Intent-Driven Report Templates from Prior Field Selections
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Solution Overview
Problem
Existing reporting applications require users to manually select numerous fields and filters, leading to complexity and frustration, and lack the ability to assist users in creating similar reports based on previous selections.
Innovation Solution
A system that extracts keywords and user-selected features from reports, classifies them, identifies commonalities, and generates prepopulated report templates based on user intent using machine learning and neural networking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually select fields and filters to create reports, then report customization and flexibility are improved, but user effort and complexity increase
Solution Approach 1:
The system performs preliminary action by automatically analyzing user intent from natural language queries and pre-selecting relevant fields and filters before the user needs to create the report. This eliminates the manual selection process while maintaining customization, as the system has already prepared the appropriate report structure based on predicted user needs.
Solution Approach 2:
The system implements self-service by autonomously generating report templates without requiring manual field selection. The intelligent agent independently analyzes the user's intent, identifies necessary data fields, applies appropriate filters, and constructs the report template automatically, allowing the system to serve itself in the report generation process.
2Loss of information
If reporting applications log user selections, then data collection for future assistance is improved, but the logging process itself does not meaningfully assist users
Solution Approach 1:
The system implements feedback by continuously analyzing logged user selections and reported intents to improve its understanding of user needs. This feedback loop enables the intelligent agent to better predict future report requirements and refine its field selection algorithms, making the logging process meaningful by directly improving future user assistance rather than merely storing data.
Solution Approach 2:
The intelligent agent acts as an intermediary between the logged user selections and future report generation. Instead of directly using raw logged data, the agent processes and interprets the logged information to infer user intent patterns, translating historical data into actionable insights that automatically assist future users in creating reports.
3Loss of time
If the system provides prepopulated report templates, then user effort and time are reduced, but the complexity of the system increases
Solution Approach 1:
The system replaces the mechanical process of manual field selection and report construction with an intelligent agent that uses natural language processing and machine learning. This substitution automates the complex analysis of user intent and field selection, reducing user time effort while managing system complexity through intelligent algorithms rather than manual procedures.
Data Source
AI summary
The present disclosure relates generally to tools to determine a user's intent and, more particularly, to a system, method and computer program product to generate a report template based on user's intent. The method includes: extracting, by a computer system, text and user selected features from one or more reports built in a reporting application; classifying, by the computer system, keywords in the text and the select features; identifying, by the computer system, common keywords and associated selected features within the one or more reports; determining, by the computer system, an intent of the user based on the common keywords and associated selected features; and generating, by the computer system, a report template with prepopulated features of the selected features based on the intent of the user.