Evaluation Item Weighting Using NLP Topics for Organizational Fit
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Solution Overview
Problem
Existing evaluation methods for evaluation targets in organizations fail to align with the organization's value system, requirements, or organizational image due to equal importance assigned to multiple evaluation items.
Innovation Solution
An information processing method that weights evaluation items based on similarity degrees between the items and topics derived from text information expressed by organization members, using topic modeling and natural language processing to align the evaluation with the organization's value system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple evaluation items are assigned equal importance, then the evaluation process is simple and easy to operate, but the evaluation cannot align with the organization's value system and requirements
Solution Approach 1:
The patent changes the parameter of evaluation item importance from equal weight to differentiated weight based on similarity degrees. The weighting unit assigns different weights to evaluation items according to their similarity degrees to organizational values, transforming the static equal-weight structure into a dynamic weighted structure that reflects organizational priorities.
Solution Approach 2:
The patent replaces manual determination of evaluation item importance with an automated NLP-based system. The topic extraction unit and similarity degree calculation unit automatically analyze organizational documents and assign weights, substituting the mechanical manual process with an intelligent automated system that better captures organizational values.
2Measurement precision
If evaluation items are weighted based on organizational values, then the evaluation accuracy improves, but the system complexity increases due to NLP processing requirements
Solution Approach 1:
The patent makes the NLP processing system universal by using it for multiple purposes: extracting organizational values from documents, calculating similarity degrees, and determining evaluation item weights. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while maintaining high evaluation accuracy.
Solution Approach 2:
The patent introduces an intermediary layer of NLP processing that mediates between raw organizational documents and evaluation item weighting. This intermediary layer abstracts the complexity of document analysis into standardized similarity degree calculations, making the system more manageable while improving evaluation precision through automated value alignment.
3Reliability
If the system processes text information from organization members, then the evaluation reflects organizational requirements, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing organizational documents and values before the actual evaluation process. The topic extraction unit pre-extracts and stores organizational values from documents, so that during evaluation, the system only needs to calculate similarity degrees rather than processing entire documents, significantly reducing processing time while maintaining accurate reflection of organizational requirements.
Data Source
AI summary
An information processing method is an information processing method for weighting an evaluation item for evaluating an evaluation target, and includes: obtaining, as text information, information expressed by one or more persons in an organization to which the one or more persons belong; obtaining one or more topics related to the text information by analyzing the text information; and weighting the evaluation item based on one or more similarity degrees between the evaluation item and the one or more topics.