Intelligent User Interface Inference Engine for Domain-Crossing Responses
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Solution Overview
Problem
Conventional digital convergence technologies limit interactions between devices to robotic command-response interactions within the same domain, failing to mimic human-like conversations and provide eloquent responses across different domains.
Innovation Solution
A method and system utilizing an intelligent user interface that employs an inference engine and Artificial Intelligence Markup Language (AIML) to determine and generate responses based on user inputs, selecting from multiple functional services and associating weights with responses to provide value-added, domain-crossing interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional command-response interaction is used between devices, then the interaction is simple and direct, but the response is limited to the same domain and sounds robotic without human-like intelligence
Solution Approach 1:
The patent introduces an inference engine as an intermediary component between the user input and the functional service selection. This inference engine uses AIML (Artificial Intelligence Markup Language) to analyze user input, determine user intent, and map inputs to appropriate functional services across different domains, thereby enabling domain-crossing capabilities without directly complicating the core device systems
Solution Approach 2:
The system implements a universal inference engine that can handle multiple types of user inputs (voice, text, gestures) and route them to various functional services across different domains. This multi-functional approach allows a single system to serve multiple purposes and domains without requiring separate specialized systems for each function
2Loss of information
If multiple functional services are integrated to provide value-added responses, then the system provides more comprehensive information, but the system complexity increases
Solution Approach 1:
The patent segments the system into distinct modular components: the inference engine for intent recognition, the functional service layer for specific tasks, and the response generation layer. Each component has a specialized function, and they communicate through standardized interfaces. This segmentation allows the system to provide comprehensive information across multiple domains while managing complexity through modular architecture
Solution Approach 2:
The inference engine acts as an intermediary that manages the complexity of multiple functional services by providing a unified interface for input processing and output coordination. It translates diverse user inputs into standardized service calls and aggregates responses from multiple services, thereby reducing the perceived complexity for users while maintaining comprehensive functionality
3Ease of operation
If traditional response templates are used, then the system is easy to implement, but it cannot provide eloquent and contextually appropriate responses
Solution Approach 1:
The system uses parameter changes in the form of weight assignments to different functional services and response options. The inference engine evaluates multiple possible responses by assigning weights based on relevance, context, and user preferences, then selects the response with the highest weighted score. This allows the system to provide eloquent and contextually appropriate responses while maintaining a structured approach similar to traditional templates
Data Source
AI summary
Various aspects of a method and system for providing information via an intelligent user interface are disclosed herein. In an embodiment, in response to the receipt of a request from an electronic device, the method includes determination of a first information response that may correspond to a first functional service. A set of second information responses that corresponds to a set of second functional services may be determined based on the first information response. Each of the determined set of second information responses is associated with a corresponding weight. One or more of the determined set of second information responses are selected based on the corresponding weight. The second information responses are value-added responses.