Digital Assistant for Transportation Vehicle Content Discovery
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Solution Overview
Problem
Conventional in-flight entertainment systems lack an efficient way for passengers to discover content and for crew to manage system functions, leading to suboptimal passenger experience and customer service in transportation vehicles.
Innovation Solution
Implementing a natural language digital assistant that interacts with users via voice or text inputs, utilizing a voice-to-text module, natural language parser, and trained neural networks to provide information and execute actions related to entertainment, crew requests, and system control within transportation vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional IFE systems use traditional navigation interfaces for content discovery, then system control functionality is provided, but passenger experience is suboptimal due to inefficient content discovery
Solution Approach 1:
The patent replaces traditional mechanical navigation interfaces (buttons, menus, and graphical user interface navigation) with a voice-based natural language processing system. The voice-to-text module converts spoken commands into text, which is then processed by natural language parsers and neural networks to execute content search and system control functions, eliminating the need for manual navigation through entertainment system menus.
Solution Approach 2:
The patent introduces a digital assistant as an intermediary layer between the passenger and the IFE system. This digital assistant processes natural language inputs, interprets user intent, and translates commands into system actions, serving as a mediator that simplifies the interaction complexities between the user and the underlying entertainment system infrastructure.
2Adaptability or versatility
If IFE systems provide comprehensive control functions, then system management capability is enhanced, but operational complexity increases making it difficult for crew to manage
Solution Approach 1:
The patent implements a universal natural language interface that handles multiple types of commands and control functions through a single unified system. The digital assistant can process various request types (content search, system control, information queries) using the same voice-based interface architecture, eliminating the need for separate control mechanisms for different functions and reducing overall system complexity.
Solution Approach 2:
The patent replaces complex mechanical control interfaces with voice-based natural language processing. Instead of requiring crew members to navigate through multiple menu levels and understand system-specific control protocols, the voice interface directly translates spoken commands into system actions, simplifying the control mechanism while maintaining comprehensive system management capability.
3Ease of operation
If IFE systems require manual navigation through menus, then system control is achieved, but passenger experience and customer service quality deteriorate
Solution Approach 1:
The patent substitutes manual menu navigation with voice-based natural language commands. Passengers can directly state their content preferences or system control requests in natural speech, which is converted to text and processed by the digital assistant, eliminating the time-consuming process of navigating through graphical menus and significantly improving both ease of operation and customer service quality.
Solution Approach 2:
The digital assistant serves as an intelligent intermediary that understands natural language inputs and translates them into precise system commands. This mediator layer bridges the gap between simple user intent and complex system operations, maintaining high customer service quality while preserving operational simplicity for passengers.
Data Source
AI summary
Methods and systems for a transportation vehicle are provided. One method includes receiving a user input for a valid communication session by a processor executable, digital assistant at a device on a transportation vehicle; tagging by the digital assistant, the user input words with a grammatical connotation; generating an action context, a filter context and a response context by a neural network, based on the tagged user input; storing by the digital assistant, a key-value pair for a parameter of the filter context at a short term memory, based on an output from the neural network; updating by the digital assistant, the key-value pair at the short term memory after receiving a reply to a follow-up request and another output from the trained neural network; and providing a response to the reply by the digital assistant.


