Dynamic Keyboard Interface for Expressive Media Retrieval

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Solution Overview

Problem

Conventional methods for communicating expressive content through messaging platforms are inefficient, as they require manual searching and copying of GIFs or images, lacking a dynamic interface for categorized content retrieval.

Innovation Solution

A media content management system that categorizes and retrieves expressive media content using a dynamic keyboard interface, allowing users to select animated inputs based on content associations, with automated content association generation and pre-processing for quick rendering.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional searching and copying methods are used, then users can share media content, but the process requires manual intervention and is time-consuming

Engineering Contradiction:

Improvecontent sharing speedVSAvoidmanual searching time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-categorizing media content into collections based on expressive intent metadata before the user needs it. The content is pre-processed and organized with associated metadata during ingestion, so when a user searches, the matching content is already prepared and immediately available for sharing, eliminating manual searching time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by automatically generating content associations and categorizing media content without requiring manual user intervention. The automated classification system independently organizes content into relevant collections based on analyzed metadata, allowing users to simply search and share without manually organizing or searching through unstructured content.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If manual searching and copying of GIFs is performed, then content can be shared, but the process lacks a dynamic interface for categorized content retrieval

Engineering Contradiction:

Improvecontent retrieval easeVSAvoidinterface complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system segments media content into distinct collections based on expressive intent categories (e.g., happy, sad, surprised). Each collection is independently organized with specific metadata tags, allowing users to easily navigate and retrieve content by selecting the desired emotional category rather than searching through a single undifferentiated pool of content.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The interface is made dynamic by allowing real-time filtering and sorting of content collections based on user-selected expressive intent. The system dynamically updates the displayed content based on metadata matching, providing an adaptive retrieval experience that responds to user input without requiring complex manual search operations.

Inventive Principle:

Principle #15Dynamics

3Extent of automation

If conventional messaging platforms are used, then users can communicate, but they lack automated content association generation and pre-processing

Engineering Contradiction:

Improveautomated content associationVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms by analyzing user interactions with content and automatically refining content associations. The metadata analysis system continuously processes user behavior data to improve the accuracy of content categorization and association generation, enabling increasingly accurate automated content matching without requiring additional user input or increasing system complexity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual mechanical processes of content organization and searching with automated computational processes. Machine learning algorithms and metadata analysis automatically generate content associations and categorize media files, substituting the manual sorting and tagging process with intelligent automated systems that scale without proportionally increasing complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11138207B2Integrated dynamic interface for expression-based retrieval of expressive media content

Publication Date: 2021.10.05 GOOGLE LLC

AI summary

Various embodiments relate generally to a system, a device and a method for expression-based retrieval of expressive media content. A request may be received to search for content items in a media content management system. Media content items may be procured from different content sources through application programming interfaces, user devices, and/or web servers. Media content items may be analyzed to determine one or more metadata attributes, including an expressions. Metadata attributes may be stored as one or more content associations. The media content items may be stored and categorized based on the content associations. A search router rules engine may determine search intent based on the search query, which may include a pictorial representation of an expression, such as an emoji. A dynamic interface may be integrated in a device operating system through various access points, including a button, a trigger key, a keyword trigger, and an overlay button.