Automatic Food Consumption Tracking via Multi-Source Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Nutritional applications require significant manual input from users to track consumed food items, leading to user abandonment due to tedious entry processes, which hampers the realization of health benefits.
Innovation Solution
A computer-implemented system that automatically detects food consumption using multiple data sources (audio, image, text, and sensor data) to generate a probability score for each food item, reducing the need for manual entry by aggregating and parsing metadata to provide nutritional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry of food items is required for nutritional tracking, then accuracy of food consumption data can be maintained, but user adherence to tracking routines decreases due to tedious entry processes
Solution Approach 1:
The system automatically detects and tracks food consumption events without requiring user intervention. Sensors detect food items, audio inputs capture consumption events, and the system autonomously processes this data to generate nutritional tracking records, eliminating the need for manual user entry while maintaining data accuracy
Solution Approach 2:
The patent replaces the mechanical manual entry process with automated detection systems including sensors, audio recording devices, and image capture components that automatically identify and record food consumption events, substituting human action with technological detection mechanisms
2Ease of operation
If multiple data sources are integrated for automatic food detection, then user adherence to nutritional tracking improves by reducing manual entry, but device complexity increases
Solution Approach 1:
The system combines multiple detection modalities including sensors, audio recording devices, and image capture components into an integrated food detection system. These diverse data sources are merged and processed together to identify food consumption events, achieving accurate automatic tracking while managing system complexity through unified processing architecture
Solution Approach 2:
The system employs multi-functional components that serve multiple purposes: sensors detect both presence and characteristics of food items, audio inputs capture both ambient sounds and specific consumption events, and the processing system handles both raw data acquisition and nutritional analysis, reducing overall system complexity through versatile components
3Ease of operation
If automatic detection systems are implemented, then manual tracking efforts are minimized improving user adherence, but measurement precision of food consumption data may decrease
Solution Approach 1:
The system incorporates user feedback mechanisms where detected food consumption events are presented to users for confirmation or correction. This feedback loop allows the system to learn from user inputs, refine its detection algorithms, and improve measurement precision over time while maintaining the benefit of reduced manual entry
Solution Approach 2:
The system uses intermediate processing steps including image recognition algorithms, audio analysis processors, and sensor data interpreters that act as mediators between raw detection data and final consumption records. These intermediaries refine and validate detected events, improving accuracy without requiring direct manual user input for each food item
Data Source
AI summary
The method provides for detecting information associated with consumed food. Data associated with detection events of food for possible consumption by a user is received from a plurality of sources. The data of detected food is parsed using a first criteria and parsing of event metadata is done using a second criteria and the data received from the detection event. Aggregate data is created for the food items by combining the detected food item data and the respective metadata of the detection event. An ordered list of food items from the aggregate data is generated and arranged according to a determined user consumption probability for the ordered list food items. The aggregate data of respective food items of the ordered list includes nutritional information of macronutrients and calories, accessed from a database, and the list of food items potentially consumed by the user are formatted into a predetermined form.