Contextual Emoji Rendering in Smart Dictation Transcription

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Solution Overview

Problem

Existing automated assistants require explicit user inputs to suggest emojis, prolonging dialog sessions and wasting computational resources due to manual interaction and inefficient emotion detection.

Innovation Solution

Process spoken utterances to determine emotion classes or associated terms, automatically rendering contextually relevant emojis for inclusion in transcriptions, allowing users to select them efficiently via single inputs or voice commands.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If automated assistants require explicit user inputs to suggest emojis, then emoji suggestions can be provided, but dialog sessions are prolonged and computational resources are wasted

Engineering Contradiction:

Improveemoji suggestion accuracyVSAvoiddialog session efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary emotion detection and emoji suggestion generation during the dictation process itself, rather than waiting for explicit user requests. The emotion detection module continuously analyzes spoken utterances and generates emoji suggestions in advance, so that when users do want emojis, they are already available for immediate selection, eliminating the need for additional interaction rounds.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The automated assistant autonomously detects emotions in spoken utterances and generates emoji suggestions without requiring explicit user commands. The system monitors its own dialog state, automatically identifies emotional content, and provides emoji options proactively, allowing the system to serve itself in the emoji suggestion task rather than waiting for user initiation.

Inventive Principle:

Principle #25Self-service

2Use of energy by moving object

If automated assistants wait for explicit user inputs to detect emotions, then computational resources are conserved, but user input requirements increase and dictation efficiency decreases

Engineering Contradiction:

Improvecomputational resource usageVSAvoiduser input effort

Core Design Contradiction:

Use of energy by moving objectVSEase of operation

Solution Approach 1:

The system applies partial emotion detection by focusing only on specific acoustic features and textual patterns that indicate emotions, rather than performing complete speech analysis on all utterances. The emotion detection module activates selectively based on confidence thresholds and contextual cues, performing detailed analysis only when emotional content is likely present, thus balancing computational effort with user benefit.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If manual keyboard interaction is required for emoji input, then precise emoji selection is possible, but user input quantity increases and transcription accuracy suffers

Engineering Contradiction:

Improveemoji selection precisionVSAvoiduser input quantity

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system segments the emoji input process into distinct components: emotion detection, emoji suggestion generation, and user selection. By breaking down the traditional monolithic keyboard input method into these modular stages, the system can provide targeted emoji options based on detected emotions, allowing users to select from a limited set of contextually relevant emojis rather than searching through a complete emoji keyboard.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP4356372B1System(s) and method(s) for causing contextually relevant emoji(s) to be visually rendered for presentation to user(s) in smart dictation

Publication Date: 2025.10.22 GOOGLE LLC

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AI summary

Implementations described herein relate to causing emoji(s) that are associated with a given emotion class expressed by a spoken utterance to be visually rendered for presentation to a user at a display of a client device of the user. Processor(s) of the client device may receive audio data that captures the spoken utterance, process the audio data to generate textual data that is predicted to correspond to the spoken utterance, and cause a transcription of the textual data to be visually rendered for presentation to the user via the display. Further, the processor(s) may determine, based on processing the textual data, whether the spoken utterance expresses a given emotion class. In response to determining that the spoken utterance expresses the given emotion class, the processor(s) may cause emoji(s) that are stored in association with the given emotion class to be visually rendered for presentation to the user via the display.