Report Recommendation Engine for Enterprise Data
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Solution Overview
Problem
In large organizations with complex datasets, users face difficulties in determining relevant reports among numerous options, relying on manual searches, user manuals, and institutional knowledge, which is inefficient and time-consuming.
Innovation Solution
A reporting system generates report recommendations based on user viewing behaviors, using a report recommendation engine that computes relevancy scores through machine learning models, click analysis, log analysis, and text analysis to provide users with relevant report suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a reporting system supports a large number of reports to handle complex datasets, then the system's functionality and coverage are improved, but it becomes harder for users to determine which reports are relevant to their needs
Solution Approach 1:
The system automatically generates report recommendations by analyzing user behavior patterns, report metadata, and contextual information without requiring manual intervention. The recommendation engine self-adjusts based on user interactions, eliminating the need for users to manually search through numerous reports or consult documentation.
Solution Approach 2:
The system implements feedback loops where user interactions with recommended reports (clicks, views, downloads) are continuously analyzed to refine future recommendations. This feedback mechanism allows the system to adapt to individual user preferences and improve recommendation accuracy over time, making report selection easier while maintaining comprehensive coverage.
2Measurement precision
If users manually search for reports using search functions or consult user manuals, then they can find relevant reports, but the process is time-consuming and inefficient
Solution Approach 1:
The system pre-computes recommendation scores and prepares personalized report suggestions before users need them. By analyzing user behavior patterns, report metadata, and contextual factors in advance, the system has recommendations ready when users access the reporting interface, eliminating the need for manual searching or consultation of documentation.
Solution Approach 2:
The patent replaces manual mechanical search processes with an automated intelligent recommendation system. Instead of users manually typing search queries, browsing categories, or consulting user manuals, the system uses machine learning algorithms, natural language processing, and behavioral analysis to automatically present relevant reports, substituting human effort with automated computational processes.
3Ease of operation
If the reporting system relies on institutional knowledge and coworker assistance, then users can obtain guidance, but the process becomes more complex and less scalable
Solution Approach 1:
The patent introduces an automated recommendation engine as an intermediary between users and the comprehensive report library. This intermediary system processes user context, analyzes behavior patterns, and filters relevant reports without requiring human intermediaries such as coworkers or support professionals. The recommendation engine serves as an intelligent mediator that simplifies the user's task while maintaining access to the full system capabilities.
Solution Approach 2:
The system enables users to obtain guidance and recommendations through self-service mechanisms rather than requiring external assistance. The recommendation engine automatically analyzes user needs and provides personalized suggestions without involving coworkers or support staff, reducing system complexity while improving ease of operation and scalability.
Data Source
AI summary
Techniques for generating a report recommendation are disclosed. A system receives a request to display a report. The system computes report relevancy scores for other reports, based at least in part on a set of rules including one or more report relevancy criteria. Each report relevancy score measures relevancy of a particular report to the requested report. The system determines that a particular report relevancy score, associated with one of the other reports, satisfies one or more report recommendation criteria. Responsive to the request to display the requested report, the system displays the requested report and, based on the particular report relevancy score satisfying the one or more report recommendation criteria, also displays a recommendation corresponding to the other report.