Patent Document ID: 8626791
Application ID: 13159882

Base Claim:
1. A computer-implemented method comprising: maintaining, in a secondary memory of a computing system, a collection of trained predictive models, wherein each trained predictive model in the collection has a respective identifier; obtaining a plurality of records, wherein each record includes a time of a previously submitted predictive request and an identifier of a trained predictive model that provided a predictive output in response to the previously submitted predictive request, and wherein each of the plurality of records identifies the respective previously submitted predictive request as having been submitted by a first user; generating a trained scheduling model using the plurality of records as training data, wherein the trained scheduling model is a trained individual scheduling model that is specific to the first user; identifying, using the trained scheduling model, a particular set of trained predictive models that are most likely to receive predictive requests at a target time; selecting, for storing, one or more of the trained predictive models in the particular set of trained predictive models that have a likelihood of receiving a predictive request at the target time that is higher relative to others of the trained predictive models in the particular set of trained predictive models; and storing the selected one or more of the trained predictive models in the particular set of trained predictive models in a primary memory of the computing system, wherein the primary memory comprises volatile memory and the secondary memory comprises a non-volatile memory.

---

Claim 12:
12. The method of claim 1 , further comprising: using the trained scheduling model to identify one or more other trained predictive models that are most likely to be used in tandem with the particular set of trained predictive models; obtaining, from the secondary memory, prior to the target time, the one or more other trained predictive models; and storing the one or more other trained predictive models in the primary memory.