Role Token Mapping for Organizational Structure Analysis
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Solution Overview
Problem
Existing organizational management systems fail to accurately identify employee activities and tasks, leading to inaccurate data classification and the inability to benchmark organizational structures or detect hidden relationships between employee activities, due to the aggregation of large data volumes without contextual or relational information.
Innovation Solution
A system and method that generates role tokens from organizational datasets using natural language processing and machine learning techniques, such as hierarchical clustering and logistic regression, to create a model organizational dataset that maps roles to their activities and tasks, enabling efficient identification of optimal organizational structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If employee information is aggregated into large data warehouses, then data volume is increased, but the data becomes less useful without contextual or relational information
Solution Approach 1:
The patent embeds multiple layers of information within the data warehouse structure: raw employee data is nested within organizational units, which are nested within departments, which are nested within the enterprise. Role tokens and activity data are nested within employee records, creating a hierarchical nested structure that preserves contextual relationships while maintaining large data volume for comprehensive analysis.
Solution Approach 2:
The patent introduces role tokens as intermediary elements that connect employees to their activities and tasks. These role tokens serve as mediators between raw employee information and organizational structure, enabling the system to detect hidden relationships and provide contextual meaning without requiring direct human interpretation of every data point.
2Ease of operation
If conventional data transformation methods are used, then data can be transformed into searchable format, but large amounts of computing resources are required
Solution Approach 1:
The patent performs preliminary transformation of employee information into role tokens and structured organizational data during the data ingestion phase, rather than transforming raw data on-demand during queries. This preliminary structuring includes creating the organizational hierarchy and tagging data with role-based metadata, enabling fast searches without requiring intensive computing resources during query execution.
3Adaptability or versatility
If basic machine learning techniques are used, then analysis capability is provided, but the system cannot fully utilize human resource records in a reasonable amount of time
Solution Approach 1:
The patent segments the analysis process into distinct phases: data ingestion and preliminary structuring, role token generation, organizational hierarchy construction, and query execution. Each segment is optimized independently, allowing the system to handle large volumes of human resource records efficiently by processing them through specialized sub-routines rather than applying a single monolithic analysis algorithm.
Data Source
AI summary
Systems and methods are provided for managing organizational or corporate structures, including the employee roles or activities administered by human resources. A portion of the role datasets received within human resource records may be used to generate role tokens comprising unique datasets that have been truncated and deduped. Such tokens may be extracted based on assigned prioritization scores, and further assigned training labels representing categorical levels. Predictive labels may be assigned to a remaining portion of the extracted tokens via a logistic regression classifier, and a model organizational dataset may be generated based on the assigned training labels and the assigned predictive labels. The prediction certainty of the role tokens in the model organizational dataset may be used to map the identified role tokens to the roles represented in the human resource records.