Topic Clustering and Metric Ranking for Sales Feed Insights
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Solution Overview
Problem
Sales engineers face challenges in finding relevant information due to limitations in current search engines' query length, ranking algorithms, and the inability to combine results from multiple sources effectively, leading to inefficient information retrieval and presentation.
Innovation Solution
A system that assembles news feed items, clusters them by topic, preprocesses to filter based on mandatory and prohibited words, and orders them using metric values such as source reputation, business activity, social buzz, and account preferences, providing personalized and relevant business insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to find relevant information, then users can search for content, but the search results are lengthy and exhausting with many similar information items
Solution Approach 1:
The patent segments search results by clustering them into topic groups based on semantic similarity. Instead of presenting a flat list of search results, the system divides them into organized clusters (e.g., by topic, source, or other dimensions), allowing users to navigate through grouped information more efficiently and reducing the time needed to review relevant content.
Solution Approach 2:
The patent changes the ranking parameters from traditional keyword-based relevance to multi-dimensional metrics including topic similarity, source reputation, recency, and user preferences. This parameter transformation enables more precise relevance measurement while reducing the cognitive load on users by presenting information organized through these new dimensional parameters.
2Quantity of substance
If search engines display more search results per screen, then users can see more information, but mobile devices have limited screen space and can only display few search results per screen
Solution Approach 1:
The patent introduces multiple dimensions for organizing search results beyond simple linear listing. By clustering results across multiple dimensions (topic, source, recency, user preferences), the system enables users to access more information effectively without increasing screen real estate. Users can navigate through clustered results using vertical or horizontal scrolling within organized groups, maximizing the utilization of limited mobile screen space.
3Productivity
If automated systems filter information to reduce noise, then users can find relevant content faster, but users need to know what piece of information is more suitable and relevant to their needs
Solution Approach 1:
The patent incorporates feedback mechanisms where the system learns from user interactions with clustered results. By tracking user behavior patterns, click-through rates, and engagement data, the system refines its clustering algorithms and relevance assessments over time. This feedback loop enables automated filtering to become increasingly accurate while maintaining ease of operation, as the system adapts to individual user needs and preferences.
Solution Approach 2:
The patent introduces topic clusters and organized groups as intermediary structures between the raw information and the user. These clusters act as mediators that automatically organize and prioritize information based on relevance metrics, reducing the cognitive effort required for users to identify suitable information while maintaining high retrieval efficiency through automated filtering.
4Measurement precision
If search engines use traditional ranking algorithms, then results can be ranked by relevance, but the algorithms cannot effectively combine results from multiple sources
Solution Approach 1:
The patent creates a universal ranking framework that handles multiple data sources uniformly. By normalizing results from diverse sources (websites, social media, forums, etc.) into a common representation space and applying consistent clustering and ranking algorithms, the system achieves both high relevance ranking accuracy and effective multi-source integration. The same algorithms process all source types, providing adaptability across different information ecosystems.
Data Source
AI summary
The technology disclosed relates to presenting important business insights to a sales engineer. In particular, the technology disclosed assembles a set of news feed items for companies of interest to a sales engineer and groups them into topics. It also qualifies some of the news feed items to return or not based on mandatory or prohibited words in the news feed items. Further, it determines a plurality of metric values for each of the returned news feed items that are based on one of a source metric, business metric, company reference metric, social buzz metric, and matched account metric. It then orders the news feed items, based on the determined metric values, with respect to one or more of source reputation, business activity-related vocabulary, company-name mention, social buzz, and correlation with accounts preferred by the sales engineer, and presents the ordered news feed items as business insights about the topics.