Content Snippet Storage With Source Context Preservation

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Solution Overview

Problem

Users face difficulties in accurately identifying and accessing relevant information from web content due to limited context provided by screenshots or links, requiring cumbersome navigation and inefficient data retrieval.

Innovation Solution

A computing system that generates and stores content snippets using user inputs and generative models to isolate and categorize selected content portions, providing enhanced context and facilitating efficient data retrieval and sharing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users save text, images, and audio from web pages, then users can locally experience the content without internet connection, but users lose context information about where the content came from and how to access it

Engineering Contradiction:
Improvecontent accessibilityVSAvoidcontext information
Core Design Contradiction:
ReliabilityVSLoss of information

Solution Approach 1:

The patent segments web page content into distinct snippets, each containing specific content elements (text, images, audio) along with their source metadata. This segmentation allows users to save and access content locally while maintaining context information about the original source through structured data fields that capture URL, page title, and position information.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary system that acts as a bridge between saved content and source information. This system stores content snippets with embedded metadata and provides retrieval mechanisms that automatically reconstruct context information, eliminating the need for users to manually track source locations while maintaining reliable local access.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Loss of information

If users navigate through browsing history to find saved content, then users can locate the original source, but users spend excessive time and effort reviewing large portions of web pages

Engineering Contradiction:
Improvesource locationVSAvoidcontent retrieval time
Core Design Contradiction:
Loss of informationVSLoss of time

Solution Approach 1:

The patent performs preliminary action by automatically capturing and storing source location metadata (URL, page title, content position) at the moment content is saved. This pre-captured information is stored with the content snippet, enabling instant retrieval without requiring users to later navigate through browsing history or search through large portions of web pages.

Inventive Principle:
Principle #10Preliminary action

3Quantity of substance

If users take screenshots to save content, then users can capture visual information, but users provide limited context and require cumbersome navigation to access related information

Engineering Contradiction:
Improvevisual dataVSAvoiddata retrieval operation
Core Design Contradiction:
Quantity of substanceVSEase of operation

Solution Approach 1:

The patent segments content into structured snippets that separate visual data (screenshots, images) from metadata (source URL, position, content type). This segmentation allows the system to store comprehensive visual information while providing easy access to context through organized data fields, eliminating cumbersome navigation requirements.

Inventive Principle:
Principle #1Segmentation

Data Source

PatentUS20250278443A1Content snippet generation and storage with generative model content grouping
Publication Date: 2025.09.04 GOOGLE LLC
  • US20250278443A1 patent drawing
  • US20250278443A1 patent drawing
  • US20250278443A1 patent drawing

AI summary

Systems and methods for content snippet generation, storage, and suggestion can include obtaining a user input, segmenting a sub-portion of displayed content based on the user input, and generating a content snippet that includes the segmented content and source data associated with the displayed content. A generative model can be leveraged to categorize the content snippet, determine similar content, and determine when to surface the content snippet to a user.