Opportunity Metadata Correlation for CRM User Notifications
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Solution Overview
Problem
In a CRM system, manually analyzing thousands of opportunities to identify correlations between related opportunities is burdensome, as it requires comparing metadata across various opportunities with different maturity levels, leading to potential issues for less mature opportunities.
Innovation Solution
A system and method that obtain opportunities from a database, analyze metadata to identify patterns, and notify users about relevant data by correlating opportunities with more mature maturity levels to guide less mature ones, using engines for obtaining, analyzing, identifying, applying weights, notifying, and updating the database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual analysis of opportunities is performed, then users can identify relevant data, but the process becomes burdensome and time-consuming
Solution Approach 1:
The system performs automatic metadata analysis and correlation identification without requiring user intervention. The analyzing engine autonomously processes opportunity metadata, identifies patterns, and notifies users of relevant correlations, allowing the system to serve itself rather than requiring manual user analysis.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. The analyzing engine uses computer-based algorithms to process metadata, identify patterns, and determine correlations, substituting human manual effort with automated mechanical computation.
2Reliability
If users sift through vast amounts of metadata, then correlations can be identified, but efficiency decreases and opportunities may be missed
Solution Approach 1:
The system extracts only the most relevant correlations from the vast metadata, rather than requiring users to examine all metadata. The analyzing engine identifies and extracts specific pattern-based correlations that are most likely to be relevant, filtering out unnecessary information and presenting only actionable insights to users.
Solution Approach 2:
The system creates simplified representations of complex metadata relationships through pattern templates. Instead of presenting raw metadata, the system copies and transforms metadata into standardized pattern formats that are easier to analyze and compare, improving both accuracy and efficiency.
3Loss of information
If comprehensive metadata analysis is performed on all opportunities, then complete information is available, but the system complexity increases
Solution Approach 1:
The system segments the complex metadata analysis task into distinct functional components: the analyzing engine for pattern identification, the determining engine for maturity level assessment, and the notifying engine for result delivery. This segmentation reduces overall system complexity by breaking down the monolithic analysis process into manageable, specialized modules.
Solution Approach 2:
The system performs preliminary metadata analysis to establish patterns and correlations before users need the information. By pre-processing the metadata and identifying correlations in advance, the system reduces the complexity of on-demand analysis while ensuring complete information is available when needed.
Data Source
AI summary
Notifying a user about relevant data for opportunities includes obtaining, from a database, opportunities, the opportunities representing a complex record structure in the database, in which each of the opportunities captures a number of fields of metadata, analyzing the metadata associated with the opportunities to identify patterns for the opportunities, identifying, based on the patterns, correlations for the opportunities, and notifying, based on the correlations, the user about relevant data for the opportunities.