Knowledge Data Encapsulation and Assembly for Reusable Executable Data

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Solution Overview

Problem

Conventional knowledge data establishment methods face challenges in establishing associations between multiple pieces of knowledge, leading to difficulties in maintaining and searching for associations, and often result in duplicate knowledge data, hindering effective reuse.

Innovation Solution

A data encapsulation device and method that automatically performs data encapsulation and assembly based on entity and relational data, using a processor and storage device to convert datasets into structurally encapsulated data, which are then assembled and compiled into executable data through model building, knowledge assembly, and compilation modules.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual methods are used to maintain and establish knowledge data, then flexibility and control are maintained, but efficiency and accuracy deteriorate when data scale increases

Engineering Contradiction:

Improvedata establishment efficiencyVSAvoiddata association complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments knowledge data into distinct entities (e.g., customer, product, order) and their relationships, allowing automated processing of each segment independently. This segmentation enables the system to handle large datasets through automated entity recognition and relationship extraction without overwhelming manual operators.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary data structure (knowledge graph framework) that mediates between raw data sources and final knowledge representations. This intermediary layer automatically processes and standardizes data, reducing the complexity of direct manual association while maintaining data quality and consistency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If manual association between knowledge pieces is performed, then association accuracy is maintained, but time consumption and labor requirements increase

Engineering Contradiction:

Improveassociation establishment timeVSAvoidassociation accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system performs self-service by automatically extracting associations between knowledge pieces through algorithms that analyze data patterns, entity relationships, and contextual information. This automated self-service reduces time consumption while maintaining accuracy through multiple validation layers and cross-referencing mechanisms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements feedback mechanisms where the system continuously validates and refines associations based on data consistency checks and pattern recognition. This feedback loop ensures that automated association processes maintain high accuracy by correcting errors and adapting to data variations.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If knowledge data is established without automated encapsulation, then data flexibility is maintained, but reusability and standardization deteriorate

Engineering Contradiction:

Improvedata reusabilityVSAvoiddata standardization effort

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The patent applies parameter changes by transforming raw data into standardized knowledge representations with consistent attributes and formats. The system automatically adjusts data parameters (such as entity types, relationship categories, and data formats) to conform to predefined standards, enabling seamless reuse across different applications without requiring manual restandardization.

Inventive Principle:

Principle #35Parameter changes

4Productivity

If automated data encapsulation is implemented, then productivity and standardization are improved, but system complexity and implementation difficulty increase

Engineering Contradiction:

Improvedata processing productivityVSAvoidencapsulation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements a universal data encapsulation framework that handles multiple data types and formats through a single standardized interface. This multi-functional system processes diverse data sources (structured, unstructured, semi-structured) using the same encapsulation mechanisms, reducing overall system complexity by consolidating functionality rather than requiring separate specialized modules for each data type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250363127A1Data encapsulation device and data encapsulation method

Publication Date: 2025.11.27 DIGIWIN SOFTWARE CO LTD

AI summary

Disclosed are a data encapsulation device and a data encapsulation method. The data encapsulation device includes a storage device and a processor. The storage device is configured to store multiple modules. The processor is coupled to the storage device and is configured to execute multiple modules. A model building module obtains a corresponding encapsulated data and a compiler based on a dataset. A knowledge assembly module receives the encapsulated data to generate component data and associated data based on the encapsulated data. The knowledge assembly module assembles multiple component data into a component dataset based on a combination command. A compilation module executes a data compilation process through the corresponding compiler to generate executable data based on the component dataset.