Systems and methods for hybrid content provisioning with dual recommendation engines

Abstract

Systems and methods for content selection with first and second recommendation engines are disclosed herein. The system can include a memory including a content library database and a model database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can receive response data from the user device; provide received response data to a first recommendation engine; alert a second recommendation engine when a selected next node is a placeholder node; receive at least one statistical model relevant to selection of the next node content; and select next node content based on an output of the at least one statistical model.

The invention claimed is: 
     
         1 . A system, comprising:
 a content library database storing, in association, a plurality of nodes, a node content for each of the plurality of nodes, and an identification of the node content as a static content or a dynamic content;   a user device operated by a user and configured to display the static content or the dynamic content; and   a server including a hardware computing device coupled to a network and including at least one processor executing within a memory instructions that, when executed, cause the system to:
 identify a location of a user within a content network including the plurality of nodes linked in a sequential relationship, each of the nodes comprising the node content; 
 determine whether a next node in the content network is associated, in the content library database, with the static content or the dynamic content; 
 responsive to the next node being associated with the static content, automatically select, by a first recommendation software module, the static content associated with the next node; 
 responsive to the next node being associated with the dynamic content, automatically select, by a second recommendation software module, the dynamic content according to:
 a predictive model stored in a model database; 
 a user skill level determined by a unique history of the user; and 
 the location of the user in the content network; and 
 
 transmit the static content or the dynamic content through the network for display on the user device. 
   
     
     
         2 . The system of  claim 1 , wherein the instructions further cause the system to:
 transmit a question through the content network to the user device;   receive a response to the question from the user device;   identify the location according to the response.   
     
     
         3 . The system of  claim 2 , wherein the instructions further cause the system to:
 analyze the response to determine whether the response was a correct response or an incorrect response; and   identify the location according to whether the response was a correct response or an incorrect response.   
     
     
         4 . A system, comprising:
 a server including a hardware computing device coupled to a network and including at least one processor executing within a memory instructions that, when executed, cause the system to:
 identify a location of a user within a content network including a plurality of nodes linked in a sequential relationship, each of the nodes comprising a node content; 
 determine whether a next node in the content network is associated, in a content library database, with a static content or a dynamic content; 
 responsive to the next node being associated with the static content, automatically select, by a first recommendation software module, the static content associated with the next node; 
 responsive to the next node being associated with the dynamic content, automatically select, by a second recommendation software module, the dynamic content according to:
 a predictive model stored in a model database; 
 a user skill level determined by a unique history of the user; and 
 the location of the user in the content network; and 
 
 transmit the static content or the dynamic content through the network for display on a user device operated by the user. 
   
     
     
         5 . The system of  claim 4 , wherein the instructions further cause the system to select a potential next node according to the location of the user in the content network. 
     
     
         6 . The system of  claim 5 , wherein the instructions further cause the system to:
 identify, within the content network, at least one rule or condition;   apply the at least one rule or condition to the unique history of the user; and   identify a potential next node according to the at least one rule or condition.   
     
     
         7 . The system of  claim 4 , wherein the predictive model represents at least one attribute of:
 the node content;   the unique history of the user;   the user skill level; or   a user learning style.   
     
     
         8 . The system of  claim 4 , wherein the instructions further cause the system to:
 receive a plurality of inputs corresponding to the user skill level; and   train the predictive model to select the node content based on the plurality of inputs.   
     
     
         9 . The system of  claim 4 , wherein the instructions further cause the system to:
 aggregate the unique history of a plurality of users in a database coupled to the network; and   store the unique history of the user in the database.   
     
     
         10 . The system of  claim 4 , wherein the instructions further cause the system to:
 receive a plurality of prerequisite input from the user; and   identify the location of the user in the content network according to the plurality of prerequisite input from the user.   
     
     
         11 . The system of  claim 10 , wherein at least one prerequisite required for the user to reach the location in the content network is stored in a content library database. 
     
     
         12 . A method, comprising the steps of:
 identifying, by a server including a hardware computing device coupled to a network and including at least one processor executing instructions within a memory, a location of a user within a content network including a plurality of nodes linked in a sequential relationship, each of the nodes comprising a node content;   determining, by the server, whether a next node in the content network is associated, in a content library database, with a static content or a dynamic content;   responsive to the next node being associated with the static content, automatically selecting, by the server, by a first recommendation software module, the static content associated with the next node;   responsive to the next node being associated with the dynamic content, automatically selecting, by the server, by a second recommendation software module, the dynamic content according to:
 a predictive model stored in a model database; 
 a user skill level determined by a unique history of the user; and 
 the location of the user in the content network; and 
   transmitting, by the server, the static content or the dynamic content through the network for display on a user device operated by the user.   
     
     
         13 . The method of  claim 12 , further comprising the steps of:
 transmitting, by the server, a question through the content network to the user device;   receiving, by the server, a response to the question from the user device;   identifying, by the server, the location according to the response.   
     
     
         14 . The method of  claim 13 , further comprising the steps of:
 analyzing, by the server, the response to determine whether the response was a correct response or an incorrect response; and   identifying, by the server, the location according to whether the response was a correct response or an incorrect response.   
     
     
         15 . The method of  claim 12 , further comprising the step of selecting, by the server, a potential next node according to the location of the user in the content network. 
     
     
         16 . The method of  claim 15 , further comprising the steps of:
 Identifying, by the server, within the content network, at least one rule or condition;   applying, by the server, the at least one rule or condition to the unique history of the user; and   identifying, by the server, a potential next node according to the at least one rule or condition.   
     
     
         17 . The method of  claim 12 , wherein the predictive model represents at least one attribute of:
 the node content;   the unique history of the user;   the user skill level; or   a user learning style.   
     
     
         18 . The method of  claim 12 , further comprising the steps of:
 receiving, by the server, a plurality of inputs corresponding to the user skill level; and   training, by the server, the predictive model to select the node content based on the plurality of inputs.   
     
     
         19 . The method of  claim 12 , further comprising the steps of:
 receiving, by the server, a plurality of prerequisite input from the user; and   identifying, by the server, the location of the user in the content network according to the plurality of prerequisite input from the user.   
     
     
         20 . The method of  claim 19 , wherein at least one prerequisite required for the user to reach the location in the content network is stored in a content library database.