Abstract: Testing prototype indices is provided. The method comprises receiving input of a number of historical index values and performing a number of Monte Carlo simulations based on the historical index values to predict future index values. A number of attributes of the historical index values are calculated, and the Monte Carlo simulations are correlated with the attributes to identify a filtered subset of predicted future index values having an accuracy above a defined threshold. The filtered subset of predicted future index values are fed into a deep neural network (DNN) that outputs a confidence score for each of the filtered subset of predictive future index values.

Abstract: Testing prototype indices is provided. The method comprises receiving input of a number of historical index values and performing a number of Monte Carlo simulations based on the historical index values to predict future index values. A number of attributes of the historical index values are calculated, and the Monte Carlo simulations are correlated with the attributes to identify a filtered subset of predicted future index values having an accuracy above a defined threshold. The filtered subset of predicted future index values are fed into a deep neural network (DNN) that outputs a confidence score for each of the filtered subset of predictive future index values.

Abstract: A computer-implemented method of improving a speed and an accuracy of a server computer executing a chat bot using a processor. An input is received at the chat bot at the server computer from a client computer over a network. The input is parsed using a natural language engine into one or more keywords. The one or more keywords are assigned to one or more part of speech tags. All of the input is stored as a profile into a non-transitory computer recordable storage medium. The graph database model is traversed, with traversing including comparing the profile to nodes in the graph database model and using relationships among the nodes as part of the comparing, and wherein traversing ends at a root node in the graph database model. A solution stored in the root node is returned to the client computer, by the processor and over the network.

Abstract: A computer-implemented method of improving a speed and an accuracy of a server computer executing a chat bot using a processor. An input is received at the chat bot at the server computer from a client computer over a network. The input is parsed using a natural language engine into one or more keywords. The one or more keywords are assigned to one or more part of speech tags. All of the input is stored as a profile into a non-transitory computer recordable storage medium. The graph database model is traversed, with traversing including comparing the profile to nodes in the graph database model and using relationships among the nodes as part of the comparing, and wherein traversing ends at a root node in the graph database model. A solution stored in the root node is returned to the client computer, by the processor and over the network.