Threshold-Based Media Mention Detection for Automatic Actions

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Solution Overview

Problem

Users often forget media content recommendations from friends and family due to the inconvenience of manually creating reminders, leading to missed opportunities for discovering new media assets.

Innovation Solution

A system that processes verbal interactions to automatically identify media assets mentioned during conversations and adds them to a user's list, considering interest levels and interaction frequency to recommend relevant content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users manually create reminders or notes to recall media recommendations, then they can remember recommended content, but the process becomes time-consuming and inconvenient

Engineering Contradiction:

Improvememory of recommendationsVSAvoidtime to create reminders

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system automatically monitors conversations, identifies media assets mentioned, and creates recommendations without requiring user intervention. The system serves itself by autonomously capturing verbal data, analyzing it to identify media assets, and managing the recommendation list, thereby eliminating the need for users to manually create reminders while maintaining reliable memory of recommendations

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by continuously monitoring conversations and identifying media assets before users need to remember them. By proactively capturing and processing verbal data in real-time during interactions, the system prepares recommendations in advance without requiring users to spend time creating reminders manually

Inventive Principle:

Principle #10Preliminary action

2Reliability

If users manually create reminders for media recommendations, then they can track recommended content, but the process becomes inconvenient and users may forgo the effort

Engineering Contradiction:

Improvetracking of recommendationsVSAvoidconvenience of creating reminders

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automatically tracks recommendations by monitoring conversations and identifying media assets without requiring user actions. The system serves itself by autonomously capturing verbal data, analyzing it to identify media assets, and maintaining the recommendation list, thereby eliminating the need for users to manually create reminders while maintaining reliable tracking

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of creating reminders and notes with an automated electronic system. The system uses voice recognition and data processing to automatically identify media assets and manage recommendations, substituting the manual mechanical action with an automated information processing system that is both reliable and convenient

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If multiple people recommend the same media asset to a user, then the user receives more recommendations, but the user may not notice that multiple people have endorsed it

Engineering Contradiction:

Improvenumber of recommendationsVSAvoidawareness of multiple endorsements

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The system merges recommendations from multiple people by aggregating them into a unified recommendation list. When multiple people mention the same media asset, the system combines these separate recommendations into a single consolidated entry, allowing users to see that multiple people have endorsed the content without requiring separate tracking for each person's recommendation

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250307283A1Systems and methods for automatic program actions based on user interactions

Publication Date: 2025.10.02 ADEIA GUIDES INC

AI summary

Methods and systems are provided for performing automatic actions related to a media asset based on a number of user interactions mentioning the media asset. In some embodiments, control circuitry receives a selection for a number of times a media asset is identified in one or more monitored interactions of a user to cause an action to be performed. The control circuitry further monitors a plurality of interactions of the user to store respective interaction data. For each interaction of the plurality of interactions of the user, the control circuitry accesses data of the respective interaction, identifies respective mentions of the media asset during the respective interaction, and increments a count of media asset mentions. The control circuitry performs the action based on determining that the count of media asset mentions equals or exceeds the selected number of times the media asset is to be identified.