Semantic Interpreter for Natural Language App Commands

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Solution Overview

Problem

User interfaces in applications become increasingly complex as they offer more functionality, negatively impacting the user experience, and users struggle to perform tasks or overlook useful features due to the complexity of navigating through various controls.

Innovation Solution

A semantic interpreter that utilizes a Large Language Model (LLM) to analyze natural language commands, generate program code in a Domain Specific Language (DSL), and correct syntax errors, enabling users to interact with applications through familiar language without needing to navigate complex interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If applications offer more functionality through traditional user interfaces, then application capability is improved, but user interface complexity increases and user experience deteriorates

Engineering Contradiction:

Improveapplication capabilityVSAvoiduser interface complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a natural language processing intermediary layer between the user and the application's complex functionality. Users provide simple natural language commands, and the system automatically translates these into appropriate application actions, eliminating the need for users to navigate complex interface controls while still accessing advanced features.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical interaction model of traditional graphical user interfaces (clicking, dragging, navigating menus) with a linguistic interaction model. Instead of manually manipulating interface elements, users communicate their intent through natural language, and the system interprets and executes the corresponding actions automatically.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If traditional user interfaces are used to support additional features, then application functionality is improved, but ease of operation deteriorates due to increased complexity

Engineering Contradiction:

Improveapplication functionalityVSAvoidease of use

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The natural language processing system serves as an intermediary that translates simple user commands into complex application operations. Users don't need to learn or navigate complex interface controls; they simply state what they want to accomplish in natural language, and the system handles the complexity of mapping this to the appropriate functionality.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If natural language processing with LLM is implemented, then ease of operation is improved, but system complexity increases

Engineering Contradiction:

Improveease of useVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system employs a self-service architecture where the LLM automatically handles the complex tasks of natural language interpretation, command generation, and action execution without requiring users to understand or manage the underlying complexity. The system serves itself by autonomously translating user intent into actionable commands and handling error correction.

Inventive Principle:

Principle #25Self-service

4Productivity

If automated code generation is implemented, then productivity is improved, but reliability may deteriorate due to potential syntax errors

Engineering Contradiction:

Improveworkflow efficiencyVSAvoidcode accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements a feedback mechanism where the generated commands are validated against the application's expected input formats and syntax rules. When syntax errors or invalid commands are detected, the system provides feedback to the LLM, which then generates corrected commands. This closed-loop validation process ensures that automated code generation maintains high reliability while preserving productivity benefits.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12554938B2Semantic interpreter for natural language commanding in applications via program synthesis

Publication Date: 2026.02.17 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A data processing system implements receiving a natural language query from a client device, the query including a textual description of one or more actions to be performed using an application on the client device, generating a prompt for a large language model (LLM) based on the natural language query; providing the prompt to the LLM to obtain program code, which when executed, causes the application to perform the one or more actions, the first program code being implemented in a domain specific language (DSL) designed to facilitate program synthesis via the LLM; analyzing a syntax of the first program code to identify syntax errors in the first program code; correcting the syntax errors in the program code to generate corrected program code; and causing the client device to execute the program code to perform the one or more actions in the application on the client device.