APPARATUS AND METHOD FOR ESTIMATING ACTUAL DIETARY CONTENT OF MEAL OF USER

• US 20180068585A1
• Filed: 09/08/2016
• Published: 03/08/2018
• Est. Priority Date: 09/08/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: HARA, Kazuhiro, NAKANO, Hiroki, NISHIYAMA, Hiroki, OKADA, Ai, YOSHIDA, Tadayuki, YOSHITAKE, Mayumi
• Granted Patent: US 11,024,198 B2
• Time in Patent Office: Days
• CPC Class Codes: A47G 21/00 Table-ware crumb trays A47L13/52; table knives B26B
• CPC Class Codes: B26B 3/02 Table-knives B26B9/02 takes precedence
• CPC Class Codes: G09B 19/0092 Nutrition

Abstract

Provided are an apparatus and method for acquiring information designating a plurality of menu items provided in a meal; reading a characteristic of each of the plurality of menu items provided in the meal, from a menu item database that stores a characteristic for each menu item; generating an classification model for classifying a specified menu item from among the plurality of menu items, based on characteristics of the menu items; acquiring measurement data measured by a sensor attached to a utensil during the meal; and classifying a menu item corresponding to the measurement data, from a characteristic corresponding to the measurement data, using the classification model.

Claims

1. An apparatus comprising:

a processor; and

one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to;

acquire information designating a plurality of menu items provided in a meal;

read a characteristic of each of the plurality of menu items provided in the meal, from a menu item database that stores a characteristic for each menu item;

generate an classification model for classifying a specified menu item from among the plurality of menu items, based on characteristics of the menu items;

acquire measurement data measured by a sensor attached to a utensil during the meal; and

classify a menu item corresponding to the measurement data, from a characteristic corresponding to the measurement data, using the classification model.

2. The apparatus according to claim 1, wherein

the classifying the menu item includes classifying a menu item having a highest confidence rating among the plurality of menu items as the menu item corresponding to the measurement data, from the characteristic corresponding to the measurement data, using the classification model.

3. The apparatus according to claim 1, wherein

the reading the characteristic of each of the plurality of menu items includes, for each menu item, reading from the menu item database at least one characteristic corresponding to at least one cooking result for which at least one of a chef and a cooking instance is different, and

the generating the classification model includes perform a learning process to classify a corresponding menu item, from each of the at least one characteristic of each of the plurality of menu items.

4. The apparatus according to claim 3, wherein

the classification model includes at least one of a support vector machine (SVM) and a neural network.

5. The apparatus according to claim 1, wherein the instructions further cause the processor to:

estimate dietary content of the meal of the user, based on time-series data of the measurement data.

6. The apparatus according to claim 5, wherein

the estimating the dietary content of the meal of the user includes estimating an amount of at least one of salt, sugar, and calories ingested by the user.

7. The apparatus according to claim 5, wherein

the measurement data includes a measurement value indicating a characteristic and a measurement value indicating an amount of a menu item that has been eaten by the user, as measured by the sensor attached to the utensil during the meal.

8. The apparatus according to claim 5, wherein the instructions further cause the processor to:

evaluate the dietary content of the meal of the user, based on the estimation results of the dietary content.

9. The apparatus according to claim 8, wherein

the evaluation of the dietary content includes advice relating to an order in which the plurality of menu items should be eaten.

10. The apparatus according to claim 1, wherein the instructions further cause the processor to:

acquire the measurement data measured by a sensor attached to the utensil during cooking or after cooking; and

register the characteristic corresponding to the measurement data in the menu item database in association with a new menu item that has not been registered in the menu item database, in response to the measurement data measured by the sensor attached to the utensil during cooking or after cooking relating to the new menu item.

11. The apparatus according to claim 1, wherein

the sensor attached to the utensil includes at least one of a salt concentration sensor, a sugar concentration sensor, a temperature sensor, an electrical resistance sensor, a warping sensor, an acceleration sensor, and a gyro sensor.

12. The apparatus according to claim 1, wherein

the utensil is an object for moving food to a mouth.

13. The apparatus according to claim 12, wherein

the utensil is at least one of a chopstick, a fork, a knife, and a spoon used only by one person during the meal.

14. The apparatus according to claim 12, wherein the instructions further cause the processor to:

detect whether the utensil has not moved for at least a predetermined time, using the measurement data measured by the utensil for moving food to the mouth; and

judge that the user of the utensil has finished the meal, in response to detecting that the utensil has not moved for at least the predetermined time.

15. A method comprising:

acquiring information designating a plurality of menu items provided in a meal;

reading a characteristic of each of the plurality of menu items provided in the meal, from a menu item database that stores a characteristic for each menu item;

generating an classification model for classifying a specified menu item from among the plurality of menu items, based on characteristics of the menu items;

acquiring measurement data measured by a sensor attached to a utensil during the meal; and

classifying a menu item corresponding to the measurement data, from a characteristic corresponding to the measurement data, using the classification model.

16. The method according to claim 15, wherein

the classifying the menu item includes classifying a menu item having a highest confidence rating among the plurality of menu items as the menu item corresponding to the measurement data, from the characteristic corresponding to the measurement data, using the classification model.

17. The method according to claim 15, wherein

the reading the characteristic of each of the plurality of menu items includes, for each menu item, reading from the menu item database at least one characteristic corresponding to at least one cooking result for which at least one of a chef and a cooking instance is different, and

the generating the classification model includes perform a learning process to classify a corresponding menu item, from each of the at least one characteristic of each of the plurality of menu items.

18. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

acquire information designating a plurality of menu items provided in a meal;

read a characteristic of each of the plurality of menu items provided in the meal, from a menu item database that stores a characteristic for each menu item;

generate an classification model for classifying a specified menu item from among the plurality of menu items, based on characteristics of the menu items;

acquire measurement data measured by a sensor attached to a utensil during the meal; and

classify a menu item corresponding to the measurement data, from a characteristic corresponding to the measurement data, using the classification model.

19. The computer program product according to claim 18, wherein

the classifying the menu item includes classifying a menu item having a highest confidence rating among the plurality of menu items as the menu item corresponding to the measurement data, from the characteristic corresponding to the measurement data, using the classification model.

20. The computer program product according to claim 18, wherein

the reading the characteristic of each of the plurality of menu items includes, for each menu item, reading from the menu item database at least one characteristic corresponding to at least one cooking result for which at least one of a chef and a cooking instance is different, and

the generating the classification model includes perform a learning process to classify a corresponding menu item, from each of the at least one characteristic of each of the plurality of menu items.