Compliance Graph Generation via Neural Language Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating compliance graphs are resource-intensive and costly, limiting their ability to keep up with changing compliance rules and new industries, leading to outdated or non-existent software products that fail to provide adequate support for determining user compliance.
Innovation Solution
A method and system utilizing a pre-trained Universal Language Model encoder and a trained decoder to generate compliance graphs, where the decoder is trained with verified and synthetic data, allowing for efficient generation of compliance graphs in various languages and domains, enabling software products to adapt to changing rules and enter new markets with reduced barriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to generate compliance graphs, then compliance determination can be achieved, but resource consumption and costs increase significantly
Solution Approach 1:
The patent applies universality by creating a multi-language compliance graph generation system that can process compliance rules in multiple languages (English, Spanish, French, German, etc.) using a single unified architecture. The system uses language model encoders trained on multiple languages to generate compliance graphs universally, eliminating the need for separate generation processes for each language and reducing overall resource consumption.
Solution Approach 2:
The patent applies preliminary action by pre-training language model encoders on compliance rules in multiple languages before actual compliance graph generation is needed. This pre-training phase prepares the models to efficiently generate compliance graphs during operation, reducing the computational resources required during actual compliance determination tasks.
2Reliability
If conventional methods are used to generate compliance graphs, then compliance rules can be implemented, but the process is time-consuming and slow
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional manual or rule-based compliance graph generation methods with neural language model-based generation. The language models automatically generate compliance graphs from compliance rules in natural language, significantly accelerating the generation process compared to conventional mechanical or manual methods.
Solution Approach 2:
The patent applies parameter changes by transforming compliance rules from natural language text into structured compliance graphs through language model processing. The system changes the parameter representation from unstructured text to structured graphical representations, enabling faster processing and implementation while maintaining accuracy.
3Reliability
If compliance graphs are generated for a particular domain, then compliance determination works for that domain, but the system cannot adapt to new industries or markets
Solution Approach 1:
The patent applies universality by designing a multi-language compliance graph generation system that can process compliance rules in multiple languages and domains. The language model encoders are trained on diverse compliance rules across different industries and markets, enabling the system to generate accurate compliance graphs for various domains while maintaining a single unified architecture.
Solution Approach 2:
The patent applies dynamics by creating a flexible system that can adapt to new industries and markets dynamically. The language models can process newly introduced compliance rules in different languages and domains without requiring fundamental system changes, allowing the system to evolve and expand into new areas as compliance requirements change.
4Reliability
If new compliance rules are implemented, then compliance coverage is improved, but a new set of compliance graphs must be generated, increasing costs
Solution Approach 1:
The patent applies preliminary action by pre-training language model encoders on diverse compliance rules across multiple languages and domains. This pre-training prepares the models to efficiently generate compliance graphs for new compliance rules without requiring expensive re-engineering, reducing the cost of implementing new compliance requirements.
Solution Approach 2:
The patent applies parameter changes by using language models to automatically transform new compliance rules from natural language into compliance graphs. This automated transformation process reduces the manual effort and costs associated with generating new compliance graphs when compliance rules are updated or new rules are implemented.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for generating a compliance graph based on a compliance rule to implement in a software program product for determining user compliance. To generate a compliance graph, an encoder receives a compliance rule in a source language and generates a set of corresponding vectors. The decoder, which has been trained using verified training pairs and synthetic data, generates a sequence of operations based on the vectors from the encoder. The sequence of operations is the used to build a graph in which each operation is a node in the graph and each node is connected to at least one other node in the same graph or a separate graph.