Machine-Learning Content Stack Graphs Reduce Navigational Burden

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Solution Overview

Problem

Existing digital content systems are inflexible and inefficient, requiring separate applications and numerous navigational operations to access and interact with different types of content items, leading to excessive resource consumption and inefficient user interfaces.

Innovation Solution

A content stack generation system utilizing a stack formulation graph and a large language model to adaptively access and provide content items from various locations, integrating multiple applications within a single interface and reducing navigational burden.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If separate applications are used to access stored content items, web-based content items, and communicate between client devices, then specific content access functions are achieved, but device complexity and resource consumption increase

Engineering Contradiction:

Improvecontent access capabilityVSAvoidnumber of applications

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent combines multiple separate applications (file management application, web browser application, communication application) into a single unified application interface. This unified interface allows users to access stored content items, web-based content items, and communicate with other users through one integrated system, thereby reducing device complexity while maintaining comprehensive content access capability

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The unified application interface is designed to perform multiple functions simultaneously: it can access stored content items through folder navigation, access web-based content items through integrated browsing, and facilitate communication between users. This multi-functional design eliminates the need for separate specialized applications while preserving all necessary content access capabilities

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If separate applications are used for different content types, then specialized functionality is provided, but resource consumption increases

Engineering Contradiction:

Improvecontent handling capabilityVSAvoidprocessing power and memory

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent merges the computational resources required for handling different content types into a single shared infrastructure. By consolidating file management, web browsing, and communication functions into one application, the system eliminates redundant resource consumption that would occur if separate applications were running simultaneously, thereby reducing overall processing power and memory requirements while maintaining full content handling capability

Inventive Principle:

Principle #5Merging (Combining)

3Stability of the object's composition

If drill-down navigation through folder hierarchies is used to access content items, then organized content structure is maintained, but user interface efficiency decreases

Engineering Contradiction:

Improvecontent organization structureVSAvoidnavigational operations

Core Design Contradiction:

Stability of the object's compositionVSEase of operation

Solution Approach 1:

The patent introduces a new dimensional approach to content access by implementing a search and direct access interface that operates parallel to the traditional folder hierarchy. Instead of requiring users to navigate through multiple folder levels, the system provides a direct search dimension where users can locate and access content items through keywords or direct links, thereby reducing navigational operations while preserving the organized folder structure for those who prefer it

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS12373492B2Generating and providing content stacks utilizing machine-learning models

Publication Date: 2025.07.29 DROPBOX INC

AI summary

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.