Semantic Setup Application for Contact Center Code Generation
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Solution Overview
Problem
Contact centers face inefficiencies and errors due to the need for manual creation and maintenance of data structures and workflows, which are prone to incompatibilities and performance issues, especially when updating systems or customizing data processing for specific architectures.
Innovation Solution
A setup application that operates at the semantic level, generating specific code and configuration files for contact center systems, allowing for customization and optimization of data processing, and accommodating site differences and regulatory needs by reading semantic definitions and generating workflow on new systems without shutting down the contact center.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual creation and maintenance of data structures and workflows is performed, then customization capability is improved, but error rate and inefficiency increase
Solution Approach 1:
The system enables self-service through automatic generation of data structures and workflows from high-level semantic definitions. The setup application automatically translates business requirements into implementation details, eliminating manual coding and reducing human error while maintaining full customization capability.
Solution Approach 2:
The patent introduces an intermediary layer (the setup application and semantic definition system) between business requirements and system implementation. This intermediary automatically generates the necessary code and configurations, acting as a mediator that transforms high-level intent into reliable, error-free implementation without manual intervention.
2Adaptability or versatility
If manual creation and maintenance of data structures and workflows is performed, then customization capability is improved, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically generating data structures, workflows, and configurations from semantic definitions. This eliminates the need for manual programming and maintenance, dramatically improving productivity while preserving full customization capability through the semantic definition interface.
Solution Approach 2:
The setup application performs preliminary actions by pre-generating all necessary code, data structures, and configurations based on semantic definitions before deployment. This preliminary automation eliminates repetitive manual work during implementation and maintenance, significantly boosting productivity.
3Adaptability or versatility
If data structures are transmitted from one component to another using many technologies, then system functionality is improved, but incompatibility and performance issues increase
Solution Approach 1:
The patent implements a universal data structure format that can be used across all components (state machines, database, Java container, reports). This single standardized format replaces multiple technology-specific formats, ensuring compatibility while maintaining full system functionality through the unified semantic definition approach.
Solution Approach 2:
The setup application serves as an intermediary that translates semantic definitions into component-specific implementations using standardized formats. This intermediary ensures that data structures are consistently formatted and compatible across all system components, eliminating incompatibility issues while preserving functionality.
4Adaptability or versatility
If data structures are transmitted from one component to another using many technologies, then system functionality is improved, but performance issues increase
Solution Approach 1:
The universal data structure format enables efficient data transmission across all system components without the overhead of multiple technology-specific formats. This standardized approach improves performance by eliminating format conversion and compatibility layer overhead while maintaining full system functionality.
Data Source
AI summary
Contact centers often employ a number of in-memory processes as one means of providing fast and efficient data processing. In-memory processes produce messages for reports or as inputs to other processes. While every available message producing option may be turned on, the processing overhead may be burdensome on even the most powerful computing platforms. As a result, contact centers selectively activate the production of messages. Furthermore, errors and/or inefficiencies may exist when a message produced is either not used or not producing the message content expected. Similarly, messages may be expected but the production of such a message has not been enabled and/or has been disabled, possibly inadvertently. By being able to apply a semantic level change, which in turn is mapped to individual reporting elements of reporting objects, the reporting objects may be modified and/or optimized to produce the desired data without requiring a programming to manually implement such changes.