Dynamic Form with ML for Eligibility Determination
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Solution Overview
Problem
Current form development and maintenance processes are inefficient due to redundancy, lack of awareness, and high administrative costs, particularly in government agencies providing social protections, where numerous forms require repetitive data entry and complex eligibility determinations.
Innovation Solution
The implementation of machine learning (ML) techniques to create dynamic forms that adapt to user needs by selectively presenting relevant questions based on previous answers and user data, using trained ML models to determine eligibility for benefits and streamline the form-filling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional static forms are used for each benefit, then complete data collection for eligibility determination is achieved, but user experience deteriorates due to redundant data entry and multiple roundtrips
Solution Approach 1:
The patent implements dynamic forms that adapt their content based on user responses and pre-existing data. The form structure changes dynamically to display only relevant questions, eliminating redundancy. This is achieved through a system that evaluates user inputs against eligibility criteria and adjusts the form in real-time, transforming static forms into adaptive, context-aware interfaces.
Solution Approach 2:
The system performs preliminary actions by pre-populating forms with user's existing data from databases before the user submits the form. This advance preparation reduces the amount of manual data entry required and eliminates multiple roundtrips, as the form is partially or fully completed based on previously collected information.
2Reliability
If individual forms are created for each benefit, then specific data requirements for each benefit are met, but development and maintenance costs increase
Solution Approach 1:
The patent creates a universal dynamic form system that can handle multiple benefit types through a single adaptable interface. Instead of developing separate static forms for each benefit, the system uses a core form framework that dynamically configures itself based on the benefit type and user responses. This multi-functional approach maintains eligibility determination accuracy while significantly reducing development and maintenance costs.
Solution Approach 2:
The system changes parameters dynamically based on the specific benefit being applied for and user responses. Form parameters such as displayed questions, required fields, and validation rules are adjusted in real-time, allowing a single form template to serve multiple benefit types with different data requirements without compromising accuracy.
3Adaptability or versatility
If comprehensive forms are provided for all possible benefits, then all eligibility scenarios are covered, but form complexity and difficulty of maintenance increase
Solution Approach 1:
The patent segments the comprehensive form into modular components or blocks that can be independently configured and displayed. Instead of presenting one large complex form, the system divides questions into logical sections that are displayed dynamically based on user needs. This segmentation maintains comprehensive coverage of eligibility scenarios while reducing perceived complexity and improving usability.
Data Source
AI summary
Methods, computer-readable media and systems are disclosed for building, deploying, operating, and maintaining an intelligent dynamic form in which a trained machine learning (ML) model is embedded. A universe of questions is associated with a plurality of output classifiers, which could represent eligibilities for respective benefits. The questions are partitioned into blocks. Each block can be associated with one or more of the classifiers, and each classifier can have a dependency on one or more blocks. An ML model is trained to make inferences from varied combinations of responses to questions and pre-existing data, and determine probabilities or predictions of values of the output classifiers. Based on outputs of the trained model, blocks of questions can be selectively rendered. The trained model is packaged with the question blocks and other components suitably for offline deployment. Uploading collected responses and maintenance of the dynamic form are also disclosed.