Method and system for case-based reasoning utilizing a belief network
First Claim
1. A case-based decision support system comprising:
- a storage device containing a belief network that contains knowledge obtained from observed cases so that the observed cases need not be stored, wherein each observed case is a problem resolution scenario previously resolved utilizing the belief network;
a memory containing;
an authoring component for receiving information from the user, creating the belief network from the received information, and storing the created belief network into the storage device; and
a reasoning component for receiving input from the user describing the problem encountered by the user, accessing the belief network in response to the received input from the user, and generating a resolution to the problem encountered by the user utilizing the belief network; and
a processing component for executing the authoring component and the reasoning component.
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Accused Products
Abstract
An improved method and system for performing case-based reasoning is provided. A belief network is utilized by the preferred case-based reasoning system for assisting a user in problem resolution. After resolving a problem of a user, the preferred embodiment of the present invention updates the probabilities in the belief network so as to provide for a more accurate problem resolution upon the next invocation of the preferred embodiment. The belief network of the preferred embodiment contains six data types relating to a problem resolution scenario. The data types utilized by the belief network of the preferred embodiment include: issues, causes, resolutions, symptoms, terms, and alternates.
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Citations
28 Claims
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1. A case-based decision support system comprising:
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a storage device containing a belief network that contains knowledge obtained from observed cases so that the observed cases need not be stored, wherein each observed case is a problem resolution scenario previously resolved utilizing the belief network; a memory containing; an authoring component for receiving information from the user, creating the belief network from the received information, and storing the created belief network into the storage device; and a reasoning component for receiving input from the user describing the problem encountered by the user, accessing the belief network in response to the received input from the user, and generating a resolution to the problem encountered by the user utilizing the belief network; and a processing component for executing the authoring component and the reasoning component. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 20)
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13. In a computer system, the computer system having a belief network containing probabilities for likelihood of resolutions to problems encountered by users and a memory containing a reasoner component for receiving input from the user describing a problem encountered by the user, a method for performing case-based reasoning comprising the steps of:
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receiving input from the user into the reasoner component describing the problem encountered by the user; generating a resolution to the encountered problem by the reasoner component utilizing the belief network; and adjusting the probabilities in the belief network in response to the generated resolution. - View Dependent Claims (14, 15, 16, 17, 18, 19, 21, 22)
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23. A case-based reasoning system for resolving a problem encountered by a user, comprising:
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a belief network for generating suggested resolutions to the encountered problem of the user, the belief network having probabilities that reflect knowledge from previously observed problem resolution scenarios; and a user interface component for interacting with the user, for receiving a description of the problem encountered by the user, for invoking the belief network to generate the suggested resolutions to the encountered problem, for updating the probabilities in the belief network to provide greater accuracy in resolving problems of the user, and for outputting the suggested resolutions to the user.
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24. In a computer system, the computer system having a belief network for resolving problems of a user, the belief network having probabilities and a plurality of types of variables, the belief network storing knowledge from previously observed problem resolution scenarios, one type of variable indicating problems of a user, one type of variable indicating causes for the problems of the user, one type of variable indicating symptoms for the problems of the user, one type of variable indicating resolutions to the problems of the user, one type of variable indicating terms acceptable for describing causes, symptoms and resolutions, and one type of variable indicating alternates acceptable for identifying the terms, a method for performing case-based reasoning comprising the steps of:
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invoking the belief network to suggest resolutions to a problem encountered by file user; generating the suggested resolutions by the belief network; and updating the probabilities in the belief network to provide greater accuracy in resolving problems of the user.
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25. A computer-readable medium whose contents cause a computer system to perform case-based reasoning, the computer system having a belief network containing probabilities for likelihood of resolutions to problems encountered by users and a memory containing a reasoner component for receiving input from the user describing a problem encountered by the user, by performing the steps of:
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receiving input from the user into the reasoner component describing the problem encountered by the user; generating a resolution to the encountered problem by the reasoner component utilizing the belief network; and adjusting the probabilities in the belief network in response to the generated resolution.
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26. A computer-readable medium whose contents cause a computer system to perform case-based reasoning, the computer system having a belief network for resolving problems of a user, the belief network having probabilities and a plurality of types of variables, the belief network storing knowledge from previously observed problem resolution scenarios, by performing the steps of:
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invoking the belief network to suggest resolutions to a problem encountered by the user; generating the suggested resolutions by the belief network; and updating the probabilities in the belief network to provide greater accuracy in resolving problems of the user.
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27. A method for resolving problems by a case-based reasoning system in a computer system having a belief network with probabilities indicating likelihoods of resolutions solving problems encountered by users, comprising:
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receiving a plurality of observed cases, wherein each observed case is a problem resolution scenario previously resolved by the case-based reasoning system; for each of the received plurality of observed cases, extracting knowledge from the observed case, wherein the knowledge influences the probabilities of the belief network; updating the probabilities of the belief network based on the extracted knowledge; and discarding the observed case; receiving input from a user describing a problem encountered by the user; and accessing the belief network and examining the probabilities to generate a resolution to the problem encountered by the user in response to receiving the input.
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28. A computer-readable medium whose contents cause a case-based reasoning system in a computer system to resolve problems, the computer system having a belief network with probabilities indicating likelihoods of resolutions solving problems encountered by users, by performing the steps of:
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receiving a plurality of observed cases, wherein each observed case is a problem resolution scenario previously resolved by the case-based reasoning system; for each of the received plurality of observed cases, extracting knowledge from the observed case, wherein the knowledge influences the probabilities of the belief network; updating the probabilities of the belief network based on the extracted knowledge; and discarding the observed case; receiving input from a user describing a problem encountered by the user; and accessing the belief network and examining the probabilities to generate a resolution to the problem encountered by the user in response to receiving the input.
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Specification