36 results about "Customer support" patented technology

The Artificial Intelligence driven service outage detection and customer support ticket management system in customer support platforms includes a customer support platform that is operatively coupled to the service outage detection system. A data collector gathers input data from a product status page and a service request tracker document. A service outage detector (SRT) detects the service outage by monitoring the product status page and incoming customer support tickets raised against the products by the users. A comparator compares the details of the product status page and the SRT document to confirm the occurrence of the service outage. A customer support ticket router updates the customer support ticket routing process based on the confirmed service outage status. A response generator generates an automated, predefined response including service outage information and any expected resolution or alternative support options and notifies the user utilizing a notification module.

We provide the system. [Solution] A means of collecting employees' closing talks as data, A method for converting collected data into text data and cleansing it, A method for training a generative AI using cleansed data, A means of providing real-time customer support using trained AI models, A means of providing employees with a simulation environment to learn closing techniques, A system that includes this.

A system is provided.SOLUTION: A system comprising: means for learning contents of an inquiry for support and generating an illustration as trouble parts for visually expressing the contents; means for distributing the generated illustration to a user terminal and providing an interface through which a user can visually select a problem; means for automatically generating an appropriate inquiry sentence based on illustration information selected by the user; means for providing an interface through which the user can finely adjust the generated inquiry sentence; and means for transmitting a final inquiry sentence adjusted by the user to customer support.SELECTED DRAWING: Figure 1

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to enable use of AI in providing customer support. Machine learning AI models are trained based on one or more previous service request lifecycles of service requests of a customer to determine latent emotions of the customer based on determined customer problem data. A customer service prioritization signal related to a current service request of the customer is generated by a predictive analytics application that includes the models. The customer service prioritization signal is indicative of a need to prioritize a current service request of the customer based on the determined latent emotions of the customer and is generated during and prior to the end of the lifecycle of the current service request whereby escalation of the current service request may be deferred or prevented.

This technology reduces the burden on users and improves engineers' ability to quickly resolve critical errors. [Solution] The computer system includes: a collection unit that monitors network connections between multiple computer peripherals and collects usage information and error information; a storage unit that stores error information describing the occurrence of an error in a database in response to the detection of an error in a computer peripheral; a code generation unit that generates a support code and stores the support code in the database in association with the error information; a code display unit that transmits the support code to the computer peripheral for display; a reading unit that reads the error information from the database in response to the receipt of a support code from a user device; and an error information display unit that displays the error information on the user interface of a customer support technician.

A computer system is configured to remotely manage technical support for a plurality of computer peripherals, by performing the steps of: monitoring network connections between the computer system and the plurality of computer peripherals to collect usage information and error information; and in response to detecting that a first error occurred in a computer peripheral of the plurality of computer peripherals: storing, in an entry of a database, error information describing an occurrence of the first error; generating a first support code and storing the first support code in the entry of the database in association with the error information; transmitting the first support code to the computer peripheral to be displayed thereby; reading the error information from the entry of the database in response to receiving the first support code from a user device; and displaying the error information on a user interface (UI) of a customer support technician.

Technologies for upgrading a service work order between two service providers include receiving, at an initial service provider, an initial service work order for resolving a problem from an affected entity affected by the problem. The initial service work order includes a set of access limited attributes of the affected entity. Based on the initial service work order, the system generates an upgraded service work order at the initial service provider. The upgraded service work order identifies the problem and omits access limited attributes. The system passes the upgraded service work order to a higher level service provider that is not authorized to access the set of access limited attributes included in the initial service work order. In response to transmitting the upgraded service work order, the system receives information corresponding to the resolution of the problem from the higher level service provider and processes the initial service work order based on the information corresponding to the resolution of the problem.

An object of a system according to an embodiment is to provide chat support that enables a flexible answer.SOLUTION: A system includes a generation AI, a tag embedding part, an information learning part, a page guide part, and a customer support part. The generation AI generates a flexible answer using the generation AI. The tag embedding unit can be introduced by embedding only one tag. The information learning unit automatically learns information in the site. The page guidance unit performs guidance to a target page. The customer support section performs customer support.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for automatically adjusting resources in real-time in response to a number of cloud server users; means for automating user responses, such as chat services and customer support, using a generative artificial intelligence model; and means for automatically generating in-game content and updates using the generative artificial intelligence model.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to collectively support planning and execution of a sales strategy in a global market.SOLUTION: A system according to an embodiment includes a market analysis unit, a customer targeting unit, a translation unit, a customer support unit, and a marketing optimization unit. The market analyzer analyzes the market using the generated AI. The customer targeting unit identifies a customer segment based on the market identified by the market analysis unit. The translation unit translates the product description to the customer segment specified by the customer targeting unit. The customer support unit responds to the inquiry from the customer based on the product description translated by the translation unit. The marketing optimization unit optimizes a marketing strategy based on the data collected by the customer support unit.SELECTED DRAWING: Figure 1

Systems and methods for automation of a customer support sorting process are described. A probability that a call from a potential caller will be received by a call system may be determined based at least in part on historical status information and call history information. The historical status information may indicate one or more past operational states of a service network. The call history information may indicate one or more calls received by the call system, as well as a subject matter of each of the one or more calls. If the determined probability satisfies a threshold, a notification to the potential caller may be transmitted. The notification may inform the potential caller of a current operational state of the service network.

Client support for applications to access services can be provided. In an example, a computing system can receive, from a client device, text input for an in-progress application to access a service. The client may be prevented from accessing the service prior to the in-progress application being approved. The computing system can detect an error associated with processing the in-progress application based on the text input and contextual information. The computing system can determine that the error is associated with the text input or with a technical issue associated with the in-progress application. The computing system can generate a recommendation associated with the error based on determining that the error is associated with the text input or the technical issue. The computing system may output the recommendation to the client device for use in resolving the error with processing the in-progress application.

Some aspects of the present disclosure relate to a computer-implemented method, to a system, and to a computer program for optimizing retrieval of similar tickets in a ticket management system using machine learning. The method comprises obtaining (110) a ticket being input into the ticket management system, extracting (120) first textual data from a description portion of the ticket, determining (130), if the ticket comprises at least one attachment, second textual data representing the at least one attachment, determining (150) combined textual data based on the first textual data and, if determined, based on the second textual data, performing (185) a similarity search based on the combined textual data and a repository of existing tickets using a lexical search and / or a semantic search to determine one or more existing tickets being similar to the ticket, and providing (190) information on the one or more existing tickets being similar to the ticket. For example, the method, system and computer program can be applied to Healthcare (Electronic Health Records), IT and Technology (Help Desk and Customer Support), Manufacturing (track production issues, quality control, and maintenance requests), or other fields.

To provide a system capable of efficiently performing the temporary examination of a housing loan, customer support and the provision of education information.SOLUTION: The system includes a first collecting unit, a first analyzing unit, a first providing unit, a second collecting unit, a generating unit, a second providing unit, a third collecting unit, a second analyzing unit, a third providing unit, a receiving unit, and an associating unit. The first collection unit collects customer information. The first analysis unit analyzes the collected information. The first provision unit provides a provisional examination result on the basis of the analyzed information. The second collection unit collects needs of the customer. The generation unit generates advice based on the collected needs. The second provision unit provides the generated advice. The third collection unit collects educational information. The second analysis unit analyzes the collected information. The third providing unit provides information based on the information. The reception unit receives an inquiry from a customer. The responder responds based on the received inquiry.SELECTED DRAWING: Figure 1

Based on behavioral logs and customer attribute information of prospective customers, the system generates email templates tailored to each prospective customer and allows customer approach support information for those prospective customers to be reflected in external systems. [Solution] An information processing system having at least one control unit, the control unit acquires behavioral logs and customer attribute information relating to prospective customers from an external system, identifies missing information indicating information that the prospective customer lacks based on the behavioral logs and customer attribute information, determines appeal axes to supplement the missing information, generates an email draft for prospective customers based on the appeal axes, and reflects customer approach support information including the email draft or information corresponding to the appeal axes or missing information in the external system.

A computer executed process for mimicking human dialog, referred to herein as a “humanoid” or “humanoid system,” can be configured to provide automated customer support. The humanoid can identify a support issue for a customer, as well as a customer support campaign corresponding to the support issue. The humanoid can identify at least one machine learning model associated with the customer support campaign and can communicate with the customer using the at least one machine learning model. The humanoid can execute a support action to resolve the support issue.

We provide the system. [Solution] A means by which the user can access customer support via an input device, A server that receives inquiries from users and connects them to the appropriate support personnel, A system that includes means for support staff to respond to users and resolve problems.

A system is provided.SOLUTION: A system for making a contract for a product or a service via a network accessible by a user, comprising: means for receiving an access request from a user terminal and displaying information on a contract product; means for providing customer support using generative artificial intelligence and guiding selection of a contract form or a plan; means for performing electronic identity verification in order to perform identity verification of the user; and means for storing contract information in a database and transmitting a contract completion notification to the user terminal.SELECTED DRAWING: Figure 1

The application belongs to the technical field of customer service work order processing, and discloses an event priority sorting and allocating method and system, which comprises the following steps: converting historical multi-round conversation data into a numerical historical work order emotion score sequence set; training a hidden Markov model by using a Baum-Welch algorithm to learn core dynamic parameters of customer emotion evolution; calibrating an optimal calibrated risk score threshold; for each ongoing work order, real-time inferring a probability distribution of each hidden state and calculating a forward-looking negative upgrade risk score; when the negative upgrade risk score exceeds the calibrated risk score threshold, automatically triggering priority adjustment and generating an updated work order record. The application provides a quantifiable and probability prediction-based framework, and changes customer support from passive and post-response work order processing to active and pre-warning risk management.

The application provides a multi-source log root cause analysis method and system based on a large model. The method comprises the following steps: multi-source log real-time collection and preprocessing, long connection differentiated log pushing, support for breakpoint continuation, and log structured analysis and semantic enhancement; a business process template library is constructed, error features are extracted, business processes are matched, and hypotheses are verified by using a large model through multiple rounds of reasoning, and error types, root cause descriptions, confidence and suggested operations are output; a dialogue template library and customer portrait are constructed, personalized guidance dialogue is matched and generated, multi-modal intelligent guidance is realized through real-time channels and state machines; a feedback optimization module collects indicators and constructs a triple tag data set, and the model is incrementally fine-tuned. The application realizes cross-component log full-link root cause positioning, generates dynamic dialogue combined with customer portrait, and continuously improves the accuracy of error analysis and guidance through closed-loop optimization, and is suitable for enterprise-level system operation and maintenance and customer support in the field of financial technology.

A computer-implemented method for augmenting customer support is disclosed in which a granular taxonomy is formed to classify tickets based on customer issue topic. A dashboard and user interface of performance metrics may be generated for the topics in the taxonomy.Recommendations may also be generated to aid servicing customer support issues for topics in the taxonomy. This may include generating information to aid in determining topics for generating automated responses or generating recommended answers for particular topics. In some implementations, an archive of historic tickets is used to generate training data for a machine learning model to classify tickets.

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to enable use of AI in providing customer support. Machine learning AI models are trained based on one or more previous service request lifecycles of service requests of a customer to determine latent emotions of the customer based on determined customer problem data. A customer service prioritization signal related to a current service request of the customer is generated by a predictive analytics application that includes the models. The customer service prioritization signal is indicative of a need to prioritize a current service request of the customer based on the determined latent emotions of the customer and is generated during and prior to the end of the lifecycle of the current service request whereby escalation of the current service request may be deferred or prevented.

The system according to this embodiment aims to automatically acquire necessary information for customer support and enable a rapid response. [Solution] The system according to the embodiment comprises a confirmation unit, a questioning unit, a presentation unit, and a linking unit. The confirmation unit grasps the content of the conversation and the input content. The questioning unit automatically asks the customer questions about the missing perspectives identified by the confirmation unit. The presentation unit presents relevant articles based on the information obtained by the questioning unit. The linking unit links with an existing customer support system.

The invention discloses a household monitoring and alarming system for self-testing fetal movement and fetal heart. According to the system, fetal movement and fetal heart rate can be monitored in real time, and it is ensured that a mother can know the condition of a fetus at any time. The system adopts a Bluetooth or Wi-Fi technology to wirelessly transmit data to a smart phone or other equipment, so that a mother can conveniently check and store the data. An intelligent algorithm is built in the system to analyze fetal movement and fetal heart data, and if abnormity is detected, the system needs to remind a pregnant woman in time and suggest to see a doctor. The system can store historical monitoring data, a doctor can conveniently carry out long-term trend analysis, a user support module in the system provides online or telephone customer support and use tutorial and health education information, pregnant women are helped to better understand and use the device, and therefore the convenience and rapidness of the whole system during use are improved, and the system is suitable for popularization and application. Therefore, anxiety of the pregnant woman can be reduced, the pregnant woman can see a doctor in time when problems are found, and prenatal and postnatal care can be promoted.

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.

We provide the system. [Solution] Methods for obtaining manual information from the customer support database, A method for training a generative AI model with acquired manual information, A user interface means for receiving inquiries from users, A means of sending received inquiries to a generative AI model, A means of receiving and providing answers generated by a generative AI model to the user, A system that includes this.

Systems and methods for handling customer support tickets are provided. A method, according to one implementation, includes a step of dynamically summarizing ticket content of a new ticket to obtain partial summaries and a final summary of the new ticket, wherein the new ticket is opened in order to resolve an incident in a domain. The method also includes a step of transforming the final summary into a numerical vector. Also, the method includes performing a similarity search to compare the numerical vector with pre-stored vectors in a vector database, wherein the pre-stored vectors are associated with previously resolved incidents in the domain. The method may further include triggering predefined actions based on the similarity search.

In an environment where customer messages are received through multiple channels, we provide a customer support system that integrates these messages to efficiently handle customer inquiries. [Solution] The system includes: an acquisition unit that acquires messages from customers transmitted from multiple different channels; a conversion unit that converts the information contained in the messages acquired by the acquisition unit into a predetermined format; a registration unit that registers the converted information on a management platform; a response unit that searches for response candidates related to the acquired message from response knowledge information managed for each facility and generates a response proposal from the searched response candidates according to predetermined conditions; and an escalation unit that switches to an operator response if no response proposal is generated for the message, or if the message matches the emergency notification conditions.

The system according to the embodiment aims to improve the efficiency of customer support and reduce costs. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a user's question. The analysis unit analyzes the question received by the reception unit. The generation unit generates an appropriate answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit.

A case assistant is provided to client support professionals, which utilizes robotic process automation (RPA) technologies to analyze large amounts of data related to historical client cases that are similar to current open cases, data related to skilled experts associated with similar client cases, and data related to business exceptions. Several processes are utilized to provide this data to client support professionals, including a document similarity finder that utilizes a vector data collector, a tokenizer, a stop word remover, a relevance finder, and a similarity finder, several of which utilize a variety of machine learning technologies. Additional processes include a skilled experts finder and a business exceptions finder.