Cognitive Enterprise System Natural Language Interface
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Solution Overview
Problem
Enterprise resource planning (ERP) systems have complex, functionally heavy interfaces that hinder user interactions and data access, making it difficult for users to make data-driven decisions and requiring advanced technologies like conversational applications and natural language processing to simplify interactions.
Innovation Solution
A cognitive enterprise system that uses machine learning and artificial intelligence to capture user intent from natural language queries, build dynamic knowledge graphs, and provide proactive applications, enabling users to interact with ERP systems in a natural way through conversational interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ERP systems use traditional functionally heavy interfaces, then they can provide integrated applications and technology to automate back office functions, but user interactions become complex and data access becomes difficult
Solution Approach 1:
The patent introduces a conversational interface as an intermediary layer between users and the ERP system. This natural language interface mediates complex data retrieval and processing operations, translating user questions into system commands without exposing users to underlying system complexity. The conversational interface acts as a buffer that simplifies user interactions while maintaining full access to ERP functionality.
Solution Approach 2:
The patent replaces traditional mechanical interfaces (forms, menus, buttons) with a natural language processing system. Instead of requiring users to navigate complex graphical interfaces and fill out structured forms, the system accepts and processes natural language queries, transforming the interaction paradigm from mechanical point-and-click operations to conversational exchanges.
2Loss of information
If ERP systems provide comprehensive data access, then users can make data-driven decisions, but the interface complexity increases and hinders user interactions
Solution Approach 1:
The conversational interface serves as an intermediary that handles the complexity of data access operations. When users ask questions in natural language, the system translates these into appropriate data queries, processes them through the ERP system, and presents results in conversational form, thereby providing comprehensive data access without exposing users to interface complexity.
Solution Approach 2:
The patent creates a conversational copy or representation of the ERP system's data and functionality. Instead of requiring users to directly interact with the complex underlying system structure, the conversational interface provides a simplified mirrored version where data can be queried and manipulated through natural language, preserving full data accessibility while eliminating interface complexity.
3Productivity
If ERP systems use traditional interfaces, then they can process millions of user entries, but users require advanced technologies like conversational applications to simplify interactions
Solution Approach 1:
The patent substitutes natural language processing mechanisms for traditional mechanical interfaces. The system maintains its capacity to process millions of entries through the backend while replacing the mechanical interaction model (forms, menus, buttons) with a conversational model that is inherently simpler and more intuitive for users.
Solution Approach 2:
The conversational interface provides a universal method for interacting with the ERP system that can handle diverse query types and data processing tasks through a single unified interface. This multi-functional approach allows users to perform various operations (data retrieval, analysis, decision-making) through natural language without needing to learn multiple interface paradigms or tools.
Data Source
AI summary
Systems and methods are provided for receiving a query created by a user, receiving output data of at least one function to retrieve data related to the query and analyzing the output data of the at least one function to retrieve data related to the query. The systems and methods further provide for generating at least one dynamic knowledge graph associated with the output data of the at least one function, wherein the at least one dynamic knowledge graph comprises data from the output data of the at least one function and indicates relationships between the data, analyzing the at least one dynamic knowledge graph to determine data relevant to the query generated by the user, and generating a response to the query based on the data relevant in the at least one dynamic knowledge graph.