Automated Form Field Prediction Using Historical Transaction Data
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Solution Overview
Problem
Users face repetitive and tedious experiences when filling out online forms that often request the same information, such as first name, last name, email address, and birthdate, across different websites.
Innovation Solution
A system that uses machine learning models to predict data values for form fields by analyzing historical transactions associated with a user, allowing for automated pre-filling of form fields with determined data values, reducing the need for users to manually input repetitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually fill out form fields with personal information, then data accuracy is ensured, but user time and effort are consumed
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and pre-filling form fields with user information from stored profiles before the user completes the form. This includes automatically filling personal details, business information, and transaction data, allowing users to review and submit with minimal manual input, thus reducing time consumption while maintaining accuracy through user verification.
Solution Approach 2:
The system enables self-service by automatically gathering and organizing user information from historical transactions and stored profiles, then autonomously pre-filling form fields. The system serves itself by managing data retrieval, processing, and form population without requiring manual user input for each field, while still allowing user review to ensure accuracy.
2Loss of information
If users manually enter repetitive information across multiple forms, then data completeness is achieved, but user effort increases
Solution Approach 1:
The system implements universality by creating a centralized user profile that stores information applicable across multiple forms and transactions. This profile contains personal details, business information, and transaction history that can be universally reused across different forms, eliminating the need for users to re-enter the same information repeatedly while ensuring data completeness through systematic retrieval.
Solution Approach 2:
The system performs preliminary actions by pre-populating form fields with information from the user profile and historical transactions before the user needs to complete the form. This includes pre-fetching and organizing relevant data, so users only need to review and confirm information rather than manually entering it, reducing effort while maintaining completeness.
3Ease of operation
If the system pre-fills form fields automatically, then user convenience is improved, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by implementing a centralized user profile that acts as a mediator between stored data and form fields. This profile stores and manages user information, transaction history, and preferences, serving as an intermediary layer that automatically retrieves and pre-fills form fields. This intermediary structure manages complexity by organizing data access patterns while providing simple pre-filled forms to users.
4Measurement precision
If the system uses machine learning models to predict data values, then prediction accuracy is improved, but computational resources are consumed
Solution Approach 1:
The system applies partial action by using machine learning models selectively only for predicting specific form fields that benefit from intelligent inference, rather than processing all fields. The ML models predict values for fields like transaction amount, category, or date based on historical patterns, while other fields are simply retrieved from the user profile. This partial application of ML reduces computational overhead while maintaining prediction accuracy for complex fields.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives from a client device an image and a user identifier associated with a user of the client device. Based on the user identifier, the program further retrieves a set of historical transactions associated with the user, the program also uses a first machine learning model to predict a string based on the image. The program further uses a second machine learning model to predict a set of digits based on the image. Based on the set of historical transaction data, the string, and the set of digits, the program also determines a data value for a field in a form.