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US6535865B1
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Automated diagnosis of printer systems using Bayesian networks
Assignee: Hewlett Packard Development Co LP
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Filing: 1999-07-14
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Granted: 2003-03-18
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Expires: 2019-07-14
Abstract
Abstract An automated troubleshooter uses Bayesian networks to troubleshoot a system. Knowledge acquisition is performed in preparation to troubleshoot the system. An issue to troubleshoot is identified. Causes of the issue are identified. Subcauses of the causes are identified. Troubleshooting steps are identified. Troubleshooting steps are matched to causes and subcauses. Probabilities for the causes and the subcauses identified are estimated. Probabilities for actions and questions set are estimated. Costs for actions and questions are estimated.
Inventors
- 👤 Claus Skaaning
- 👤 Finn V. Jensen
- 👤 Uffe Kjærulff
- 👤 Paul A. Pelletier
- 👤 Lasse Rostrup Jensen
- 👤 Marilyn A. Parker
- 👤 Janice L. Boborad
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-29 14:14
CPC Classifications
G; G05; G05B; G05B23/00; G05B23/02; G05B23/0205; G05B23/0259; G05B23/0275; G05B23/0281
Claims Analysis
Total Claims: 49
Claim 1
Independent Claim
115 words
A Bayesian network that models a system component causing failure of a system, the Bayesian network comprising: an indicator node that has a state that indicates whether the system component is causing a failure; a plurality of cause nodes, coupled to the indicator node, each cause node in the plurality of cause nodes representing a cause of the system component producing a failure; and, a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled.
Claim 2
Dependent Claim
115 words
A Bayesian network that models a system component causing failure of a system, the Bayesian network comprising: an indicator node that has a state that indicates whether the system component is causing a failure; a plurality of cause nodes, coupled to the indicator node, each cause node in the plurality of cause nodes representing a cause of the system component producing a failure; and, a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled.
Claim 3
Dependent Claim
58 words
A Bayesian network as set out in claim 1 , additionally comprising: a question node, the question node being coupled to at least one cause node from the plurality of cause nodes, the question node representing a question, which when answered, provides potential information about causes represented by any cause nodes to which the question node is coupled.
Claim 4
Dependent Claim
39 words
A Bayesian network as set out in claim 1 , wherein the plurality of cause nodes are coupled to the indicator node through a causes node which represents a probability distribution over causes for failure of the system component.
Claim 5
Dependent Claim
51 words
A Bayesian network as set out in claim 3 , additionally comprising: a question node, the question node being coupled to the causes node, the question node representing a question of a general type which is not necessarily related to a symptom or a cause of failure of the system component.
Claim 6
Dependent Claim
49 words
A Bayesian network as set out in claim 1 wherein for a first troubleshooting step suggesting a first action, when calculating whether the first action will solve a first cause, utilizing an inaccuracy factor, the inaccuracy factor representing a probability that a user will incorrectly perform the first action.
Claim 7
Dependent Claim
25 words
A Bayesian network as set out in claim 1 wherein at least two cause nodes, from the plurality of cause nodes, have a common subcause.
Claim 8
Dependent Claim
98 words
A printing diagnosis system comprising: a first Bayesian network that handles all errors where a customer does not get output from a printer when attempting to print, and where the customer gets corrupted output; a second Bayesian network that handles all errors where the customer gets unexpected output; a third Bayesian network that handles all types of errorcodes that can be seen on a control panel of the printer; and, a fourth Bayesian network that handles miscellaneous erroneous behavior of the printer not covered by the first Bayesian network, the second Bayesian network and the third Bayesian network.
Claim 9
Dependent Claim
98 words
A printing diagnosis system comprising: a first Bayesian network that handles all errors where a customer does not get output from a printer when attempting to print, and where the customer gets corrupted output; a second Bayesian network that handles all errors where the customer gets unexpected output; a third Bayesian network that handles all types of errorcodes that can be seen on a control panel of the printer; and, a fourth Bayesian network that handles miscellaneous erroneous behavior of the printer not covered by the first Bayesian network, the second Bayesian network and the third Bayesian network.
Claim 10
Dependent Claim
27 words
A printing diagnosis system as in claim 7 , additionally comprising: a fifth Bayesian network that represents all possible settings in a printing system for the printer.
Claim 11
Independent Claim
91 words
A method for performing knowledge acquisition used to troubleshoot a system, the method comprising the following steps: (a) identifying an issue to troubleshoot; (b) identifying causes of the issue; (c) identifying subcauses of the causes; (d) identifying troubleshooting steps; (e) matching troubleshooting steps to causes and subcauses; (f) estimating probabilities for the causes identified in step (b) and the subcauses identified in step (c); (g) estimating probabilities for actions and questions set out in the troubleshooting steps; and, (h) estimating costs for actions and questions set out in the troubleshooting steps.
Claim 12
Independent Claim
91 words
A method for performing knowledge acquisition used to troubleshoot a system, the method comprising the following steps: (a) identifying an issue to troubleshoot; (b) identifying causes of the issue; (c) identifying subcauses of the causes; (d) identifying troubleshooting steps; (e) matching troubleshooting steps to causes and subcauses; (f) estimating probabilities for the causes identified in step (b) and the subcauses identified in step (c); (g) estimating probabilities for actions and questions set out in the troubleshooting steps; and, (h) estimating costs for actions and questions set out in the troubleshooting steps.
Claim 13
Dependent Claim
41 words
A method as in claim 9 wherein in step (d) each troubleshooting step involves an action in one of the following categories: (a) a solution action that provides an action that may solve the issue; and, (b) an information gathering action.
Claim 14
Dependent Claim
26 words
A method as in claim 9 additionally comprising the following step: (i) identifying actions and questions, set out in the troubleshooting steps, that require special handling.
Claim 15
Dependent Claim
20 words
A method as in claim 9 wherein in step (b) domain experts are used to identify causes of the issue.
Claim 16
Dependent Claim
33 words
A method as in claim 9 wherein in step (d) troubleshooting steps include: actions that can solve any of the causes or subcauses; and, questions that provide additional information about causes or subcauses.
Claim 17
Dependent Claim
49 words
A method as in claim 13 wherein in step (e) each troubleshooting step that includes an action is matched to any cause or subcause the action can solve and each troubleshooting step that includes a question is matched to any cause or subcause to which the question is related.
Claim 18
Dependent Claim
29 words
A method as in claim 9 wherein when new causes are identified in performing steps (c), (d) and (e), steps (b) through (e) are repeated for the new causes.
Claim 19
Dependent Claim
28 words
A method as in claim 9 wherein when new troubleshooting steps are identified in performing step (e), steps (d) and (e) are repeated for the new troubleshooting steps.
Claim 20
Dependent Claim
55 words
A method as in claim 9 wherein step (g) comprises the following substeps: (g.1) determining for each related cause or related subcause a first probability that indicates whether performing a first action correctly will solve the issue; and (g.2) determining a second probability that indicates a likelihood a customer will perform the first action correctly.
Claim 21
Dependent Claim
41 words
A method as in claim 17 wherein step (g) additionally comprises the following substep: (g.3) combining the first probability and the second probability to determine whether instructing the customer to perform the first action will result in the issue being solved.
Claim 22
Dependent Claim
66 words
A method as in claim 9 wherein in step (h), costs for a first action include factors that take into account the following: time to perform the first action; risk of breaking something when performing the first action; amount of money required to purchase any parts necessary to perform the first action; and, degree of insult a user may experience when the first action is suggested.
Claim 23
Dependent Claim
36 words
A method as in claim 9 wherein in step (h), costs for a first action include a factor that take into account a degree of insult a user may experience when the first action is suggested.
Claim 24
Dependent Claim
31 words
A method as in claim 9 wherein in step (h), costs for a first action include a factor that take into account risk of breaking something when performing the first action.
Claim 25
Dependent Claim
112 words
A method as in claim 9 additionally comprising the following step: (i) identifying actions and questions, set out in the troubleshooting steps, that require special handling, the actions and questions that require special handling including at least one of the following: initial steps that identify default causes that should be ruled out initially; workarounds which may solve a problem, in a way that may not be satisfactory to a user; information pertaining to replacement of components; information pertaining to irreversible actions; information pertaining to included actions; information pertaining to special-case steps; and, information pertaining to persistence of old observations as to whether the old obserservations are rendered invalid by later performed actions.
Claim 26
Dependent Claim
360 words
A dataflow model of a print job comprising: a first Bayesian network model representing how a file can cause erroneous printer output; a second Bayesian network model, coupled to the first Bayesian network, representing how an application can cause erroneous printer output; a third Bayesian network model, coupled to the second Bayesian network, representing how a printer driver can cause erroneous printer output; a fourth Bayesian network model, coupled to the third Bayesian network, representing how a spooler can cause erroneous printer output; a fifth Bayesian network model, coupled to the fourth Bayesian network, representing how operating system redirect code can cause erroneous printer output; a sixth Bayesian network model, coupled to the fifth Bayesian network, representing how a network driver can cause erroneous printer output; a seventh Bayesian network model, coupled to the sixth Bayesian network, representing how a network card can cause erroneous printer output; a eighth Bayesian network model, coupled to the seventh Bayesian network, representing how a first network connection can cause erroneous printer output; a ninth Bayesian network model, coupled to the eighth Bayesian network, representing how a server can cause erroneous printer output; a tenth Bayesian network model, coupled to the ninth Bayesian network, representing how a queue can cause erroneous printer output; a eleventh Bayesian network model, coupled t o the tenth Bayesian network, representing how a second network connection can cause erroneous printer output; a twelfth Bayesian network model, coupled to the eleventh Bayesian network, representing how printer hardware can cause erroneous printer output; a thirteenth Bayesian network model, coupled to the twelfth Bayesian network, representing how the printer hardware output can cause erroneous printer output; a fourteenth Bayesian network model, coupled to the fifth Bayesian network, representing how a local parallel cable can cause erroneous printer output; a fifteenth Bayesian network model, coupled to the fourteenth Bayesian network, representing how a switchbox can cause erroneous printer output; and, a sixteenth Bayesian network model, coupled to the fifth Bayesian network, representing how a third network connection can cause erroneous printer output; wherein the twelfth Bayesian network model is also coupled to the fifteenth Bayesian network model and the sixteenth Bayesian network model.
Claim 27
Dependent Claim
360 words
A dataflow model of a print job comprising: a first Bayesian network model representing how a file can cause erroneous printer output; a second Bayesian network model, coupled to the first Bayesian network, representing how an application can cause erroneous printer output; a third Bayesian network model, coupled to the second Bayesian network, representing how a printer driver can cause erroneous printer output; a fourth Bayesian network model, coupled to the third Bayesian network, representing how a spooler can cause erroneous printer output; a fifth Bayesian network model, coupled to the fourth Bayesian network, representing how operating system redirect code can cause erroneous printer output; a sixth Bayesian network model, coupled to the fifth Bayesian network, representing how a network driver can cause erroneous printer output; a seventh Bayesian network model, coupled to the sixth Bayesian network, representing how a network card can cause erroneous printer output; a eighth Bayesian network model, coupled to the seventh Bayesian network, representing how a first network connection can cause erroneous printer output; a ninth Bayesian network model, coupled to the eighth Bayesian network, representing how a server can cause erroneous printer output; a tenth Bayesian network model, coupled to the ninth Bayesian network, representing how a queue can cause erroneous printer output; a eleventh Bayesian network model, coupled t o the tenth Bayesian network, representing how a second network connection can cause erroneous printer output; a twelfth Bayesian network model, coupled to the eleventh Bayesian network, representing how printer hardware can cause erroneous printer output; a thirteenth Bayesian network model, coupled to the twelfth Bayesian network, representing how the printer hardware output can cause erroneous printer output; a fourteenth Bayesian network model, coupled to the fifth Bayesian network, representing how a local parallel cable can cause erroneous printer output; a fifteenth Bayesian network model, coupled to the fourteenth Bayesian network, representing how a switchbox can cause erroneous printer output; and, a sixteenth Bayesian network model, coupled to the fifth Bayesian network, representing how a third network connection can cause erroneous printer output; wherein the twelfth Bayesian network model is also coupled to the fifteenth Bayesian network model and the sixteenth Bayesian network model.
Claim 28
Dependent Claim
190 words
A Bayesian network that models dataflow through a system, the Bayesian network comprising: an indicator node that has a state that indicates whether there is a failure in dataflow; a dataflow components node defining all components of the data flow; a plurality of component indicator nodes, a state of each component indicator node representing whether failure in the dataflow is due to an associated component; a plurality of flow nodes presenting data flow through the components; a plurality of global troubleshooting nodes, each global troubleshooting node being associated with at least one of the plurality of component indicator nodes or at least one of the flow nodes, each global troubleshooting node providing a global troubleshooting step; a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled; and, a plurality of component network nodes, each of the component network nodes representing a Bayesian network modeling causes of an associated component.
Claim 29
Dependent Claim
190 words
A Bayesian network that models dataflow through a system, the Bayesian network comprising: an indicator node that has a state that indicates whether there is a failure in dataflow; a dataflow components node defining all components of the data flow; a plurality of component indicator nodes, a state of each component indicator node representing whether failure in the dataflow is due to an associated component; a plurality of flow nodes presenting data flow through the components; a plurality of global troubleshooting nodes, each global troubleshooting node being associated with at least one of the plurality of component indicator nodes or at least one of the flow nodes, each global troubleshooting node providing a global troubleshooting step; a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled; and, a plurality of component network nodes, each of the component network nodes representing a Bayesian network modeling causes of an associated component.
Claim 30
Dependent Claim
27 words
A Bayesian network as in claim 24 wherein each component network node in the plurality of component network nodes includes local troubleshooting steps for the associated component.
Claim 31
Independent Claim
277 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, and selecting on the basis of ECR and ECO, one of the optimal actions and the optional question; and, wherein when calculating ECO and ECR, taking into account that when an action that can solve multiple causes fails, probabilities of each of the multiple causes decreases and probabilities of other remaining causes increases accordingly such that a sum of cause probabilities is still 1.
Claim 32
Independent Claim
277 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, and selecting on the basis of ECR and ECO, one of the optimal actions and the optional question; and, wherein when calculating ECO and ECR, taking into account that when an action that can solve multiple causes fails, probabilities of each of the multiple causes decreases and probabilities of other remaining causes increases accordingly such that a sum of cause probabilities is still 1.
Claim 33
Independent Claim
287 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECR, using the following algorithm: find first action A 1 as action with highest P i /C i for i=0 to #actions recompute probabilities of causes assuming A i−1 =no (fails) find action A i as action with highest p(A i =yes|A 1 =no, . . . , A i−1 =no)/C i .
Claim 34
Independent Claim
287 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECR, using the following algorithm: find first action A 1 as action with highest P i /C i for i=0 to #actions recompute probabilities of causes assuming A i−1 =no (fails) find action A i as action with highest p(A i =yes|A 1 =no, . . . , A i−1 =no)/C i .
Claim 35
Independent Claim
335 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECO, approximating probabilities of actions solving a problem p(A=yes|Q=s,e) given observation of a question Q in state s utilizing the following equation: p ( A = yes | Q = s , e ) = p ( Q = s | A = yes , e ) ↔ p ( A = yes | e ) p ( Q = s | e ) ⋃ p ( Q = s | A = yes ) ↔ p ( A = yes | e ) p ( Q = s | e )
Claim 36
Independent Claim
335 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECO, approximating probabilities of actions solving a problem p(A=yes|Q=s,e) given observation of a question Q in state s utilizing the following equation: p ( A = yes | Q = s , e ) = p ( Q = s | A = yes , e ) ↔ p ( A = yes | e ) p ( Q = s | e ) ⋃ p ( Q = s | A = yes ) ↔ p ( A = yes | e ) p ( Q = s | e )
Claim 37
Independent Claim
334 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECO, using an expression for a probability that a question Q identifies a cause which can be compared with a probability that an action solves the problem, the expression identifying a cause being expressed as a maximization over causes and states of the question Q, the expression being set out below: p Qid c = max C max s p ( C = y | Q = s ) - p ( C = y ) 1 - p ( C = y ) ↔ p ( Q = s )
Claim 38
Independent Claim
334 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein step (b) includes: calculating expected cost of repair (ECR) for performing an optimal action, calculating expected cost of observation (ECO) for asking an optimal question, selecting on the basis of ECR and ECO, one of the optimal action and the optional question, and wherein when calculating ECO, using an expression for a probability that a question Q identifies a cause which can be compared with a probability that an action solves the problem, the expression identifying a cause being expressed as a maximization over causes and states of the question Q, the expression being set out below: p Qid c = max C max s p ( C = y | Q = s ) - p ( C = y ) 1 - p ( C = y ) ↔ p ( Q = s )
Claim 39
Independent Claim
209 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein in step (b) a question is asked only if it can potentially make a difference in a next selected troubleshooting step.
Claim 40
Independent Claim
209 words
A method for troubleshooting a problem within a system, the method comprising the following steps performed by an automated troubleshooter: (a) identifying the problem; (b) selecting one of any remaining actions or remaining questions; (c) when in step (b) a remaining question is selected, performing the following substeps: (c.1) asking a user the selected remaining question, (c.2) receiving any answer provided by the user, (c.3) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (c.4) if there are more actions or questions, returning to step (b); and, (d) when in step (b) a remaining action is selected, performing the following substeps: (d.1) performing the selected remaining action, (d.2) determining whether the problem is solved, (d.3) if performing the selected remaining action solved the problem, terminating the troubleshooter as successfully fixing the problem, and (d.4) if performing the selected remaining action did not solve the problem, performing the following substeps: (d.4.1) if there are no more actions or questions terminating the troubleshooter as unsuccessfully fixing the problem, and (d.4.2) if there are more actions or questions, returning to step (b); wherein in step (b) a question is asked only if it can potentially make a difference in a next selected troubleshooting step.
Claim 41
Independent Claim
97 words
A method for performing knowledge acquisition to be used by an expert system to troubleshoot a product, the method comprising the following steps: (a) identifying an issue to troubleshoot; (b) identifying causes of the issue; (c) identifying subcauses of the causes; (d) identifying troubleshooting steps; (e) matching troubleshooting steps to causes and subcauses; (f) estimating probabilities for the causes identified in step (b) and the subcauses identified in step (c); (g) estimating probabilities for actions and questions set out in the troubleshooting steps; and, (h) estimating costs for actions and questions set out in the troubleshooting steps.
Claim 42
Independent Claim
97 words
A method for performing knowledge acquisition to be used by an expert system to troubleshoot a product, the method comprising the following steps: (a) identifying an issue to troubleshoot; (b) identifying causes of the issue; (c) identifying subcauses of the causes; (d) identifying troubleshooting steps; (e) matching troubleshooting steps to causes and subcauses; (f) estimating probabilities for the causes identified in step (b) and the subcauses identified in step (c); (g) estimating probabilities for actions and questions set out in the troubleshooting steps; and, (h) estimating costs for actions and questions set out in the troubleshooting steps.
Claim 43
Dependent Claim
119 words
An expert system comprising: a Bayesian network that models a system component causing failure of a system, the Bayesian network including: an indicator node that has a state that indicates whether the system component is causing a failure; a plurality of cause nodes, coupled to the indicator node, each cause node in the plurality of cause nodes representing a cause of the system component producing a failure; and, a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled.
Claim 44
Dependent Claim
119 words
An expert system comprising: a Bayesian network that models a system component causing failure of a system, the Bayesian network including: an indicator node that has a state that indicates whether the system component is causing a failure; a plurality of cause nodes, coupled to the indicator node, each cause node in the plurality of cause nodes representing a cause of the system component producing a failure; and, a first plurality of troubleshooting nodes, each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes, each troubleshooting step suggesting an action to remedy causes represented by any cause nodes to which the troubleshooting node is coupled.
Claim 45
Dependent Claim
62 words
An expert system as set out in claim 32 , wherein the Bayesian network additionally includes: a question node, the question node being coupled to at least one cause node from the plurality of cause nodes, the question node representing a question, which when answered, provides potential information about causes represented by any cause nodes to which the question node is coupled.
Claim 46
Dependent Claim
39 words
An expert system as set out in claim 32 , wherein the plurality of cause nodes are coupled to the indicator node through a causes node which represents a probability distribution over causes for failure of the system component.
Claim 47
Dependent Claim
55 words
An expert system as set out in claim 34 , wherein the Bayesian network additionally includes: a question node, the question node being coupled to the causes node, the question node representing a question of a general type which is not necessarily related to a symptom or a cause of failure of the system component.
Claim 48
Dependent Claim
49 words
An expert system as set out in claim 32 wherein for a first troubleshooting step suggesting a first action, when calculating whether the first action will solve a first cause, utilizing an inaccuracy factor, the inaccuracy factor representing a probability that a user will incorrectly perform the first action.
Claim 49
Dependent Claim
25 words
An expert system as set out in claim 32 wherein at least two cause nodes, from the plurality of cause nodes, have a common subcause.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2006-03-17 | 2006-09-16 | 2007-03-18 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2010-03-17 | 2010-09-16 | 2011-03-18 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2014-03-17 | 2014-09-16 | 2015-03-18 | $7700.00 | Window Closed / Lapsed |