Emotional Impact Factor Recommendation System
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Solution Overview
Problem
Current recommendation systems for media and products fail to effectively match users with content based on emotional impact, relying on preselected genres or past consumption patterns, and lack improvement in verifying the veracity of media information.
Innovation Solution
Developing systems that determine Emotional Impact Factors (EIF) by analyzing anthropological, ethnographic, and psycho-social parameters across sentient and non-sentient agents, integrating these factors into psychometric profiles to provide personalized recommendations and verify media authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems rely on preselected genres or past consumption patterns, then implementation is simple, but recommendation accuracy and user engagement are insufficient
Solution Approach 1:
The system transforms the recommendation approach by changing the parameters used for matching - instead of using simple genre tags or consumption history, it calculates Emotional Impact Factors (EIF) that quantify the emotional response to media content. This involves measuring multiple parameters including emotional valence, arousal levels, and user psychometric profiles, thereby improving recommendation accuracy through more nuanced emotional parameter matching
Solution Approach 2:
The system introduces an intermediary computational layer that processes raw media content and user responses into standardized Emotional Impact Factors. This intermediary layer includes algorithms that analyze media characteristics, measure user emotional responses, and compute EIF values that serve as the basis for recommendations, thereby bridging the gap between simple input data and accurate recommendations
2Adaptability or versatility
If systems analyze multiple anthropological, ethnographic, and psycho-social parameters, then recommendation personalization improves, but data processing complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modular components: (1) media content analysis module that extracts emotional characteristics from media, (2) user response measurement module that captures user emotional reactions, (3) EIF calculation module that computes emotional impact factors, and (4) recommendation generation module that matches EIF with user profiles. This segmentation allows each module to handle specific parameters independently, reducing overall processing complexity while maintaining comprehensive personalization
Solution Approach 2:
The system transforms multiple complex anthropological, ethnographic, and psycho-social parameters into a standardized set of Emotional Impact Factor parameters. By converting diverse input parameters (cultural background, personality traits, consumption patterns) into unified EIF metrics, the system achieves comprehensive personalization while simplifying the data processing architecture through parameter standardization
3Measurement precision
If systems determine Emotional Impact Factors through comprehensive data analysis, then recommendation quality improves, but computational time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing Emotional Impact Factor profiles for media content in a database. When a user requests recommendations, the system retrieves pre-computed EIF values and compares them with the user's current psychometric profile, avoiding the need to re-analyze the entire media content. This preliminary processing significantly reduces computational time during actual recommendation generation while maintaining measurement accuracy
Solution Approach 2:
The system replaces comprehensive real-time data analysis with a more efficient computational approach using pre-computed Emotional Impact Factors and psychometric profile matching. Instead of mechanically analyzing all media parameters and user responses in real-time, the system uses stored EIF values and algorithmic profile comparison, substituting heavy computational mechanics with efficient data retrieval and matching operations
Data Source
AI summary
Some embodiments of the present disclosure relate to systems, devices, and methods for making recommendations to users for, individually or together, goods, services, and information/media (e.g., audio, video, publications), as well as verifying the veracity of data/information, based upon an emotional impact of such goods/services and/or media/information on the user, other users, and/or other individuals, as well some embodiments directed to systems, devices, and methods for treating one or more brain functions, including neurological conditions and episodes associated therewith by determining (and optionally providing) media (e.g., audio, but in some embodiments, any of audio, textual information, and video), which may be referred to as optimal medical media (“OMM”).