Knowledge Sharing Analysis from Documents, Chats, and Emails
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Solution Overview
Problem
Existing technologies fail to effectively visualize and manage knowledge sharing within organizations, leading to inefficiencies in knowledge utilization and retention.
Innovation Solution
An information processing apparatus and method that acquires, extracts, and determines the sharing status of knowledge by analyzing document, chat, and email data to generate an association diagram and provide determination results on knowledge sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge is stored in multiple data types (documents, chats, emails), then knowledge completeness is improved, but knowledge sharing status becomes difficult to determine
Solution Approach 1:
The patent segments the determination process into distinct functional units: an extraction unit that identifies knowledge-related character strings from various data types, a determination unit that evaluates sharing status based on extraction sources, and an output unit that presents results. This segmentation allows the system to handle multiple data types systematically without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary extraction unit that acts as a bridge between the raw data (documents, chats, emails) and the determination unit. This extraction unit identifies and isolates knowledge-related character strings, transforming unstructured multi-type data into a standardized format that the determination unit can efficiently evaluate for sharing status.
2Measurement precision
If all data types are analyzed for knowledge extraction, then knowledge extraction accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential knowledge-related character strings from the comprehensive data set, rather than analyzing every piece of data in full detail. The extraction unit identifies and isolates relevant knowledge elements, allowing the system to achieve high extraction accuracy while minimizing the time required for full data processing.
Solution Approach 2:
The patent applies partial action by focusing analysis on knowledge-related character strings rather than processing all data equally. The extraction unit selectively identifies relevant portions of documents, chats, and emails, enabling the system to achieve sufficient knowledge extraction accuracy without the excessive time cost of comprehensive full-text analysis of all data types.
Data Source
AI summary
An information processing apparatus includes circuitry to acquire, from a first data group, a second data group related to a person. The first data group includes a plurality of types of data. The circuitry extracts a character string indicating knowledge related to the person from the second data group and determines whether a sharing status of the knowledge is appropriate based on a third data group to obtain a determination result. The third data group is an extraction source of the character string in the second data group. The circuitry outputs information based on the determination result.