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 machine learning system for generating a request for improvement of a data object in a neural network, the system comprising: a supervisor device configured to edit one or several data objects in a neural network; a content management server configured to: generate a plurality of connecting vectors between data objects via machine learning from binary indicators generated based on a user experience; continuously update the plurality of connecting vectors based on received data identifying successes and failures in traversing the plurality of connecting vectors; determine a deficiency in the content of at least one of the data objects based on the magnitude of an associated connecting vector; alert the supervisor device of the at least one of the data objects having a determined deficiency; and update the data packet with a received modification. 2. The machine learning system of claim 1, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of the associated connecting vector comprises: retrieving a strength threshold value, wherein the strength threshold value indicates a minimum acceptable strength; and comparing the magnitude of at least some of the plurality of connecting vectors to the strength threshold value. 3. The machine learning system of claim 2, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of the associated connecting vector comprises assigning a value to the connecting vectors of the plurality of connecting vectors according to a Boolean function, wherein a first value is assigned to one of the connecting vectors of the plurality of connecting vectors if the strength of the one of the connecting vectors of the plurality of connecting vectors exceeds the strength threshold value, and wherein a second value is assigned to one of the connecting vectors of the plurality of connecting vectors if the strength of the one of the connecting vectors of the plurality of connecting vectors does not exceed the strength threshold value. 4. The machine learning system of claim 3, wherein the content management server is further configured to output a message indicating a deficiency in the at least one of the data objects if the connecting vector associated with the data object is assigned the second value. 5. The machine learning system of claim 4, wherein the content management server is further configured to identify connecting vectors assigned the second value. 6. The machine learning system of claim 5, wherein the content management server is further configured to relatively rank the plurality of connecting vectors. 7. The machine learning system of claim 6, wherein the content management server relatively ranks the plurality of connecting vectors according to the degree to which users successfully traverse the plurality of connecting vectors. 8. The machine learning system of claim 7, wherein the strength threshold value identifies a minimum acceptable relative rank. 9. The machine learning system of claim 1, wherein the content management server is configured to identify a set of the plurality of connecting vectors, wherein the connecting vectors in the set of the plurality of connecting vectors have stabilized. 10. The machine learning system of claim 9, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of the associated connecting vector comprises selecting at least one of the connecting vectors from the set of the plurality of connecting vectors and identifying the at least one of the data objects that is connected by the connecting vector. 11. A method of generating a request for improvement of a data object in a neural network, the method comprising: generating a plurality of connecting vectors between data objects in a neural network via machine learning from binary indicators based on a user experience; continuously updating the plurality of connecting vectors based on received data identifying successes and failures in traversing the plurality of connecting vectors; determining a deficiency in the content of at least one of the data objects based on the magnitude of an associated connecting vector; alerting the supervisor device of the at least one of the data objects having a determined deficiency; and updating the at least one of the data objects with received information. 12. The method of generating a request for improvement of a data object in a neural network of claim 11, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of an associated connecting vector comprises: retrieving a strength threshold value, wherein the strength threshold value indicates a minimum acceptable strength; and comparing the magnitude of at least some of the plurality of connecting vectors to the strength threshold value. 13. The method of generating a request for improvement of a data object in a neural network of claim 12, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of an associated connecting vector comprises assigning a value to the connecting vectors of the plurality of connecting vectors according to a Boolean function, wherein a first value is assigned to one of the connecting vectors of the plurality of connecting vectors if the strength of the one of the connecting vectors of the plurality of connecting vectors exceeds the strength threshold value, and a second value is assigned to one of the connecting vectors of the plurality of connecting vectors if the strength of the one of the connecting vectors of the plurality of connecting vectors does not exceed the strength threshold value. 14. The method of generating a request for improvement of a data object in a neural network of claim 13, further comprising outputting a message indicating a deficiency in a data object if the connecting vector associated with the data object is assigned the second value. 15. The method of generating a request for improvement of a data object in a neural network of claim 14, wherein the binary indicators are generated for successful traversal of the connecting vector and for failed traversals of the connecting vector. 16. The method of generating a request for improvement of a data object in a neural network of claim 15, further comprising identifying connecting vectors assigned the second value. 17. The method of generating a request for improvement of a data object in a neural network of claim 16, further comprising relatively ranking the plurality of connecting vectors. 18. The method of generating a request for improvement of a data object in a neural network of claim 17, wherein the plurality of connecting vector are relatively ranked according to the degree to which students successfully traverse the plurality of connecting vectors. 19. The method of generating a request for improvement of a data object in a neural network of claim 18, wherein the strength threshold value identifies a minimum acceptable relative rank. 20. The method of generating a request for improvement of a data object in a neural network of claim 11, further comprising: identifying a set of the plurality of connecting vectors, wherein the connecting vectors in the set of the plurality of connecting vectors have stabilized, wherein determining a deficiency in the content of at least one of the data objects based on the magnitude of an associated connecting vector comprises selecting at least one of the connecting vectors from the set of the plurality of connecting vectors and identifying the at least one of the data objects that is connected by the connecting vector.