1. A method for automatically managing service delivery or resource allocation by a health care facility, comprising:
processing at least one element of first input data specific to at least one individual of a plurality of individuals under the care of said health care facility to generate a first output dataset, said first output dataset including information associated with a patient profile, said patient profile including at least one sleep profile associated with said at least one individual;
processing either (i) at least one element of second input data associated with one or more resources specific to said health care facility and at least one element of said first output dataset to generate a second output dataset, said second output dataset including information associated with a delivery of services to said at least one individual; or (ii) at least one element of second input data associated with one or more resources specific to said health care facility and at least one element of said first output dataset to generate a third output dataset, said third output dataset including information associated with allocating one or more resources associated with said health care facility; and
communicating at least one element of at least one of said first output dataset, said second output dataset, and said third output dataset to a user.
2. The method of claim 1, wherein said at least one element of first input data includes at least one element of first input data specific to at least one individual of a plurality of individuals under the care of at least one of a hospital, a nursing facility, and a long-term care facility.
3.-4. (canceled)
5. The method of claim 1, wherein said one or more of a resources allocation include at least one unit of facility equipment or service allocation.
6. The method of claim 1, wherein said one or more of a resources allocation include a facility accommodation.
7. (canceled)
8. The method of claim 1, wherein said at least one element of first input data includes at least one data element associated with at least one of a sleep habit, a sleep cycle, a chronotype, and a circadian cycle for said at least one individual.
9. (canceled)
10. The method of claim 8, wherein said sleep cycle includes at least one of stage 1 non-rapid eye movement (NREM) sleep, stage 2 NREM sleep, stage 3 NREM sleep, stage 4 NREM sleep, and rapid eye movement (REM) sleep.
11. (canceled)
12. The method of claim 1, wherein said at least one element of first input data includes a care protocol for said at least one individual.
13. The method of claim 1, wherein said at least one element of first input data includes at least one element of data acquired by a sensor.
14. The method of claim 13, wherein said sensor includes at least one of a polysomnographic array, an actigraph, a thermometer, a chemical sensor, an electroencephalograph, an electrooculograph, an electrocardiograph, and an electromyelograph.
15.-16. (canceled)
17. The method of claim 1, wherein said at least one element of first input data includes at least one data element associated with timing the administration of a medication.
18. The method of claim 1, wherein said at least one element of first input data includes a patient medical history.
19.-21. (canceled)
22. The method of claim 1, wherein said at least one element of first input data is acquired from at least one of an electronic medical record, a database, and a response to a questionnaire.
23.-25. (canceled)
26. The method of claim 1, wherein said at least one element of second input data includes at least one data element specific to at least one location within said health care facility.
27. The method of claim 26, wherein said at least one location includes at least one bed, room, ward, floor level, and other subdivision within said health care facility.
28.-31. (canceled)
32. The method of claim 26, further comprising:
assigning said at least one individual to said at least one location, based on said second output dataset.
33. The method of claim 1, further comprising:
assigning at least one delivery of services to said at least one individual, based on said second output dataset.
34.-36. (canceled)
37. The method of claim 33, further comprising:
generating at least one schedule comprising at least one delivery of services, said at least one schedule being specific to at least one of a location and a plurality of individuals assigned to a location.
38.-45. (canceled)
46. The method of claim 1, further comprising:
allocating a responsibility of care for said at least one individual to at least one staff member, based on said third output dataset.
47.-50. (canceled)
51. The method of claim 46, further comprising:
generating at least one schedule comprising at least one allocation of responsibility, said at least one schedule being specific to at least one of a location within said health care facility, a plurality of individuals assigned to a location, and a staff member.
52.-55. (canceled)
56. The method of claim 1, wherein said user includes at least one of an external record, an external database, an external program, and an external device.
57.-181. (canceled)
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
What is claimed is:
1. A method for optimizing geometric stretch of a parametrization scheme in connection with computer graphics, comprising:
parametrizing a mesh utilizing a geometric stretch metric, wherein the geometric stretch metric is used to measure a plurality of geometric stretch metric values corresponding to how much undersampling exists for different points on the surface of the mesh in accordance with spatial relationships of the mesh; and
minimizing the plurality of geometric stretch metric values to minimize undersampling over all points on the surface of the mesh.
2. A method according to claim 1, wherein the plurality of geometric stretch metric values are measured by integrating a pointwise undersampling metric over the surface area of the mesh.
3. A method according to claim 2, wherein the geometric stretch metric is based on at least one of a L2 and L norm, which correspond to the root mean square stretch over all directions in the domain and the maximum singular value obtained when mapping unit-length vectors from the texture domain to the surface, respectively.
4. A method according to claim 1, wherein said minimizing includes updating individual vertex positions of the chart using line searches.
5. A method according to claim 1, wherein the spatial relationships include x, y and z coordinate spatial relationships of the mesh.
6. A method according to claim 1, wherein said parametrizing includes partitioning the unparametrized mesh into a plurality of charts, and wherein said partitioning includes considering geometry of the mesh.
7. A method according to claim 6, wherein said minimizing includes resizing polygons of said plurality of charts, and wherein said resizing is based upon geometric stretch.
8. A method according to claim 7, further comprising:
forming a texture atlas, wherein said forming includes performing a second resizing of the polygons of said plurality of charts and packing the polygons of said plurality of charts to define said texture atlas.
9. A method according to claim 8, further comprising sampling texture images based upon said texture atlas.
10. A method according to claim 9, wherein said sampling includes applying a pull-push algorithm to fill in unsampled regions.
11. A computer readable medium having stored thereon a plurality of computer-executable instructions for performing the method of claim 1.
12. A modulated data signal carrying computer executable instructions for performing the method of claim 1.
13. At least one of a coprocessing device and a computing device comprising means for performing the method of claim 1.
14. A method for optimizing geometric stretch of a parametrization scheme in connection with computer graphics, comprising:
receiving a mesh; and
generating a progressive mesh (PM) sequence from said mesh utilizing a geometric stretch metric, wherein the geometric stretch metric measures a plurality of geometric stretch metric values corresponding to how much undersampling exists for different points on the surface of a mesh of said PM sequence in accordance with spatial relationships of the mesh.
15. A method according to claim 14, further including minimizing the plurality of geometric stretch metric values to minimize undersampling at any point on the surface of the mesh of said PM sequence.
16. A method according to claim 15, wherein said minimizing includes updating individual vertex positions of the chart using line searches.
17. A method according to claim 14, wherein the plurality of geometric stretch metric values are measured by integrating a pointwise undersampling metric over the surface area of the mesh.
18. A method according to claim 15, wherein the geometric stretch metric is based on at least one of a L2 and L norm, which correspond to the root mean square stretch over all directions in the domain and the maximum singular value obtained when mapping unit-length vectors from the texture domain to the surface, respectively.
19. A method according to claim 14, wherein the spatial relationships include x, y and z coordinate spatial relationships of the mesh.
20. A method according to claim 15, wherein said minimizing is based upon minimization of texture deviation-and geometric stretch over all meshes in the PM sequence.
21. A method according to claim 14, wherein said generating includes partitioning the mesh into a plurality of charts, and wherein said partitioning includes considering geometry of the mesh.
22. A method according to claim 21, wherein said generating further includes forming an initial parametrization of said plurality of charts, and wherein said forming includes minimizing geometric stretch.
23. A method according to claim 22, wherein said generating further includes resizing polygons of said plurality of charts, and wherein said resizing is based upon geometric stretch.
24. A method according to claim 23, wherein said minimizing further includes simplifying the mesh after said resizing to produce an initial PM sequence, and wherein said simplifying includes minimizing texture deviation.
25. A method according to claim 24, wherein said minimizing further includes optimizing said initial PM sequence to construct said PM sequence, and wherein said optimizing includes minimizing both geometric stretch and texture deviation over all of said initial PM sequence.
26. A method according to claim 23, further comprising:
forming a texture atlas based upon said PM sequence, wherein said forming includes performing a second resizing of the polygons of the plurality of charts and packing the polygons of the plurality of charts to define said texture atlas.
27. A method according to claim 26, further comprising sampling texture images based upon said texture atlas.
28. A method according to claim 27, wherein said sampling includes applying a pull-push algorithm to fill in unsampled regions.
29. A computer readable medium having stored thereon a plurality of computer-executable instructions for performing the method of claim 14.
30. A modulated data signal carrying computer executable instructions for performing the method of claim 14.
31. At least one of a coprocessing device and a computing device comprising means for performing the method of claim 14.
32. A method for optimizing geometric stretch of a parametrization scheme in connection with computer graphics, comprising:
partitioning a mesh into a plurality of charts utilizing a geometric stretch metric, wherein the geometric stretch metric measures a plurality of geometric stretch metric values corresponding to how much undersampling exists for different points on the surface of the mesh in accordance with spatial relationships of the mesh.
33. A method according to claim 32, further including minimizing the plurality of geometric stretch metric values to minimize undersampling at any point on the surface of the mesh of said PM sequence.
34. A method according to claim 33, wherein said minimizing includes updating individual vertex positions of the chart using line searches.
35. A method according to claim 32, wherein the plurality of geometric stretch metric values are measured by integrating a pointwise undersampling metric over the surface area of the mesh.
36. A method according to claim 35, wherein the geometric stretch metric is based on at least one of a L2 and L norm, which correspond to the root-mean-square stretch over all directions in the domain and the maximum singular value obtained when mapping unit-length vectors from the texture domain to the surface, respectively.
37. A method according to claim 32, wherein the spatial relationships include x, y and z coordinate spatial relationships of the mesh.
38. A method according to claim 32, wherein said partitioning includes scaling at least one chart area of the plurality of charts relative to at least one other chart area of the plurality of charts.
39. A method according to claim 32, wherein said partitioning includes resizing polygons of said plurality of charts, and wherein said resizing is based upon geometric stretch.
40. A computer readable medium having stored thereon a plurality of computer-executable instructions for performing the method of claim 32.
41. A modulated data signal carrying computer executable instructions for performing the method of claim 32.
42. At least one of a coprocessing device and a computing device comprising means for performing the method of claim 32.