Chatbot Intent Classification Using Word2Vec and LSTM for Technical Support
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Solution Overview
Problem
Current chatbot technologies face challenges in automating technical support issues, including identifying product issue categories, training AI models without manual labeling, and integrating domain expertise, which limits their ability to accurately resolve customer intents and handle complex scenarios.
Innovation Solution
A two-stage approach is implemented, where a just-in-time labeling method and novel classification models using Word2Vec and LSTM models are employed to map customer intent to product issue categories, gradually reducing human dependency and enabling unsupervised learning to identify new issue categories through similarity scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional chatbot technologies are used for technical support, then basic customer service can be provided, but accuracy in identifying product issue categories and resolving complex scenarios remains limited
Solution Approach 1:
The patent segments the chatbot system into distinct functional modules: intent identification module, issue category classification module, and troubleshooting guidance module. Each module uses specialized AI models (LSTM for intent, Word2Vec for categorization) trained on specific datasets, allowing high accuracy in each function while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent implements preliminary action by pre-training Word2Vec models on product manuals and documentation before deployment. This pre-processing creates ready-to-use vector representations of product concepts that accelerate real-time issue categorization and reduce computational complexity during actual customer interactions.
2Reliability
If manual labeling methods are used for training AI models, then domain expertise can be integrated, but time consumption and human dependency increase
Solution Approach 1:
The patent implements self-service through automated label generation using Word2Vec similarity scoring. The system automatically compares customer issues against known issue categories using pre-trained vector models, generating labels without human intervention. This maintains reliability by using domain-specific vocabulary from product manuals while eliminating manual labeling time.
Solution Approach 2:
The patent uses feedback mechanisms where customer interactions and resolution outcomes continuously refine the AI models. Resolution data from successful troubleshooting cases feeds back into retraining the classification models, progressively improving accuracy while reducing the need for extensive manual labeling as the system learns from accumulated experience.
3Productivity
If chatbot automation is implemented, then productivity increases, but ability to handle unseen issues and adapt to changing product lines decreases
Solution Approach 1:
The patent implements dynamics through continuous model retraining and updates. The Word2Vec models are periodically retrained on new product documentation and the LSTM intent identification model is updated with new issue patterns from accumulated data. This dynamic adaptation allows the automated system to handle unseen issues and changing product lines while maintaining high productivity.
Solution Approach 2:
The patent uses parameter changes in the Word2Vec similarity threshold to balance automation and adaptability. By adjusting the similarity score threshold, the system can dynamically control when to automatically categorize issues versus when to escalate to human agents, allowing flexible handling of both common and novel issues while maintaining automation efficiency.
4Extent of automation
If unsupervised learning is used to identify new issue categories, then human dependency reduces, but measurement precision of new category identification may worsen
Solution Approach 1:
The patent uses Word2Vec vector similarity as an intermediary between unsupervised learning and precise categorization. The system clusters similar customer issues using unsupervised algorithms, then uses Word2Vec similarity scoring as a mediator to match these clusters to existing issue categories or identify new ones, maintaining precision while reducing human dependency through automated vector-based comparison.
Data Source
AI summary
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.