Context-Aware UI Pre-fill via Offline Tokenization
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Solution Overview
Problem
Existing user interface (UI) systems for online forms, such as invoicing, face challenges in efficiently pre-filling data while minimizing computing resource usage and latency, and often fail to capture context correctly, leading to user dissatisfaction and increased churn.
Innovation Solution
The system automatically determines UI context and pre-fills relevant data by extracting interchangeable entities like dates and names using Named Entity Recognition (NER) and regular expressions, replacing them with tokens, and injecting context-specific information at runtime, creating context-specific UI elements that are pre-filled and available for selection, thereby reducing resource overload and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-filling data is performed using existing UI systems, then user time savings are achieved, but computing resource usage increases and latency occurs
Solution Approach 1:
The system performs data extraction, entity recognition, and template creation in advance before the user actually needs to fill out the form. Historical data is processed offline to create pre-computed templates with identified entities and contexts, so that during runtime, only lightweight template matching and data injection are required, significantly reducing real-time computing resources while maintaining fast user experience
Solution Approach 2:
The pre-filling process is divided into distinct phases: historical data extraction, entity recognition and tokenization, template creation, context identification, and runtime data injection. Each phase is handled separately with appropriate resource allocation, allowing complex processing to occur offline while keeping online operations lightweight and efficient
2Loss of time
If pre-filling data is performed using existing UI systems, then user time savings are achieved, but latency increases
Solution Approach 1:
All heavy processing including entity recognition, template creation, and context analysis is performed in advance during offline data preparation. During runtime, the system only needs to match user input against pre-created templates and inject data, which are much faster operations that minimize latency while still providing comprehensive pre-filling capabilities
3Use of energy by moving object
If pre-filling data is performed without context analysis, then computing resources are saved, but context accuracy decreases
Solution Approach 1:
Context analysis is performed in advance during the template creation phase using historical data. The system identifies entities, determines their contexts, and creates structured templates with context information embedded. This offline context analysis enables accurate context-aware pre-filling during runtime without requiring heavy real-time computing resources
Solution Approach 2:
The system creates simplified copies of historical data in the form of structured templates that capture essential context information. These templates are pre-computed with identified entities and contexts, allowing the system to replicate accurate context matching during runtime without re-processing the full historical data, thus balancing resource usage with context accuracy
Data Source
AI summary
A processor may receive a plurality of text samples generated by a user and identify at least one variable text element in at least one of the plurality of text samples. The processor may tokenize the at least one variable text element, thereby producing a plurality of tokenized text samples including at least one token. The processor may build a longest common substring from the plurality of tokenized text samples and add the longest common substring and the at least one token to a set of selectable user interface options specific to the user. The processor may generate a user interface comprising the set of selectable user interface options. This can include detecting a user interface context and automatically replacing the at least one token with information specific to the user interface context within the set of selectable user interface options.