837results about "Customer relationship" patented technology

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, 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.

The invention discloses a multi-modal-based digital intelligent customer service system. Data compensation is carried out on initial multi-modal problem data by adopting a dynamic adaptive compensation mechanism through a multi-modal input module. And carrying out dynamic weight distribution on the target multi-modal problem data by adopting a cross-modal attention mechanism through a multi-modal comparison learning module. And performing intention recognition on the target multi-modal semantic vector through an intention recognition module according to the historical interaction data and a preset dynamic knowledge graph. And inputting the user intention data into the target reinforcement learning reply model through a reply generation module to construct reply data. And performing potential problem prediction by adopting the target multi-modal semantic vector, the historical interaction sequence corresponding to the user and the time data through a potential problem prediction module. And by utilizing the dynamic updating capability of the knowledge graph, the context sensitive identification of the user problem is realized, and the identification accuracy is improved. And the potential problem prediction module turns from passive response to active service, so that the interaction efficiency and the user satisfaction are remarkably improved.

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 relates to the technical field of multi-channel intelligent customer service, in particular to a digital system intelligent customer service multi-contact method based on artificial intelligence, which comprises the following steps of: analyzing operation time, contact identification and data structure based on a multi-channel user request entry, judging text and voice content characteristics, and identifying standardized fields; and matching the keyword and the knowledge tag, analyzing the portrait and behavior of the user, identifying the authority and the sensitive item, and outputting a desensitization authority result. According to the method, feature classification and standardized arrangement can be executed for different entrance data by combining user behavior parameters under a multi-channel entrance, the request recognition precision is improved through multi-dimensional parameter fusion, effective aggregation of cross-channel user identities and request sequences is supported, content semantic deep analysis is promoted, and the user identification efficiency is improved. High matching of a content structure and a knowledge label is realized, an individualized service instruction and an authority identifier are automatically generated, and sensitive content identification and safe output are realized.

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.

Disclosed herein are methods and systems to generate code for a workflow. A non-limiting example of a method comprises executing, by a processor, a large language model, which receives an input of an intent associated with a workflow and provides a machine-readable description by: identifying, by searching a hierarchical data structure using a vector embedding associated with the intent, an object corresponding to the intent; identifying a set of data paths within the hierarchical data structure to retrieve the identified object; and generating the machine-readable description, the machine-readable description describing the object and at least one data path within the set of data paths; and generating, by the processor, code for the workflow using the machine-readable description.

A multi-channel ticket creation and service management system that includes: a graphical interface controller configured to provide a graphical user interface (GUI) accessible through a plurality of predefined Uniform Resource Locators (URLs), the GUI including interactive service request forms associated with a plurality of hierarchical service and subservice classifications; a form processing circuit operatively coupled to the graphical interface controller and configured to receive a selected service-subservice combination from a user and dynamically populate subsequent input fields based on a predefined service taxonomy, the input fields including contact information, email address, subject, location identifier, area of ​​responsibility field, and a description field; a rule execution logical processing unit coupled to the form processing circuitry, the rule execution logical processing unit configured to enable or disable one or more context-sensitive fields based on logical rules stored in a service-subservice dependency tree, the logical rules governing the enablement of date and time fields exclusively when the selected service is journalism and the selected subservice is newspaper reporting; an authentication mapping processor comprising a session parser and a user-to-department resolver, wherein the session parser is configured to extract user credentials and associated metadata from session cookies or authentication tokens, and the user-to-department resolver is configured to auto-populate an activity-driven scope field based on the extracted metadata and an organizational mapping scheme; and a validation and transmission control unit configured to check the completeness and data type conformity of all enabled fields, generate a structured ticket record, and transmit it to a central ticket management platform.

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.

The invention relates to the technical field of data processing, and discloses a decision-making auxiliary method, device and equipment based on portrait data and a medium, which can be applied to financial and medical scenarios, and the method comprises the following steps: processing an original data set to obtain standard data; respectively constructing a basic attribute portrait, a service behavior portrait and a consumption behavior portrait based on the standard data to extract multi-dimensional feature information, and converting the multi-dimensional feature information into a customer portrait vector; identifying customer behaviors according to the customer portrait vector, and constructing a portrait dynamic updating mechanism to obtain dynamic portrait data; and constructing a decision-making auxiliary model by using the dynamic portrait data, and operating the decision-making auxiliary model to output a decision-making auxiliary scheme. In the invention, aiming at the problem of unreasonable resource allocation caused by incomplete client portrait construction, the decision-making auxiliary model can be constructed and operated by utilizing the calculated dynamic portrait data, so as to output the decision-making auxiliary scheme. Therefore, resources can be reasonably distributed, and the working efficiency of personnel is improved.

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.

A system (100) for multi-channel purchase authorization with dynamic role validation, comprising: (a) a multi-channel request capture module configured to receive and standardise purchase requests initiated through a variety of communication channels, including corporate portals, mobile applications, emails and messaging platforms; (b) a role identification and contextual mapping module configured to dynamically retrieve user roles and contextual information based on real-time access management data and organizational hierarchy; c) a policy and rules module configured to evaluate purchase requests based on pre-configured and context-sensitive authorization policies, thresholds and compliance rules; (d) a dynamic role validation and escalation module configured to validate whether the requester has the authority to initiate or authorise a transaction and, if not, to automatically escalate the request to an appropriate authorised role; (e) an authorization workflow orchestration module configured to sequence approval steps, apply business logic, manage parallel or sequential workflows, and interface with external procurement systems; f) and an audit, logging and analytics module configured to record all request, validation and authorization actions in a tamper-proof log and generate real-time insights and compliance metrics, g) where the system enables real-time, role-based purchasing authorization across multiple channels with dynamic policy enforcement and escalation handling.

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.

The invention provides a work order generation method and device based on large model intelligent classification, equipment and a medium, and the method comprises the steps: constructing a plurality of first labels and second labels according to first-level classification, second-level classification and third-level keywords, the third-level keywords being classification subjects of classification description information; when the work order description information is obtained, candidate tags are determined from the second tags based on a classification model, and the corresponding classification description information is configured as model cue words to determine identification tags; and determining the first tag corresponding to the secondary classification included in the identification tag as a target tag, and generating a target work order based on the target tag and the work order description information. According to the technical scheme, each scene classification only corresponds to one second label, the detailed classification description information serves as additional information of the three-level keywords, the number of the classification labels is reduced, illusion classification is avoided by taking the classification description information as the model cue word, and the recognition efficiency and accuracy of the large model are improved.

Automatic analyses of customer-agent interactions provide valuable, actionable feedback for managers, agents, and customers. To provide such analyses, methods for recording and analysis of customer-agent interactions using a customer relationship management (CRM) system are disclosed. A recorder application records the customer-agent interaction, and sensitive information may be identified. Sensitive portions of the recording may then be redacted and removed from the recording. The redacted recording is then analyzed to generate useful summary and analytics information.

The invention provides an AI-based customer service intelligent interaction method and system, and a medium. The method comprises the following steps: collecting attribute information and behavior track information of a target customer, processing to obtain a customer information portrait, extracting personalized demand information of the target customer according to the customer information portrait, executing intelligent interaction service based on the personalized demand information, monitoring an interaction service process in real time, collecting service state parameters, and sending the service state parameters to the target customer. Whether manual customer service switching needs to be executed or not is judged according to the service state parameters, intelligent interaction service in a preset time period is monitored, service index data is extracted, quantitative evaluation processing is conducted according to the service index data, a service reliability coefficient is obtained, the reliability of the intelligent interaction service is judged, and corresponding optimization measures are taken. Therefore, the AI-based customer service intelligent interaction technology is realized.

The invention discloses a data analysis decision-making method, device and equipment and a medium, and is suitable for data analysis decision-making in the financial and medical fields. The data analysis decision-making method comprises the following steps: firstly, performing distributed data acquisition from a target data source by combining a passive collection mode and an active collection mode to obtain original data; and then performing efficient cleaning and processing on the original data through a preset segmentation rule and a pre-trained detection model. And through text mining analysis and association rule mining technologies, key modes and potential associations in a large amount of data are extracted, so that an accurate target portrait is constructed, and dynamic adjustment is performed according to real-time updated original data. And finally, through a preset decision-making algorithm, based on the target portrait, carrying out deep analysis and generating a decision-making report. According to the method, the efficiency and accuracy of data processing and decision support are remarkably improved.

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 relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. An information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening out target users with loss risks according to the user original information data; s2, extracting customer loss risk features from the original information data of the target users, importing the customer loss risk features into the trained risk assessment calculation model, and outputting results to indicate loss risk levels of the target users; and S3, generating a reservation scheme according to the loss risk level of the target user and the corresponding original information data, and outputting the reservation scheme to the corresponding target user. According to the invention, grading intervention is carried out on the target users based on different risk grades, a uniform retention strategy is prevented from being adopted for all users, and the retention rate of clients is improved.

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.