Abstract: The present invention relates to a system and method for providing adaptive digital therapy, enhancing the emotional well-being of users through the analysis and translation of expressive and physiological states. Aspects of the invention are directed to identifying the neuroplastic window via user inputs, with or without the aid of psychedelics. This invention is particularly suited for supporting psychoactive therapies involving compounds such as 5-HT2A agonists, dissociatives, and empathogens, as well as applications beyond psychoactive treatments. By utilizing multi-modal deep learning methods, the system captures a wide array of user inputs, including text, speech, vision, symbols, and brain waves, to form a dynamic user representation. This representation is then used to provide real-time, tailored generative communications informed by established psychological therapeutic frameworks.

Abstract: The present invention relates to a system and method for providing adaptive digital therapy, enhancing the emotional well-being of users through the analysis and translation of expressive and physiological states. Aspects of the invention are directed to identifying the neuroplastic window via user inputs, with or without the aid of psychedelics. This invention is particularly suited for supporting psychoactive therapies involving compounds such as 5-HT2A agonists, dissociatives, and empathogens, as well as applications beyond psychoactive treatments. By utilizing multi-modal deep learning methods, the system captures a wide array of user inputs, including text, speech, vision, symbols, and brain waves, to form a dynamic user representation. This representation is then used to provide real-time, tailored generative communications informed by established psychological therapeutic frameworks.

Abstract: The pose of a wide-angle image is determined by dewarping regions of the wide-angle image, determining estimated poses of the dewarped regions of the wide-angle image and deriving a pose of the wide-angle image from the estimated poses of the of the dewarped regions. The estimated poses of the dewarped regions may be determined by comparing features in the dewarped regions with features in prior dewarped regions from one or more prior wide-angle images, as well as by comparing features in the dewarped regions with features in a point cloud.

Abstract: The pose of a wide-angle image is determined by dewarping regions of the wide-angle image, determining estimated poses of the dewarped regions of the wide-angle image and deriving a pose of the wide-angle image from the estimated poses of the of the dewarped regions. The estimated poses of the dewarped regions may be determined by comparing features in the dewarped regions with features in prior dewarped regions from one or more prior wide-angle images, as well as by comparing features in the dewarped regions with features in a point cloud.

Abstract: The pose of a wide-angle image is determined by dewarping regions of the wide-angle image, determining estimated poses of the dewarped regions of the wide-angle image and deriving a pose of the wide-angle image from the estimated poses of the of the dewarped regions. The estimated poses of the dewarped regions may be determined by comparing features in the dewarped regions with features in prior dewarped regions from one or more prior wide-angle images, as well as by comparing features in the dewarped regions with features in a point cloud.

Abstract: The pose of a wide-angle image is determined by dewarping regions of the wide-angle image, determining estimated poses of the dewarped regions of the wide-angle image and deriving a pose of the wide-angle image from the estimated poses of the of the dewarped regions. The estimated poses of the dewarped regions may be determined by comparing features in the dewarped regions with features in prior dewarped regions from one or more prior wide-angle images, as well as by comparing features in the dewarped regions with features in a point cloud.