Natural Language Interface for Command Translation

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Solution Overview

Problem

Users of multi-function devices face frustration in navigating through numerous menu levels to find the correct options, as existing interfaces require typing or clicking through extensive menus to access information, which lacks efficiency and user-friendly interaction.

Innovation Solution

A natural language interface, such as a chatbot, utilizes a machine-learning model to translate user queries into predefined commands by converting them into feature vectors, allowing for efficient matching and execution of commands, thereby providing responses without the need for extensive menu navigation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional menu interfaces are used, then device functionality is comprehensive, but user interaction time and complexity increase significantly

Engineering Contradiction:

ImproveUser interaction easeVSAvoidTime to access information

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent replaces the mechanical menu navigation system (typing commands, clicking through menus) with a natural language processing system. The NLM translates natural language queries directly into feature vectors that map to device commands, eliminating the need for structured menu traversal and significantly reducing interaction time and complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a natural language interface as an intermediary between the user and the device commands. This intermediary layer translates human-friendly natural language queries into the device's internal command structure through feature vector representation, making the interaction more intuitive and faster without requiring users to navigate complex menus.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If comprehensive menu options are provided, then device versatility is maintained, but interface complexity increases

Engineering Contradiction:

ImproveDevice command coverageVSAvoidInterface complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent replaces the complex mechanical menu structure with a semantic feature vector space. Instead of presenting users with hierarchical menus, the system represents all device commands as vectors in a continuous space, allowing natural language queries to directly access any command through semantic similarity matching, thereby reducing interface complexity while maintaining full functionality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the discrete, hierarchical menu parameter structure into a continuous feature vector representation. By mapping commands to continuous vector spaces and using semantic similarity metrics, the system enables flexible, natural language-based access to all device functions without requiring users to navigate complex hierarchical structures.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11509794B2Machine-learning command interaction

Publication Date: 2022.11.22 HEWLETT PACKARD DEVELOPMENT COMPANY LP

AI summary

Examples disclosed herein relate to receiving a query via a chat interaction, translating the received query into one of a set of predefined commands according to a trained machine-learning model, and providing a result of the one of the set of predefined commands to the chat interaction.