Media Retrieval System for Digital Content Discovery

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Solution Overview

Problem

Users face challenges in discovering and accessing relevant digital media content within vast digital media services due to time constraints, as existing systems fail to efficiently select and present short, relevant media segments that match user interests and preferences.

Innovation Solution

A media retrieval and organization system that identifies recommended media items based on user preferences and recency metrics, streams preview segments, enables user feedback, and allows for deferred consumption of full-length media items, utilizing machine learning to personalize content selection and presentation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If users manually search through vast digital media content, then they can find relevant media items, but the time required increases significantly

Engineering Contradiction:

Improvemedia discovery accuracyVSAvoidtime to find media

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-selecting and pre-processing media items based on user preferences and recency metrics before users need them. Media items are pre-organized and pre-ranked, so when users access the system, relevant content is already prepared and presented, eliminating the need for manual searching through vast content libraries.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual mechanical searching with an automated machine learning-based recommendation system. Instead of users manually browsing and filtering content, the system automatically analyzes user preferences, processes media metadata, and generates personalized recommendations, substituting human effort with automated computational processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Quantity of substance

If the system presents only full-length media items, then complete content is provided, but users cannot quickly assess relevance before committing time

Engineering Contradiction:

Improvemedia content completenessVSAvoiduser decision efficiency

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system segments media content by presenting brief previews or summaries of full-length media items before the user commits to viewing the complete content. This segmentation allows users to quickly assess relevance without investing significant time, while still providing access to the complete media item if desired. The preview acts as a filter that helps users make efficient decisions about which full-length items warrant their attention.

Inventive Principle:

Principle #1Segmentation

3Speed

If the system stores all full-length media items for every user, then immediate access is provided, but storage requirements and costs increase

Engineering Contradiction:

Improvemedia access speedVSAvoidstorage capacity

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The system applies local quality by providing different levels of media content to different users based on their specific needs and preferences. Rather than storing complete media items for all users, the system stores and provides full-length items only for users who have expressed interest through positive interactions with previews. This creates a personalized, sparse storage structure where each user has access to complete items relevant to them while the overall system storage requirements remain manageable.

Inventive Principle:

Principle #3Local quality

4Productivity

If the system uses simple recommendation algorithms, then processing is fast and simple, but personalization accuracy decreases

Engineering Contradiction:

Improvecontent selection speedVSAvoidpersonalization accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system employs machine learning models that dynamically adjust parameters based on user behavior patterns, preferences, and feedback. These models process multiple features and parameters simultaneously to generate personalized recommendations, achieving both speed and accuracy through optimized computational approaches. The system balances processing complexity with recommendation quality by using efficient algorithms that can handle sophisticated personalization without excessive computational overhead.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11451598B1Systems and methods for digital media storage

Publication Date: 2022.09.20 META PLATFORMS INC

AI summary

Systems and methods for creating automatic digital representations of events may include (1) transmitting a preview segment of a recommended media item to a user's device for presentation within a media consumption interface presented within a display element of the device, (2) receiving, from the device, an indication that the user has selected a user-selectable save element presented in association with the preview segment within the media consumption interface, and (3) in response to receiving the indication, adding a full-length version of the recommended media item to a digital container maintained for the user. Various other methods, systems, and computer-readable media are also disclosed.