Contextual Timeline for Data Communications
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Solution Overview
Problem
Current data communications systems lack effective methods to aggregate and analyze context information across disparate communication systems, making it difficult to assess the sentiment and health of relationships between client entities and their communication partners, which is crucial for proactive monitoring and predictive modeling.
Innovation Solution
Implementing machine learning-based algorithms and artificial intelligence in data communications networks to collect, correlate, and aggregate context information from various communication systems, such as VoIP, email, and CRM services, to determine sentiment and relationship health, and provide predictive models for future communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data communications systems collect and aggregate context information from multiple disparate systems, then the ability to monitor and analyze communication relationships improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent combines multiple disparate data communications systems (VoIP, email, CRM, messaging) into a unified context aggregation framework. The server consolidates context information from these separate systems, merging previously isolated data streams into a comprehensive view of communication relationships, thereby improving monitoring reliability without requiring separate analysis for each system
Solution Approach 2:
The patent introduces a context aggregation server as an intermediary component between disparate communications systems and the analysis layer. This mediator collects, standardizes, and aggregates context information from multiple sources, simplifying the architecture by providing a single point of integration rather than requiring direct connections between all systems
2Measurement precision
If machine learning algorithms analyze large volumes of communication data, then predictive analytics and sentiment analysis improve, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and aggregating context information from multiple communication systems before applying machine learning algorithms. The system collects, cleanses, and structures data in advance, creating ready-to-analyze context aggregates that reduce the computational burden during actual sentiment analysis and predictive modeling operations
Solution Approach 2:
The patent segments the data processing workflow into distinct stages: data collection from disparate systems, context aggregation and standardization, and subsequent machine learning analysis. This segmentation allows for optimized processing at each stage, with context aggregation preparing data in a format that accelerates the later analytical phase
3Loss of information
If the system aggregates context information from multiple disparate data communications systems, then the comprehensiveness of relationship analysis improves, but the difficulty of integrating and standardizing data increases
Solution Approach 1:
The patent implements universality by creating a standardized context aggregation framework that can interface with multiple types of disparate communication systems (VoIP, email, CRM, messaging) through common protocols and data structures. The context aggregation server provides universal functionality to handle diverse data sources, enabling comprehensive relationship analysis without requiring system-specific integration logic for each communication platform
Data Source
AI summary
Certain aspects of the disclosure are directed to providing aggregated context information in a data communications network. According to a specific example, a data communications server can operate to provide user-data communications sessions each involving a client-specific endpoint device and another participating endpoint device, where the client-specific endpoint device is associated with a client-entity among a plurality of remotely-situated client entities. User-data communications between the client-specific endpoint device and the other participating endpoint device can be retrieved from a plurality of disparate interconnected data communications systems, where the data communications systems each provide at least one data communications service to the client entity on a subscription basis. Context information for each respective user-data communication between the client-specific endpoint device and the other participating endpoint device can be aggregated and displayed in a disparate-system data room, where the aggregated context information includes an indication of the context for each user-data communication.