Runtime Enforcement of Partnership Conditions From Natural Language
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Solution Overview
Problem
Customizing software code to manage and validate computer-detected events based on specific conditions is time-consuming, labor-intensive, and error-prone, leading to scalability and throughput issues.
Innovation Solution
A system extracts conditions from natural language documents using machine learning, generates machine-readable data structures, and validates events in real-time to reduce storage and network resource demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software code is customized to manage and validate computer-detected events based on specific conditions, then the system can accurately enforce partnership agreement terms, but the implementation becomes time-consuming, labor-intensive, and error-prone
Solution Approach 1:
The patent creates machine-readable copies of partnership agreement conditions from natural language documents. These structured data representations can be directly processed and enforced by software systems without requiring manual code customization, thus maintaining enforcement accuracy while dramatically reducing implementation time and human effort.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts natural language conditions into structured machine-readable formats. This intermediary step acts as a mediator between the human-written agreements and the software enforcement mechanisms, eliminating the need for direct code customization while ensuring accurate condition representation.
2Measurement precision
If software code is customized to capture various attributes specific to partnership conditions, then the system can precisely validate events, but the process becomes vulnerable to errors and difficult to maintain
Solution Approach 1:
The patent transforms the representation of conditions by changing parameters from unstructured natural language to structured machine-readable formats with defined schemas. This parameter transformation maintains validation precision while reducing software complexity, as the structured formats can be processed through standardized validation logic rather than custom-coded solutions.
Solution Approach 2:
The patent segments partnership agreement conditions into discrete, structured data elements that can be independently processed and validated. This segmentation allows the system to handle complex validation requirements through modular, standardized operations rather than monolithic custom code, reducing both complexity and error susceptibility.
3Loss of information
If traditional methods are used to manage partnership conditions, then detailed tracking is possible, but storage needs and network traffic increase, reducing scalability
Solution Approach 1:
The patent changes the data representation parameters from detailed natural language storage to compact structured formats. This transformation maintains all necessary compliance information while significantly reducing storage requirements and network transmission overhead, enabling scalable deployment across distributed systems.
Data Source
AI summary
Context information applicable to an operation is obtained. A first computer-readable data structure encoding a first set of conditions applicable to performance of the operation is generated based on the context information. A second computer-readable data structure corresponding to an identifier of an entity for which the operation is performable is obtained, where the second computer-readable data structure encodes a second set of conditions applicable to the performance of the operation. A third computer-readable data structure is generated based on a third set of conditions encoded by both the first computer-readable data structure and the second computer-readable data structure. The third computer-readable data structure is caused to be used to apply the third set of conditions to control the performance of the operation for the entity.