Odor Sensor Data Processing for Automated Assistant Identification
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Solution Overview
Problem
Current automated assistants are inadequate in identifying odors in environments due to insufficient data from natural language understanding, leading to increased user inputs and computational resources, especially when multiple terms describe the same odor and individuals perceive odors differently.
Innovation Solution
Incorporating odor sensors in client devices to generate odor data instances, which are processed to identify odors, and utilizing machine learning models or indexes to provide accurate responses, while establishing baseline odors to filter out familiar scents and alert users to unfamiliar or potentially harmful ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated assistant relies on natural language understanding pipeline to identify odors, then it can process user inputs through ASR and NLU components, but the NLU output contains insufficient slot values to accurately describe the odor
Solution Approach 1:
The patent combines multiple data sources including NLU output, user input descriptions, and environmental context data into a unified odor identification approach. This merging of information sources compensates for insufficient slot values in NLU output by supplementing with additional descriptors and contextual information from other sources.
2Measurement precision
If user provides additional requests to identify the odor, then odor identification accuracy may improve, but the quantity of user inputs and computational resources consumed increases
Solution Approach 1:
The system performs preliminary odor identification using available NLU output and environmental data before engaging the user for additional inputs. By pre-processing the information and only requesting additional user input when necessary, the system reduces the total number of user inputs required while maintaining identification accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the automated assistant presents preliminary odor identification results to the user and refines the identification based on user confirmation or correction. This iterative feedback process improves accuracy while minimizing the number of interaction rounds needed compared to requiring multiple sequential user requests.
3Adaptability or versatility
If automated assistant processes multiple descriptive terms for the same odor, then it can accommodate different user perceptions, but the fulfillment output may not correctly identify the odor due to insufficient discrimination
Solution Approach 1:
The patent introduces environmental context data and sensor information as an intermediary that mediates between multiple user-described odor terms and the actual odor identification. This intermediary objective data source helps discriminate between different odor descriptions and identifies the correct odor despite variations in user perception and terminology.
Data Source
AI summary
Implementations described herein are directed to leveraging odor sensor(s) of client device(s) in responding to user request(s) and/or in generating notification(s). Processor(s) of a given client device can receive a request to identify an odor in an environment of the given client device, process an odor data instance generated by the odor sensor(s) of the given client device, identify the odor based on processing the odor data instance, generate a response that identifies the odor and/or a source of the odor, and cause the response to the request to be rendered via the given client device. Processor(s) of the given client device can additionally, or alternatively, establish baseline odor(s) in the environment and generate a notification when an odor is detected that does not correspond to the baseline odor(s) and/or exclude the baseline odor(s) in generating the response to the request.