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EP1069487A1 Expired / Lapsed EP ⏱️ 0.0 years remaining

Automated diagnosis of printer systems using bayesian networks

Assignee: HP Inc
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Filing: 2000-07-03
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Granted: —
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Expires: 2020-07-03
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Abstract

Abstract An automated troubleshooter uses Bayesian networks to troubleshoot a system (209,210). Knowledge acquisition is performed in preparation to troubleshoot the system (209,210). 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 Skaanning
  • 👤 Finn V. Jensen
  • 👤 Uffe Kjaerulff
  • 👤 Paul A. Pelletier
  • 👤 Lasse Rostrup Jensen
  • 👤 Marilyn A. Parker
  • 👤 Janice L. Bogorad

Bibliographic Details

Application Number —
Jurisdiction EP
Data Source google_patents
Last Synchronized 2026-10-24 15:42
CPC Classifications
G; G05; G05B; G05B23/00; G05B23/02; G05B23/0205; G05B23/0259; G05B23/0275; G05B23/0281

Claims Analysis

Total Claims: 10
Claim 1
Independent Claim 127 words
A Bayesian network (1-11,500-531,601-608,610-629) that models a system component (209,210) causing failure of a system, the Bayesian network (1-11,500-531,601-608,610-629) comprising: an indicator node (502-512) that has a state that indicates whether the system component (209,210) is causing a failure; a plurality of cause nodes (602-605,611-620), coupled to the indicator node (502-512), each cause node in the plurality of cause nodes (602-605,611-620) representing a cause of the system component (209,210) producing a failure; and, a first plurality of troubleshooting nodes (606-608,621-627), each troubleshooting node representing a troubleshooting step, each troubleshooting node being coupled to at least one cause node from the plurality of cause nodes (602-605,611-620), each troubleshooting step suggesting an action to remedy causes represented by any cause nodes (602-605,611-620) to which the troubleshooting node is coupled.
Claim 2
Dependent Claim 64 words
A Bayesian network (1-11,500-531,601-608,610-629) as set out in claim 1, additionally comprising: a question node (628,629), the question node (628,629) being coupled to at least one cause node from the plurality of cause nodes (602-605,611-620), the question node (628,629) representing a question, which when answered, provides potential information about causes represented by any cause nodes (602-605,611-620) to which the question node (628,629) is coupled.
Claim 3
Dependent Claim 42 words
A Bayesian network (1-11,500-531,601-608,610-629) as set out in claim 1, wherein the plurality of cause nodes (602-605,611-620) are coupled to the indicator node (502-512) through a causes node which represents a probability distribution over causes for failure of the system component (209,210).
Claim 4
Dependent Claim 56 words
A Bayesian network (1-11,500-531,601-608,610-629) as set out in claim 3, additionally comprising: a question node (628), the question node (628) being coupled to the causes node (610), the question node (628) representing a question of a general type which is not necessarily related to a symptom or a cause of failure of the system component (209,210).
Claim 5
Dependent Claim 50 words
A Bayesian network (1-11,500-531,601-608,610-629) 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 6
Independent Claim 92 words
A method for performing knowledge acquisition used to troubleshoot a system (209,210), 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 7
Dependent Claim 41 words
A method as in claim 6 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 8
Dependent Claim 49 words
A method as in claim 6 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 9
Dependent Claim 82 words
A method as in claim 6 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; (g.2) determining a second probability that indicates a likelihood a customer will perform the first action correctly; and (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 10
Dependent Claim 66 words
A method as in claim 6 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.

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