Webpage Chatbot Query Generation From Customer Point Selection
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Solution Overview
Problem
Existing systems struggle to effectively engage browsing customers through chatbots by initiating relevant queries based on their interests, leading to suboptimal customer interaction and engagement.
Innovation Solution
A chatbot feature with a point of interest query generator that identifies customer-selected points of interest on a webpage and generates queries based on associated data, using a backend service to determine and provide relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a chatbot feature is implemented to engage browsing customers, then customer engagement is improved, but the system complexity increases due to the need for point of interest identification and query generation mechanisms
Solution Approach 1:
The system is divided into distinct functional modules: a chatbot feature for customer interaction, a point of interest indicator for element selection, a query generation process for creating relevant queries, and a backend service for processing. This segmentation allows each component to handle specific tasks independently, improving customer engagement while managing system complexity through modular architecture.
Solution Approach 2:
The system pre-queues web content messages in a message queue before they are needed for query generation. This preliminary action prepares the data in advance, allowing the chatbot to quickly access and process relevant information when a customer selects a point of interest, thereby improving engagement response time without overwhelming the system with complex real-time processing.
2Measurement precision
If the chatbot generates queries based on customer-selected points of interest, then interaction quality is improved, but the processing time increases due to web content message queuing and searching
Solution Approach 1:
Web content messages are queued in advance before the customer interaction occurs. This preliminary queuing of content data ensures that when a customer selects a point of interest, the relevant web content is already prepared and available for quick retrieval and processing, reducing the perceived processing time while maintaining high interaction quality.
Solution Approach 2:
The system replaces traditional synchronous query processing with an asynchronous message queue mechanism. Instead of immediately searching and processing web content when a customer selects a point of interest, the system uses a message queue to handle processing in the background, substituting the mechanical synchronous search with a more efficient asynchronous processing model that reduces waiting time.
3Ease of operation
If the pointing tool is made operably movable for selecting visual elements, then ease of operation is improved, but the device complexity increases due to the interactive selection mechanism
Solution Approach 1:
The point of interest indicator serves multiple functions: it acts as a visual marker, a selection tool, and a trigger for query generation. This multi-functionality allows a single interface element to handle customer interaction, element selection, and query initiation, improving ease of operation while avoiding the need for multiple separate controls that would increase interface complexity.
Solution Approach 2:
The chatbot system automatically generates queries based on the customer's point of interest selection without requiring the customer to manually formulate the query. The system self-services by taking the selected visual element and automatically creating the appropriate query, simplifying the customer's interaction while managing the complexity of query generation internally.
Data Source
AI summary
A process for generating a query in response to a customer indicating a point of interest on a webpage. The query generation process includes: queuing, in a web content message queue, web content messages of web content used to render the webpage and storing in a datastore as stored web content messages; receiving input from the customer via a point of interest indicator selecting a visual element on the webpage as the point of interest; determining a web element associated with the visual element for transmitting to a point of interest backend service; initiating a search of the stored web content messages using the web element to determine matching data; gathering associated data related to the matching data for indicating a context of the first query; and providing the associated data to the chatbot feature for generating the first query.