초록 ▼

In a navigation device, user profiles may be stored and used to navigate a user who may be driving in a vehicle, on foot, or in other mode of transportation. Each user profile corresponds to one of the user's personae. For example, the user business profile corresponding to the user's business perso

In a navigation device, user profiles may be stored and used to navigate a user who may be driving in a vehicle, on foot, or in other mode of transportation. Each user profile corresponds to one of the user's personae. For example, the user business profile corresponding to the user's business persona may be different from the user personal profile corresponding to the user's personal persona. For instance, the user business profile may include fine-dining type restaurants for business meetings while the user personal profile may instead include fast-food type restaurants for personal dining. The navigation device provides the user with a navigated route, together with information concerning the favorite facilities and events surrounding the navigated route, which satisfy the preferences in a selected user profile. In addition, blockages may also be established using the device to avoid selected areas, e.g., high crime rate areas, in the navigated route, or to block transmission of selected information concerning, e.g., uninteresting facilities and events, to the navigation device.

대표청구항 ▼

In a navigation device, user profiles may be stored and used to navigate a user who may be driving in a vehicle, on foot, or in other mode of transportation. Each user profile corresponds to one of the user's personae. For example, the user business profile corresponding to the user's business perso

In a navigation device, user profiles may be stored and used to navigate a user who may be driving in a vehicle, on foot, or in other mode of transportation. Each user profile corresponds to one of the user's personae. For example, the user business profile corresponding to the user's business persona may be different from the user personal profile corresponding to the user's personal persona. For instance, the user business profile may include fine-dining type restaurants for business meetings while the user personal profile may instead include fast-food type restaurants for personal dining. The navigation device provides the user with a navigated route, together with information concerning the favorite facilities and events surrounding the navigated route, which satisfy the preferences in a selected user profile. In addition, blockages may also be established using the device to avoid selected areas, e.g., high crime rate areas, in the navigated route, or to block transmission of selected information concerning, e.g., uninteresting facilities and events, to the navigation device. im 4 wherein said processor uses said model iteratively to populate ones of said plurality of accessible fields of said first data structure with said range of possible values of said at least one measurable characteristic wherein said model gives a prediction form for any point p in the future and wherein said prediction is of the form: 6. The computer system set forth in claim 5 wherein said model includes at least one feedback variable representing, at least in part, an output of said at least one process of said associated processes. 7. The computer system set forth in claim 6 wherein said processor populates at least one of said plurality of accessible fields of said first data structure in response to said at least one feedback variable of said at least one process of said associated processes. 8. The computer system set forth in claim 3 wherein said model includes a manipulable variable. 9. The computer system set forth in claim 8 wherein said processor at least substantially maintains a value of said manipulable variable during at least a portion of said iterative population of said ones of said plurality of accessible fields of said first data structure. 10. The computer system set forth in claim 1, wherein said circuitry maintains statically said range of possible values of said at least one measurable characteristic associated with at least one process of said plurality of associated processes. 11. The computer system set forth in claim 1 wherein said processor uses said range of possible values of said at least one measurable characteristic to predict an unforced response associated with said at least one process. 12. A method of operating a computer system that is for use with a process facility having a plurality of associated processes, said method of operation comprising the steps of: maintaining a first data structure having a plurality of accessible fields in circuitry associated with said computer system; populating ones of said plurality of accessible fields of said first data structure using a processor, that is associated with said circuitry, with a range of possible values of at least one process of said plurality of associated processes; and maintaining a model in said circuitry of at least a portion of said plurality of associated processes wherein said model comprises a discrete state space model of the form: xk+1=Axk+Bukand yk=Cxk+Duk where xkand ukand ykrepresent states of a modeled process and where k is a time period and k+1 is a next time period and where A, B, C, and D respectively represent measurable characteristics of said modeled process at any given time period. 13. The method of operation set forth in claim 12 further comprising the step of storing a task in said circuitry that directs said processor to populate said ones of said plurality of accessible fields of said first data structure with said range of possible values. 14. The method of operation set forth in claim 12 further comprising the step of maintaining said model comprising a second data structure of at least a portion of said plurality of associated processes in said circuitry wherein said second data structure comprises an AB0I matrix of the form: where A and B represent measurable characteristics of a modeled process and where I is an identity matrix and 0 is a null matrix and wherein said second data structure comprises a feedback vector of the form: where xkand ukrepresent states of said modeled process and where k is a time period. 15. The method of operation set forth in claim 14 wherein said model includes a mathematical representation of at least a portion of said at least one process of said plurality of associated processes, said mathematical representation defining relationships among inputs and outputs of said at least one process of said associated processes, wherein said mathem atical relationship is of the form: where Zkrepresents a state space vector of the form: and said method further comprises the step of using said model iteratively by said processor to populate ones of said plurality of accessible fields of said first data structure with said range of possible values of said at least one measurable characteristic wherein said model gives a prediction form for any point p in the future and wherein said prediction is of the form: 16. The method of operation set forth in claim 15 wherein said model includes at least one feedback variable representing, at least in part, an output of said at least one process of said associated processes, and said method further comprises the step of using said processor, in response to said at least one feedback variable of said at least one process of said associated processes, to populate at least one of said plurality of accessible fields of said first data structure. 17. The method of operation set forth in claim 14 wherein said model includes a manipulable variable, and said method further comprises the step of at least substantially maintaining a value of said manipulable variable during at least a portion of said iterative population of said ones of said plurality of accessible fields of said first data structure. 18. The method of operation set forth in claim 12 wherein said circuitry maintains statically said range of possible values of said at least one measurable characteristic associated with at least one process of said plurality of associated processes. 19. The method of operation set forth in claim 12 further comprising the step of predicting an unforced response associated with said at least one process using said processor and said range of possible values of said at least one measurable characteristic.