A method/apparatus/system for generating a request for improvement of a data object in a neural network is described herein. The neural network contains a plurality of data objects each made of an aggregation of content. The data objects of the neural network are interconnected based on one or several skill levels embodied in the content of the data objects via a plurality of connecting vectors. These connecting vectors can be generated and/or modified based on data collected from the iterative transversal of the connecting vectors by one or several users of the neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of adding a data object to a multi-dimensional network, the method comprising: identifying a first data object comprising first content, wherein the first data object is connected within a data object network based on a common subject of the data object network; determining a non-subject skill level of the first data object that is independent of the common subject of the data object network; adding a value indicative of the non-subject skill level of the first data object; identifying a second data object comprising second content independent of the common subject of the data object network and having a non-subject skill level lower than the non-subject skill level of the first data object; identifying a third data object having a non-subject skill level higher than the non-subject skill level of the first data object; and creating a non-subject object network by generating a second learning vector extending from the second data object to the first data object and a third learning vector extending from the first data object to the third data object. 2. The method of claim 1, wherein the non-subject skill level comprises at least one of a quantile level and a lexile level. 3. The method of claim 1, wherein the first content comprises a plurality of content objects and an assessment. 4. The method of claim 3, wherein determining the non-subject skill level of the first data object comprises determining that the first data object has a corresponding non-subject skill level identified in the information associated with the first data object. 5. The method of claim 4, wherein determining the non-subject skill level of the first data object comprises determining a skill level of the first data object when a non-subject skill level is not identified in the information associated with the first data object. 6. The method of claim 5, wherein the non-subject skill level is determined by analyzing the content of the first data object. 7. The method of claim 5, wherein the non-subject skill level is determined by analyzing one of the content objects or the assessment is analyzed. 8. The method of claim 1, further comprising determining a subject matter of the first data object. 9. The method of claim 8, wherein determining the subject matter of the first data object comprises extracting information identifying the subject matter of the first data object from the information associated with the first data object. 10. The method of claim 1, wherein at least one of the second and third data objects comprise the same subject matter as the first data object. 11. A system for maintaining a multi-dimensional network comprising: memory comprising a plurality of data objects associated with information that identifies an aspect of the therewith associated data object; a processor configured to: identify a first data object connected within a data object network based on a common subject of the data object network; determine a non-subject skill level of the first data object, the non-subject skill level corresponding to a skill independent from the common subject of the data object network; identify a second data object independent of the common subject of the data object network and comprising a non-subject skill level lower than the non-subject skill level of the first data object; identify a third data object comprising a non-subject skill level higher than the non-subject skill level of the first data object; and create a non-subject object network by generating a second learning vector extending from the second data object to the first data object and a third learning vector extending from the first data object to the third data object. 12. The system of claim 11, wherein the processor is further configured to retrieve information associated with the first data object, which information identifies an aspect of the first data object. 13. The system of claim 11, wherein the non-subject skill level comprises at least one of a quantile level and a lexile level. 14. The system of claim 11, wherein the aggregation of learning content comprises a plurality of content objects. 15. The system of claim 11, wherein the processor is configured to cause user traversal of the second connecting vector. 16. The system of claim 15, wherein the processor is configured to cause user traversal of the third connecting vector. 17. The system of claim 16, wherein the processor is configured to: receive an input indicative of the traversal of one of the second and third connecting vectors; and update the traversed one of the second and third connecting vectors based on the received input. 18. The system of claim 17, wherein the processor is configured to determine the self-optimization of the second connecting vector. 19. The system of claim 18, wherein determining the self-optimization of the second connecting vector can comprise determining stabilization of a magnitude of the second connecting vector. 20. The system of claim 19, wherein determining stabilization of the magnitude of the second connecting vector comprises determining an oscillation decay across data contained in a shifting time-window.