Machine-Learning Query Execution for Electronic Form Error Correction
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Solution Overview
Problem
Complex electronic forms often result in errors that are difficult to identify and correct, leading to user frustration and inefficiency, especially when multiple forms interact, and subject matter experts lack context or intent understanding.
Innovation Solution
A system that utilizes a machine learning model to analyze user queries, extract relevant data sets, apply rules and data schema to generate an output data object that automatically corrects errors or provides guidance to complete forms, and facilitates communication with subject matter experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a subject matter expert manually analyzes user queries and form errors, then the expert can provide comprehensive assistance, but the time required to understand the user's situation and identify the correct form increases significantly
Solution Approach 1:
The system performs preliminary analysis of user queries, form errors, and customer context before the expert receives the case. The automated system extracts relevant information, identifies potential issues, and prepares a summary that includes recommended forms and actions, so the expert receives pre-processed information rather than raw data
Solution Approach 2:
An automated intermediate system acts as a mediator between the user and the expert. This system collects user queries, analyzes form errors, retrieves relevant customer context from multiple sources, and presents synthesized information to the expert, reducing the expert's manual analysis workload
2Adaptability or versatility
If the system processes multiple interacting electronic forms, then comprehensive form completion can be achieved, but error identification becomes more difficult and less efficient
Solution Approach 1:
The system segments the complex multi-form processing task into separate analyzable components. Each form error is individually extracted and analyzed, and the relationships between forms are mapped to identify propagation paths. This segmentation allows automated processing of complex interactions without overwhelming the error analysis system
Solution Approach 2:
The system implements feedback mechanisms that automatically track form errors through multiple interacting forms. When an error is detected in one form, the system feeds this information back through the form interaction graph to identify related errors in dependent forms, enabling automated comprehensive error identification across the entire form set
3Productivity
If the system automatically identifies and corrects form errors, then user frustration is reduced and efficiency improves, but the complexity of the automated correction system increases
Solution Approach 1:
The system enables self-service automated correction by allowing users to directly interact with the system to correct errors. The automated error identification provides clear guidance on what needs correction, and users can make corrections through intuitive interfaces without requiring complex system intervention or expert manual processing
Solution Approach 2:
The system manages complexity by dynamically adjusting processing parameters based on error severity and form context. Not all errors are processed with the same level of automated analysis; the system modifies its automated correction behavior based on the specific error type, form structure, and user context, simplifying the overall system architecture
Data Source
AI summary
A method including receiving a user query relating to completion of an electronic form stored as a data object. A context topic is generated that describes a category of data relevant to the user query. Data sets that correspond to the context topic are extracted. A machine learning model is applied to the user query and the data sets to generate a prediction text representing a predicted intent of the user. A subset of rules and a data schema are generated by applying the prediction text and the data sets to a data file including rules and data schema applicable to the rules. A subset of the data sets is determined based on the subset of rules. The subset of rules is applied to the subset of the data sets to generate the output data object. The output data object is returned.