Dynamic AI Engine Access in Cloud Architecture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for integrating an artificial intelligence engine with cloud computing architectures are complex and time-consuming, requiring multiple API calls and development experience, making dynamic access and interaction challenging for users.
Innovation Solution
A cloud computing architecture that allows dynamic access to an AI engine by storing a record with an AI engine identification and parameters, enabling the AI engine to run with these parameters without the need for separate API calls, and providing a declarative solution for obtaining predicted outputs directly from an external data source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current techniques are used to integrate AI engine with cloud computing architecture, then AI analytics service can be provided, but the integration process becomes complex and time-consuming requiring multiple API calls
Solution Approach 1:
The patent combines multiple separate API calls and integration steps into a single unified declarative statement that can be executed in one operation. The AI engine integration is merged with the existing cloud computing architecture operations, allowing users to access AI analytics services through standard database query mechanisms rather than requiring separate integration protocols.
Solution Approach 2:
The patent creates a universal interface that allows the AI engine to be accessed through the same declarative statement mechanism used for standard database operations. This multi-functional approach enables the system to handle both traditional data retrieval and AI analytics requests through a common interface, eliminating the need for specialized integration code.
2Productivity
If multiple API calls are used to access AI engine, then predicted output can be obtained, but the process becomes time-consuming and requires development experience
Solution Approach 1:
The patent performs preliminary actions by pre-configuring the AI engine connection and authentication within the cloud computing architecture itself. This setup is done once during system initialization, so that subsequent AI analytics requests can be executed immediately through declarative statements without requiring repeated authentication or connection establishment steps.
Solution Approach 2:
The patent introduces an intermediary layer within the cloud computing architecture that mediates between the user's declarative statements and the AI engine. This intermediary handles the complexity of API communications, data formatting, and response processing, allowing users to obtain predicted outputs quickly through simple statements without needing to understand the underlying complex communication protocols.
3Adaptability or versatility
If users want to dynamically interact with AI engine, then flexible access is achieved, but the process requires storing results and running multiple predictions
Solution Approach 1:
The patent implements dynamic access by allowing users to modify AI analytics requests at any time through updated declarative statements. The system dynamically re-executes the AI engine with new parameters or data without requiring pre-stored results or batch processing, enabling flexible on-demand analytics that adapt to changing user needs in real-time.
Solution Approach 2:
The patent enables the system to automatically handle the entire AI analytics process end-to-end through declarative statements. The cloud computing architecture self-manages data retrieval, AI engine invocation, result processing, and response delivery without requiring users to manually store intermediate results or orchestrate multiple prediction steps, thereby simplifying the dynamic interaction process.
Data Source
AI summary
A method and a cloud-computing architecture for enabling dynamic access of an artificial intelligence engine are described. A record that includes a set of one or more fields is stored in a database. A first field from the set of fields includes an identification of an artificial intelligence (AI) engine and one or more additional fields from the set of fields respectively include one or more parameters for the AI engine. The record is accesses causing the AI engine to run with the one or more parameters. As a result of the AI engine running with the one or more parameters upon access of the record, a desired predicted output is obtained.