new customer, performing preprocessing, and performing new customer ID marking on the new customer; s105, establishing an emotion change curve, and obtaining a comprehensive intention score based on the emotion change curve and the keyword-emotion model; and S106, the comprehensive intention score is judged and compared, intention level marking is carried out, new customers who intend to purchase can be marked through the method, and convenience is improved.

Computerized systems and methods are provided for managing alerts and notifications within a technology distribution platform. A Single Pane of Glass User Interface (SPoG UI) presents notifications to users enabling interaction and customization. A Real-Time Data Mesh (RTDM) collects, filters, enriches, and standardizes event data from multiple sources into a uniform format. An Event Adapter formats data to be processed by a Notification Engine, configured to determine one or more notification triggers and generate alert content based on established rules and algorithms. A logging and user interaction module tracks user interactions with notifications. User feedback is processed by an Advanced Analytics and Machine Learning (AAML) Module configured to dynamically adapt notification logic. Notification content and delivery mechanisms are refined by a Distribution Module to ensure effective dissemination across various communication channels.

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.

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.

The invention relates to an intelligent customer service question-answering system answer generation method and device and a storage medium. Belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining text information input by a user; inputting the text information into the created knowledge base for retrieval recall, and determining a knowledge base slice recall result matched with the text information; inputting the text information and a knowledge base slice recall result into a large language model to obtain an information illusion cognition result; if the information illusion cognition result indicates that the illusion exists, not feeding back the question and answer based on retrieval to the user; if the information illusion cognition result indicates that illusion does not exist, the text information and the knowledge base slice recall result are input into a large language model, and a final answer result is obtained. The information illusion confirmation is performed based on the user input text and the knowledge base recall result, and the final reply result is output based on the confirmation result, so that the overall accuracy is improved, and the overall effect of the intelligent customer service question-answering system is improved.

Techniques for using collaborative conversational artificial intelligence during video conferencing are provided. In an example method, a video conference provider joins first and second client devices to a video conference. The video conference provider receives, from the first client device, a first prompt and relays the first prompt to a conversational AI, which is followed by receipt of a first response. The video conference provider then outputs, to the second client device, a data structure including information about the first prompt and the first response. In some examples, the video conference provider receives, from the second client device, a second prompt and relays the second prompt to the conversational AI, which is followed by receipt of a second response. The video conference provider then outputs, to the first client device, a data structure including information about the second prompt and the second response.

The invention is suitable for the field of intelligent customer service, and discloses an optimization method, system and device of an intelligent customer service system and a medium. Performing natural language processing on the dialogue content; according to the personalized characteristics of the user, adjusting the word segmentation spacing and the boundary of the semantic unit, and generating an optimized word segmentation and semantic analysis result; an emotion analysis model is applied to the optimized word segmentation and semantic analysis result, the emotion state of the user is recognized, and an emotion recognition result is obtained; identifying the question type of the user by adopting a classification model; and generating a reply suggestion in combination with the emotional state and the question type of the user. By identifying the personalized characteristics of the user and adjusting the word segmentation spacing and the semantic unit boundary of the dialogue content, not only can the coherence of expressing brief user semantic understanding be improved, but also the accuracy of redundant expression user semantic analysis can be enhanced, so that a more accurate word segmentation and semantic analysis result is generated.

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.