Semantic CRM Transcripts via Knowledge Graph
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Solution Overview
Problem
Current Customer Relationship Management (CRM) systems lack the infrastructure to fully utilize the information they collect, particularly in tracking customer contacts across multiple channels and agents, leading to difficulties in managing complex interactions.
Innovation Solution
The implementation of a computer system that uses a semantic graph database and artificial intelligence to create an enterprise knowledge graph, which stores and processes data as semantic triples, enabling the tracking and integration of customer interactions across various platforms and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional CRM systems are used to collect customer contact data, then data collection capability is improved, but data tracking and integration capability deteriorates
Solution Approach 1:
The patent transitions from traditional tabular data structures to a graph database structure, adding a dimensional layer of semantic relationships. Customer contacts are represented as nodes with attributes, and relationships between contacts, agents, and interactions are explicitly modeled as edges, enabling comprehensive tracking across multiple dimensions without losing information.
Solution Approach 2:
The patent introduces an intermediary layer of semantic triples and knowledge graphs that mediate between raw contact data and analytical queries. This intermediary structure transforms unstructured contact information into structured semantic relationships, enabling effective tracking and integration while preserving the original data quantity.
2Adaptability or versatility
If multiple communication channels are supported, then customer contact capability is improved, but interaction tracking complexity increases
Solution Approach 1:
The patent implements a universal graph database structure that can represent multiple communication channels (phone, email, text, social media) using the same underlying model. All channels are represented as interaction types between customer and agent nodes, providing multi-functionality without increasing structural complexity.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from channel-specific formats to a unified semantic graph structure. By transforming diverse channel data into standardized triples (subject-predicate-object), the system handles multiple channels uniformly, reducing tracking complexity despite increased versatility.
3Loss of information
If comprehensive customer data is collected, then CRM information completeness is improved, but data usability and analysis capability deteriorates
Solution Approach 1:
The patent segments comprehensive customer data into discrete semantic triples (subject-predicate-object) that can be independently queried and analyzed. This segmentation allows the system to maintain complete information while enabling flexible, targeted analysis of specific relationship types without processing the entire dataset.
Solution Approach 2:
The patent replaces traditional mechanical data querying mechanisms with semantic graph traversal. Instead of joining multiple tables and filtering rows, the system uses graph database queries that naturally traverse relationships, making comprehensive data analysis more efficient and easier to operate.
Data Source
AI summary
Customer relationship management (‘CRM’) implemented in a computer system, including administering by the computer system a communications session that includes a sequence of communications contacts between a tele-agent and one or more customer representatives, the session and each contact composed of structured computer memory of the computer system; and generating by the computer system a digital transcript of the content of the communications contacts.