Task Query System Using Feature Identification Codes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In e-commerce voice unmanned customer service scenarios, users face difficulties in querying tasks as they need to remember and input order numbers, leading to inefficiencies and poor user experience due to incorrect inputs.
Innovation Solution
A task query method and device that determine the target task without requiring users to input a key template or dictate a task number by using feature identification codes and query keywords to generate question prompts, allowing users to input feedback and iteratively refine their queries until the correct task is identified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users are required to input order number or dictate order number according to verbal trick guide, then the system can identify the specific order for query, but the user operation difficulty increases and query efficiency decreases due to memory burden and potential input errors
Solution Approach 1:
The system automatically retrieves and identifies order information using the user's mobile phone number as the identification code without requiring user input of order numbers. The server end performs automatic matching and retrieval based on pre-stored order data associated with user accounts, making the system serve itself by eliminating the need for user memory and manual input.
Solution Approach 2:
The mobile phone number serves as an intermediary between the user and the order identification system. Instead of directly requiring order number input, the system uses the mobile phone number (which users already possess and remember) as a mediator to automatically retrieve and identify the relevant order information through server-side matching.
2Reliability
If users must remember and input order number correctly, then the system can provide accurate order query service, but the query efficiency decreases and user experience deteriorates due to time consumption and potential errors
Solution Approach 1:
The system performs preliminary actions by pre-storing and associating order information with user mobile phone numbers in the database before the query occurs. When a user initiates a query, the system automatically retrieves the pre-associated order data using the mobile phone number, eliminating the need for real-time order number input and manual matching processes.
Solution Approach 2:
The query system automatically identifies and retrieves order information using the user's mobile phone number without requiring user intervention for order number input. The server end autonomously performs the matching and retrieval operations, making the system self-serve the user by eliminating manual input steps and accelerating the query process.
Data Source
AI summary
The method comprises: acquiring a task query request (S101); determining a first task according to a feature identification code corresponding to an input end of the task query request (S102); when there are a plurality of first tasks, determining, according to a query keyword contained in the task query request, first feature information of the first tasks, and generating a question prompt according to the first feature information (S103); and receiving feedback information inputted by a user according to the question prompt, and determining, according to the feedback information, a target task corresponding to the task query request from the first tasks (S104). The method can determine a task corresponding to a task query request inputted by a user without the need for a user to use a key template or dictate a task number, thereby improving the user experience.