Syntactic Interface for Generative Model Configuration in EMR Workflows

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Solution Overview

Problem

Existing processes for configuring generative machine learning models are inefficient and require significant user interaction, and existing systems fail to leverage the power of natural language processing effectively.

Innovation Solution

A system and method for configuring a generative machine learning model using a syntactic interface to configure a syntactic interface for leveraging the power of natural language processing effectively.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If free-form input interface is used for configuring generative machine learning models, then user input flexibility is improved, but input collection efficiency deteriorates as users must input the same information multiple times

Engineering Contradiction:

Improveuser input flexibilityVSAvoidinput collection efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

A syntactic interface acts as an intermediary between the user and the generative machine learning model. This interface translates user selections from predefined categories into structured prompts that automatically incorporate relevant information from knowledge graphs, eliminating the need for users to manually input the same information multiple times while maintaining input flexibility through category-based selection.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary action by pre-organizing information from knowledge graphs and pre-defining input categories before the user makes selections. This allows the system to automatically generate comprehensive prompts based on user selections without requiring users to repeatedly provide the same information, thereby improving input collection efficiency while maintaining ease of operation.

Inventive Principle:

Principle #10Preliminary action

2Extent of automation

If traditional input collection processes are used, then user interaction requirements are reduced, but model configuration effectiveness deteriorates

Engineering Contradiction:

Improveinput collection automationVSAvoiduser interaction requirements

Core Design Contradiction:

Extent of automationVSEase of operation

Solution Approach 1:

The syntactic interface enables self-service by allowing users to configure model parameters through intuitive category selections without requiring complex manual input. The system automatically retrieves relevant information from knowledge graphs and generates structured prompts based on user selections, reducing the burden on users while maintaining high automation in the input collection process.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The syntactic interface serves as an intermediary that bridges simple user selections with complex model configuration requirements. It translates user-friendly category choices into technically accurate prompts that automatically incorporate relevant information, achieving both high automation and ease of operation simultaneously.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If existing systems are used for model configuration, then system simplicity is maintained, but natural language processing capability is underutilized

Engineering Contradiction:

Improvenatural language processing utilizationVSAvoidsystem structure complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The syntactic interface provides multi-functionality by serving multiple purposes: it guides users through category selections, retrieves information from knowledge graphs, generates structured prompts, and handles the complexity of natural language processing. This universal interface approach increases adaptability and versatility in utilizing natural language processing while managing system complexity through a unified design.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The syntactic interface acts as an intermediary layer that abstracts the complexity of natural language processing and knowledge graph queries from the user. It translates simple user selections into complex NLP operations and information retrieval, thereby increasing NLP utilization without requiring users to interact with the underlying system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250391523A1Configuring a generative machine learning model using a syntactic interface

Publication Date: 2025.12.25 NFERENCE INC

AI summary

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input, generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model and using a conversational interface displayed using the user interface, display the first generative model output to the user.