Sparse Intent Clustering for Report Feature Selection
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Solution Overview
Problem
Users face confusion and inefficiency when selecting features for new reports due to a wide range of potential options, and current applications fail to recognize the intent and context behind user actions during report creation.
Innovation Solution
A computer-implemented method using sparse intent clustering, which encodes features of user reports into binary vectors, projects them into a N-dimensional space, and clusters them based on cosine similarities to identify user intent, allowing for suggested features in new reports based on past report patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide set of potential features is provided in report creation, then the completeness and versatility of reports is improved, but user confusion and time consumption increase
Solution Approach 1:
The system performs self-service by automatically analyzing user intent from report titles and descriptions, then autonomously selecting and recommending appropriate features without requiring manual user configuration. This resolves the contradiction by maintaining feature completeness while eliminating user time investment in feature selection.
Solution Approach 2:
The system implements feedback loops where user report creation patterns are continuously analyzed, and the system learns from these patterns to improve future feature recommendations. This resolves the contradiction by using accumulated feedback to progressively reduce user effort while maintaining or improving report quality.
2Measurement precision
If manual analysis of report features is performed, then accurate understanding of user intent is achieved, but the process becomes time consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis of report features with an automated machine learning system that uses natural language processing and clustering algorithms to analyze user intent. This substitution maintains high measurement precision in understanding user intent while dramatically improving productivity by eliminating manual analysis time.
Solution Approach 2:
The system introduces an intermediary layer of automated analysis between the user's report description and the final feature selection. This intermediary uses clustering algorithms and pattern recognition to bridge the gap between user intent and appropriate feature selection, maintaining accuracy while improving efficiency.
3Measurement precision
If clustering algorithms are applied to user reports, then user intent is accurately identified, but computational complexity increases
Solution Approach 1:
The system segments the complex task of intent identification into distinct processing stages: text preprocessing, feature extraction, clustering analysis, and result application. This segmentation reduces computational complexity by breaking down the overall complex operation into manageable, optimized sub-tasks that can be processed efficiently.
Solution Approach 2:
The system transforms unstructured text data into structured numerical representations (vectors) that are suitable for clustering algorithms. This parameter change from text to numerical space enables the application of efficient clustering techniques while maintaining the semantic meaning necessary for accurate intent identification.
Data Source
AI summary
A method of sparse intent clustering is provided. The method comprises identifying features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description. The features of each user report are encoded into a binary vector. The binary vector for each user report is fed into an autoencoder neural network that creates a N-dimensional vector representing the user report. The float vectors representing the user reports are projected into a N-dimensional space. The float vectors are clustered according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports. The intent of each vector cluster is then labeled.