Chatbot Deep-Link Interface Pre-Population
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Solution Overview
Problem
Existing chatbots lack the ability to automatically and dynamically populate complex digital payment interfaces with parameter values during a single user input, making it difficult for users with limited functionality devices to initiate transactions efficiently.
Innovation Solution
A computer-implemented system that uses natural language processing and predictive algorithms to determine missing parameter values for transactions, pre-populating digital interfaces and generating deep links, allowing users to initiate transactions with a single input on devices with limited functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing chatbots generate responses programmatically without automatic interface population, then the system complexity remains low, but the ease of operation deteriorates as users must manually fill out complex digital payment interfaces
Solution Approach 1:
The system performs preliminary action by automatically determining candidate parameter values and pre-filling interface elements before the user needs to complete the transaction. The chatbot analyzes prior exchanges of data, identifies missing parameters, and pre-populates the digital interface with appropriate values, so that when the user views the interface, most fields are already completed or can be easily confirmed.
2Measurement precision
If the chatbot determines candidate parameter values based on prior data exchanges, then the accuracy of parameter filling improves, but the loss of time increases due to additional processing requirements
Solution Approach 1:
The system applies self-service by having the chatbot autonomously analyze prior data exchanges and automatically determine appropriate parameter values without requiring manual intervention. The chatbot serves itself by extracting relevant information from historical conversations and using it to auto-configure the interface parameters, eliminating the need for users to manually specify these values.
3Productivity
If deep-linked interfaces are automatically populated with parameter values, then the productivity of transaction initiation improves, but the device complexity increases due to the need for natural language processing and predictive algorithms
Solution Approach 1:
The system introduces an intermediary layer in the form of a chatbot that mediates between the user's simple device input and the complex digital interface. The chatbot translates minimal user input into comprehensive interface configurations by leveraging natural language processing and predictive algorithms, thereby enabling high productivity without requiring the user's device to be complex.
Data Source
AI summary
The disclosed exemplary embodiments include computer-implemented apparatuses and processes that automatically populate deep-linked interfaces based n programmatically established chatbot sessions. For example, an apparatus may determine a candidate parameter value for a first parameter of an exchange of data based on received messaging information and on information characterizing prior exchanges of data between a device and the apparatus. The apparatus may also generate interface data that associates the first candidate parameter value with a corresponding interface element of a first digital interface, and may store the store interface data within a data repository. In some instances, the apparatus may transmit linking data associated with the stored interface data to the device, and an application program executed by the device may present a representation of the linking data within a second digital interface.