Activity-Based Distributed Graph for Relationship Strength
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Solution Overview
Problem
In cloud-based CRM systems, users face challenges in determining which organization member has the strongest connection to a target contact, leading to inefficient communication efforts, as existing systems lack effective methods to calculate and utilize relationship strengths based on communication data.
Innovation Solution
Implementing an activity-based distributed graph within the database system to process communication messages, using natural language processing to extract metadata and calculate closeness scores, thereby identifying users with the strongest connections to targets within the organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a user contacts a new contact without previous communication history, then communication can be initiated, but the success rate is low
Solution Approach 1:
The patent introduces an intermediary mechanism by identifying and recommending organization members who serve as bridges between the user and the target contact. These recommended members act as intermediaries who have established relationships with the target, thereby increasing the likelihood of communication success without requiring the user to have direct prior contact history.
Solution Approach 2:
The system implements feedback by analyzing communication data and interaction history to calculate relationship strengths between organization members and contacts. This feedback loop enables the system to learn from past interactions and provide increasingly accurate recommendations for identifying the most suitable communication partners.
2Productivity
If the system analyzes communication data to determine relationship strength, then communication success rate improves, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional relationship analysis system that serves multiple purposes: it analyzes communication data, calculates relationship strengths, identifies recommended contacts, and provides communication guidance. This single system handles diverse functions that would otherwise require separate tools, thereby managing complexity through consolidation.
Solution Approach 2:
The system implements self-service by automatically analyzing communication data and generating relationship strength calculations without requiring manual input or intervention. The system self-updates its understanding of relationships through ongoing analysis of communication patterns, reducing the need for complex manual configuration and maintenance.
3Measurement precision
If the system processes communication messages to extract metadata and calculate closeness scores, then relationship strength determination improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing communication data in a structured format that enables rapid retrieval and analysis. The system prepares and indexes communication messages in advance, allowing for quick extraction of metadata and calculation of relationship strengths when needed, rather than processing raw data in real-time.
Solution Approach 2:
The system implements partial action by selectively analyzing only the most relevant communication data points and metadata fields necessary for relationship strength calculation, rather than processing every aspect of all communication messages. This selective approach maintains measurement precision while significantly reducing processing time.
Data Source
AI summary
Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.