51 results about "Customer support" patented technology

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

Embodiments of the present disclosure provide methods, systems, and computer program products for assigning and dynamically managing a Customer Proficiency Rating for a specific customer for implementing enhanced customer support operations for a supported product or service. Disclosed embodiments provide an AI virtual support agent that receives a customer support request for a current problem, obtains a customer statement of understanding for the current problem and provides a set of questions, to obtain customer responses. In an embodiment, the AI virtual support agent evaluates the customer statement and customer responses, and calculates a customer proficiency rating for a specific customer for the support request to identify a customer skill level for the current problem. The AI virtual support agent routes customers to an optimal human support agent based on the customer proficiency rating.

Systems / techniques that facilitate automatic product support systems and methods via generative artificial intelligence (GAI) and customer interactions are provided. In various embodiments, a system can access an electronic interaction record pertaining to a software product. In various aspects, the system can synthesize, via execution of GAI on the electronic interaction record, first text that describes a problem afflicting the software product. In various instances, the system can determine, based on executing the GAI on the first text, whether there is an available software feature in an available software feature repository that addresses or solves the problem. In various cases, the system can, in response to a determination that there is no available software feature that addresses or solves the problem, synthesize, via execution of the GAI on the first text, a recommended design alteration to the software product that would address or solve the problem.

A method and a system for building a knowledge base for industrial use and using the knowledge base to manage and control industrial products, processes, resources and systems. The knowledge base uses semantic processing modules and artificial intelligence tools to enable continuous extraction and entry of knowledge into the knowledge base from various sources including user inputs, operation status reports, manuals, websites, diagnosis reports, sensor data, operation sequence, and other static or ephemeral sources. The knowledge base is supported by a knowledge base management system that includes a suite of tools to support knowledge entry, update, retrieval and visualization using both natural language based queries and structured queries with predefined syntax. The system also provides tools to retrieve and assemble knowledges on-the-fly to support industrial applications including to design products, services or production lines, build production process, manage scheduling and flow, monitor and optimize operations, diagnose issues, apply business logics, procure materials and supplies, manage and train workforce, and provide customer supports.

An autonomous customer support Chatbot Agent utilizes a large language model to aid in implementing a workflow to solve a customer issue. A natural language workflow policy may be selected by an admin, along with tools such as API calls. The large language model determines the implementation details for the workflow based on the workflow policy and the selected tools.

The invention belongs to the technical field of customer service work order processing, and discloses an event priority ranking distribution method and system, and the method comprises the steps: converting historical multi-round dialogue data into a numeralized historical work order emotion score sequence set; the Hidden Markov model is trained by using a Boem-Weili algorithm, and core dynamic parameters of customer emotional evolution are learned; an optimal risk score threshold value after calibration is obtained through calibration; for each work order in progress, the probability distribution of each current hidden state is deduced in real time, and a forward-looking negative upgrading risk score is calculated; and when the negative upgrading risk score exceeds the calibrated risk score threshold, automatically triggering priority adjustment, and generating an updated work order record. According to the invention, a quantifiable framework based on probability prediction is provided, and customer support is converted from passive and post-response type work order processing into an active and pre-warning type risk management normal form.

Various embodiments provide for a unified interface for testing a system, which can include a device under test (DUT). In particular, some embodiments described herein can be used to implement a Unified Test Framework (UTF) that comprises an architecture that provides a unified (or common) software interface to one or more test methods used during a development life cycle of a system. Example test methods can include, without limitation, modeling, verification, simulation, emulation, validation, customer support, and the like.

An industrial technical support system leverages generative artificial intelligence (AI) techniques to generate technical support guidance and recommendations in response to natural language technical support requests submitted as natural language input. The system can maintain and leverage one or more sets of custom models trained with sets of domain-specific training data specific for different industrial domains. When a natural language technical support query is received, the system leverages the domain-specific training data as well as responses prompted form a generative AI model to formulate and render technical support recommendations for addressing a performance or design issue described by the query.

Techniques for locking and unlocking clusters of content items are disclosed. A clustering algorithm is executed to assign content items to corresponding clusters. A lock is then applied to the clusters, ensuring that content items associated with the locked clusters will not be reassigned to a new cluster while the cluster is in a locked state. Characteristics associated with the first clustering algorithm, the set of clusters, and / or the content items are monitored for cluster unlocking criteria. After determining that cluster unlocking criteria has been met for a cluster, the cluster is unlocked. The clustering algorithm is then applied to content items from the unlocked cluster.

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.

To perform guidance at a customer window contacted by a user even when it is difficult to acquire information necessary for performing guidance for solution from the user.SOLUTION: An image forming apparatus 100 includes a reception unit 31 that receives abnormality information indicating that an operation of the image forming apparatus 100 is abnormal, a related log extraction unit 33 that extracts related log information determined in advance to be highly related to the abnormality information received by the reception unit 31 from log information included in the image forming apparatus 100, a feature extraction unit 34 that extracts a feature by performing pattern matching on the related log information extracted by the related log extraction unit 33, and an output unit 36 that outputs a log summary generated from the feature extracted by the feature extraction unit 34.SELECTED DRAWING: Figure 3

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 automated helpdesk explainer for use by a customer-support ticket platform is integrated with an online gaming platform. The automated helpdesk explainer receives a plurality of helpdesk tickets and determines a ticket volume parameter based on the plurality of helpdesk tickets. In response to the ticket volume parameter satisfying a predefined threshold, the automated helpdesk explainer applies one or more of the plurality of helpdesk tickets to a natural language processor configured to assign the one or more of the plurality of helpdesk tickets to one or more topics, creates an infographic image representing the one or more of the plurality of the helpdesk tickets based on the topics, applies at least a portion of the topics to a large language model configured to generate a natural language incident analysis summary, and provides the infographic image and incident analysis summary for presentation at a client device.

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.