Autofill Field Classification via Secure Content Subsets
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Solution Overview
Problem
Existing user interfaces on mobile devices are cumbersome for entering data, leading to inefficiencies and errors, especially with small screens, and current autofill solutions are unreliable, often requiring user intervention and compromising security by sending data to remote providers without proper consent.
Innovation Solution
A system that allows an operating system to send a secure subset of content to a remote provider for autofill requests, receiving suggested user values and field classification, and determining similarity metrics without storing user-inputted data, thus providing intelligent autofill suggestions while maintaining user privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autofill data is sent to remote providers for processing, then data entry efficiency is improved, but user data security deteriorates
Solution Approach 1:
The operating system acts as an intermediary between the user device and remote providers. It processes and anonymizes data locally before sending to remote providers, and filters incoming autofill suggestions. This mediator role allows efficient remote processing while protecting user data security by preventing direct exposure of sensitive information.
2Measurement precision
If complete user data is transmitted to remote providers, then autofill accuracy is improved, but loss of information (privacy) worsens
Solution Approach 1:
The system extracts only the necessary identifying features from user data for remote processing, while keeping sensitive personal information local. The operating system sends anonymized datasets to remote providers for training and matching, achieving accurate autofill without transmitting complete user privacy information.
3Measurement precision
If manual data entry is required, then data accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system implements self-service through intelligent autofill that automatically completes forms based on learned patterns from user behavior. The operating system monitors typing patterns, form interactions, and data entry habits to provide accurate autocomplete suggestions, reducing manual input while maintaining data accuracy through user-specific learning.
Data Source
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AI summary
An example method includes determining a subset of content excluding user-specific information displayed by an application on a user interface (UI) of a user device. The method further includes transmitting a request to a remote provider an autofill request for the application. The method also includes receiving one or more suggested user values and a field classification request for a text input field displayed by the application on the UI. The method further includes detecting a user input value in the text input field and determining a similarity metric for each of the one or more suggested user values based on a comparison of the user input value to each of the one or more suggested user values. The method also includes transmitting a response to the field classification request to the remote provide comprising the similarity metric for at least one of the one or more suggested user values.