Media Recommendation Capture from Verbal Interaction Analysis

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Solution Overview

Problem

Users often forget media recommendations from friends and family due to the inconvenience of manually creating reminders, and multiple recommendations can be overlooked without proper tracking.

Innovation Solution

A system that processes verbal interactions to identify media assets mentioned during conversations and automatically adds them to a user's list based on interest thresholds and contact relevance, enabling automated media asset recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users manually create reminders or notes to recall media recommendations, then they can remember the recommendations, but it is time consuming and inconvenient

Engineering Contradiction:

Improverecall accuracyVSAvoidtime to create reminders

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system automatically monitors user interactions and identifies media assets without requiring user action. The device itself performs the task of creating reminders by detecting media asset references in conversations and automatically adding them to the user's media asset list, eliminating the need for manual reminder creation while ensuring reliable recall

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system proactively identifies and captures media asset recommendations as they occur during conversations, before the user would need to manually record them. By continuously monitoring interactions and automatically processing media asset identification, the system prepares and stores recommendation data in advance, ready for user access without requiring reactive manual input

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If users manually track multiple media recommendations, then they can remember all recommendations, but it is inconvenient and users quickly forget

Engineering Contradiction:

Improverecommendation trackingVSAvoidtracking convenience

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The system automatically performs the entire tracking process without user intervention. It monitors conversations, identifies media asset references, determines user interest levels based on interaction analysis, and maintains the media asset list automatically. This eliminates the manual tracking burden while ensuring comprehensive recommendation capture and follow-through

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors user interactions and provides feedback by automatically updating the media asset list based on detected recommendations. It analyzes conversation patterns and user responses to adjust tracking accuracy, ensuring that relevant recommendations are captured while filtering out irrelevant information, all without requiring user effort

Inventive Principle:

Principle #23Feedback

3Productivity

If the system automatically identifies media assets from conversations, then users receive comprehensive recommendations, but the system complexity increases

Engineering Contradiction:

Improverecommendation capture efficiencyVSAvoidautomatic identification system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system uses an intermediary processing layer that analyzes conversation data to identify media asset references. This intermediary component translates raw interaction data into structured media asset identifications, bridging the gap between user conversations and the recommendation system without requiring complex direct integration between all system components

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The automatic identification system is divided into separate functional modules: interaction monitoring, media asset detection, interest level determination, and list management. Each module handles a specific aspect of the recommendation process independently, reducing overall system complexity while maintaining comprehensive functionality through modular architecture

Inventive Principle:

Principle #1Segmentation

4Loss of information

If the system monitors all user interactions to identify media recommendations, then no recommendations are missed, but the processing time and resources increase

Engineering Contradiction:

Improverecommendation detection completenessVSAvoidprocessing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system monitors all interactions but applies selective processing based on detected relevance. It uses preliminary filtering to identify only those interactions that contain potential media asset references, then performs detailed analysis only on those segments. This approach ensures comprehensive recommendation detection while minimizing processing time by avoiding exhaustive analysis of all conversation data

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12353453B2Systems and methods for automatic program recommendations based on user interactions

Publication Date: 2025.07.08 ADEIA GUIDES INC

AI summary

Methods and systems are provided for generating automatic program recommendations based on user interactions. In some embodiments, control circuitry processes verbal data received during an interaction between a user of a user device and a person with whom the user is interacting. The control circuitry analyzes the verbal data to automatically identify a media asset referred to during the interaction by at least one of the user and the person with whom the user is interacting. The control circuitry adds the identified media asset to a list of media assets associated with the user of the user device. The list of media assets is transmitted to a second user device of the user.