In the claims:
1. A process for determining the confidence factor for an insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
estimating the conditional probability of misclassification for the at least one identified parameter;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
defining a run-time function to evaluate the confidence threshold based on the soft constraint.
2. The process according to claim 1, where at least one identified parameter comprises at least one of:
a) the number of retrieved cases;
b) the variability of the retrieved cases;
c) the number of retrieved cases thresholded by similarity value;
d) the variability of retrieved cases thresholded by similarity value
e) the number of refined cases;
f) the variability of refined cases
g) the number of refined cases, thresholded by similarity value
h) the variability of refined cases thresholded by similarity value
i) the measure of strength of mode;
j) the number of retrieved cases weighted by similarities;
k) the variability of retrieved cases weighted by similarities;
1) the number of refined cases weighted by similarities; and
m) the variability of refined cases weighted for similarities
3. The process according to claim 1, wherein the step of estimating the conditional probability of misclassification for the at least one identified parameter further comprises:
accessing a case base containing a plurality of cases whose associated application decisions have been certified correct;
selecting one of the plurality of cases, where the selected case is defined as the probe case;
identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
comparing the underwriting classification determination with the original certified decision of the probe case; and
recording the comparison and at least one identified internal parameter for the probe case.
4. The process according to claim 1, wherein the step of translating the conditional probability of misclassification into a soft constraint for each parameter further comprises a penalty for misclassification and a reward for correct classification.
5. The process according to claim 1, wherein the step of translating the conditional probability of misclassification into a soft constraint for each parameter further comprises an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications.
6. The process according to claim 1, further comprising the step generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
7. The process according to claim 6, further comprising the step of computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
8. The process according to claim 1, wherein the insurance underwriting decision is an automated decision.
9. A process for determining the confidence factor for an application decision based on a comparison of at least one previous application decision, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the application;
estimating the conditional probability of misclassification for the at least one identified parameter;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
defining a run-time function to evaluate the confidence threshold based on the soft constraint.
10. The process according to claim 9, wherein the step of estimating the conditional probability of misclassification for the at least one identified parameter further comprises:
accessing a case base containing a plurality of cases whose associated application decisions have been certified correct;
selecting one of the plurality of cases, where the selected case is defined as the probe case;
identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
comparing the underwriting classification determination with the original certified decision of the probe case; and
recording the comparison and the at least one identified internal parameter for the probe case.
11. The process according to claim 9, wherein the step of translating the conditional probability of misclassification into a soft constraint for each parameter further comprises a penalty for misclassification and a reward for correct classification.
12. The process according to claim 9, wherein the step of translating the conditional probability of misclassification into a soft constraint for each parameter further comprises an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications.
13. The process according to claim 9, further comprising the step generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
14. The process according to claim 13, further comprising the step of computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
15. The process according to claim 9, wherein the application decision is an automated application decision.
16. A process for determining the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
estimating the conditional probability of misclassification for the at least one identified parameter, including the sub-steps of:
a) accessing a case base containing a plurality of cases whose associated application decisions have been certified correct;
b) selecting one of the plurality of cases, where the selected case is defined as the probe case;
c) identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
d) determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
e) comparing the underwriting classification determination with the original certified decision of the probe case; and
f) recording the comparison and the at least one identified internal parameter for the probe case;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
defining a run-time function to evaluate the confidence threshold based on the soft constraint.
17. A process for determining the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
estimating the conditional probability of misclassification for the at least one identified parameter;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter, including comprising a penalty for misclassification and a reward for correct classification; and
defining a run-time function to evaluate the confidence threshold based on the soft constraint.
18. A process for determining the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
estimating the conditional probability of misclassification for the at least one identified parameter;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter through an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications; and
defining a run-time function to evaluate the confidence threshold based on the soft constraint.
19. A process for determining the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the process comprising:
identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
estimating the conditional probability of misclassification for the at least one identified parameter;
translating the conditional probability of misclassification into a soft constraint for the at least one parameter;
defining a run-time function to evaluate the confidence threshold based on the soft constraint; and
generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
20. The process according to claim 19, further comprising the step of computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
21. A medium storing code for causing a processor to determine the confidence factor for an insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
code for estimating the conditional probability of misclassification for the at least one identified parameter;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
code for defining a run-time function to evaluate the confidence threshold for each new query.
22. The medium according to claim 21, where the at least one identified parameters comprise at least one of:
a) the number of retrieved cases;
b) the variability of the retrieved cases;
c) the number of retrieved cases thresholded by similarity value;
d) the variability of retrieved cases thresholded by similarity value
e) the number of refined cases;
f) the variability of refined cases
g) the number of refined cases, thresholded by similarity value
h) the variability of refined cases thresholded by similarity value
i) the measure of strength of mode;
j) the number of retrieved cases weighted by similarities;
k) the variability of retrieved cases weighted by similarities;
l) the number of refined cases weighted by similarities; and
m) the variability of refined cases weighted for similarities
23. The medium according to claim 21, wherein the code for estimating the conditional probability of misclassification for the at least one identified parameters further comprises:
code for accessing a case base containing a plurality of cases whose associated application decisions have been certified correct;
code for selecting one of the plurality of cases, where the selected case is defined as the probe case;
code for identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
code for determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
code for comparing the underwriting classification determination with the original certified decision of the probe case; and
code for recording the comparison and the at least one identified internal parameter for the probe case.
24. The medium according to claim 21, wherein translating the conditional probability of misclassification into a soft constraint for each parameter further comprises a penalty for misclassification and a reward for correct classification.
25. The medium according to claim 21, wherein translating the conditional probability of misclassification into a soft constraint for each parameter further comprises an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications.
26. The medium according to claim 21, further comprising code for generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
27. The medium according to claim 26, further comprising code for computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
28. The medium according to claim 21, wherein the insurance underwriting decision is an automated decision.
29. A medium storing code for causing a processor to determining the confidence factor for an application decision based on a comparison of at least one previous application decision, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the application;
code for estimating the conditional probability of misclassification for the at least one identified parameters;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
code for defining a run-time function to evaluate the confidence threshold based on the soft constraint.
30. The medium according to claim 29, wherein the code for estimating the conditional probability of misclassification for the at least one identified parameter further comprises:
code for accessing a case base containing a plurality of cases whose associated application decisions have been were certified correct;
code for selecting one of the plurality of cases, where the selected case is defined as the probe case;
code for identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
code for determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
code for comparing the underwriting classification determination with the original certified decision of the probe case; and
code for recording the comparison and the at least one identified internal parameter for the probe case.
31. The medium according to claim 29, wherein translating the conditional probability of misclassification into a soft constraint for each parameter further comprises a penalty for misclassification and a reward for correct classification.
32. The medium according to claim 29, wherein translating the conditional probability of misclassification into a soft constraint for each parameter further comprises an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications.
33. The medium according to claim 29, further comprising code for generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
34. The medium according to claim 29, further comprising code for computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
35. The medium according to claim 29, wherein the application decision is an automated application decision.
36. A medium storing code for causing a processor to determine the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
code for estimating the conditional probability of misclassification for the at least one identified parameter, including:
a) code for accessing a case base containing a plurality of cases whose associated insurance application decisions have been certified correct;
b) code for selecting one of the plurality of cases, where the selected case is defined as the probe case;
c) code for identifying at least one internal parameter of the probe case that may affect the probability of misclassification;
d) code for determining the underwriting classification of the probe case based on the remaining plurality of cases within the case base;
e) code for comparing the underwriting classification determination with the original certified decision of the probe case; and
f) code for recording the comparison and the at least one identified internal parameter for the probe case;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter; and
code for defining a run-time function to evaluate the confidence threshold based on the soft constraint.
37. A medium storing code for causing a processor to determine the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
code for estimating the conditional probability of misclassification for the at least one identified parameter;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter, including comprising a penalty for misclassification and a reward for correct classification; and
code for defining a run-time function to evaluate the confidence threshold for based on the soft constraint.
38. A medium storing code for causing a processor to determine the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
code for estimating the conditional probability of misclassification for the at least one identified parameter;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter through an aggregating functions using a weighted sum of rewards and penalties with increasing penalties for misclassifications; and
code for defining a run-time function to evaluate the confidence threshold for based on the soft constraint.
39. A medium storing code for causing a processor to determine the confidence factor for an automated insurance application underwriting decision based on a comparison of at least one previous underwritten insurance application, the medium comprising:
code for identifying at least one internal parameter that may affect the probability of misclassification of the insurance application;
code for estimating the conditional probability of misclassification for the at least one identified parameter;
code for translating the conditional probability of misclassification into a soft constraint for the at least one parameter;
code for defining a run-time function to evaluate the confidence threshold based on the soft constraint; and
code for generating a soft constraint evaluation vector that contains the degree to which each parameter satisfies its corresponding soft constraint.
40. The medium according to claim 39, further comprising code for computing a confidence factor based on the intersection of all the soft constraints evaluations contained in the soft constraint evaluation vector.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer-implemented method, comprising:
determining, by a remote access protocol server executed by a computing device, that a monitor configuration of a primary client device is not compatible with a multiple monitor configuration of a virtual machine (VM) hosted by a hypervisor of the computing device;
establishing, by the remote access protocol server, a communication session with remote access protocol clients of multiple client devices of a logical client unit comprising the primary client device, wherein each of the multiple client devices comprises at least one monitor; and
for each VM monitor of the multiple monitors configured for the VM, mapping, by the remote access protocol server, the VM monitor to a monitor of a client device in the logical client unit.
2. The method of claim 1, further comprising:
receiving data representing the multiple monitors of the VM;
separating the data representing the multiple monitors of the VM into individual monitor data for each VM monitor of the multiple monitors of the VM;
identifying, by referencing the mapping of VM monitors to client device monitors, a client device that is associated with each VM monitor of the multiple monitors of the VM; and
for each individual monitor data, sending the individual monitor data to the identified client device associated with the VM monitor represented by the individual monitor data.
3. The method of claim 2, wherein the identifying the client device that is associated with each monitor further comprises referencing a mapping of VM monitors to client devices.
4. The method of claim 2, wherein the sending comprises transmitting the individual monitor data to a remote access protocol client executing on the client device receiving the individual monitor data.
5. The method of claim 1, wherein one of the multiple client devices is designated as a primary client device of the logical client unit.
6. The method of claim 5, wherein the primary client device manages inputoutput (IO) devices for the VM by:
receiving input events from the IO devices; and
sending the input events to the VM for processing by the VM.
7. The method of claim 5, wherein the primary client device manages inputoutput (IO) devices for the VM by:
receiving a mouse event from a mouse device of the IO devices;
determining one of the multiple client devices where an icon representing the mouse was located when the mouse event was generated; and
sending the mouse event to the determined one of the multiple client devices.
8. The method of claim 7, wherein the determined one of the multiple client devices renders the mouse event on a monitor of the client device by transforming the mouse event into primitive of a platform of the determined one of the multiple client devices.
9. A system, comprising:
a memory;
a processing device communicably coupled to the memory; and
a remote access protocol server executable from the memory by the processing device, the remote access protocol server configured to:
determine that a monitor configuration of a primary client device is not compatible with a multiple monitor configuration of a virtual machine (VM) hosted by a hypervisor executable from the memory by the processing device;
establish a communication session with remote access protocol clients of multiple client devices of a logical client unit comprising the primary client device, wherein each of the multiple client devices comprises at least one monitor; and
for each VM monitor of the multiple monitors configured for the VM, mapping the VM monitor to a monitor of a client device in the logical client unit.
10. The system of claim 9, wherein the remote access protocol server is further configured to:
receive data representing the multiple monitors of the VM;
separate the data representing the multiple monitors of the VM into individual monitor data for each VM monitor of the multiple monitors of the VM;
identify, by referencing the mapping of VM monitors to client device monitors, a client device that is associated with each VM monitor of the multiple monitors of the VM; and
for each individual monitor data, send the individual monitor data to the identified client device associated with the VM monitor represented by the individual monitor data.
11. The system of claim 10, wherein the identifying the client device that is associated with each monitor further comprises referencing a mapping of VM monitors to client devices.
12. The system of claim 10, wherein the sending comprises transmitting the individual monitor data to a remote access protocol client executing on the client device receiving the individual monitor data.
13. The system of claim 9, wherein one of the multiple client devices is designated as a primary client device of the logical client unit.
14. The system of claim 13, wherein the primary client device manages inputoutput (IO) devices for the VM by:
receiving input events from the IO devices; and
sending the input events to the VM for processing by the VM.
15. The system of claim 13, wherein the primary client device manages inputoutput (IO) devices for the VM by:
receiving a mouse event from a mouse device of the IO devices;
determining one of the multiple client devices where an icon representing the mouse was located when the mouse event was generated; and
sending the mouse event to the determined one of the multiple client devices.
16. A non-transitory machine-readable storage medium including data that, when accessed by a machine, cause the machine to perform operations comprising:
determining, by a remote access protocol server executed by a computing device, that a monitor configuration of a primary client device is not compatible with a multiple monitor configuration of a virtual machine (VM) hosted by a hypervisor of the computing device;
establishing, by the remote access protocol server, a communication session with remote access protocol clients of multiple client devices of a logical client unit comprising the primary client device, wherein each of the multiple client devices comprises at least one monitor; and
for each VM monitor of the multiple monitors configured for the VM, mapping, by the remote access protocol server, the VM monitor to a monitor of a client device in the logical client unit.
17. The non-transitory machine-readable storage medium of claim 16, wherein the data, when accessed by the machine, causes the machine to perform operations further comprising:
receiving data representing the multiple monitors of the VM;
separating the data representing the multiple monitors of the VM into individual monitor data for each VM monitor of the multiple monitors of the VM;
identifying, by referencing the mapping of VM monitors to client device monitors, a client device that is associated with each VM monitor of the multiple monitors of the VM; and
for each individual monitor data, sending the individual monitor data to the identified client device associated with the VM monitor represented by the individual monitor data.
18. The non-transitory machine-readable storage medium of claim 17, wherein the identifying the client device that is associated with each monitor further comprises referencing a mapping of VM monitors to client devices.
19. The non-transitory machine-readable storage medium of claim 17, wherein the sending comprises transmitting the individual monitor data to a remote access protocol client executing on the client device receiving the individual monitor data.
20. The non-transitory machine-readable storage medium of claim 16, wherein one of the multiple client devices is designated as a primary client device of the logical client unit, the primary client device to manage inputoutput (IO) devices for the VM by:
receiving input events from the IO devices; and
sending the input events to the VM for processing by the VM.