Method and system for improving search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems
First Claim
1. A computing system implemented method for improving search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems, comprising:
- providing, with one or more computing systems, a customer self-help system associated with a financial management system;
storing, with the customer self-help system, customer support content data representing customer support content, in memory allocated for use by the customer self-help system;
determining content relevance weight data for the customer support content data by applying content relevance data to a content relevance analytics model, the content relevance data being acquired by the customer self-help system from one or more sources of content relevance data, the content relevance weight data representing content relevance weights;
determining topic term probabilities, wherein the topic term probabilities are for topic terms that are assigned to individual portions of the customer support content represented by the customer support content data;
determining context characteristics probabilities, wherein the context characteristics probabilities represent a probabilistic relationship between topics and user context characteristics data;
receiving search query terms data, by the customer self-help system, from a user of the customer self-help system, the search query terms data representing one or more search query terms that formulate at least part of a search query;
providing search algorithm data representing a search algorithm that matches the search query with portions of the customer support content that are topically related to the search query;
applying search query data to the search algorithm data to identify the portions of the customer support content data that are topically related to the search query, the search query data including the search query terms data;
determining context characteristics associated with the user of the customer self-help system, wherein the context characteristics are present during the submission of the search query terms data, and further wherein the context characteristics include user financial data characteristics and user profile characteristics;
determining topic relevance scores for customer support content associated with the search query by summing the topic term probabilities associated with topic terms in the identified portions of the customer support content data and the context characteristics probabilities for the context characteristics associated with the user;
determining weighted relevance scores for customer support content associated with the search query by multiplying the topic relevance scores by the content relevance weights associated with the customer support content;
applying the weighted relevance scores to the customer support content data to transform the customer support content into recency boosted customer support content data, the recency boosted customer support content data representing recency boosted customer support content;
updating user experience display data to include portions of the recency boosted customer support content data that are topically related to the search query, the user experience display data representing a user experience display; and
transmitting the user experience display data to a user computing system, to display the portions of the recency boosted customer support content data that are topically related to the search query, in response to the search query terms data received from the user, to assist the user in finding an answer to the search query.
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Accused Products
Abstract
Disclosed methods and systems improve search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems. The customer self-help system retrieves content relevance from a variety of sources, such as media outlets, taxation agencies and news feeds for the financial management system. The customer self-help system generates content relevance weights from the content relevance data, and applies the content relevance weights to customer support content maintained by the customer self-help system. In response to receiving a search query from a user, the customer self-help system provides relevant portions of customer support content that has been recency boosted (e.g., adjusted by the content relevance weights), to increase the likelihood that the customer support content provided to the user is relevant to the user'"'"'s search query.
252 Citations
23 Claims
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1. A computing system implemented method for improving search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems, comprising:
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providing, with one or more computing systems, a customer self-help system associated with a financial management system; storing, with the customer self-help system, customer support content data representing customer support content, in memory allocated for use by the customer self-help system; determining content relevance weight data for the customer support content data by applying content relevance data to a content relevance analytics model, the content relevance data being acquired by the customer self-help system from one or more sources of content relevance data, the content relevance weight data representing content relevance weights; determining topic term probabilities, wherein the topic term probabilities are for topic terms that are assigned to individual portions of the customer support content represented by the customer support content data; determining context characteristics probabilities, wherein the context characteristics probabilities represent a probabilistic relationship between topics and user context characteristics data; receiving search query terms data, by the customer self-help system, from a user of the customer self-help system, the search query terms data representing one or more search query terms that formulate at least part of a search query; providing search algorithm data representing a search algorithm that matches the search query with portions of the customer support content that are topically related to the search query; applying search query data to the search algorithm data to identify the portions of the customer support content data that are topically related to the search query, the search query data including the search query terms data; determining context characteristics associated with the user of the customer self-help system, wherein the context characteristics are present during the submission of the search query terms data, and further wherein the context characteristics include user financial data characteristics and user profile characteristics; determining topic relevance scores for customer support content associated with the search query by summing the topic term probabilities associated with topic terms in the identified portions of the customer support content data and the context characteristics probabilities for the context characteristics associated with the user; determining weighted relevance scores for customer support content associated with the search query by multiplying the topic relevance scores by the content relevance weights associated with the customer support content; applying the weighted relevance scores to the customer support content data to transform the customer support content into recency boosted customer support content data, the recency boosted customer support content data representing recency boosted customer support content; updating user experience display data to include portions of the recency boosted customer support content data that are topically related to the search query, the user experience display data representing a user experience display; and transmitting the user experience display data to a user computing system, to display the portions of the recency boosted customer support content data that are topically related to the search query, in response to the search query terms data received from the user, to assist the user in finding an answer to the search query. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computing system implemented method for improving search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems, comprising:
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providing, with one or more computing systems, a customer self-help system associated with a financial management system; storing, with the customer self-help system, customer support content data representing customer support content, in memory allocated for use by the customer self-help system; acquiring, with the customer self-help system, content relevance from one or more sources of content relevance data; determining content relevance weight data for the customer support content data by applying the content relevance data to a content relevance analytics model, the content relevance weight data representing content relevance weights, the content relevance analytics model being at least partially based on a probabilistic topic model; determining topic term probabilities, wherein the topic term probabilities are for topic terms that are assigned to individual portions of the customer support content represented by the customer support content data; determining context characteristics probabilities, wherein the context characteristics probabilities represent a probabilistic relationship between topics and user context characteristics data; receiving search query terms data, by the customer self-help system, from a user of the customer self-help system, the search query terms data representing one or more search query terms that formulate at least part of a search query; providing search algorithm data representing a search algorithm that matches the search query with portions of the customer support content, at least partially based on topics that are common to both the search query and the portions of the customer support content; applying search query data to the search algorithm data to identify the portions of the customer support content data that are topically related to the search query, the search query data including the search query terms data; determining context characteristics associated with the user of the customer self-help system, wherein the context characteristics are present during the submission of the search query terms data, and further wherein the context characteristics include user financial data characteristics and user profile characteristics; determining topic relevance scores for customer support content associated with the search query by summing the topic term probabilities associated with topic terms in the identified portions of the customer support content data and the context characteristics probabilities for the context characteristics associated with the user; determining weighted relevance scores for customer support content associated with the search query by multiplying the topic relevance scores by the content relevance weights associated with the customer support content; applying the weighted relevance scores to the customer support content data to transform the customer support content into recency boosted customer support content data, the recency boosted customer support content data representing recency boosted customer support content; updating user experience display data to include portions of the recency boosted customer support content data that are topically related to the search query, the user experience display data representing a user experience display; and transmitting the user experience display data to a user computing system, to display the portions of the recency boosted customer support content data that are topically related to the search query, in response to the search query terms data received from the user, to assist the user in finding an answer to the search query. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22)
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23. A system that increases a likelihood of providing relevant search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems, comprising:
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one or more processors; and memory having instructions which, if executed by the one or more processors, cause the one or more processors to perform a process for improving search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems, the process comprising; providing, a customer self-help system associated with a financial management system; storing, with the customer self-help system, customer support content data representing customer support content, in memory allocated for use by the customer self-help system; determining content relevance weight data for the customer support content data by applying content relevance data to a content relevance analytics model, the content relevance data being acquired by the self-help system from one or more sources of content relevance data, the content relevance weight data representing content relevance weights; determining topic term probabilities, wherein the topic term probabilities are for topic terms that are assigned to individual portions of the customer support content represented by the customer support content data; determining context characteristics probabilities, wherein the context characteristics probabilities represent a probabilistic relationship between topics and user context characteristics data; receiving search query terms data, by the customer self-help system, from a user of the customer self-help system, the search query terms data representing one or more search query terms that formulate at least part of a search query; providing search algorithm data representing a search algorithm that matches the search query with portions of the customer support content that are topically related to the search query; applying search query data to the search algorithm data to identify the portions of the customer support content data that are topically related to the search query, the search query data including the search query terms data; determining context characteristics associated with the user of the customer self-help system, wherein the context characteristics are present during the submission of the search query terms data, and further wherein the context characteristics include user financial data characteristics and user profile characteristics; determining topic relevance scores for customer support content associated with the search query by summing the topic term probabilities associated with topic terms in the identified portions of the customer support content data and the context characteristics probabilities for the context characteristics associated with the user; determining weighted relevance scores for customer support content associated with the search query by multiplying the topic relevance scores by the content relevance weights associated with the customer support content; applying the weighted relevance scores to the customer support content data to transform the customer support content into recency boosted customer support content data, the recency boosted customer support content data representing recency boosted customer support content; updating user experience display data to include portions of the recency boosted customer support content data that are topically related to the search query, the user experience display data representing a user experience display; and transmitting the user experience display data to a user computing system, to display the portions of the recency boosted customer support content data that are topically related to the search query, in response to the search query terms data received from the user, to assist the user in finding an answer to the search query.
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Specification