Cognitive SaaS Platform for Avionics Using Reinforcement Learning
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Solution Overview
Problem
Traditional SaaS platforms for connected flight management systems lack the ability to understand user intent, adapt to changing circumstances, and learn from experience, leading to inefficient and costly custom coding for interaction with multiple sub-systems, and they often fail to provide context-sensitive data and reasoning capabilities.
Innovation Solution
The implementation of reinforcement learning models to analyze user queries, determine intent, entities, emotions, and context, and dynamically invoke appropriate services, enabling a cognitive service interface that can adapt and learn over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hard-wired interfaces are used to interact with sub-systems, then specific user needs can be met, but the cost and time to build increase prohibitively
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between users and sub-systems. This intermediary translates user queries into appropriate sub-system interactions, eliminating the need for hard-wired interfaces for each specific user need. The intermediary acts as a mediator that dynamically routes requests based on language understanding rather than pre-programmed pathways.
Solution Approach 2:
The system implements a universal natural language interface that can handle multiple different user needs through a single flexible system. Instead of creating dedicated interfaces for each sub-system interaction, the universal language processor can adapt to various query types and route them appropriately, making one interface serve multiple functions.
2Adaptability or versatility
If custom coding is used for each point-to-point interaction of sub-systems, then specific functionality is achieved, but the complexity and cost increase extremely
Solution Approach 1:
The natural language processing system serves as an intermediary that replaces complex custom coding with language-based routing. Instead of writing custom code for each point-to-point interaction, the system uses language understanding to dynamically determine which sub-systems to invoke and how to connect them, significantly reducing complexity.
Solution Approach 2:
The system transitions from static hard-wired connections to dynamic language-based routing. The interaction pathways are not fixed but are determined dynamically at runtime based on the user's natural language query, allowing the system to adapt to new functionalities without additional coding.
3Ease of operation
If traditional SaaS platforms are used, then basic data storage and computing are provided, but the ability to understand user intent and provide context-sensitive data is lacking
Solution Approach 1:
The patent replaces traditional mechanical interface systems with cognitive language processing systems. Instead of requiring users to navigate complex menu structures or know specific command protocols, the system uses natural language understanding to interpret user intent, substituting rigid mechanical interaction patterns with flexible cognitive processing.
Solution Approach 2:
The system implements feedback loops where the natural language processing model learns from user interactions and improves its understanding of intent over time. This feedback mechanism allows the platform to become increasingly adept at understanding user needs and providing context-sensitive responses.
Data Source
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AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for cognitive services for a FMS SaaS platform. For instance, the method may include obtaining training data; training reinforcement learning model(s) using the obtained training data; in response to receiving a request for cognitive services from a user device, analyzing a query of the request for cognitive services using at least one reinforcement learning model of the trained reinforcement learning model(s); determining intent, entity(s), emotion, and/or context of the query based on an output of the at least one reinforcement learning model to form a cognitive services request; applying a second at least one reinforcement learning model of the trained reinforcement learning model(s) to the cognitive services request to determine one or more services to invoke; and transmitting a result to the user device based on an output of the one of more invoked services.