A method is provided that includes obtaining individual behavior data of a target user and crowd behavior data of other users, and executing a machine learning algorithm to determine performance benchmarks for tasks based on the crowd behavior data. The method further includes aggregating the other users into a plurality of user clusters, classifying the target user into one of the clusters, identifying one or more focus features of the target user that underperform at least one benchmark of the one or more features of the plurality of users in the user cluster to which the target user is classified, identify targeted content associated with the one or more tasks or chains of tasks based on the one or more identified features of the target user, and deliver the targeted content via the computing device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method performed by one or more computing devices, the method comprising: obtaining individual behavior data from interactions of a target user with an application program on at least one computing device; obtaining crowd behavior data from interactions of a plurality of users with other instances of the application program on other computing devices; determining one or more performance benchmarks for one or more tasks or chains of tasks based on the crowd behavior data; aggregating the plurality of users into a plurality of user clusters based on similarity of one or more features between users; classifying the target user into one of the plurality of user clusters based on similarity of one or more features between the target user and users in the user clusters; from the individual behavior data and the crowd behavior data, identifying one or more focus features of the target user that underperform one or more of the performance benchmarks of the one or more features of the plurality of users in the user cluster to which the target user is classified; identifying targeted content associated with the one or more tasks or chains of tasks based on the one or more identified features of the target user; and delivering the targeted content via the computing device. 2. The method of claim 1, wherein determining the one or more performance benchmarks is accomplished at least in part by executing a machine learning algorithm; wherein executing the machine learning algorithm includes: training a neural network having a plurality of layers on the individual and crowd behavior data, at least one of the layers including one or more feature detectors detecting one or more features, each of the feature detectors having a corresponding set of weights, each feature being associated with the one or more tasks or chains of tasks and the one or more performance benchmarks; and evaluating the individual and crowd behavior data based on the corresponding set of weights; and wherein the one or more features detected by the one or more feature detectors are predetermined by the target user and/or the neural network. 3. The method of claim 1, wherein determining the one or more performance benchmarks is accomplished at least in part by executing a machine learning algorithm; wherein the machine learning algorithm utilizes a machine learning technique selected from the group consisting of a support vector machine, decision tree learning, and supervised machine learning. 4. The method of claim 2, wherein the method is iteratively repeated following the delivery of targeted content, so that the individual behavior data of the target user is re-evaluated, the target user is reclassified, one or more focus features are re-identified, and targeted content is re-delivered based on change in performance of one or more focus features against the one or more performance benchmarks. 5. The method of claim 4, wherein the corresponding sets of weights for the features detected by the one or more feature detectors are adjusted with each iterative repetition of the method. 6. The method of claim 1, wherein the one or more tasks associated with the one or more identified focus features of the target user are arranged in a linked sequence and associated with customized triggers. 7. The method of claim 6, wherein the customized triggers are associated with targeted content cues that are observable and actionable by the target user. 8. The method of claim 6, wherein the customized triggers are adjusted based on the user cluster to which the target user is classified. 9. The method of claim 6, wherein the customized triggers are temporal ranges and/or geographical ranges. 10. The method of claim 9, wherein the geographical ranges correspond to positions on a constrained path along which the task or chain of tasks are organized, and the temporal ranges are associated with timings of the one or more tasks or chains of tasks along the constrained path. 11. The method of claim 1, wherein the targeted content is delivered by a hint engine accessible via an application programming interface and provided with a hint library that is instantiated on the one or more computers. 12. The method of claim 1, wherein the targeted content is delivered via textual, auditory, visual, and/or tactile medium. 13. The method of claim 1, wherein the delivery of targeted content includes ranking the one or more tasks or chains of tasks associated with the one or more identified focus features based on an evaluated potential of the target user for improvement on the one or more tasks or chains of tasks. 14. A computing device, comprising: a processor and non-volatile memory, the non-volatile memory storing instructions which, upon execution by the processor, cause the processor to: obtain individual behavior data from interactions of a target user with an application program on at least one computing device; obtain crowd behavior data from interactions of a plurality of users with other instances of the application program on other computing devices; determine one or more performance benchmarks for one or more tasks or chains of tasks based on the crowd behavior data; aggregate the plurality of users into a plurality of user clusters based on similarity of one or more features between users; classify the target user into one of the plurality of user clusters based on similarity of one or more features between the target user and users in the user clusters; from the individual behavior data and the crowd behavior data, identify one or more focus features of the target user that underperform the one or more performance benchmarks of the one or more features of the plurality of users in the user cluster to which the target user is classified; and identify targeted content associated with the one or more tasks or chains of tasks based on the one or more identified features of the target user; and deliver the targeted content via the computing device. 15. The device of claim 14, wherein the processor is configured to determine the one or more performance benchmarks at least in part by executing a machine learning algorithm, according to which the processor is further configured to: train a neural network having a plurality of layers on the individual and crowd behavior data, at least one of the layers including one or more feature detectors detecting one or more features, each of the feature detectors having a corresponding set of weights, each feature being associated with one or more tasks or chains of tasks and one or more performance benchmarks; and evaluate the individual and crowd behavior data based on the corresponding set of weights; and wherein one or more features detected by the one or more feature detectors are predetermined by the target user and/or the neural network. 16. The device of claim 15, wherein the method is iteratively repeated following the delivery of targeted content, so that the individual behavior data of the target user is re-evaluated, the target user is reclassified, one or more focus features are re-identified, and targeted content is re-delivered based on change in performance of one or more focus features against the one or more performance benchmarks. 17. The device of claim 16, wherein the corresponding sets of weights for the features detected by the one or more feature detectors are adjusted with each iterative repetition of the method. 18. The device of claim 14, wherein the one or more tasks associated with the one or more identified focus features of the target user are arranged in a linked sequence and associated with customized triggers; wherein the customized triggers are adjusted based on the user cluster to which the target user is classified. 19. The device of claim 18, wherein the customized triggers are temporal ranges and/or geographical ranges. 20. A computing device, comprising: a processor and non-volatile memory, the non-volatile memory storing instructions which, upon execution by the processor, cause the processor to: obtain individual behavior data from interactions of a target user with an application program on at least one computing device; obtain crowd behavior data from interactions of a plurality of users with other instances of the application program on other computing devices; execute a machine learning algorithm means for determining one or more performance benchmarks for one or more tasks or chains of tasks based on the crowd behavior data; aggregate the plurality of users into a plurality of user clusters based on similarity of one or more features between users; classify the target user into one of the plurality of user clusters based on similarity of one or more features between the target user and users in the user clusters; from the individual behavior data and the crowd behavior data, identify one or more focus features of the target user that underperform the one or more performance benchmarks of the one or more features of the plurality of users in the user cluster to which the target user is classified; identify targeted content associated with the one or more tasks or chains of tasks based on the one or more identified features of the target user; and deliver the targeted content via the computing device.