Automated Intelligence Engine for Entity Model Maintenance
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Solution Overview
Problem
Modern businesses face challenges in maintaining accurate and up-to-date communication-based information, as legacy systems lack automation for creating and updating FAQs and entity models, leading to inefficiencies in identifying competitors and understanding customer interactions.
Innovation Solution
A system utilizing machine learning, a user interface, sentiment analysis, and automated bots to create and maintain entity models, analyze communication data, and update FAQs, while clustering entities to identify competitors and improve customer service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to create and update FAQs and entity models, then human judgment can be applied to ensure accuracy, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables automated self-updating of FAQs and entity models by monitoring communications between the entity and secondary entities. The intelligence engine automatically extracts information, updates entity models, and generates FAQ entries without requiring manual human intervention, thus resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent replaces manual mechanical processes of creating and updating FAQs with an automated intelligence engine that uses machine learning and natural language processing. This substitution eliminates human labor while maintaining information quality through algorithmic analysis of communication patterns.
2Productivity
If automated systems are used to update communication-based information, then efficiency is improved, but the system complexity increases
Solution Approach 1:
The intelligence engine is designed as a universal system that performs multiple functions: monitoring communications, analyzing data, updating entity models, generating FAQs, and identifying competitors. By consolidating these diverse functions into a single multi-functional platform, the system achieves high productivity while managing complexity through integration rather than proliferation of separate systems.
Solution Approach 2:
The patent introduces an intelligence engine as an intermediary layer between raw communication data and the entity's information systems. This intermediary automatically processes and structures unstructured communication data, updating entity models and generating FAQs without requiring complex direct integration with multiple internal systems, thus simplifying the overall architecture.
3Loss of information
If comprehensive communication-based information is collected and analyzed, then better competitor identification and customer understanding are achieved, but data processing requirements and system resources increase
Solution Approach 1:
The intelligence engine selectively extracts only the most relevant information from comprehensive communication data through automated analysis. It identifies key entities, relationships, and patterns while filtering out redundant or less important data, thus achieving complete entity models without processing every single data point in detail, reducing computational resource requirements.
Solution Approach 2:
The system applies partial analysis to comprehensive data by focusing computational resources on critical communication patterns and high-value information while using lighter processing for routine or less important data. This approach maintains information completeness where it matters most while optimizing resource utilization across the entire dataset.
Data Source
AI summary
A system that creates and automatically builds one or more entity models that are derived from communication-based information received over a network. Building the entity models uses machine learning, a user interface, a sentiment analyzer, a communication monitoring agent and an automated bot. Bots are created based on a bot template. New communication-based information is analyzed and processed to improve the entity model, to keep the bot up-to-date, and to update other services and products that the entity relies on. The system enables a business to cluster other entities together to assist in identifying competitors. The system provides analytical information about how users journey through an entity model. The system automatically maintains listings such as frequently asked questions. The system works in a networked environment, which may be a distributed network.