Document Selection System Using Confidence Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for selecting documents in industries like automotive are complex, time-consuming, and prone to human error, leading to increased transaction times and potential customer loss due to mistakes, which can result in lost sales and damaged customer relationships.
Innovation Solution
A computer-implemented method and system that aid users in selecting the appropriate documents for transactions by receiving transaction parameters, accessing historical data, determining a confidence threshold, and automatically selecting the necessary documents based on user input, utilizing a database, network interface, and processor to streamline the document selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a dealer management system is set up to manage transaction complexity, then the ability to handle multiple deal types is improved, but the system complexity and setup resources increase
Solution Approach 1:
The system automatically selects documents based on transaction parameters without requiring manual configuration or complex setup by users. The document selection process is self-service, where the system retrieves historical data, compares parameters, and determines required documents autonomously, eliminating the need for complex manual system management.
Solution Approach 2:
The system uses historical transaction data as templates to guide current document selection. By copying and analyzing patterns from past transactions with similar parameters, the system determines appropriate documents without requiring complex rule-based configurations, simplifying the system while maintaining adaptability.
2Ease of operation
If manual document selection is performed by salespersons, then flexibility in handling different transactions is maintained, but time consumption and error rates increase
Solution Approach 1:
The system acts as an intermediary between the salesperson and the document selection process. It automatically retrieves transaction parameters, compares them with historical data, and determines required documents, freeing the salesperson from manual selection tasks while maintaining flexibility through parameter-based automation.
Solution Approach 2:
The manual mechanical process of reviewing and selecting documents is replaced with an automated computer-based system that retrieves parameters, queries historical data, and determines document requirements algorithmically, significantly reducing time while maintaining operational flexibility.
3Reliability
If comprehensive document selection criteria are applied, then accuracy in selecting proper documents is improved, but the complexity of the selection process increases
Solution Approach 1:
The system uses historical transaction data as feedback to improve document selection accuracy. By continuously analyzing past transactions and their outcomes, the system refines its parameter comparisons and document determination logic, achieving high reliability through data-driven feedback rather than complex rule sets.
Solution Approach 2:
The system copies successful document selection patterns from historical transactions with similar parameters. By replicating proven document combinations based on parameter matching, the system achieves accurate document selection without requiring complex selection criteria, as the logic is derived from actual historical performance.
4Productivity
If real-time document selection is required, then customer satisfaction is improved, but the time and resources needed for setup and operation increase
Solution Approach 1:
The system performs preliminary actions by pre-storing transaction parameters and document requirements in historical databases. When a new transaction occurs, the system quickly retrieves relevant historical data and parameters, enabling real-time document selection without requiring complex real-time analysis or extensive setup resources.
Solution Approach 2:
The system achieves real-time performance by copying and matching parameter patterns from historical transactions. This approach allows rapid document determination through parameter comparison and historical pattern recognition, providing fast service without requiring complex real-time processing infrastructure or extensive setup resources.
Data Source
AI summary
Examples described herein include methods, techniques, and systems that aid a user in selecting one or more documents of a plurality of documents to complete an instant transaction. In some embodiments, a method may include receiving, from the user, one or more parameters associated with the instant transaction. The method may also include accessing data of one or more completed transactions over a past time period. Based on the one or more parameters received from the user, the method includes determining a confidence threshold of each document of the plurality of documents needed to complete the instant transaction. The method may also include receiving, from the user, a desired confidence threshold of the one or more documents of the plurality of documents to complete the instant transaction. Based on the desired confidence threshold, the method may include automatically listing the one or more documents to complete the instant transaction.