Adaptive Mapping Computing Device for Route Optimization

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Solution Overview

Problem

Existing planning and scheduling systems are cumbersome and difficult to use for managing numerous social activities, errands, and events, especially when determining optimal travel paths between multiple locations across a wide geographic region.

Innovation Solution

The development of an adaptive mapping computing device that retrieves user tasks and geographic mapping data to generate an optimal route model, determining the most efficient travel plan by considering scheduled tasks, real-time conditions, and user preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If conventional planning and scheduling systems are used to manage numerous social activities, errands, and events, then task organization is achieved, but the systems become cumbersome and difficult to use

Engineering Contradiction:

Improveease of useVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system automatically retrieves tasks from multiple sources (calendars, contacts, social media), generates route options, and optimizes travel plans without requiring manual user input for each detail. The adaptive mapping computing device performs self-service by autonomously managing the complex scheduling and routing operations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-retrieves and pre-processes task information from various sources before the user needs to plan their day. By advance-fetching calendar events, contact information, and social media updates, the system prepares the data foundation for rapid route optimization when needed.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If manual coordination of event times and activities is performed, then scheduling flexibility is maintained, but the total combination of possible schedules becomes beyond human capability for solving

Engineering Contradiction:

Improvescheduling flexibilityVSAvoidschedule generation speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system incorporates user feedback through the user interface, allowing users to select from generated route options, adjust preferences, and provide constraints. This feedback loop enables the system to maintain flexibility while leveraging computational power to evaluate numerous schedule combinations efficiently.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The adaptive mapping computing device acts as an intermediary between the user's scheduling needs and the complex computational task of evaluating all possible schedule combinations. It translates user requirements into optimized routes, bridging the gap between human decision-making and computational optimization.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If GPS devices and mapping data are used to calculate distances between current position and desired targets, then basic navigation is provided, but determining optimal travel paths between multiple activities requires highly sophisticated analysis

Engineering Contradiction:

Improvedistance calculation accuracyVSAvoidroute optimization complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system merges GPS location data, mapping information, task schedules, and user preferences into a unified route optimization model. By combining these multiple data sources and considerations, the system determines optimal travel paths that account for all relevant factors simultaneously.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The route optimization system dynamically adjusts travel paths based on real-time conditions such as traffic, weather, and changing user preferences. The adaptive mapping computing device continuously re-evaluates and updates optimal routes as conditions change, providing dynamic rather than static navigation.

Inventive Principle:

Principle #15Dynamics

4Adaptability or versatility

If additional waypoints are spontaneously added to a planned route, then task completion options are increased, but the complexity in determining minimal travel time and cost substantially increases

Engineering Contradiction:

Improveroute flexibilityVSAvoidroute calculation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system pre-calculates and stores route options between various locations and task combinations. When users spontaneously add waypoints, the system can efficiently retrieve and recombine pre-computed route segments rather than calculating entirely new routes from scratch, reducing the computational complexity of handling dynamic changes.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250131376A1Systems and methods for adaptive route optimization for learned task planning

Publication Date: 2025.04.24 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

An adaptive mapping (AM) computing device having at least one processor in communication with at least one memory device is provided. The AM computing device may be configured to retrieve a plurality of tasks associated with a user, and retrieve geographic mapping data. The AM computing device may also generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The AM computing device may execute the route model to determine an optimal route, and transmit, to the user, an optimized travel plan based upon the optimal route.