ML Content Depiction Generation via Profile-Template Matching

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Solution Overview

Problem

Existing methods for generating content depictions, such as movie posters, are expensive and time-consuming due to manual creation, and often ineffective as they fail to cater to the diverse preferences of different consumer profiles, leading to broad but untargeted marketing.

Innovation Solution

A machine learning system that processes user profiles, content metadata, and feature depictions to generate tailored content images using neural networks and natural language processing, optimizing depictions based on user preferences and feedback for improved consumption outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual creation of content depictions is used, then customization to consumer profiles is possible, but cost and time consumption increase significantly

Engineering Contradiction:

Improvecustomization to consumer profilesVSAvoidtime consumption

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system uses template-based generation where standardized depiction templates are copied and automatically customized with consumer profile data, eliminating manual creation while maintaining personalization. Templates contain pre-defined layouts and design elements that can be rapidly instantiated for different consumers.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system dynamically adjusts depiction parameters (images, text, colors, layout) based on consumer profile parameters. By changing these parameters automatically through algorithms, the system achieves customization without manual intervention, resolving the contradiction between adaptability and time consumption.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If manual creation of content depictions is used, then quality control is possible, but cost increases significantly

Engineering Contradiction:

Improvecustomization to consumer profilesVSAvoidcost

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

Reusable templates and standardized design elements are copied across multiple depictions, eliminating repetitive manual work. This reduces cost while maintaining quality through consistent template design that has been pre-approved.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs automatic quality control through algorithmic validation of generated depictions against predefined quality criteria. The generation process itself self-regulates by selecting and combining elements that meet quality standards, eliminating the need for expensive manual review while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

3Productivity

If generalized content depictions are used, then distribution efficiency is improved, but effectiveness in attracting specific consumers decreases

Engineering Contradiction:

Improvedistribution efficiencyVSAvoideffectiveness for specific consumer profiles

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system applies different depiction elements and parameters to different consumer segments while maintaining a standardized overall structure. This allows efficient bulk generation (distribution efficiency) while tailoring specific elements to local consumer preferences (effectiveness).

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The depiction generation system dynamically adapts content based on real-time consumer profile data, allowing the same distribution infrastructure to deliver personalized content. This dynamic adaptation maintains distribution efficiency while improving effectiveness for specific consumer groups.

Inventive Principle:

Principle #15Dynamics

4Productivity

If automated generation systems are implemented, then productivity increases, but complexity of the system increases

Engineering Contradiction:

Improvedepiction generation speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated generation system is segmented into independent modular components (template selection, parameter adjustment, image generation, quality validation). Each module handles a specific task, making the overall complex system manageable through clear separation of concerns while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240422371A1Systems and methods for generating adapted content depictions

Publication Date: 2024.12.19 ADEIA GUIDES INC

AI summary

A method for generating a content depiction of particular content that includes a machine learning system programmed to receive profile data representing preferences for content. The machine learning system identifies preferences for content features based upon the profile data, accesses content data representing the particular content and other content, and classifies features of the content data and content structure data within a content structure database system according to content categories. The machine learning system generates a content structure depiction of the particular content by combining content structure data from the content structure database system, wherein the combining is based upon correlating the identified preferences of the profile with the classified content categories. The machine learning system receives feedback data responsive to the content depiction and reprograms a configuration of the machine learning system for generating a content depiction based upon the feedback data.