Patent Document ID: 9336483
Application ID: 14678312
Patent Flag: 1

Claim One:
1. A dynamically updating neural network system for evaluating and updating artificial neural networks for electronic learning systems, the dynamically updating neural network system comprising: a database server comprising one or more databases that: receive and store neural network training data corresponding to input and output data associated with an electronic learning system; a network interface configured to provide one or more electronic learning system servers with access to the database server via one or more computer networks; and a neural network management server of the electronic learning system comprising: a processing unit comprising one or more processors; and memory coupled with and readable by the processing unit and storing therein a set of instructions which, when executed by the processing unit, causes the neural network management server to: retrieve, from the database server and via the network interface, first neural network training data corresponding to input and output data of the electronic learning system; generate and train a first electronic learning system neural network using the first neural network training data; determine an error threshold associated with the trained first electronic learning system neural network; receive additional input data and corresponding output data associated with the electronic learning system; execute a plurality of predictive analyses using the first electronic learning system neural network, based on the additional input data associated with the electronic learning system; evaluate the first electronic learning system neural network by: comparing the results of each of the plurality of predictive analyses with the corresponding additional output data; aggregating the results of the plurality of predictive analyses to generate an aggregate error rate for the trained first electronic learning system neural network; and determining whether the aggregate error rate has exceeded the error threshold associated with the trained first electronic learning system neural network; in response to determining that the aggregate error rate has exceeded the error threshold associated with the trained first electronic learning system neural network, generate and train a plurality of additional electronic learning system neural networks using at least the first neural network training data and the additional input and output data associated with the electronic learning system; evaluate the plurality of additional electronic learning system neural networks by executing one or more identical predictive analyses using each of the plurality of additional electronic learning system neural networks; select a replacement electronic learning system neural network based on the evaluation of the plurality of additional electronic learning system neural networks; and replace the first electronic learning system neural network with the selected replacement electronic learning system neural network.