Neural Network Role Mapping Platform for Organizational Efficiency
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Solution Overview
Problem
Organizations face inefficiencies in managing roles due to mismatched titles and tasks, as conventional natural language processing struggles to map organization-specific roles to standardized roles, leading to resource wastage and inability to optimize processes.
Innovation Solution
A role management platform that converts title data into semantic vectors, uses a data model with weighted values to score likelihoods of title-class associations, and performs actions to modify or eliminate tasks based on standardized role information, optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing is used to map organization-specific roles to standardized roles, then the process is simple to implement, but the mapping accuracy is low leading to resource wastage
Solution Approach 1:
The patent introduces semantic vectors as an intermediary representation between organization-specific role titles and standardized role classifications. The neural network data model processes these semantic vectors to determine mapping likelihoods, thereby improving mapping accuracy while maintaining manageable system complexity through the use of established NLP techniques.
Solution Approach 2:
The patent replaces conventional natural language processing methods with a neural network-based data model. This substitution enables the system to process semantic vectors and compute mapping likelihoods more accurately, resolving the contradiction between simple implementation and high mapping accuracy.
2Productivity
If organization-specific roles are not standardized, then the organization maintains flexibility in role definitions, but resource wastage occurs due to mismatched titles and tasks
Solution Approach 1:
The patent implements a feedback mechanism where the data model evaluates the likelihood of title-class associations and provides scoring information. This feedback enables the organization to identify and eliminate mismatched tasks, thereby improving process efficiency and reducing resource wastage associated with poorly defined roles.
3Measurement precision
If a data model with weighted values is used to score title-class associations, then the mapping precision is improved, but the computational complexity increases
Solution Approach 1:
The patent applies weighted values to specific title-class identifier associations within the data model rather than processing all possible combinations equally. This partial action approach improves mapping precision for critical associations while reducing unnecessary computational power consumption on less important mappings.
Data Source
AI summary
A device receives a request associated with standardizing organization-specific roles within an organization, where the request includes data that identifies titles for the organization-specific roles. The device converts the data to vectors that represent semantic meanings of the titles. The device sets a configuration of a data model by assigning weighted values to title-class identifiers that are used to associate titles, of a standardized set of titles, to a hierarchy of role classifications. The device uses the data model to determine scores that indicate likelihoods of the titles mapping to the title-class identifiers. The device identifies, based on scores, a subset of title-class identifiers that associate particular titles, of the standardized set of titles, and particular role classifications. The subset of title-class identifiers is stored in association with information relating to the particular titles. The device performs an action based on the information relating to the particular titles.