Recency Boosting Customer Support Search Results
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Solution Overview
Problem
Traditional customer self-help systems fail to provide relevant search results due to poor prioritization of customer support content, leading to user dissatisfaction, loss of trust, and inefficient use of resources.
Innovation Solution
Implementing a recency boosting mechanism that gathers content relevance data from various sources to generate relevance weights, enhancing the prioritization of customer support content and improving search results by increasing the relevance of recent and relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customer support content is prioritized based on creation date, then newer content is prioritized over older content, but relevant older content (e.g., tax laws or regulations) is de-prioritized leading to irrelevant search results
Solution Approach 1:
The patent changes the prioritization parameter from creation date to a dynamic relevance score that incorporates multiple factors including recency, user feedback, and content type. This allows the system to adaptively prioritize content based on actual relevance rather than a fixed temporal parameter, resolving the contradiction between preferring new content and maintaining relevant older content.
Solution Approach 2:
The system implements dynamic content prioritization where the ranking of customer support content changes based on real-time factors such as user interactions, feedback, and recency. This dynamic approach allows the system to flexibly adjust priorities to maintain reliability while adapting to different content types and user needs.
2Ease of operation
If traditional content prioritization techniques are used, then content is ranked by creation date, but the system fails to provide relevant information leading to user dissatisfaction and loss of trust
Solution Approach 1:
The patent implements feedback mechanisms where user interactions with search results (clicks, dwell time, feedback ratings) are continuously collected and used to refine content prioritization. This feedback loop ensures that the system learns from user behavior to improve relevance, thereby maintaining user satisfaction while preventing loss of relevant information.
Solution Approach 2:
The system performs preliminary actions by pre-processing and tagging customer support content with metadata including content type, recency indicators, and relevance factors before search queries are submitted. This preliminary preparation enables faster and more accurate retrieval of relevant information, improving both user satisfaction and information delivery.
3Manufacturing precision
If newer customer support content is prioritized, then product feature information is updated, but outdated content (e.g., resolved product errors) continues to be provided
Solution Approach 1:
The patent changes the content prioritization parameter from static creation date to a dynamic relevance score that incorporates recency, accuracy indicators, and content type. This allows the system to accurately prioritize current and relevant content while deprioritizing outdated or resolved information, improving both content accuracy and reducing user search time.
Solution Approach 2:
The system applies partial prioritization to different content types based on their specific requirements. For example, product feature content may be prioritized by recency while troubleshooting content may be prioritized by relevance to current issues. This selective approach improves content accuracy without unnecessarily increasing user search time across all content types.
Data Source
AI summary
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.