Entity Relation Detection from Unlabeled Data for Efficient Routing
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Solution Overview
Problem
Existing machine learning systems struggle to efficiently determine topics of unlabeled data objects and relate them, leading to increased computational resources and inefficiencies in tasks such as customer service request routing.
Innovation Solution
A machine learning model is trained using unlabeled data objects to identify topics, which are then used to automatically relate similar data objects, reducing the need for human intervention and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems use traditional labeled data approaches to determine topics and relationships, then accuracy of topic identification is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The system uses unlabeled data objects to train the machine learning model, allowing the model to self-learn topic identification without requiring manual labeling. This self-service approach eliminates the need for human annotators while reducing computational overhead compared to traditional labeled training methods
Solution Approach 2:
The patent extracts and utilizes only the essential features from unlabeled data objects for training, rather than processing complete labeled datasets. This extraction approach reduces the volume of data that needs to be processed while maintaining the core learning signal needed for topic identification
2Measurement precision
If manual intervention is used to route and relate data objects, then routing accuracy is improved, but productivity and efficiency decrease
Solution Approach 1:
The machine learning model automatically determines topics and relationships among data objects without human intervention. The system self-routes service requests based on the learned relationships, eliminating manual routing while maintaining high accuracy through continuous learning from unlabeled data
Solution Approach 2:
The system performs preliminary topic identification and relationship mapping on data objects before routing decisions are needed. By pre-processing and organizing data objects into topic-based relationships in advance, the system enables faster and more accurate routing when actual service requests occur
3Measurement precision
If comprehensive data analysis is performed to identify relationships among data objects, then relationship detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the data analysis process into distinct phases: topic determination phase and relationship identification phase. By dividing the comprehensive analysis into smaller, manageable segments that can be performed independently and iteratively, the system reduces processing time while maintaining relationship detection accuracy
Solution Approach 2:
The system performs partial analysis on unlabeled data objects, focusing only on the specific features and patterns necessary for relationship detection rather than comprehensive analysis of all data attributes. This partial action approach achieves sufficient relationship detection accuracy with reduced processing time
Data Source
AI summary
Example methods and systems are directed to determining topics of data objects. A machine learning model may be trained and used to determine topics of data objects. After topics for data objects are determined by the trained machine learning model, data objects having similar topics can be automatically related. A semantic web approach relies upon the metadata of the data objects being generated along with the metadata of the insights being generated (such as topic groups). Such a semantic association between various objects (using metadata) forms a metadata driven network of analytical representation of business entities/objects. A data-stream comprising the semantic web, indicating the relationships between the metadata of the data objects and the metadata for the topics, may be pushed continuously into a central tool or repository to allow users to generate seamless analytical dashboards with minimal efforts.