A system and method for intelligently routing calls between customers and agents. The system and method use knowledge graphs to generate route recommendations for a route selection system. The system uses dynamically selected objective functions to generate the route recommendations. The objective functions may be selected according to the intent of the call. The system and method can also be used to reroute ongoing calls when the intent of the call changes.

Implementations of the subject matter described herein provide a solution for generating a text from the structured data. In this solution, the structured data is converted into its representation, where the structured data comprises a plurality of cells, and the representation of the structured data comprises plurality of representations of the plurality of cells. A natural language sentence associated with the structured data may be determined based on the representation of the structured data, thereby implementing the function of converting the structured data into a text.

Implementations of the subject matter described herein provide a solution for generating a text from the structured data. In this solution, the structured data is converted into its representation, where the structured data comprises a plurality of cells, and the representation of the structured data comprises plurality of representations of the plurality of cells. A natural language sentence associated with the structured data may be determined based on the representation of the structured data, thereby implementing the function of converting the structured data into a text.

A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

A method for model customization according to an embodiment includes providing a user with prediction results of each of a plurality of pre-trained natural language processing models for a document subjected to analysis selected from a document set including a plurality of documents, acquiring user feedback on the prediction results from the user, generating a plurality of augmented documents from at least one of the plurality of documents based on data attributes of each of the plurality of documents and the user feedback; and retraining at least one of the plurality of natural language processing models, using training data including the plurality of augmented documents.

Various aspects of the disclosure relate to test automation systems with pre-compilers to validate various steps associated with a test script. An artificial intelligence (AI)-based pre-compiler may use natural language processing (NLP) to validate various steps associated with a test script associated with an application. Other aspects of this disclosure relate to automated encryption and mocking of test input data associated with test scripts.

Various aspects of the disclosure relate to test automation systems with pre-compilers to validate various steps associated with a test script. An artificial intelligence (AI)-based pre-compiler may use natural language processing (NLP) to validate various steps associated with a test script associated with an application. Other aspects of this disclosure relate to automated encryption and mocking of test input data associated with test scripts.

A user authentication device includes: a collection part collecting information of a user; a generation part generating a question for the user on the basis of the information of the user collected by the collection part and a skill model of the user; a presentation part presenting the question for the user generated by the generation part to the user; a reception part receiving, from the user, a response to the question presented by the presentation part; and a determination part determining authentication of the user on the basis of the response received by the reception part.

A customer who is contacting customer care via a support session regarding a problem is classified into a customer category of multiple customer categories based at least on customer account information of the customer. A customer care topic in a predetermined set of multiple customer care topics that correspond to the problem is then identified via machine learning. A topic script that corresponds to the customer category of the customer for the customer care topic in the predetermined set of customer care topics is further retrieved or generated, in which the topic script includes one or more topic issues related to the customer care topics. The topic script is provided for presentation to a customer service representative (CSR) to prompt the CSR to discuss the one or more topic issues related to the customer care topic with the customer.

A customer who is contacting customer care via a support session regarding a problem is classified into a customer category of multiple customer categories based at least on customer account information of the customer. A customer care topic in a predetermined set of multiple customer care topics that correspond to the problem is then identified via machine learning. A topic script that corresponds to the customer category of the customer for the customer care topic in the predetermined set of customer care topics is further retrieved or generated, in which the topic script includes one or more topic issues related to the customer care topics. The topic script is provided for presentation to a customer service representative (CSR) to prompt the CSR to discuss the one or more topic issues related to the customer care topic with the customer.