Hierarchical Product Parameter Generation System
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Solution Overview
Problem
Conventional approaches face challenges in efficiently creating and managing hierarchies of operational factors for products and services, due to high human effort, time-consuming processes, and difficulties in keeping pace with rapid product and service evolution.
Innovation Solution
The system automatically generates and updates hierarchical product parameters by transforming product parameter seeds into a hierarchical structure, using data extraction, grouping, merging, and transformation processes to maintain data integrity and adapt to changing organizational needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual methods are used to create and manage hierarchies of operational factors, then flexibility and adaptability are maintained, but high human effort and time consumption are required
Solution Approach 1:
The system performs self-service by automatically extracting data from multiple sources, generating product parameter seeds, grouping them by similarity, merging duplicates, and transforming them into hierarchical product parameters without requiring manual intervention at each step. The system serves itself by maintaining and updating the hierarchy structure autonomously based on incoming data streams from various ecosystems.
Solution Approach 2:
The patent segments the complex task of hierarchy creation into distinct modular steps: data extraction from multiple sources, generation of product parameter seeds, grouping by similarity, merging of duplicate seeds, and transformation into final hierarchical parameters. This segmentation allows each module to be independently developed, maintained, and optimized, reducing overall system complexity while enabling automation.
2Productivity
If manual creation of hierarchies is performed, then control over data accuracy is maintained, but the process is time-consuming and slow to update
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring data streams from multiple ecosystems, comparing extracted product parameter seeds against existing hierarchies, and automatically updating the structure based on detected changes. This closed-loop approach ensures that the hierarchy remains accurate and synchronized with real-time data while operating at automated speeds.
Solution Approach 2:
The patent applies preliminary action by pre-processing and validating data during the extraction phase, generating standardized product parameter seeds before grouping and merging. This upfront preparation ensures data quality and accuracy are established early in the automated pipeline, reducing the need for manual correction later while maintaining high update speed.
3Quantity of substance
If comprehensive data is collected from multiple sources, then completeness of hierarchy is improved, but data management complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional data extraction framework that can simultaneously connect to and process data from multiple disparate ecosystems and sources. The standardized product parameter seed structure serves as a universal intermediate format that accommodates data from various sources, enabling comprehensive data collection without proportionally increasing management complexity.
Solution Approach 2:
The patent introduces product parameter seeds as an intermediary structure between raw data from multiple sources and the final hierarchical product parameters. This intermediary layer standardizes and normalizes data from diverse ecosystems into a common format, simplifying the integration process while ensuring comprehensive data collection across all sources.
4Loss of time
If frequent updates are performed to keep pace with product evolution, then data currency is improved, but resource consumption increases
Solution Approach 1:
The system implements periodic action by scheduling automated updates at optimized intervals rather than continuously processing all incoming data. The system periodically scans data streams, extracts changes, and updates the hierarchy structure only when necessary, reducing computational resource consumption while maintaining data currency. This periodic processing balances timeliness with resource efficiency.
Solution Approach 2:
The patent applies partial action by selectively processing only the portions of data streams that contain changes or updates relevant to the hierarchy structure. Rather than re-processing entire data sets, the system identifies and processes only the necessary subsets of data, reducing computational overhead while ensuring timely updates when changes occur.
Data Source
AI summary
The present subject matter discloses techniques for automatically generating and updating hierarchical product parameters in a hierarchy of operational factors for offerings deployed in a connected environment. The system extracts data from multiple connected data sources, generates product parameter seeds by converting the extracted data into a standard format, groups the product parameter seeds into partition sets based on similarities, merges product parameter seeds in each partition set to form a merged product parameter seed, and transforms the merged product parameter seed into a hierarchical product parameter. The hierarchical product parameter is then updated in a hierarchical product parameter map. The system handles data from diverse sources with different formats by converting inputs into a common product parameter seed format for consistent processing.