Framework Processor for Unified Channel Interaction Data
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Solution Overview
Problem
Existing systems face challenges in providing a unified, non-siloed view of user interactions across multiple channels for organizations, leading to inefficiencies in data dissemination and accessibility, particularly in real-time and unstructured data processing.
Innovation Solution
A system framework that uses machine learning and business rules to process interaction data from various channels, extracting and transforming it into a unified, searchable format through microservices, enabling real-time data collection and decryption, and providing secure access and analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected and stored in a silo or compartmentalized format by individual channels, then each channel can maintain its own data structure and processing logic, but the organization cannot achieve a unified view of user interactions across channels
Solution Approach 1:
The patent merges data from multiple channel silos into a centralized data hub that provides a unified view of user interactions. The hub consolidates interaction data, user data, and channel data into a single accessible location, allowing the organization to see the complete customer journey across all channels while maintaining manageable architecture through standardized data models.
Solution Approach 2:
The data hub acts as an intermediary layer between individual channel systems and the organization's analytics and decision-making processes. It receives data from various channels, standardizes the format, and provides unified access without requiring changes to the underlying channel systems, thus reducing architectural complexity while enabling unified views.
2Loss of information
If real-time data processing and decryption is implemented across all channels, then the organization can achieve comprehensive visibility of user interactions, but the system requires high-end server networks and significant computational resources
Solution Approach 1:
The patent segments the data processing architecture into distributed microservices that process data locally at the channel level before aggregating results at the hub. This segmentation allows real-time processing capabilities to be distributed across multiple smaller units rather than requiring a single high-end server network, reducing the concentration of computational resource requirements.
Solution Approach 2:
The system performs preliminary data processing, filtering, and encryption at the source channels before data reaches the central hub. By pre-processing data where it is generated, the system reduces the computational burden on the central hub and enables real-time processing without requiring excessive computational resources at any single point.
3Ease of operation
If unstructured data from multiple channels is decrypted and stored for searchability, then the organization can access and analyze user interaction data efficiently, but data security and access control become more complex
Solution Approach 1:
The patent implements a universal encryption and access control framework that works across all data types and channels. The data hub provides standardized security protocols and authentication mechanisms that handle diverse data formats uniformly, making security management simpler rather than more complex. The same security principles apply whether accessing structured or unstructured data from any channel.
Solution Approach 2:
The system creates encrypted copies of sensitive data with controlled access permissions while maintaining the ability to search and analyze data. Through selective decryption and controlled data replication, the system enables efficient data access for authorized users and processes while maintaining security, rather than requiring all data to be fully decrypted and stored in plaintext.
4Productivity
If microservices are deployed at the interface level for data collection, then the system can process data from multiple channels independently and scalably, but the system architecture becomes more distributed and potentially harder to manage
Solution Approach 1:
The patent implements dynamic microservices that can be independently deployed, activated, or deactivated based on channel requirements and data processing needs. The microservices architecture allows the system to adapt its complexity dynamically - adding or removing processing capabilities as needed without requiring complete system redesign, thus improving productivity while keeping manageable complexity through on-demand configuration.
Data Source
AI summary
Embodiments of the invention are directed to a system, method, or computer program product for a framework processing for channel contacts data such as interaction data. In this way, the invention receives and processes raw interaction data from one or more deployed micro services. The framework subsequently provides for advanced operational execution not possible by the micro services. The processing of the interaction data includes machine learning predictive analytics framework and business rule application. As such, the system may learn from the data received at the micro servers in real-time and continue to monitor the raw interaction data based on the implemented business rules and machine learning to issue future alerts if necessary. Furthermore, the system may calculate analytics and perform advanced operation executions based on the demands/requests and/or rules. Finally the framework presents data in an interface picture format based on the calculated analytics.