Navigation Destination Assignment via Character Group Categorization
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Solution Overview
Problem
Conventional navigation systems require clear and complete destination entries, which can be time-consuming and cumbersome, especially in situations like driving in heavy traffic, and often fail to adequately consider user preferences for route selection.
Innovation Solution
A device and method that assigns a navigation destination to a character sequence by categorizing character groups into information categories, determining probability values for correct assignments, and evaluating interpretations to provide a list of possible destinations sorted by probability, allowing for quicker and more intuitive destination entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional navigation systems require clear and complete destination entries, then navigation accuracy is improved, but user convenience and input speed deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-categorizing character groups into information categories (such as street name, city, state, ZIP code) before complete destination verification. This allows the system to start processing potential destinations early in the input process, rather than waiting for complete clear input, thereby improving both accuracy and convenience simultaneously
Solution Approach 2:
The system accepts partial destination information by allowing ambiguous or incomplete character sequences to be processed. Instead of requiring complete clear input, the system processes partial matches and uses probability values to rank potential destinations, enabling users to enter less information while still achieving accurate navigation
2Measurement precision
If conventional navigation systems require complete destination entries, then destination identification accuracy is improved, but input time and user effort increase
Solution Approach 1:
The system performs preliminary categorization of character groups into information categories during the input process itself. As users type, the system continuously assigns character groups to categories like street name, city, or state, and begins generating potential destination matches before the user finishes typing, significantly reducing input time while maintaining accuracy
Solution Approach 2:
The system provides continuous feedback by displaying a list of probable destinations ranked by probability values as the user types. This real-time feedback allows users to see potential matches and confirm or correct the system's interpretation, enabling faster destination selection without sacrificing accuracy
3Measurement precision
If the system processes multiple interpretations of character sequences, then destination accuracy is improved, but computing complexity increases
Solution Approach 1:
The system processes multiple interpretations by generating multiple potential destination matches with different probability values. Instead of exhaustively analyzing every possible interpretation, the system ranks results by probability and can limit processing to the top candidates, managing computing complexity while maintaining high destination accuracy
Solution Approach 2:
The system uses probability values as a parameter to evaluate and rank different interpretations of character sequences. By assigning numerical probability scores to each potential destination based on how well it matches the categorized character groups, the system can efficiently compare multiple interpretations and select the most likely destination without excessive computing complexity
Data Source
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AI summary
Exemplary embodiments relate to a device (100) for assigning a navigation destination to a sequence of characters comprising a plurality of character groups. The device (100) includes an assignment module (102) configured to assign at least one character group to a first information category from a plurality of information categories for the purpose of interpreting the sequence of characters. The device (100) also includes a storage module (104) configured to provide, for a plurality of cities, a plurality of features from different information categories that are uniquely assigned to each city. The device (100) further includes a selection module (106) configured to select a city from the plurality of cities if the character group corresponds to a feature of the first information category assigned to the selected city.The assignment module (102) is further designed to assign additional character groups from the character sequence to the second information category for a selected city only if a feature corresponding to the respective character group is assigned to the second information category of the city.