Abstract: Embodiments described herein are generally directed to training of classification models and their use by a chatbot to identify a customer-specified product support issue and provide appropriate troubleshooting guidance. According to an example, text describing an issue associated with a product line of a vendor is received via the chatbot. A vector representation of the issue is created using a word association model corresponding to the product line and trained based on a set of historical support cases relating to multiple supported issue categories for the product line. It is determined whether the issue matches a category within the supported issue categories for the product line by applying a classification model to the vector representation. When the determination is affirmative, an automated, interactive, conversational troubleshooting dialog is initiated with the user via the chatbot and guided based on a decision tree for the category within the product line.

Abstract: Embodiments described herein are generally directed to use of a chatbot to identify a customer-specified product support issue and provide appropriate troubleshooting guidance. According to an example, free text input describing an issue associated with a product line of a vendor is received from a user via a chatbot. A vector representation of the issue is created by tokenizing and vectorizing the free text input using a word association model corresponding to the product line. It is determined whether the issue matches at least one category within multiple issue categories for the product line by performing similarity scoring between the vector representation and multiple vectors created based on top words per issue category of the multiple issue categories. Responsive to an affirmative determination, an automated, interactive, conversational troubleshooting dialog is initiated with the user via the chatbot and guided based on a decision tree for the at least one category.

Abstract: Embodiments described herein are generally directed to use of a chatbot to identify a customer-specified product support issue and provide appropriate troubleshooting guidance. According to an example, free text input describing an issue associated with a product line of a vendor is received from a user via a chatbot. A vector representation of the issue is created by tokenizing and vectorizing the free text input using a word association model corresponding to the product line. It is determined whether the issue matches at least one category within multiple issue categories for the product line by performing similarity scoring between the vector representation and multiple vectors created based on top words per issue category of the multiple issue categories. Responsive to an affirmative determination, an automated, interactive, conversational troubleshooting dialog is initiated with the user via the chatbot and guided based on a decision tree for the at least one category.

Abstract: Embodiments described herein are generally directed to training of classification models and their use by a chatbot to identify a customer-specified product support issue and provide appropriate troubleshooting guidance. According to an example, text describing an issue associated with a product line of a vendor is received via the chatbot. A vector representation of the issue is created using a word association model corresponding to the product line and trained based on a set of historical support cases relating to multiple supported issue categories for the product line. It is determined whether the issue matches a category within the supported issue categories for the product line by applying a classification model to the vector representation. When the determination is affirmative, an automated, interactive, conversational troubleshooting dialog is initiated with the user via the chatbot and guided based on a decision tree for the category within the product line.