Image Generation System for Historic Event Clustering

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Solution Overview

Problem

Records of historic events often lack intuitive labeling and context, making it difficult for users to identify related events and verify their authenticity.

Innovation Solution

An image generation system that clusters related historic events based on location and timestamp, assigns importance levels, and uses machine learning to generate images depicting these clusters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If descriptions are added to records of historic events, then clarity and context are improved, but the descriptions often lack clarity and do not provide necessary context

Engineering Contradiction:

Improvecontext informationVSAvoidinterpretation difficulty

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent introduces an image generation system as an intermediary between raw historic event records and user comprehension. The system clusters related events and generates visual representations that mediate the information gap, providing context without requiring users to interpret ambiguous text descriptions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical approach of adding text descriptions with a machine learning-based image generation system. Instead of manually crafting descriptions, the system automatically generates visual representations that convey contextual information more effectively.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Quantity of substance

If records are labeled with codes or shorthand, then information density is improved, but intuitiveness and understandability deteriorate

Engineering Contradiction:

Improveinformation densityVSAvoidunderstandability

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The patent uses visual imagery to replace text-based codes and shorthand. Instead of relying on symbolic representations that require decoding, the system generates images that directly convey the meaning and context of historic events, making information both dense and understandable simultaneously.

Inventive Principle:

Principle #32Color changes

3Loss of information

If clustering algorithms are applied to identify related events, then relationship identification is improved, but system complexity increases

Engineering Contradiction:

Improverelationship informationVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent implements a multi-functional system that performs both clustering analysis and image generation within a single integrated platform. The machine learning model serves multiple purposes: identifying relationships between events, generating contextual images, and presenting information to users, thereby managing complexity through functional consolidation.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Loss of information

If machine learning models are used to generate images, then contextual visualization is improved, but computational resources and processing time increase

Engineering Contradiction:

Improvecontextual informationVSAvoidcomputational energy

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by generating images only for clustered groups of related historic events rather than processing every individual record. This selective approach reduces computational energy consumption while still providing contextual visualization where it is most needed for user understanding.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250029287A1Generating images based on generated clusters

Publication Date: 2025.01.23 CAPITAL ONE SERVICES LLC

AI summary

Methods and systems are described herein for generating images based on generated clusters. The system retrieves, from an account of a user, historic events associated with the user and determines clusters of the historic events based on location and time data associated with the historic events. The system generates an input for a machine learning model for a first cluster of historic events, where the machine learning model has been trained to generate images based on clusters of historic events. The system then inputs the input into the machine learning model to cause the machine learning model to output images depicting locations associated with the first cluster of historic events. The system outputs the images in conjunction with the first cluster of historic events.