Semantic Asset Identification System for IT Reuse
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Solution Overview
Problem
Current solutions for managing reusable IT assets in organizations are limited in handling unified interfaces, sophisticated access control, workflow management, lifecycle management, efficient search, versioning, licensing, and cloud enablement, among other features, which hinders efficient asset reuse and migration.
Innovation Solution
A method and system for identifying relevant assets in an asset store by generating semantic-based segments of user input requirements, mapping parameters such as metadata and source code, and searching across native and external stores, with features like crowd-sourcing and SaaS model presentation, to ensure efficient asset retrieval and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional asset management solutions are used, then basic asset storage is achieved, but efficient search and identification of relevant assets is not possible
Solution Approach 1:
The patent transforms asset search from traditional keyword matching to semantic parameter-based matching. By extracting semantic parameters from natural language queries and mapping them to asset metadata parameters, the system enables intelligent identification of relevant assets without requiring complex search algorithms. This parameter transformation approach resolves the contradiction by improving search efficiency while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces semantic parameter segments as an intermediary between user queries and asset metadata. These segments act as a bridge that translates natural language requirements into structured search criteria, enabling efficient asset identification without direct complex querying. The intermediary layers simplify the search process while improving productivity.
2Measurement precision
If comprehensive asset metadata is collected for better matching, then asset identification accuracy improves, but data processing time increases
Solution Approach 1:
The patent extracts only the relevant semantic parameters from comprehensive asset metadata based on the specific query requirements. Instead of processing all metadata fields, the system identifies and extracts only those parameters that are relevant to the current search intent, thereby maintaining high matching accuracy while reducing processing time significantly.
Solution Approach 2:
The patent segments the asset metadata into distinct parameter categories and processes only the relevant segments based on query semantics. By dividing the comprehensive metadata into manageable parameter segments and selectively processing only those needed for the current search, the system achieves accurate matching without the overhead of processing entire metadata sets.
3Adaptability or versatility
If multiple external asset stores are integrated for broader search, then asset availability increases, but system complexity increases
Solution Approach 1:
The patent implements a universal semantic parameter mapping framework that works across multiple external asset stores with different schemas and structures. By defining a common set of semantic parameters that can map to various source systems, the system achieves broad asset store coverage without requiring complex integration logic for each individual store. The universal parameter framework handles diverse sources uniformly.
4Reliability
If sophisticated gating criteria are applied for asset quality control, then asset quality improves, but deployment time increases
Solution Approach 1:
The patent applies gating criteria as preliminary actions during asset ingestion and registration, rather than during deployment. By pre-validating asset quality, checking metadata completeness, and verifying compliance with standards when assets are first added to the store, the system ensures high asset quality without delaying subsequent deployment operations. The gating checks are performed upfront, allowing rapid deployment of pre-validated assets.
Data Source
AI summary
A method system and computer readable medium for a method of identifying assets in an asset store, said method comprising, receiving an input representing a predetermined requirement, generating semantic based segments of the predetermined requirement, from the received input, mapping at least one parameter for each of the assets in the asset store with the generated segments, the parameter being one of metadata, supporting documents and source code, identifying at least one asset in the asset store based on the mapping, the asset being relevant to the predetermined requirement and providing as output the identified asset.