Data Structure Tool Using ML to Generate Descriptor Files
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Solution Overview
Problem
Organizations face significant resource expenditure in developing and testing new data structures, and there is a need for efficient sharing of pre-developed data structures and APIs across systems, as many organizations store similar types of data.
Innovation Solution
A data structure tool utilizing a machine learning algorithm to generate descriptor files that specify input and output types for various data structures and APIs, allowing for efficient retrieval and implementation of existing services without the need for organizations to develop them from scratch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an organization develops a new data structure from scratch, then the data structure can be customized to fit specific needs, but significant time and resources are expended on development and testing
Solution Approach 1:
The patent applies preliminary action by having organizations publish their data structures in advance to a marketplace before needing them. This allows other organizations to access and reuse these pre-developed data structures, eliminating the need to develop them from scratch and significantly reducing development and testing time while maintaining customization options through the marketplace selection process
Solution Approach 2:
The patent applies copying by enabling organizations to replicate and reuse data structures created by other organizations through the marketplace. Instead of developing unique data structures for each organization, the system allows copying of proven, tested data structures that have already been validated by their original creators, reducing both time and resource expenditure
2Loss of energy
If an organization locates a suitable data structure from another organization, then resource expenditure is reduced, but significant time is required to locate the appropriate structure
Solution Approach 1:
The patent applies the intermediary principle by introducing a marketplace as a mediating platform between data structure creators and users. This marketplace indexes and organizes available data structures with metadata, enabling efficient searching and retrieval. Organizations can locate suitable data structures quickly through the marketplace's search capabilities rather than manually searching through multiple external sources
Solution Approach 2:
The patent applies universality by creating a marketplace that serves multiple functions: it stores data structures, indexes them with metadata for searchability, enables discovery by potential users, and facilitates retrieval. This multi-functional platform consolidates what would otherwise require multiple separate systems into a single universal access point
3Reliability
If each organization develops its own data structures independently, then data structures can be optimized for specific organizational needs, but overall productivity across the industry decreases
Solution Approach 1:
The patent applies merging by combining individual organizational data structures into a collective marketplace. This allows each organization to maintain their optimized, needs-specific data structures while simultaneously contributing them to a shared resource. The marketplace merges these diverse data structures into a unified catalog that benefits all participants, increasing industry-wide productivity through reuse while preserving individual customization
Data Source
AI summary
An apparatus includes a database and a processor. The database stores a set of services, each of which produces outputs when provided inputs. The processor determines a machine learning policy that can be applied to a first service to determine a descriptor file assigned to the service. The first service can produce a first output when provided a first input. The descriptor file includes the first output type and the first input type. The processor applies the machine learning policy to the set of services to determine a set of descriptor files. The processor receives a request from a user for a second service that produces a second output given a second input. The processor then locates a second descriptor file that includes the second output type and the second input type, determines a third service assigned to the descriptor file, and sends the third service to the user.