Sensory Communication Translation Using ML User Capability Profiles

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Solution Overview

Problem

Existing automated machines often assume uniform user capabilities, failing to accommodate users with diverse sensory communication abilities, leading to barriers in seamless interaction.

Innovation Solution

A computer-implemented method using Machine Learning (ML) models to translate sensory inputs to outputs, mapping communication capabilities between entities, including human users and machines, by obtaining profiles and training models to minimize sensory capability mismatches.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing automated machines use fixed sensory communication interfaces, then device complexity is reduced, but adaptability to users with diverse sensory capabilities deteriorates

Engineering Contradiction:

Improveadaptability to users with diverse sensory capabilitiesVSAvoidcomplexity of sensory communication translation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a translation system as an intermediary component that sits between the machine's fixed sensory interfaces and users with diverse sensory capabilities. This mediator translates between standardized machine communication protocols and various user-specific sensory modalities (visual, auditory, tactile, olfactory, gustatory), enabling adaptability without requiring the machine itself to be complex or reconfigurable.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The translation system is designed to handle multiple sensory modalities through a single universal framework. Rather than requiring separate specialized systems for each sense type, the patent creates a multi-functional translation apparatus that can process and convert between visual, auditory, tactile, olfactory, and gustatory signals, making the system adaptable to any user sensory capability profile.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If existing electronic devices are designed for specific sensory functions, then device complexity is reduced, but adaptability to different communication needs deteriorates

Engineering Contradiction:

Improveadaptability to different communication needsVSAvoidcomplexity of multi-functional translation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system adapts to different communication needs by dynamically changing operational parameters such as the selected translation model, sensory modality priorities, and communication protocols based on user profiles and contextual information. This allows a single device to serve multiple functions by adjusting its configuration rather than requiring multiple specialized devices.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If existing sensory communication tools are made personalized, then ease of operation is improved, but device complexity increases

Engineering Contradiction:

Improveease of interaction for users with specific sensory capabilitiesVSAvoidcomplexity of personalized communication system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-configuring user profiles with sensory capability information and pre-selecting appropriate translation models before actual interaction occurs. User profiles are established in advance with their preferred sensory modalities and communication preferences, allowing the system to automatically adapt without requiring complex real-time configuration or user input during the interaction itself.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The translation system operates autonomously by automatically selecting appropriate translation models and sensory output modalities based on stored user profiles and detected contexts. Rather than requiring the system to be manually configured for each user or requiring complex user input to customize the interface, the system self-adjusts its behavior based on pre-established profiles, simplifying the user experience while maintaining personalization.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12542835B2Translating sensory communications

Publication Date: 2026.02.03 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

AI summary

A computer implemented method for translating a sensory input to a sensory output for communication between first and second entities. The first entity includes a user and the second entity includes a user or a computing system. The method includes obtaining a first profile specifying a communication capability of the first entity, and a second profile specifying a communication capability of the second entity and obtaining a trained Machine Learning (ML) model operable to map an input sensory communication in accordance with one of the first or second profiles to an output sensory communication in accordance with the other of the profiles. The method further includes receiving an input sensory communication from one of the first or second entities, using the ML model to map the input sensory communication to an output sensory communication, and providing the output sensory communication to the other of the entities.