METHOD AND SYSTEM FOR PERFORMING A PROBABILISTIC TOPIC ANALYSIS OF SEARCH QUERIES FOR A CUSTOMER SUPPORT SYSTEM
First Claim
1. A method for improving a likelihood of user satisfaction with a customer support response that is provided by a customer support system in response to receiving search query terms, by using context information to reduce a likelihood of inaccurately identifying a relevant topic for the search query terms with a probabilistic topic model, the method comprising:
- receiving search query terms data representing one or more search query terms received by a customer support system with one or more computing systems, from a current user;
applying the search query terms data to a probabilistic topic model to identify topics data representing one or more topics that are relevant to the one or more search query terms, and to determine topic relevance scores data for the topics data representing the one or more topics, the topic relevance scores data representing one or more topic relevance scores that quantify a likelihood of relevance between the one or more topics and the one or more search query terms received from the current user;
generating length data for the search query terms data, the length data for the search query terms data representing a combined length of the one or more search query terms;
comparing the length data for the search query terms data to search query length threshold data representing a search query length threshold, below which a likelihood of inaccuracy increases for the probabilistic topic model;
if the combined length of the one or more search query terms is less than the search query length threshold, updating the topic relevance scores data representing the one or more topic relevance scores with context characteristics probabilities data to reduce the likelihood of inaccuracy for the probabilistic topic model, the context characteristics probabilities data representing one or more context characteristics probabilities that quantify a likelihood that a question about the one or more topics occurs while one or more context characteristics for the search query terms exist, wherein updating the topic relevance scores data includes;
identifying context characteristics data representing the context characteristics for the search query terms, the context characteristics data being selected from a group of context characteristics data consisting of;
data representing user characteristics of the current user;
data representing identification of user experience displays visited by the current user, the user experience displays being provided by one or more service provider systems associated with the customer support system; and
data representing identification of the one or more service provider systems associated with the customer support system and used by the current user;
applying the context characteristics data to the probabilistic topic model to generate the context characteristics probabilities data representing the one or more context characteristics probabilities; and
combining the context characteristics probabilities data with the topic relevance scores data to update the topic relevance scores data to reflect a combination of the context characteristics probabilities data and the topic relevance scores data;
selecting a relevant topic from the one or more topics that is likely most relevant to the one or more search query terms, at least partially based on a highest one of the one or more topic relevance scores represented by the topic relevance scores data; and
providing a personalized customer support response to the current user for the search query terms, at least partially based on the relevant topic, to increase a likelihood of customer satisfaction of the current user with a user experience within the customer support system, by reducing a likelihood of inaccurately identifying the relevant topic for the search query terms received by the customer support system.
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Abstract
A method and system uses a probabilistic topic model to identify topics that are relevant search query terms received by a customer support system from a user, according to one embodiment. The probabilistic topic model identifies topics that are relevant to the search query terms at least partially based on the context around the receipt of the search query terms, according to one embodiment. By identifying relevant topics at least partially based on the context around the receipt of the search query terms, a likelihood of inaccurately identifying a relevant topic is reduced, according to one embodiment.
43 Citations
21 Claims
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1. A method for improving a likelihood of user satisfaction with a customer support response that is provided by a customer support system in response to receiving search query terms, by using context information to reduce a likelihood of inaccurately identifying a relevant topic for the search query terms with a probabilistic topic model, the method comprising:
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receiving search query terms data representing one or more search query terms received by a customer support system with one or more computing systems, from a current user; applying the search query terms data to a probabilistic topic model to identify topics data representing one or more topics that are relevant to the one or more search query terms, and to determine topic relevance scores data for the topics data representing the one or more topics, the topic relevance scores data representing one or more topic relevance scores that quantify a likelihood of relevance between the one or more topics and the one or more search query terms received from the current user; generating length data for the search query terms data, the length data for the search query terms data representing a combined length of the one or more search query terms; comparing the length data for the search query terms data to search query length threshold data representing a search query length threshold, below which a likelihood of inaccuracy increases for the probabilistic topic model; if the combined length of the one or more search query terms is less than the search query length threshold, updating the topic relevance scores data representing the one or more topic relevance scores with context characteristics probabilities data to reduce the likelihood of inaccuracy for the probabilistic topic model, the context characteristics probabilities data representing one or more context characteristics probabilities that quantify a likelihood that a question about the one or more topics occurs while one or more context characteristics for the search query terms exist, wherein updating the topic relevance scores data includes; identifying context characteristics data representing the context characteristics for the search query terms, the context characteristics data being selected from a group of context characteristics data consisting of; data representing user characteristics of the current user; data representing identification of user experience displays visited by the current user, the user experience displays being provided by one or more service provider systems associated with the customer support system; and data representing identification of the one or more service provider systems associated with the customer support system and used by the current user; applying the context characteristics data to the probabilistic topic model to generate the context characteristics probabilities data representing the one or more context characteristics probabilities; and combining the context characteristics probabilities data with the topic relevance scores data to update the topic relevance scores data to reflect a combination of the context characteristics probabilities data and the topic relevance scores data; selecting a relevant topic from the one or more topics that is likely most relevant to the one or more search query terms, at least partially based on a highest one of the one or more topic relevance scores represented by the topic relevance scores data; and providing a personalized customer support response to the current user for the search query terms, at least partially based on the relevant topic, to increase a likelihood of customer satisfaction of the current user with a user experience within the customer support system, by reducing a likelihood of inaccurately identifying the relevant topic for the search query terms received by the customer support system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for identifying a relevant topic for search query terms based on context characteristics for the search query terms, to provide customer support responses to the search query terms based on the relevant topic for the search query terms, the system comprising:
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a customer support engine that receives search query terms data and context characteristics data for a customer support system hosted by one or more computing systems, the search query terms data representing one or more search query terms, the context characteristics data representing one or more context characteristics, wherein the context characteristics data are selected from a group of context characteristics data, consisting of; data representing user characteristics of a current user; data representing identification of user experience displays visited by the current user, the user experience displays being provided by one or more service provider systems associated with the customer support system; and data representing identification of the one or more service provider systems associated with the customer support system and used by the current user; and an analytics module that identifies one of a plurality of topics as being a relevant topic for the one or more search query terms, at least partially based on the search query terms data and at least partially based on the context characteristics data, wherein the analytics module identifies one of the plurality of topics as being the relevant topic by applying the search query terms data and the context characteristics data to a probabilistic topic model that generates topic relevance scores data representing a plurality of topic relevance scores for the plurality of topics, wherein the analytics module identifies the relevant topic from the plurality of topics by selecting a highest one of the plurality of topic relevance scores for the plurality of topics, wherein the customer support engine provides a customer support response that is responsive to receipt of the search query terms data, at least partially based on the relevant topic, to provide the customer support response at least partially based on the context characteristics data to reduce a likelihood of inaccurately identifying the relevant topic and to increase a likelihood of correctly addressing the search query terms with the customer support response. - View Dependent Claims (11, 12, 13)
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14. A system for improving a likelihood of user satisfaction with a customer support response that is provided by a customer support system in response to receiving search query terms, by using context information to reduce a likelihood of inaccurately identifying a relevant topic for the search query terms with a probabilistic topic model, the system comprising:
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at least one processor; and at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process for using context information to reduce a likelihood of inaccurately identifying a relevant topic for the search query terms with a probabilistic topic model, the process including; receiving search query terms data representing one or more search query terms received by a customer support system with one or more computing systems, from a current user; applying the search query terms data to a probabilistic topic model to identify topics data representing one or more topics that are relevant to the one or more search query terms, and to determine topic relevance scores data for the topics data representing the one or more topics, the topic relevance scores data representing one or more topic relevance scores that quantify a likelihood of relevance between the one or more topics and the one or more search query terms received from the current user; generating length data for the search query terms data, the length data for the search query terms data representing a combined length of the one or more search query terms; comparing the length data for the search query terms data to search query length threshold data representing a search query length threshold, below which a likelihood of inaccuracy increases for the probabilistic topic model; if the combined length of the one or more search query terms is less than the search query length threshold, updating the topic relevance scores data representing the one or more topic relevance scores with context characteristics probabilities data to reduce the likelihood of inaccuracy for the probabilistic topic model, the context characteristics probabilities data representing one or more context characteristics probabilities that quantify a likelihood that a question about the one or more topics occurs while one or more context characteristics for the search query terms exist, wherein updating the topic relevance scores data includes; identifying context characteristics data representing the context characteristics for the search query terms, the context characteristics data being selected from a group of context characteristics data consisting of; data representing user characteristics of the current user; data representing identification of user experience displays visited by the current user, the user experience displays being provided by one or more service provider systems associated with the customer support system; and data representing identification of the one or more service provider systems associated with the customer support system and used by the current user; applying the context characteristics data to the probabilistic topic model to generate the context characteristics probabilities data representing the one or more context characteristics probabilities; and combining the context characteristics probabilities data with the topic relevance scores data to update the topic relevance scores data to reflect a combination of the context characteristics probabilities data and the topic relevance scores data; selecting a relevant topic from the one or more topics that is likely most relevant to the one or more search query terms, at least partially based on a highest one of the one or more topic relevance scores represented by the topic relevance scores data; and providing a personalized customer support response to the current user for the search query terms, at least partially based on the relevant topic, to increase a likelihood of customer satisfaction of the current user with a user experience within the customer support system, by reducing a likelihood of inaccurately identifying the relevant topic for the search query terms received by the customer support system. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21)
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Specification