Runtime API Insertion for AI-Based Condition Enforcement
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Solution Overview
Problem
Customizing software code to implement and manage sets of conditions for computer-detected events is time-consuming, labor-intensive, and prone to errors, hindering scalability and throughput.
Innovation Solution
A system that uses machine learning models to extract and validate sets of conditions from natural language text, generating machine-readable data structures to evaluate events in real-time, reducing the need for customized code and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software code is customized to implement sets of conditions for computer-detected events, then the conditions can be implemented and enforced, but the process becomes time-consuming, labor-intensive, and error-prone
Solution Approach 1:
The patent uses template-based code generation to create standardized implementations of condition enforcement logic. Instead of customizing code from scratch for each set of conditions, the system copies and adapts pre-defined templates, significantly reducing development time and human error while maintaining reliability through proven patterns
Solution Approach 2:
The system automatically generates and inserts the necessary code snippets into executable application code without requiring manual customization. The condition enforcement mechanism serves itself by programmatically creating the implementation details, reducing labor-intensive work while ensuring consistent and error-free code generation
2Adaptability or versatility
If software code is customized to manage sets of conditions, then specific attributes can be captured, but scalability and throughput are hindered
Solution Approach 1:
The patent implements a universal condition enforcement mechanism that can handle multiple types of conditions and attributes through a single standardized API interface. The system uses polymorphic design where different condition types share common enforcement logic, allowing the system to adapt to various condition specifications without sacrificing throughput or requiring separate custom implementations for each case
Solution Approach 2:
The system dynamically generates and inserts code snippets at runtime based on the specific conditions that need to be enforced. This dynamic approach allows the system to adapt to different condition requirements without static compilation overhead, maintaining high throughput while capturing diverse attributes through on-demand code generation
3Ease of operation
If machine learning models are used to extract and validate sets of conditions from natural language text, then machine-readable data structures can be generated for real-time evaluation, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer that translates natural language condition specifications into structured machine-readable data. This intermediary handles the complexity of parsing and validation, allowing users to specify conditions in simple natural language while the ML model manages the transformation to executable formats, isolating complexity from the end user
Solution Approach 2:
The system performs preliminary extraction and validation of conditions from natural language text before the actual exchange execution. By pre-processing the condition specifications through ML models and generating ready-to-execute code snippets in advance, the system reduces runtime complexity and prepares all necessary data structures beforehand, making the overall process more manageable
Data Source
AI summary
Metadata corresponding to a set of conditions applicable to an exchange to be performed among a plurality of entities is obtained, the set of conditions determined using one or more trained artificial intelligence models. A set of application programming interfaces (APIs) are generated based, at least in part, on the set of conditions. One or more points to insert the set of APIs to an executable application code to be executed for the exchange are determined. The set of APIs to be called during runtime of the executable application code to implement the set of conditions.