View source for OVOMIND K.K (20240245986). METHOD FOR AUTOMATICALLY PREDICTING THE EMOTIONAL EFFECT PRODUCTED BY A VIDEO GAME SEQUENCE simplified abstract

=METHOD FOR AUTOMATICALLY PREDICTING THE EMOTIONAL EFFECT PRODUCTED BY A VIDEO GAME SEQUENCE=


==Organization Name==

[[:Category:OVOMIND K.K|OVOMIND K.K]]


[[Category:OVOMIND K.K]]

==Inventor(s)==

[[:Category:Yann Frachi of Marseille (FR)|Yann Frachi of Marseille (FR)]][[Category:Yann Frachi of Marseille (FR)]]

==METHOD FOR AUTOMATICALLY PREDICTING THE EMOTIONAL EFFECT PRODUCTED BY A VIDEO GAME SEQUENCE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240245986 titled 'METHOD FOR AUTOMATICALLY PREDICTING THE EMOTIONAL EFFECT PRODUCTED BY A VIDEO GAME SEQUENCE

'''Simplified Explanation''': 

This patent application describes a method for automatically predicting the emotional effect produced by a video game sequence by analyzing audio and video streams, as well as biosignals, using neural networks and NLP coding layers.

* Labeling game sequences by generating descriptors at specific time points
* Applying digital processing to audio stream using neural network architecture and NLP coding layer
* Extracting timestamped descriptors from audio and video streams
* Processing biosignals to extract timestamped signals
* Transmitting descriptors and signals to neural network for analysis
* Predicting emotional state brought about by audiovisual sequences

'''Key Features and Innovation''':

* Automatic prediction of emotional effects in video game sequences
* Integration of audio, video, and biosignal analysis using neural networks
* Real-time processing of data streams for accurate emotional state prediction

'''Potential Applications''':

* Video game development for enhancing user experience
* Virtual reality applications for immersive environments
* Mental health monitoring and therapy using biosignal analysis

'''Problems Solved''':

* Difficulty in predicting emotional responses to audiovisual content
* Lack of real-time analysis tools for emotional state assessment
* Limited integration of multiple data streams for emotion prediction

'''Benefits''':

* Enhanced user engagement in video games
* Personalized content delivery based on emotional responses
* Improved mental health monitoring and intervention

'''Commercial Applications''''':

* "Automatic Emotional State Prediction in Video Games: Enhancing User Experience and Engagement"
* This technology can be utilized in the gaming industry to create more immersive and emotionally engaging experiences for players.
* Market Implications: Increased user retention, higher customer satisfaction, and potential for new revenue streams through personalized content delivery.

'''Prior Art''':

Prior research in affective computing and emotion recognition using audio and video signals can provide insights into similar technologies and methodologies.

'''Frequently Updated Research''':

Ongoing research in affective computing, neural network architectures, and biosignal analysis can provide valuable updates and advancements in this field.

'''Questions about Automatic Emotional State Prediction in Video Games''':

1. How does this technology compare to traditional methods of assessing emotional responses in video games?
2. What are the potential ethical considerations surrounding the use of biosignals for emotional state prediction in gaming environments?


==Original Abstract Submitted==

a method is provided for automatically predicting the emotional effect produced by a video game sequence, comprising labeling sequences of the game by automatically generating descriptors at time sequences of the game, the labeling comprising applying digital processing to the audio stream of the video game sequence using a neural network architecture and an nlp coding layer, to extract a first series of timestamped descriptors, and applying digital processing to the video stream to provide a second series of timestamped descriptors for characterizing the scenes of each image of the video stream, and transmitting them as m-tuples to a neural network. the method also comprises processing biosignals to extract timestamped signals and transmit them as n-tuples to a neural network and processing the m-tuples corresponding to the timestamped descriptors and the n-tuples to provide at least one indicator predicting the emotional state brought about by a type of audiovisual sequence.



[[Category:A63F13/212]]
[[Category:A63F13/67]]
[[Category:CPC_A63F13/212]]