Automated Child Object Labeling via ML Relevance Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in identifying relevant child objects within large datasets or reports due to the lack of effective labeling methods, which can lead to overlooked valuable information, especially when child objects are deeply buried and lack tags, limiting the ability of machine learning components to evaluate their relevance accurately.
Innovation Solution
An automated system using a machine learning component to rank parent objects and generate restricted objects by removing child objects, allowing for the assignment of labels to child objects based on query relevance, enhancing search result reporting, ML training data generation, and report modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If child objects are manually labeled to improve search result reporting and relevance identification, then measurement precision of child object relevance is improved, but loss of time and productivity deteriorate due to the prohibitively expensive and time-consuming process
Solution Approach 1:
The system enables child objects to automatically label themselves by having the machine learning component evaluate restricted objects (parent objects with missing child objects) and infer the most relevant child object based on query relevance rankings, eliminating the need for manual human labeling
Solution Approach 2:
The system creates restricted object copies of parent objects with specific child objects removed, uses these copies for ML evaluation, and then applies the learned relevance patterns back to the original child objects for automatic labeling
2Reliability
If child objects are manually labeled to improve search result reporting, then reliability of search results is improved, but device complexity increases due to the need for manual intervention processes
Solution Approach 1:
The system replaces the mechanical manual labeling process with an automated machine learning-based system that uses restricted object evaluation and relevance ranking to automatically assign labels to child objects
3Measurement precision
If all child objects within parent objects are evaluated for labeling, then measurement precision of relevance is improved, but productivity deteriorates due to the large number of objects to process
Solution Approach 1:
The system processes parent objects selectively by creating restricted objects for evaluation, focusing computational resources on evaluating the relevance of specific child objects through the ML component rather than uniformly processing all child objects in all parent objects
Data Source
AI summary
Solutions for automated labeling of child objects within tagged parents include: receiving a plurality of parent objects, each having a tag and including a plurality of child objects; receiving a machine learning (ML) component operable to rank objects according to relevance to queries; for each parent object: generating a set of restricted objects, wherein each restricted object corresponds to each child object in the plurality of child objects; for each of a plurality of queries, ranking, with the ML component, the restricted objects according to relevance; based at least on the query and an inverse of the rank of the restricted objects, assigning a child object label.