Customer self service subsystem for context cluster discovery and validation
First Claim
1. A user context clustering system for a customer self service system that performs resource search and selection, said system comprising:
- a context attribute database comprising types of user contexts and one or more context attributes associated with each user context for processing by said system;
a context attribute function database comprising funtions for computing values for each context attribute;
a database of user interaction records including user interaction data; and
, a processing mechanism for receiving said user interaction data including traces of previous user interactions with the system and including context information that is a function of said user including that user'"'"'s interaction state, and receiving a distance metric for associating closeness of said user interaction data and, for performing unsupervised learning by clustering related aspects of multiple user interactions with said system from said user interaction data according to said distance metric to determine new user contexts and associated attributes for use in subsequent resource searches initiated by users in said system, wherein improved query definition and resource lookup results from said new determined user contrxt attributes due to user validation based on a combination of said unsupervised learning and supervised learning.
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Abstract
A system and method for clustering user contexts in a customer self service system that performs resource search and selection. The customer self service system includes a context attribute database comprising types of user contexts and one or more context attributes associated with each user context for processing by the system, and context attribute function database comprising functions for computing values for each context attribute. The context clustering system receives user interaction data from among a database of user interaction records and a distance metric for associating closeness of the user interaction data and clusters the user interaction data according to the distance metric to determine new user contexts and associated attributes for use in subsequent resource searches initiated by users in the system. Improved user query definition and resource lookup results from the new determined user context attributes. The user interaction data comprises past and present user queries, system responses to user queries, raw context information including: one or more of static, historical context, transient context, organizational context, community context, and environment context, and, other raw context associated with the user and dependent upon that user'"'"'s interaction state and customer query domain.
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Citations
30 Claims
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1. A user context clustering system for a customer self service system that performs resource search and selection, said system comprising:
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a context attribute database comprising types of user contexts and one or more context attributes associated with each user context for processing by said system;
a context attribute function database comprising funtions for computing values for each context attribute;
a database of user interaction records including user interaction data; and
,a processing mechanism for receiving said user interaction data including traces of previous user interactions with the system and including context information that is a function of said user including that user'"'"'s interaction state, and receiving a distance metric for associating closeness of said user interaction data and, for performing unsupervised learning by clustering related aspects of multiple user interactions with said system from said user interaction data according to said distance metric to determine new user contexts and associated attributes for use in subsequent resource searches initiated by users in said system, wherein improved query definition and resource lookup results from said new determined user contrxt attributes due to user validation based on a combination of said unsupervised learning and supervised learning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for clustering user contexts in a customer self service system that performs resource search and selection, said customer self service system including a context attribute database comprising types of user contexts and one or more context attributes associated with each user context for processing by said system, and context attribute function database comprising functions for computing values for each context attribute, the method comprising the steps of:
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a) receiving user interaction data including traces of previous user interactions with the system including context information that is a function of said user including that user'"'"'s interaction state from among a database of user interaction records and a distance metric for associating closeness of said user interaction data; and
,b) performing unsupervised learning by clustering related aspects of multiple user interactions with said system from said user interaction data according to said distance metric to determine new user contexts and associated attributes for use in subsequent resource searches initiated by users in said system, wherein improved query definition and resource lookup result from said unsupervised learning and supervised learning. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22)
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23. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for clustering user contexts for a customer self service system that performs resource search and selection, said customer self service system including a context attribute database comprising types of user contexts and one or more context attributes associated with each user context for processing by said system, and context attribute function database comprising functions for computing values for each context attribute, said method comprising the steps of:
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a) receiving user interaction data including traces of previous user interactions with the system including context information that is a function of said user including that user'"'"'s interaction state from among a database of user interaction records and a distance metric for associating closeness of said user interaction data; and
,b) performing unsupervised learning by clustering related aspects of multiple user interactions with said system from said user interaction data according to said distance metric to determine new user contexts and associated attributes for use in subsequent resource searches initiated by users in said system, wherein improved query definition and resource lookup results from said new determined user context attributes due to user validation based on a combination of said unsupervised learning and supervised learning. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30)
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Specification