A method for recommending choices to a user comprises receiving data. A first set of recommendation choices based on the data is derived using a first model that models a user's subconscious reactions (i.e., intuition) to the data, and a second set of recommendation choices based on the data is derived using a second model that models the user's consciousness. The first and second sets of recommendation choices are blended using a blending function based on characteristics of a domain of the data to create a third set of recommendation choices that are arranged in an order, which is presented to the user. A conscious decision of the user and subconscious reactions of the user are monitored, and data representing the subconscious reactions of the user and data representing the conscious decisions of the user are appended to a first and second memory space, respectively.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for recommending choices to a user, the method comprising: receiving data; deriving, using a first model, a first set of recommendation choices based at least in part on the data; deriving, using a second model, a second set of recommendation choices based at least in part on the data; blending, using a blending function, the first set of recommendation choices and the second set of recommendation choices to create a third set of recommendation choices that are arranged in an order; presenting the third set of recommendation choices to the user; monitoring a conscious decision made by the user, the monitoring including: receiving a selection from the third set of recommendation choices performed by the user; and monitoring what was not selected from the third set of recommendation choices by the user; monitoring subconscious reactions of the user, the monitoring including: monitoring a subconscious reaction of the user while the user is performing the selection; and monitoring a subconscious reaction of the user while the user is not performing the selection; appending data representing the subconscious reactions of the user to a first memory space as previous subconscious reactions; and appending data representing the conscious decisions of the user to a second memory space as previous conscious decisions. 2. The method of claim 1 further including: determining a context of the user in which the conscious decision of the user is made; wherein: deriving the first set of recommendation choices is further based on the determined context of the user; deriving the second set of recommendation choices is further based on the determined context of the user; and blending is further based on the determined context of the user. 3. The method of claim 2, wherein determining a context of the user further includes: deriving connection links between the conscious decision made by the user and the data; and storing the connection links to a third memory space as previous connection links; wherein the connection links are unique to the user. 4. The method of claim 3, wherein presenting the third set of recommendation choices to the user further includes presenting a reason why the third set of recommendation choices is presented to the user in the order the third set of recommendation choices is presented to the user, wherein the reason why is based on the previous connection links. 5. The method of claim 1, wherein blending is further based on input from the user's kith. 6. The method of claim 1, wherein monitoring the subconscious reaction of the user further includes monitoring reflexive responses of the user including select at least one of: physiological measurements; affective observables; and paralinguistics. 7. The method of claim 1, wherein monitoring the subconscious reaction of the user further includes monitoring reflexive responses of the user with noninvasive sensors. 8. The method of claim 1, wherein monitoring the subconscious reaction of the user further includes monitoring the amount of time the user takes to make the selection. 9. The method of claim 1, wherein deriving, using a first model, a first set of recommendation choices is further based on the previous subconscious reaction of the user and the previous conscious decision of the user. 10. The method of claim 1, wherein deriving, using a second model, a first set of recommendation choices is further based on the previous subconscious reaction of the user and the previous conscious decision of the user. 11. The method of claim 1, wherein the first model models the user's subconscious process. 12. The method of claim 11, wherein the first model further comprises: taking measurements of the data; and mapping the measurements of the data to the monitoring of the subconscious reaction of the user. 13. The method of claim 1 further including: modifying the first model based on the previous conscious decisions and the previous subconscious reactions; and modifying the second model based on the previous conscious decisions and the previous subconscious reactions. 14. The method of claim 1, wherein the second model models the user's conscious process. 15. The method of claim 14, wherein deriving, using a second model, a second set of recommendation choices further includes: creating a third-party model that models a third party; and simulating an interaction between a model of the user and the third-party model to derive the second set of recommendation choices. 16. The method of claim 1, wherein: deriving a first set of recommendation choices is further based upon previous subconscious reactions from people other than the user; and deriving a second set of recommendation choices is further based upon previous conscious decisions from people other than the user. 17. The method of claim 1, wherein monitoring a conscious decision made by the user further includes monitoring conscious deliberations of the user. 18. The method of claim 1, wherein monitoring a subconscious reaction of the user further includes monitoring subconscious reactions of the user after the conscious decision is made. 19. The method of claim 1, wherein the blending function places weight on the first set of recommendation choices inversely proportional to the amount of time predicted for the user to make a decision about the data. 20. The method of claim 1, wherein the blending function is a veto function that ignores the first set of recommendation choices entirely.