Conversation Analysis for Navigation Alerting
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Solution Overview
Problem
Current navigation systems require explicit user invocation and preconfigured inputs, missing alerts for suboptimal driving conditions when navigation instructions are provided verbally between humans, as they lack the ability to analyze conversational context for navigation cues.
Innovation Solution
A method that passively listens to conversations, performs shallow and deep parsing to identify navigational cues, determines the context of spoken phrases, and alerts users about suboptimal driving conditions on routes derived from conversational directions, using Natural Language Processing (NLP) and data sources for real-time traffic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If passive listening is used to detect preconfigured trigger words or phrases, then the system can launch intelligent assistant applications, but it cannot detect navigation cues from casual conversations between humans
Solution Approach 1:
The system performs multiple functions using a unified conversation analysis framework: it detects both preconfigured trigger phrases and casual navigation cues from human conversations. The NLP engine serves as a universal processor that handles both structured assistant invocations and unstructured conversational navigation requests, making the system adaptable to multiple input types without requiring separate detection mechanisms.
Solution Approach 2:
The patent introduces an NLP engine as an intermediary between the audio input and the navigation system. This intermediary component performs deep parsing of conversations to extract navigation cues, acting as a mediator that translates casual human speech into actionable navigation instructions without requiring explicit trigger words or preconfigured phrases.
2Productivity
If the system requires explicit user invocation of navigation apps, then it can provide navigation services, but it misses navigation instructions provided verbally between humans
Solution Approach 1:
The system performs self-service by automatically detecting navigation cues from conversations without requiring explicit user invocation. The NLP engine continuously monitors and parses conversation audio, automatically identifying navigation instructions and triggering navigation services when cues are detected, thereby eliminating the need for users to explicitly launch navigation apps or use specific trigger phrases.
Solution Approach 2:
The system performs preliminary analysis of conversations to identify potential navigation cues before the user would normally need to invoke navigation services. By continuously parsing conversation audio in real-time, the system is already prepared to detect and act on navigation instructions as soon as they are mentioned, rather than waiting for explicit user activation.
3Loss of information
If shallow parsing is performed to detect trigger words, then the system can identify preconfigured phrases, but it cannot extract contextual meaning from conversation
Solution Approach 1:
The patent replaces simple mechanical keyword matching (shallow parsing) with an intelligent NLP-based deep parsing system. Instead of merely detecting predefined trigger words, the NLP engine analyzes the semantic meaning, context, and structure of conversation sentences to extract navigation cues, thereby preserving contextual information that would be lost in shallow parsing approaches.
4Reliability
If the system analyzes every conversation in real-time, then it can detect navigation cues, but it consumes significant processing resources
Solution Approach 1:
The system applies partial action by selectively analyzing only those conversation segments that contain potential navigation cues, rather than processing every word uniformly. The NLP engine uses contextual analysis to identify sentences that may contain navigation instructions and focuses computational resources on those specific segments, thereby maintaining reliable detection while reducing overall processing energy consumption.
Data Source
AI summary
At an application executing in a device in a vehicle, a phrase is detected in a conversation occurring between two users. A determination is made that the phrase is usable in providing a navigation directive. Using NLP, a deep parsing the conversation is performed to extract a context applicable to the phrase. the context is evaluated to determine whether the context is related to a navigation of the vehicle. At the device, from the conversation, a future location is computed of the vehicle during the navigation. Using data from a data source, a suboptimal driving condition is identified on a route between a present location of the vehicle and the future location. A user in the vehicle is alerted about the suboptimal driving condition on the route.