Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for caching a predictive model on a mobile computing device, comprising: at the mobile computing device: caching a predictive model into a memory of the mobile computing device, wherein the predictive model is stored as a contiguous file within the memory; establishing a set of training data that is specific to the predictive model; identifying a time at which to update the predictive model based on the set of training data; retrieving the predictive model from the memory; updating the predictive model based on the set of training data; and caching the predictive model into the memory. 2. The method of claim 1, wherein the predictive model is provided by a server computing device, and the predictive model is: configured according to capabilities of the mobile computing device, and based on a raw predictive model managed by the server computing device. 3. The method of claim 1, wherein storing the predictive model as a contiguous file within the cache enables a hierarchical structure of the predictive model to be retained without requiring a deconstruction of the hierarchical structure of the predictive model prior to storing the predictive model into the memory. 4. The method of claim 3, wherein, when the predictive model is retrieved from the memory, the hierarchical structure of the predictive model is intact and does not require a reconstruction of the predictive model. 5. The method of claim 3, wherein the predictive model is contiguously stored within the file based on a hardware configuration and/or software configuration of the mobile computing device. 6. The method of claim 3, wherein the predictive model is contiguously stored within the file based on a format that is specific to an application executing on the mobile computing device. 7. The method of claim 2, further comprising: receiving, from the server, an update to the predictive model; determining that the update to the predictive model is different from the predictive model cached in the memory; and in response to determining, and based on the update to the predictive model: updating or replacing the predictive model cached in the memory. 8. A non-transitory computer readable storage medium configured to store instructions that, when executed by a processor included in a mobile computing device, cause the computing device to cache a predictive model, comprising: at the mobile computing device: caching a predictive model into a memory of the mobile computing device, wherein the predictive model is stored as a contiguous file within the memory; establishing a set of training data that is specific to the predictive model; identifying a time at which to update the predictive model based on the set of training data; retrieving the predictive model from the memory; updating the predictive model based on the set of training data; and caching the predictive model into the memory. 9. The non-transitory computer readable storage medium of claim 8, wherein the predictive model is provided by a server computing device, and the predictive model is: configured according to capabilities of the mobile computing device, and based on a raw predictive model managed by the server computing device; 10. The non-transitory computer readable storage medium of claim 8, wherein storing the predictive model as a contiguous file within the cache enables a hierarchical structure of the predictive model to be retained without requiring a deconstruction of the hierarchical structure of the predictive model prior to storing the predictive model into the memory. 11. The non-transitory computer readable storage medium of claim 10, wherein, when the predictive model is retrieved from the memory, the hierarchical structure of the predictive model is intact and does not require a reconstruction of the predictive model. 12. The non-transitory computer readable storage medium of claim 10, wherein the predictive model is contiguously stored within the file based on a hardware configuration and/or software configuration of the mobile computing device. 13. The non-transitory computer readable storage medium of claim 10, wherein the predictive model is contiguously stored within the file based on a format that is specific to an application executing on the mobile computing device. 14. The non-transitory computer readable storage medium of claim 9, further comprising: receiving, from the server, an update to the predictive model; determining that the update to the predictive model is different from the predictive model cached in the memory; and in response to determining, and based on the update to the predictive model: updating or replacing the predictive model cached in the memory. 15. A mobile computing device configured to cache a predictive model, comprising: a processor; and a memory configured to store instructions that, when executed by the processor, cause the mobile computing device to carry out steps that include: caching a predictive model into a memory of the mobile computing device, wherein the predictive model is stored as a contiguous file within the memory; establishing a set of training data that is specific to the predictive model; identifying a time at which to update the predictive model based on the set of training data; retrieving the predictive model from the memory; updating the predictive model based on the set of training data; and caching the predictive model into the memory. 16. The mobile computing device of claim 15, wherein the predictive model is provided by a server computing device, and the predictive model is: configured according to capabilities of the mobile computing device, and based on a raw predictive model managed by the server computing device; 17. The mobile computing device of claim 15, wherein storing the predictive model as a contiguous file within the cache enables a hierarchical structure of the predictive model to be retained without requiring a deconstruction of the hierarchical structure of the predictive model prior to storing the predictive model into the memory. 18. The mobile computing device of claim 17, wherein, when the predictive model is retrieved from the memory, the hierarchical structure of the predictive model is intact and does not require a reconstruction of the predictive model. 19. The mobile computing device of claim 15, wherein the predictive model is contiguously stored within the file based on a hardware configuration and/or software configuration of the mobile computing device. 20. The mobile computing device of claim 15, wherein the predictive model is contiguously stored within the file based on a format that is specific to an application executing on the mobile computing device. 21. The mobile computing device of claim 16, wherein the steps further include: receiving, from the server, an update to the predictive model; determining that the update to the predictive model is different from the predictive model cached in the memory; and in response to determining, and based on the update to the predictive model: updating or replacing the predictive model cached in the memory.