Abstract: A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Abstract: Automatically recommending sound effects based on visual scenes enables sound engineers during video production of computer simulations, such as movies and video games. This recommendation engine may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX.

Abstract: Sound effects (SFX) are registered in a database for efficient search and retrieval. This may be accomplished by classifying SFX and using a machine learning engine to output a first of the classified SFX for a first computer simulation based on learned correlations between video attributes of the first computer simulation and the classified SFX. Subsequently, videos without sound may be processed for object, action, and caption recognition to generate video tags which are semantically matched with SFX tags to associate SFX with the video.

Abstract: A system enhances existing audio-visual content with audio describing the setting of the visual content. A scene annotation module classifies scene elements from an image frame received from a host system and generates a caption describing the scene elements.

Abstract: A system enhances existing audio visual content with audio describing the action and setting of the visual content. The system may also provide subtitle content describing the important sound or sounds occurring within audio. Accommodation for color or visual impairments may be implemented by selective color substitution. A Graphical Style Modification module may apply a style from one image to another to adapt the style of a video per a gamer's preference.

Abstract: Graphical style modification may be implemented using machine learning. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. A Graphical Style Modification module receives a first image frame from a host system and applies a style adapted from a second image frame to the first image frame to generate a style adapted first image frame.

Abstract: A system and method for multimodal classification of user characteristics is described. The method comprises receiving audio and other inputs, extracting fundamental frequency information from the audio input, extracting other feature information from the video input, classifying the fundamental frequency information, textual information and video feature information using the multimodal neural network.

Abstract: A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Abstract: A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Abstract: A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Abstract: A video game console, a video game system, and a computer-implemented method are described. Generally, a video game and video game assistance are adapted to a player. For example, a narrative of the video game is personalized to an experience level of the player. Similarly, assistance in interacting with a particular context of the video game is also personalized. The personalization learns from historical interactions of players with the video game and, optionally, other video games. In an example, a deep learning neural network is implemented to generate knowledge from the historical interactions. The personalization is set according to the knowledge.

Abstract: A mobile wireless device, such as a mobile wireless phone, is adapted based on a user's current abstracted contextual situation, where the context of a user is determined using devices enabled with near-field communication technology. Dynamic information of a user of a mobile device, such as the identity of his or her current environment, is determined using near-field communication, such as radio-frequency identification (RFID) tags. Static information regarding the user is also determined, where such static information can include the user's preferences regarding how the mobile device should adapt to certain environments. An abstracted contextual situation of the user is synthesized based on this dynamic and static information. One or more adaptation directives for the mobile device of the user are determined based on the user's abstracted contextual situation.

Abstract: A mobile wireless device, such as a mobile wireless phone, is adapted based on a user's current abstracted contextual situation, where the context of a user is determined using devices enabled with near-field communication technology. Dynamic information of a user of a mobile device, such as the identity of his or her current environment, is determined using near-field communication, such as radio-frequency identification (RFID) tags. Static information regarding the user is also determined, where such static information can include the user's preferences regarding how the mobile device should adapt to certain environments. An abstracted contextual situation of the user is synthesized based on this dynamic and static information. One or more adaptation directives for the mobile device of the user are determined based on the user's abstracted contextual situation.