Automatic Diet Tracking System Using Text Parsing
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Solution Overview
Problem
Current diet tracking systems are tedious and inefficient, requiring users to undergo a lengthy process of selecting and inputting food items, quantities, and units, with limited automation and accuracy.
Innovation Solution
An automatic diet tracking system that parses user-submitted text to identify food items and quantities, utilizing machine learning, real-time user data, and text aliasing to streamline the tracking process, allowing for complete automation and reducing the number of steps required from 35 to 3 for tracking a five-food meal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual food tracking process is used, then food tracking accuracy can be maintained through user selection, but the process becomes extremely tedious requiring 35 steps to track a five-food meal
Solution Approach 1:
The system automatically tracks food by analyzing images and detecting food items, quantities, and nutritional information without requiring user intervention. The system serves itself by autonomously completing the tracking process, eliminating the need for manual food selection and data entry that previously required 35 steps.
Solution Approach 2:
The patent replaces the manual mechanical process of selecting and entering food data with an automated image recognition and analysis system. Computer vision algorithms and machine learning models substitute for the manual mechanical steps of food selection, quantity estimation, and nutritional data lookup.
2Productivity
If complete automation is implemented, then the number of steps is reduced from 35 to 3, but the system complexity increases significantly
Solution Approach 1:
The system integrates multiple functions into a single automated platform: image capture, food item detection, quantity estimation, nutritional analysis, and database searching. This multi-functional approach consolidates what would otherwise require separate manual operations into one unified automated process, achieving high productivity without proportionally increasing apparent system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-processing images, pre-detecting food items, and pre-calculating nutritional information before user review. This allows the system to prepare tracking data in advance, reducing the steps users need to take from 35 to just 3 confirmation actions.
3Productivity
If automated food identification is used, then tracking efficiency improves, but food identification accuracy may decrease without proper verification
Solution Approach 1:
The system incorporates feedback mechanisms where automated food identification results are presented to users for verification and correction. This feedback loop allows the system to learn from user corrections and improve accuracy over time while maintaining high tracking efficiency. Users can confirm or adjust automatically detected food items, ensuring precision without sacrificing productivity.
Data Source
AI summary
An automatic diet tracking system and methods comprising: i) voice-transcribed or typed text natural language processing and automatic tracking to record food, food quantity, and nutrition data, ii) multi-food administration to record multiple foods and related data in a single user voice-transcribed or typed text submission, and iii) location-based diet recommendations system that provides customized food recommendations to users based on user preferences and user physical location. Further, such automatic diet tracking system and location-based diet recommendations system are usable through computers, tablets, mobile phones, smart watches, wearables and other similar devices.