Adaptive Logistics Platform for Schedule Generation
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Solution Overview
Problem
Creating schedules for events with numerous competing priorities among users and companies is challenging due to the large number of variables involved, making it difficult to balance priorities such as safety, health, budget, and free time, especially when thousands of individuals are involved, and updating these schedules in response to external events is resource-intensive.
Innovation Solution
An adaptive logistics platform that uses processors to receive user and company preference data, external preference data, and generates schedules based on category scores, ranking them to select the best schedule while conserving resources through natural language processing and artificial intelligence models to predict user deviations and account for contextual changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual creation of schedules is used, then flexibility and customization are improved, but it becomes impossible when thousands or tens of thousands of objects need to be considered
Solution Approach 1:
The patent replaces manual mechanical schedule creation with an automated system that uses natural language processing and machine learning models to generate schedules automatically, enabling the system to handle thousands of objects that would be impossible to manage manually
Solution Approach 2:
The system allows schedules to be created and updated automatically through natural language inputs without requiring manual intervention, with the AI models autonomously generating and optimizing schedule configurations based on user needs and constraints
2Reliability
If comprehensive preference data from multiple sources is collected, then schedule quality and user satisfaction are improved, but processing resources and system complexity increase
Solution Approach 1:
The patent segments preference data into distinct categories (user preferences, company preferences, external preferences) and processes each category separately through specialized AI models, reducing overall system complexity while maintaining comprehensive data utilization
Solution Approach 2:
The system introduces natural language processing as an intermediary layer that translates diverse preference data from multiple sources into structured inputs that the scheduling AI models can process efficiently, simplifying the integration of comprehensive data
3Adaptability or versatility
If schedules are updated in response to external events, then adaptability and relevance are improved, but processing resources are consumed
Solution Approach 1:
The system implements periodic monitoring of external events and updates schedules at appropriate intervals rather than continuously, reducing processing resource consumption while maintaining schedule adaptability to relevant changes
Solution Approach 2:
The system proactively monitors external events and updates schedules before users are affected by changes, allowing for more efficient processing by anticipating needs rather than reacting to crises
Data Source
AI summary
A device may receive user preference data and company preference data associated with one or more bookable items. The device may obtain external preference data associated with the one or more bookable items. The external preference data may be different from the user preference data and the company preference data. The device may generate a list of schedules based on the user preference data, the company preference data, and the external preference data. The device may generate one or more category scores for a plurality of schedules included in the list of schedules based on the company preference data. The device may rank the list of schedules based on the one or more category scores. The device may select a schedule, from the ranked list of schedules, based on the one or more category scores. The device may perform an action based on selecting the schedule.