Abstract: Embodiments of the present invention provide a system for analyzing using artificial intelligence engines depicted in audio visual content. The system is configured for determining that a user has accessed a first audio visual content on an audio visual content providing platform, performing a frame-by-frame analysis to determine one or more resources depicted in the first audio visual content, identifying one or more third party systems associated with the one or more resources, crawling the one or more third party systems associated with the one or more resources, extracting resource information associated with the one or more resources from the one or more third party systems based on crawling the one or more third party systems, and embedding the resource information associated with the one or more resources in the first audio visual content.

Abstract: Assessing Susceptibility to Epilepsy and Epileptic Seizures A method and system adapted to assist with assessing susceptibility to epilepsy and/or epileptic seizures in a patient receives (202) patient brain data and generates (204) a network model from the received patient brain data. The system further generates (206) synthetic brain activity data in at least some of the nodes of the network model and computes (208) seizure frequency from the synthetic brain activity data by monitoring transitions from non-seizure states to seizures states in at least some of the nodes over time. The system further includes a device (104, 110) configured to use the seizure frequency to compute (210) a likelihood of susceptibility to epilepsy and/or epileptic seizures in the patient, and a device (104, 110) configured to compare (212) the computed likelihood with another likelihood of susceptibility to epilepsy and/or epileptic seizures in order to assess whether the likelihood has increased or decreased.

Abstract: Assessing Susceptibility to Epilepsy and Epileptic Seizures A method and system adapted to assist with assessing susceptibility to epilepsy and/or epileptic seizures in a patient receives (202) patient brain data and generates (204) a network model from the received patient brain data. The system further generates (206) synthetic brain activity data in at least some of the nodes of the network model and computes (208) seizure frequency from the synthetic brain activity data by monitoring transitions from non-seizure states to seizures states in at least some of the nodes over time. The system further includes a device (104, 110) configured to use the seizure frequency to compute (210) a likelihood of susceptibility to epilepsy and/or epileptic seizures in the patient, and a device (104, 110) configured to compare (212) the computed likelihood with another likelihood of susceptibility to epilepsy and/or epileptic seizures in order to assess whether the likelihood has increased or decreased. FIG.