Abstract: A method for assisting a patient through a therapy session using a live real-time voice interaction with a narrative audio is disclosed. The method includes steps of: extracting an emotional content from the set of multimedia in a playlist; calculating an emotional score for each emotional content in the playlist using a deep machine learning model; generating an emotional arc for the emotional content based on the calculated emotional score, previous response from the patient to different genres and personal information of the patient; generating a narrative text from the set of multimedia and generating a narrative text story based on the generated narrative text and the personal information of the patient using an artificial intelligence (AI) story model; converting the generated narrative text story to an audio; and generating a narrative audio by combining the audio file with the emotional arc.

Abstract: A wellness measurement method for measuring a level of wellness of an individual based on wellness function using a wellness measurement system is disclosed. The method includes steps of: measuring an approximation of the wellness function for computing the level of wellness of the individual based on microbiome data of the individual; determining wellness areas using symptom classes and corresponding diseases associated with symptoms based on the approximation of the wellness function; determining possible wellness gain using the maximization technique by assigning weightage to the determined wellness areas and a microbiome-wellness association network, and predicting an optimal value of microbiome configuration for the individual based on the wellness areas and the microbiome-wellness association network, being assigned with weightage; and outputting recommendations with the wellness gain through a plurality of constraints using a convex optimization.

Abstract: A method for assisting a patient through a therapy session using a live real-time voice interaction with a narrative audio is disclosed. The method includes steps of: extracting an emotional content from the set of multimedia in a playlist; calculating an emotional score for each emotional content in the playlist using a deep machine learning model; generating an emotional arc for the emotional content based on the calculated emotional score, previous response from the patient to different genres and personal information of the patient; generating a narrative text from the set of multimedia and generating a narrative text story based on the generated narrative text and the personal information of the patient using an artificial intelligence (AI) story model; converting the generated narrative text story to an audio; and generating a narrative audio by combining the audio file with the emotional arc.

Abstract: In a plurality of data analysis apparatuses communicatively coupled to one another, a database is accessed at a given access level. Based on the data acquired from the database, an application program is executed to perform data analysis. Communication control is performed to allow transmission and reception of a data analysis result with respect to another data analysis apparatus at a different access level.

Abstract: In a plurality of data analysis apparatuses communicatively coupled to one another, a database is accessed at a given access level. Based on the data acquired from the database, an application program is executed to perform data analysis. Communication control is performed to allow transmission and reception of a data analysis result with respect to another data analysis apparatus at a different access level.

Abstract: The present invention involves retrieving edges that do not constitute a designated network model which is a network model designated by a user and are linked to nodes that constitute the designated network model, displaying retrieval results that include the retrieved edges and network model IDs corresponding to the edges in a selectable manner, and generating an integrated network model in which, when the retrieval results are selected by the user, the edges that are included in the selected retrieval results and the nodes that do not constitute the designated network model and are linked to the edges, are integrated into the designated network model.

Abstract: The present invention acquires compound structure data and candidate protein structure data on a candidate protein serving as a candidate for interaction with the compound. The present invention calculates a binding strength between the candidate protein and the compound using a docking simulation method, determines a predicted binding strength corresponding to the binding strength predicted by making a comprehensive evaluation of the binding strength, and determines a predicted protein corresponding to the candidate protein predicted to interact with the compound. The present invention calculates an interaction strength using a binding strength simulation method and determines a predicted interaction strength corresponding to the interaction strength predicted by making the comprehensive evaluation of the interaction strength.

Abstract: A simulation system includes an interface component connected to a public or shared network that has less restrictive access than networks inside the firewall, generating a simulation job and registering the simulation job in a database, a job control component accessing said database to retrieve the simulation job and scheduling the simulation job for execution, and a simulation execution component receiving the simulation job from the job control component, creating executable codes for numerical and parallel computing algorithms and distributing computing processes to multiple computers to execute the simulation job.

Abstract: The present invention involves retrieving edges that do not constitute a designated network model which is a network model designated by a user and are linked to nodes that constitute the designated network model, displaying retrieval results that include the retrieved edges and network model IDs corresponding to the edges in a selectable manner, and generating an integrated network model in which, when the retrieval results are selected by the user, the edges that are included in the selected retrieval results and the nodes that do not constitute the designated network model and are linked to the edges, are integrated into the designated network model.