Multimodal Trip Planning System Using Machine Learning
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Solution Overview
Problem
Conventional methods for planning trips are inefficient, requiring users to manually search for destinations, excursions, and itineraries, leading to increased network occupancy and processing power, and making it difficult to identify pertinent information in a timely manner.
Innovation Solution
A computing system that uses a machine-learning model to analyze media inputs, extract features, identify user intents, and generate intelligent recommendations, reducing the need for multiple searches by providing a user interface with options based on the analyzed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search for destinations, excursions, and itineraries separately, then comprehensive trip information can be found, but the time and network resources required increase significantly
Solution Approach 1:
The patent combines multiple separate search functions (destination search, excursion search, itinerary search) into a single integrated trip planning system. The machine learning model processes multiple data types (images, text, structured data) simultaneously to generate comprehensive trip recommendations, eliminating the need for users to perform multiple separate searches and thereby reducing time loss while maintaining information completeness.
Solution Approach 2:
The system performs preliminary analysis of user preferences, past behavior, and media inputs before generating trip recommendations. The machine learning model pre-processes and structures unstructured data (images, text) into actionable insights, so when users search for trips, the system already has prepared recommendation sets ready, significantly reducing the time required for trip planning information retrieval.
2Loss of information
If users manually search for trip information, then detailed itineraries can be found, but network occupancy and processing power increase
Solution Approach 1:
The patent extracts only the essential and relevant features from large volumes of unstructured data (images, text, user preferences) using machine learning models. Instead of processing and transmitting all raw data, the system extracts key trip planning information (destinations, excursions, itineraries) and presents only what is necessary, thereby reducing network occupancy and processing power requirements while maintaining information completeness.
Solution Approach 2:
The system transforms unstructured media inputs (images, text) into structured parameters that can be efficiently processed and stored. By changing the data representation from unstructured to structured format through machine learning, the system reduces the computational resources and network bandwidth needed for subsequent trip planning operations, while preserving all essential trip information.
3Measurement precision
If the system provides detailed analysis of media inputs, then accurate trip recommendations can be generated, but processing complexity increases
Solution Approach 1:
The patent segments the complex media analysis process into distinct machine learning model components: one model for extracting features from images, another for processing text inputs, and a third for generating trip recommendations. This segmentation allows each component to specialize in a specific task, improving recommendation accuracy while managing processing complexity through modular architecture that can be developed and maintained independently.
Data Source
AI summary
A computing system includes a memory device and a processor structured to: receive, from a user device, a media input; extract, using a machine-learning model, at least one feature of the media input; identify, using the machine-learning model, at least one intent; determine, using the machine-learning model, at least one action policy; generate a user interface comprising the media input, the at least one extracted feature of the media input, and the at least one action policy; provide the user interface to the user device; receive, via an input to the user interface, an indication of a selection of the at least one action policy displayed on the user interface; generate a second user interface comprising a plurality of options associated with the selected at least one action policy; and provide the second user interface to the user device.