Vernacular User Segmentation via Multi-Source ML Analysis

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Solution Overview

Problem

Current systems fail to effectively identify languages used by individuals across vernacular contexts, determine language proficiency, and segment users based on linguistic parameters, limiting personalized marketing campaigns in competitive online environments.

Innovation Solution

A computer-implemented method using machine learning algorithms to analyze user data from various sources, including name, audio, and device data, to identify languages, language attributes, and segment users in real-time, enabling personalized marketing campaigns tailored to vernacular contexts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multi-lingual campaigning systems are implemented to identify languages and segment users, then personalized marketing effectiveness is improved, but system complexity and data processing requirements increase

Engineering Contradiction:

Improvelanguage identification capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments users based on multiple language attributes including primary language, secondary language, language proficiency levels, and vernacular context. This segmentation enables targeted marketing campaigns for different language groups while maintaining manageable system complexity through modular processing of each language dimension separately.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The multi-lingual campaigning system performs multiple functions simultaneously: it identifies primary and secondary languages, determines language proficiency, detects vernacular contexts, and segments users for personalized marketing. This multi-functional approach consolidates what would otherwise require separate systems into a unified platform.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If real-time analysis of multiple data sets is performed to identify language attributes, then user segmentation accuracy is improved, but processing time and computational resources increase

Engineering Contradiction:

Improvelanguage detection accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system collects and pre-processes multiple data sets including user profile information, communication device data, and content interaction data before the actual language identification process. This preliminary preparation of data structures and cleaning enables faster real-time analysis when language attributes need to be determined.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Traditional rule-based language detection methods are replaced with machine learning algorithms that automatically analyze patterns in user data. These algorithms process multiple data sets simultaneously and identify language attributes more efficiently than sequential mechanical processing, reducing overall processing time while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If comprehensive user data is collected from multiple sources to determine language proficiency, then marketing campaign personalization is improved, but data privacy concerns and compliance requirements increase

Engineering Contradiction:

Improvecampaign personalization capabilityVSAvoiddata privacy risks

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The system extracts only the specific language-related attributes needed for marketing personalization from comprehensive user data sets. Instead of storing and processing all user information, it selectively extracts primary language, secondary language, proficiency levels, and vernacular preferences, minimizing data retention and reducing privacy risks.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system uses an intermediary processing layer that aggregates language attributes from multiple data sources without exposing raw personal data. This intermediary layer processes user profiles, device data, and content interactions to derive language characteristics while maintaining data security and enabling compliance with privacy regulations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20210117862A1Method and system for adopting user learnings across vernacular contexts

Publication Date: 2021.04.22 AFFLE INT PTE LTD

AI summary

The present disclosure provides a method and system to adopt user learnings across vernacular contexts. The system receives a first set of data associated with a plurality of users. The system collects a second set of data associated with the plurality of users. The system fetches a third set of data associated with one or more communication devices of the plurality of users. The system analyzes the first set of data, the second set of data, and the third set of data using one or more machine learning algorithms. The system enables segmentation of the plurality of users in one or more segments based on one or more patterns of a plurality of languages and a plurality of language attributes. The system triggers initialization of one or more personalized marketing campaigns for the one or more segments based on the plurality of languages and the plurality of language attributes.