17542382. AUTOMATIC TRACKING OF PROBABLE CONSUMED FOOD ITEMS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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AUTOMATIC TRACKING OF PROBABLE CONSUMED FOOD ITEMS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Stephen Haertel of Ajax (CA)

Wing Hon Lee of Richmond Hill (CA)

Lior Aronovich of Thornhill (CA)

AUTOMATIC TRACKING OF PROBABLE CONSUMED FOOD ITEMS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17542382 titled 'AUTOMATIC TRACKING OF PROBABLE CONSUMED FOOD ITEMS

Simplified Explanation

The patent application describes a method for detecting information about consumed food. Here are the key points:

  • The method receives data from multiple sources about food that a user may consume.
  • The data is parsed based on specific criteria, including the type of food and event metadata.
  • Aggregate data is created by combining the detected food item data and the respective metadata.
  • An ordered list of food items is generated based on the user's probability of consuming them.
  • The aggregate data includes nutritional information obtained from a database.
  • The list of potentially consumed food items is formatted in a predetermined form.

Potential applications of this technology:

  • Personalized nutrition tracking: The method can be used in apps or devices to track and analyze a user's food consumption, providing personalized nutritional information.
  • Dietary planning: The ordered list of food items can help users plan their meals and make healthier choices based on their consumption probability.
  • Food waste reduction: By analyzing the data of detected food items, the method can help identify patterns of food waste and provide insights for reducing it.

Problems solved by this technology:

  • Manual tracking and analysis: The method automates the process of detecting and analyzing consumed food, eliminating the need for users to manually input their meals.
  • Incomplete data: By combining data from multiple sources and parsing event metadata, the method can provide more comprehensive information about consumed food.
  • Personalized recommendations: The ordered list of food items based on consumption probability helps users make informed decisions about their diet.

Benefits of this technology:

  • Convenience: Users can easily track their food consumption without the need for manual input, making it more convenient and accurate.
  • Personalization: The method takes into account the user's specific consumption patterns to provide personalized recommendations and nutritional information.
  • Improved health outcomes: By providing insights into nutritional information and consumption patterns, the method can help users make healthier choices and improve their overall health.


Original Abstract Submitted

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.

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