Chat Bot Aspect Pre-Selection via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search techniques are repetitive and require users to manually reenter aspects of categories, leading to user frustration and computational inefficiency, especially in natural-language conversation systems.
Innovation Solution
The implementation of aspect pre-selection techniques using machine learning, where an artificial assistant system engages in natural-language conversations to prompt users for category aspects, and uses this data to generate search queries automatically, reducing the need for user intervention in subsequent searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search techniques are used, then users can perform searches, but users must repeatedly manually enter aspects of categories leading to user frustration and computational inefficiency
Solution Approach 1:
The system performs preliminary action by learning user preferences and category aspects during initial interactions and storing them in a model. This preliminary learning enables the system to automatically include relevant aspects in subsequent search queries without requiring users to manually reenter them, thereby reducing repetitive interactions and improving operational efficiency
Solution Approach 2:
The system implements self-service by automatically generating search queries with pre-selected aspects based on learned user preferences. The machine learning model autonomously determines which aspects to include in searches without continuous user input, allowing the system to serve itself in reducing the burden of manual aspect entry while maintaining search relevance
2Productivity
If conventional search techniques are used, then searches can be performed, but the system requires repeated user interventions which reduces productivity
Solution Approach 1:
The system uses feedback from initial user interactions to train and refine the machine learning model. The model learns from the data describing natural-language conversations and adjusts its understanding of user preferences. This feedback mechanism enables the system to improve its automatic aspect selection in subsequent searches, enhancing productivity while managing complexity through iterative learning
Data Source
AI summary
Aspect pre-selection techniques using machine learning are described. In one example, an artificial assistant system is configured to implement a chat bot. A user then engages in a first natural-language conversation. As part of this first natural-language conversation, a communication is generated by the chat bot to prompt the user to specify an aspect of a category that is a subject of a first natural-language conversation and user data is received in response. Data that describes this first natural-language conversation is used to train a model using machine learning. Data is then received by the chat bot as part of a second natural-language conversation. This data, from the second natural-language conversation, is processed using the model as part of machine learning to generate the second search query to include the aspect of the category automatically and without user intervention.