Semantic AI Data Object Generation for Self-Service Integration
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Solution Overview
Problem
Existing integration platforms require technical expertise for generating data objects, leading to a high learning curve and inefficiency for novice users, as current solutions rely on static definitions or simple merges.
Innovation Solution
Automated generation of data objects using semantic comparisons and artificial intelligence, involving the determination of reference vector embeddings, graph database searches, and a generative language model to dynamically create data objects based on user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static, pre-existing definitions are used for data object generation, then the system is simple to operate, but the adaptability and intelligence are insufficient
Solution Approach 1:
The patent transforms static data object definitions into dynamic, AI-generated definitions. The system uses machine learning models to dynamically create and update data object definitions based on contextual information, user interactions, and evolving business requirements, making the system both easy to operate and highly adaptable
Solution Approach 2:
The patent introduces an AI intermediary layer between the user and the integration platform. This intermediary automatically generates data object definitions by analyzing user requests and contextual data, eliminating the need for users to manually define complex data structures while maintaining high adaptability
2Manufacturing precision
If manual construction of integration processes is required, then the precision and control are high, but the productivity and time consumption are poor
Solution Approach 1:
The patent performs preliminary actions by pre-defining data object templates, validation rules, and integration patterns. The AI system uses these pre-prepared elements to automatically construct integration processes, maintaining precision while dramatically improving productivity by eliminating manual configuration steps
Solution Approach 2:
The patent enables self-service integration process construction through AI automation. The system automatically analyzes business requirements, selects appropriate integration patterns, configures data mappings, and validates processes without human intervention, achieving both high precision and rapid deployment
3Reliability
If technical expertise is required for data object generation, then the reliability and quality are high, but the ease of operation and accessibility are poor
Solution Approach 1:
The patent replaces the mechanical system of manual technical configuration with an intelligent AI-based system. The machine learning model automatically generates reliable data object definitions by learning from historical data and expert knowledge, making the process accessible to users without specialized technical expertise while maintaining high quality standards
Data Source
AI summary
Currently, technical expertise is required to construct data objects within integration processes. Disclosed embodiments enable automated generation of data objects using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings. The input vector embeddings may be semantically compared to the reference vector embeddings to identify semantically similar data, and related data may be identified using a graph database. This semantically similar and related data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data object for use in an integration process.