Message-Guided Navigation Suggestions to Reduce Driver Distraction
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Solution Overview
Problem
Existing navigation systems divert user attention with unorganized and irrelevant incoming communications, such as traffic updates or personal messages, during navigation, leading to distractions and inefficiencies.
Innovation Solution
A computing system uses machine-learning models to parse messages, determine entities and objectives, and generate relevant navigation suggestions, reducing distractions by integrating message content into the navigation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If navigation systems provide incoming communications (traffic updates, personal messages) to users during navigation, then users receive information updates, but user attention is diverted and distractions increase
Solution Approach 1:
The patent introduces an intermediary system (computing system with machine-learning models) that mediates between incoming communications and the user. The system automatically processes messages, determines relevance, and generates navigation suggestions without requiring direct user engagement with the communication interface, thus delivering information while preventing distraction
Solution Approach 2:
The navigation system performs self-service by automatically parsing messages, determining entities and objectives, and generating relevant navigation suggestions without user intervention. The system serves itself by filtering and processing communications autonomously, eliminating the need for users to manually interpret messages while driving
2Loss of information
If navigation systems provide generic traffic updates while users are driving, then users receive information, but the information is not directly relevant and requires careful reading
Solution Approach 1:
The patent applies local quality by customizing information delivery to each user's specific navigation context. The machine-learning model analyzes the user's current route, destination, and message content to generate locally relevant suggestions tailored to that specific situation, rather than providing generic updates applicable to all users
Solution Approach 2:
The system changes the parameters of information delivery by transforming generic traffic updates into personalized navigation suggestions. The machine-learning models process message data and route data to generate suggestions that are parameterized according to the user's specific navigation context, making information both relevant and easily actionable
3Adaptability or versatility
If navigation systems provide personally directed messages while driving, then users receive customized information, but messages are hastily written and require careful reading to distinguish relevant information
Solution Approach 1:
The computing system acts as an intermediary that processes personally directed messages and translates them into clear navigation suggestions. The machine-learning models parse the hastily written messages, determine entities and objectives, and generate organized navigation recommendations, eliminating the need for users to carefully read and interpret ambiguous personal messages
Solution Approach 2:
The patent replaces the mechanical process of manual message reading and interpretation with an automated machine-learning system. The models automatically parse message data, identify relevant information, and generate navigation suggestions, substituting the user's manual cognitive processing with an automated system that handles the complexity of message interpretation
4Ease of operation
If navigation systems provide manually processed route changes and message responses, then users have control over navigation, but journey time increases due to manual interactions
Solution Approach 1:
The system performs preliminary action by automatically processing messages and generating navigation suggestions before the user needs to make decisions. The machine-learning models continuously monitor incoming communications and pre-process them into actionable navigation suggestions, so that when the user needs to respond, the work is already done, eliminating time-consuming manual processing
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors user responses to navigation suggestions and adjusts its processing accordingly. The machine-learning models learn from user interactions to improve future message processing, creating a feedback loop that optimizes both automation level and user control over time
Data Source
AI summary
Methods, systems, devices, and tangible non-transitory computer readable media for using incoming communications to generate suggestions for navigation. The disclosed technology can include accessing route data that includes information associated with navigation from a starting location to a destination. Based on the route data, one or more routes from the starting location to the destination can be determined. Message data including one or more messages to a user can be accessed. Based on the message data and one or more machine-learned models, at least one entity and objectives that are associated with the one or more messages can be determined. Based on the one or more routes, the at least one entity, and the objectives, suggestions associated with the one or more messages can be determined. Furthermore, output including indications associated with the suggestions directed to the user can be generated via a user interface.