Database System for Filtering Data Presentations via User Links
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Solution Overview
Problem
Companies face challenges in efficiently managing customer service interactions on social media, as many user comments are noise and do not require customer relationship management (CRM) support, leading to resource inefficiencies.
Innovation Solution
A database system that uses a term weighting algorithm to identify relevant articles by extracting terms from user queries, ranking them based on relevance, and displaying them to users, thereby bridging the gap between customer problem vocabularies and solution vocabularies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If companies hire armies of agents to manage user comments on social media, then customer service coverage is improved, but operational cost and resource consumption increase significantly
Solution Approach 1:
The patent implements a self-service mechanism where users can find answers to their questions through automated article recommendations based on their comments. The system extracts terms from user comments, searches for relevant articles, and presents them to users without requiring human agent intervention. This allows the system to serve itself in resolving customer queries, reducing the need for human agents while maintaining service coverage.
Solution Approach 2:
The patent replaces the mechanical system of human agents manually reviewing and responding to comments with an automated computational system. The system uses term extraction algorithms, relevance scoring mechanisms, and automated article recommendation to substitute human labor in the customer service process, thereby reducing operational costs while maintaining or improving service efficiency.
2Reliability
If all user comments are processed by CRM agents, then no customer query is left unanswered, but productivity decreases due to handling noise and irrelevant comments
Solution Approach 1:
The patent extracts relevant terms from user comments using natural language processing techniques. By identifying and extracting key terms that indicate actual customer problems or queries, the system separates meaningful comments from noise. This extraction process allows the system to focus only on comments that require article recommendations or agent attention, improving productivity by eliminating the need to process irrelevant comments.
Solution Approach 2:
The patent introduces an intermediary automated system between user comments and CRM agents. This intermediary system processes comments, extracts terms, searches for relevant articles, and presents recommendations to users or agents. By acting as a mediator, it filters out noise and pre-processes comments, allowing agents to focus only on complex cases that require human judgment, thereby improving overall productivity.
3Reliability
If manual CRM case creation is used for every user comment, then thorough customer service is provided, but time consumption and operational complexity increase
Solution Approach 1:
The patent performs preliminary actions by automatically searching for and recommending relevant articles to users based on their comments before human agents need to intervene. The system extracts terms from comments, queries the article database, and presents potential solutions in advance. This preliminary automated processing reduces the time required for agents to research and respond to comments, while maintaining thoroughness by ensuring relevant articles are identified and presented.
4Loss of energy
If automated article recommendation systems are implemented, then operational cost is reduced, but the ability to handle complex or nuanced customer queries may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system learns from user interactions with recommended articles. When users click on, view, or use recommended articles, this feedback is captured and used to improve the term extraction and article recommendation algorithms. Additionally, the system can learn which articles are most effective for specific types of queries, continuously improving the accuracy of recommendations while maintaining cost efficiency.
Data Source
AI summary
A database system may include a data storage configured to store one or more data sets and a processor coupled to the data storage. The processor may receive a query for first data included in the one or more data sets and access the data storage to obtain the first data based on the query. The processor may transmit the first data to a user system. The processor may receive an indication of a filter request to filter the first data based on a characteristic. The processor may identify linked data within second data based on the characteristic, the linked data linked to a portion of the first data associated with the filter request. The processor may access the data storage to obtain the linked data and transmit the linked data to the user system, to enable updating of a data presentation to display the linked data.