Generative Learning for Enriched Automated Chatbot Generation
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Solution Overview
Problem
Existing chatbot development platforms face challenges in efficiently generating coherent and accurate conversation flows across multiple topics and functions, particularly in managing complex consumer interactions, often requiring user expertise that many consumers lack.
Innovation Solution
Utilizing large language models to generate enriched chatbot datasets from initial datasets, which are cleaned and processed to include additional properties, enabling the training of machine learning models to create automated chatbots that can adapt to user inputs and generate chatbot flows, dialogues, and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual-based chatbot building platforms are used to develop chatbot solutions, then users can design conversation flows and interactions, but the process requires user expertise that many consumers lack
Solution Approach 1:
The system enables automated chatbot generation through self-service mechanisms where the chatbot builder automatically creates conversation flows and dialogues using trained machine learning models, eliminating the need for users to manually design complex conversation structures
Solution Approach 2:
A trained machine learning model acts as an intermediary between the user's simple input and the complex chatbot generation task, translating user requirements into structured conversation flows and dialogues without requiring users to understand the underlying complexity
2Manufacturing precision
If manual chatbot development is used to ensure accurate conversation flows, then coherent and accurate automated solutions can be achieved, but the process is time-consuming and requires expertise
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on enriched datasets before deployment, so that when the chatbot builder is used, the models are already prepared to generate accurate conversation flows without requiring manual refinement
Solution Approach 2:
The system uses copying by generating conversation flows and dialogues through machine learning models that replicate effective patterns from training data, allowing accurate chatbot creation without manual design of each interaction
3Productivity
If enriched datasets are generated using large language models to train machine learning models, then automated chatbots can be generated more efficiently, but additional data processing steps are required
Solution Approach 1:
The system merges multiple functions into an integrated pipeline where data collection, cleaning, enrichment through large language models, and machine learning training are combined into a unified automated process that improves productivity despite the multiple steps involved
Data Source
AI summary
A system may collect at least one initial chatbot dataset, and the system may clean the at least one initial chatbot dataset. The system may generate at least one processed dataset, wherein the at least one processed dataset is based on the at least one cleaned initial chatbot dataset. The system may provide the at least one processed dataset to a large language model and request a dataset property from the large language model, wherein the dataset property is based on a query submitted to the large language model. The system may receive the dataset property from the large language model provide, and the system may generating at least one enriched dataset incorporating the dataset property.