Computational Disambiguation for Dynamic Hierarchical Data Structures
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Solution Overview
Problem
Customer relationship management (CRM) systems face challenges in maintaining accurate user profiles due to missing disambiguation, time dependency, incompleteness, subjectiveness, and loss of information, particularly across cultural and language barriers, leading to difficulties in identifying target customers and predicting changing user profiles.
Innovation Solution
A system and method utilizing computational, statistical, and machine learning methods, including taxonomy and profile clustering, to disambiguate and enrich dynamic datasets, creating ideal customer profiles that can be used to identify similar profiles across cultural and language barriers, and predict changes in work environments and company structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If CRM systems compile data from multiple communication channels to improve customer understanding, then the quantity and diversity of data increases, but the accuracy and reliability of user profiles deteriorates due to outdated and inconsistent information
Solution Approach 1:
The system performs preliminary disambiguation and validation actions on data before it is stored in the CRM database. By pre-processing data from multiple channels to resolve ambiguities and verify consistency, the system ensures that only accurate and reliable information is retained, thus maintaining profile accuracy while still benefiting from diverse data sources
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor and update user profiles based on new interactions and data. This ongoing validation and correction process ensures that profiles remain current and accurate despite the dynamic nature of customer data across multiple communication channels
2Loss of information
If CRM systems store detailed user profiles to improve customer understanding, then the completeness of information increases, but the complexity of maintaining accurate profiles worsens due to velocity of change in customer interactions
Solution Approach 1:
The system segments user profile data into distinct modules or layers (e.g., demographic information, interaction history, preferences, behavioral patterns). This segmentation allows different components to be updated independently based on their specific update frequencies and requirements, reducing the overall complexity of maintaining complete profiles while preserving information completeness
Solution Approach 2:
The system implements dynamic data structures and update mechanisms that automatically adjust profile completeness and detail based on recency and relevance. Frequently changing data elements are updated more aggressively, while stable information is maintained less frequently, allowing the system to handle complete profiles without proportionally increasing maintenance complexity
3Productivity
If pre-classification is applied to data inputs to improve processing efficiency, then the speed of data analysis increases, but the accuracy of identification deteriorates due to loss of information and distorted representation
Solution Approach 1:
The system performs preliminary disambiguation and context-enrichment actions before classification. By pre-processing data to resolve ambiguities and add contextual information, the system enables efficient processing without sacrificing identification accuracy, as the classification operates on already-disambiguated data
Solution Approach 2:
The system introduces an intermediary disambiguation layer between raw data input and classification. This intermediary process resolves ambiguities and preserves critical information before classification occurs, allowing the classification step to operate efficiently on clean, context-rich data without losing identification accuracy
4Adaptability or versatility
If international user profiles are analyzed to expand global market reach, then the scope of business opportunities increases, but the difficulty of identification worsens due to language and position title differences
Solution Approach 1:
The system introduces disambiguation and normalization intermediaries that translate diverse international position titles and descriptors into standardized categories. This intermediary layer handles language and cultural variations, allowing the system to analyze international profiles efficiently without being hindered by title differences
Solution Approach 2:
The system transforms position title data from their original local forms into standardized parameters or categories that are comparable across different countries and languages. By changing the representation parameters of position information, the system enables consistent analysis of international profiles while maintaining adaptability to global markets
Data Source
AI summary
A system and method for inferring an organizational structure of a record based on position role transitions from a parsed plurality of record profiles using machine learning techniques described herein. A piece-wise graph of transitions between positions across a normalized user employment landscape are computed to recover properties of user hierarchical structures across a plurality of position information by analysis of transition trajectories.