Context Aggregation for Client-Specific Data Communications
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Solution Overview
Problem
In data communications networks, existing systems lack the ability to effectively aggregate and analyze context from multiple communication channels to improve interaction quality and predictive modeling for client relationships, leading to inefficient communication handling and service delivery.
Innovation Solution
Implementing machine learning-based algorithms for context aggregation in data communications networks, which collect and analyze data from various interconnected systems to determine sentiment, relationship health, and predictive models for future communications, enabling proactive monitoring and improved communication routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data communications are handled through multiple separate systems without context aggregation, then system simplicity is maintained, but communication efficiency and interaction quality deteriorate
Solution Approach 1:
The patent combines multiple separate communication systems and their context data into a unified context aggregation framework. The context aggregation service merges data from various communication channels (email, chat, video conferencing, etc.) and external systems (CRM, calendar, task management) into a single cohesive context representation, enabling efficient communication handling without requiring complex modifications to individual systems.
Solution Approach 2:
The patent introduces a context aggregation service as an intermediary layer between multiple communication systems and the communication handler. This mediator collects, processes, and aggregates context data from various sources, then provides unified context information to routing and notification services, improving efficiency without increasing the complexity of individual systems.
2Measurement precision
If context data is collected from multiple interconnected systems, then predictive modeling accuracy improves, but data collection complexity increases
Solution Approach 1:
The context aggregation service is designed as a universal platform that can collect and process data from multiple types of interconnected systems including CRM, calendar, task management, and various communication channels. It provides a unified interface and standardized data processing approach that works across diverse data sources, improving predictive modeling accuracy without proportionally increasing collection complexity.
Solution Approach 2:
The patent transforms raw data from multiple systems into standardized context parameters and features that are suitable for predictive modeling. By changing the parameter representation of data from various sources into a unified context framework, the system achieves high predictive accuracy while managing data collection complexity through standardized transformation processes.
3Measurement precision
If machine learning algorithms are implemented for context aggregation, then communication routing accuracy improves, but computational resource requirements increase
Solution Approach 1:
The patent implements machine learning algorithms selectively for context aggregation and routing decisions rather than applying them to all communication data uniformly. The system uses ML to identify and aggregate relevant context features that significantly impact routing accuracy, while using simpler rule-based approaches for less critical decisions, thereby improving routing accuracy without excessive computational resource consumption.
4Reliability
If proactive monitoring and predictive insights are provided, then service delivery quality improves, but system complexity increases
Solution Approach 1:
The patent implements proactive monitoring and predictive insights by performing context aggregation and analysis in advance of communication events. The system pre-processes data from multiple systems, identifies patterns and predictions, and prepares routing recommendations before communications occur, thereby improving service delivery quality without requiring complex real-time processing architecture.
Data Source
AI summary
Certain aspects of the disclosure are directed to context aggregation in a data communications network. According to a specific example, user-data communications between a client-specific endpoint device and the other participating endpoint device during a first time period can be retrieved from a plurality of interconnected data communications systems. The client entity can be configured and arranged to interface with a data communications server providing data communications services on a subscription basis. A context can be determined for each respective user-data communication between the endpoint devices during the first time period. A plurality of user-data communications between the client-specific endpoint device and the other participating endpoint device can be aggregated during a second time period, and a context can be determined for the aggregated user-data communications during the second time period based on a comparison of the aggregated user-data communications and the user-data communications during the first time period.