Author: claimsbot

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.

1-10. (canceled)
11. An digital signature generating apparatus, which is provided so as to be communicated to the user authentication apparatus for executing a user authentication in accordance with a user authentication system that has been set in advance upon receipt of a request of the user authentication; generating the first assertion for asserting the user authentication system when a result of this user authentication indicates validity; and outputting the result of the user authentication and the first assertion, the apparatus comprising:
a key management device configured to manage an digital signature generation key in accordance with a key management system that has been set in advance for each generation request source of the digital signature;
an authentication request transmission device configured to transmit a user authentication request for the generation request source of the digital signature to the user authentication apparatus upon receipt of the generation request of the digital signature;
an digital signature generation device configured to generate the digital signature by using the corresponding digital signature generation key in the key management device when a result of this user authentication received from the user authentication apparatus indicates validity;
a second assertion generation device configured to generate the second assertion for asserting the key management system;
means for applying the conversion processing to the digital signature and the first and second assertion and relating the digital signature and the first and second assertion each other by the acquired conversion value; and
an output device configured to output the digital signature, the first and second assertion, and the conversion value.
12. The digital signature generating apparatus according to claim 11, wherein
the conversion processing is an arithmetic processing of the hash function,
the conversion value is a hash value.
13. The digital signature generating apparatus according to claim 11, wherein
the conversion processing is the signature processing using a private key specific to the digital signature generating device,
the conversion value is a second digital signature.

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. An image fixing apparatus for fixing an unfixed toner image formed on a recording medium, comprising:
a fixing rotation member that forms a fixing nip portion through which the recording medium passes; and
a roller having a peripheral surface around which a web is wound, said roller forming a cleaning nip portion for cleaning the fixing rotation member with the web by pressing a portion on which a web is winded up, against the fixing rotation member,
wherein a used portion for cleaning of the web moves from the cleaning nip portion by rotation of the roller, and
wherein during non-cleaning in which the web does not clean the fixing rotation member, said roller leaves the fixing rotation member further than a position of during cleaning so that the web separates from the fixing rotation member.
2. An image fixing apparatus according to claim 1,
wherein said roller is a supplying roller on which an unused portion for cleaning of the web is wound up, and
the supplying roller supplies the unused portion for cleaning of the web to the cleaning nip portion.
3. An image fixing apparatus according to claim 1, wherein the roller is a winding roller for winding the used portion for cleaning of the web from the cleaning nip portion.
4. An image fixing apparatus according to claim 1, further comprising a second roller having a peripheral surface around which the web is wound,
wherein the web is stretched between the roller and the second roller, and
wherein when the web is separated from the fixing rotation member during non-cleaning, the web is loosened between the roller and the second roller.
5. An image fixing apparatus according to claim 4, wherein a position of the roller during non-cleaning is a position at which the roller is closer to the second roller than a position during the cleaning so as to allow the web to loosen between the roller and the second roller.
6. An image fixing apparatus according to claim 1, further comprising a cylindrical fixing film and a heater which comes in contact with an inside surface of the fixing film,
wherein the fixing rotation member is a pressure roller without a heat source therein,
the fixing film which comes in pressing contact with an unfixed toner image bearing surface of the recording medium is nipped between the heater and the fixing rotation member to form the fixing nip portion,
when the recording medium is not in the fixing nip portion, a toner component adhered to the fixing film is transited to the fixing rotation member.
7. An image fixing apparatus according to claim 1, wherein a state where the web roll-up portion is pressed on the fixing rotation member is maintained until the fixing rotation member is rotated at least once after the recording medium passes through the fixing nip portion.
8. An image fixing apparatus according to claim 1, wherein in a case where a time until a next recording medium passes after the recording medium passes through the fixing nip portion is shorter than a predetermined time, a state where the web roll-up portion is pressed on the fixing rotation member is maintained at a time interval until the next recording medium reaches the fixing nip portion.