Brand Voice Attribute Extraction for User-Aligned Content Generation

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Solution Overview

Problem

Existing automated content generation techniques fail to produce content that aligns with a user's preferred brand voice, as existing generative machine learning models are trained on diverse text corpora lacking user-specific brand voices, and users struggle to quantify their brand voice attributes accurately.

Innovation Solution

A method involving a language processing machine learning model to extract brand voice attributes from user-generated content, using prompts to identify narrative, trait classifications, and writing style attributes, and iteratively improve content generation through user feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If existing generative machine learning models are used for automated content generation, then content can be generated automatically, but the content does not align with user-specific brand voice

Engineering Contradiction:

Improveautomated content generationVSAvoidbrand voice alignment

Core Design Contradiction:

Extent of automationVSManufacturing precision

Solution Approach 1:

The system performs preliminary extraction of brand voice attributes from user-generated content before the actual content generation process. This preliminary analysis captures the user's specific brand voice characteristics (narrative style, trait classifications, writing style attributes) which are then stored and applied during automated content generation, ensuring alignment while maintaining automation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces brand voice attributes as an intermediary layer between the user's existing content and the generative model. These attributes (extracted through language processing) serve as mediators that translate the user's unique brand voice into parameters that the automated generation system can utilize, bridging the gap between automated generation and brand voice fidelity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If users manually define brand voice attributes, then content can be tailored to brand voice, but users struggle to quantify attributes accurately

Engineering Contradiction:

Improvebrand voice alignmentVSAvoidattribute quantification

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system enables self-service by automatically extracting brand voice attributes from the user's own generated content without requiring manual input or expertise from the user. The language processing model analyzes the user's existing content and autonomously identifies narrative style, trait classifications, and writing style attributes, making the process easy while maintaining precision.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of defining brand voice attributes with an automated language processing system. Instead of users manually quantifying attributes (which is difficult and subjective), the system uses machine learning models to automatically analyze and extract these attributes from text, substituting human effort with computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated content generation is performed without brand voice attributes, then resource utilization is high, but content quality and user satisfaction are low

Engineering Contradiction:

Improvecontent generation efficiencyVSAvoidcontent quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system changes the parameters of content generation by incorporating brand voice attributes (narrative style, trait classifications, writing style attributes) as additional control variables. These parameter changes guide the generative model to produce content that maintains both efficiency and high quality by aligning with the user's established brand voice characteristics.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements a feedback mechanism where the system analyzes user-generated content to extract brand voice attributes, then uses these attributes to guide subsequent content generation. This closed-loop feedback ensures that generated content consistently matches the user's brand voice, improving quality and satisfaction while maintaining productivity through automation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4647958A1Automated extraction of brand voice attributes for generation of content in brand voice through machine learning

Publication Date: 2025.11.12 INTUIT INC

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AI summary

Aspects of the present disclosure provide techniques for automated extraction of brand voice attributes for content generation in a brand voice through machine learning. Embodiments include determining a set of user-generated content items associated with a user from which to extract brand voice attributes and providing the set of user-generated content items to a language processing machine learning model along with a prompt that instructs the language processing machine learning model to extract the brand voice attributes from the set of user-generated content items. Embodiments include receiving the brand voice attributes from the language processing machine learning model in response to the prompt. Embodiments include automatically generating content based on the brand voice attributes. Embodiments include outputting the content for display via a user interface.