Programming-Language Data Structures for Domain-Specific ML Tasks

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Solution Overview

Problem

Existing machine learning models struggle to effectively utilize domain-specific information and syntax, particularly in unstructured and semi-structured electronic data, limiting their ability to perform tasks that require understanding of domain-specific programming languages.

Innovation Solution

Implementing a computer-code-based domain-specific data structure that encodes domain information, allowing machine learning models to understand and leverage domain-specific syntax for tasks such as document/content analysis and generative tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine learning models process unstructured and semi-structured electronic data, then they can handle diverse data types, but they struggle to effectively utilize domain-specific information and syntax

Engineering Contradiction:

Improveability to handle diverse data typesVSAvoideffectiveness in utilizing domain-specific information

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces a domain-specific programming language data structure as an intermediary layer between raw unstructured data and machine learning models. This intermediary structure encodes domain-specific information and syntax in a standardized format, enabling models to effectively process diverse data types while maintaining reliable understanding of domain-specific concepts. The data structure acts as a mediator that translates varied input formats into a unified representation that models can reliably process.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If machine learning models are trained on domain-specific programming languages, then they can understand and leverage domain-specific syntax, but the complexity of encoding and processing increases

Engineering Contradiction:

Improveunderstanding of domain-specific syntaxVSAvoidcomplexity of encoding operation

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the complex task of processing domain-specific data into distinct components: (1) an encoding operation that converts domain-specific programming languages into a standardized data structure format, and (2) a machine learning model that processes the encoded data. This segmentation allows the encoding operation to be optimized independently for syntax understanding, while the model focuses on pattern recognition and prediction, reducing overall system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The encoding operation performs preliminary action by pre-processing and transforming domain-specific programming languages into a standardized data structure format before the machine learning model receives the data. This preliminary encoding step simplifies the model's processing task by providing cleaned, structured input, reducing the computational complexity required for syntax understanding during model execution.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If encoded domain information is generated from multiple data structures, then the encoding operation can handle diverse inputs, but the encoding process becomes more complex

Engineering Contradiction:

Improveability to handle multiple data structuresVSAvoidcomplexity of encoding operation

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The domain-specific programming language data structure serves as a universal container that can represent multiple types of domain-specific information and data structures through a common syntax framework. This universal structure allows the encoding operation to handle diverse inputs (multiple data structures) by translating them all into the same standardized format, reducing encoding complexity through reuse of the same encoding rules and patterns.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250299093A1Programming language as a data structure

Publication Date: 2025.09.25 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Embodiments of the invention provide a computer-implemented method that includes executing a machine learning (ML) model operable to perform a ML task that includes generating a ML output responsive to a ML input. The ML output includes encoded domain information associated with a domain. The encoded domain information is encoded in a computer-code-based domain-specific data structure, and the ML task is associated with the domain.