A system for operating a food serving system

Patent metadata

Field	Value
Publication number	US-2025020503-A1
Application number	US-202218715893-A
Country	US
Kind code	A1
Filing date	Nov 30, 2022
Priority date	Dec 1, 2021
Publication date	Jan 16, 2025
Grant date	—

Abstract

According to an aspect, there is provided a system for managing a food serving system. The food collected by a user is weighed and sensor data, for example, image data about the food collected by the user is obtained. This data may be used as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food.

First claim

1 . A system comprising:
a plurality of food serving points configured to serve food, each food serving point being configured to serve a predetermined dish, each food serving point being associated with a weighing device configured to weigh the amount of the food collected from the food serving point to provide a weighing result, and a reader configured to read an identifier associated with a food collecting session of a user; at least one sensing point, each sensing point comprising at least one sensor configured to provide sensor data about the food collected by the user and a reader configured to read the identifier associated with the food collecting session of the user; a data system configured to
store data relating to food served in each of the plurality of food serving points;
obtain the weighing result and the identifier from each food serving point; and
associate weighing results having the same identifier with each other;
a training system configured to
obtain sensor data originating from the at least one sensing point, the sensor data being associated with the identifier;
obtain the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points from the data system; and
use the obtained sensor data associated with the identifier, the weighing results associated with the identifier and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food.

2 . The system of claim 1 , further comprising a control unit configured to:
receive a trigger event; and trigger storing of sensor data of the food collected by the user with the at least one sensor.

3 . The system of claim 2 , wherein the control unit is configured to receive the trigger event from the reader.

4 . The system of claim 1 , further comprising a control unit and a sensing point at each of the plurality of food serving points, the control unit of a food serving point being configured to:
receive a trigger event; and trigger storing of sensor data associated with the food collected by the user with the at least one sensor of the sensing point.

5 . The system of claim 4 , wherein the control unit is configured to receive the trigger event from the reader associated with the food serving point.

6 . The system of claim 4 , wherein the control unit is configured to receive the trigger event from the weighing device associated with the food serving point.

7 . The system of claim 1 , wherein the data relating to food served in the plurality of food serving points comprises at least one of:
the food served by each of the plurality of food serving points; and nutrient content information associated with each food served by each of the plurality of food serving points.

8 . The system of claim 1 , wherein the data system is configured to:
generate a session having a session identifier when obtaining the identifier for the first time and determining that there does not exist an active session; associate a session start time with the session; and link the identifier with the session having the session identifier.

9 . The system of any claim 8 , further comprising user identifier reader, and the data system is configured to:
obtain a user identifier from the user identifier reader; and associate the user identifier with the session.

10 . The system of claim 1 , wherein the identifier comprises a radio frequency identifier, a near filed communication identifier, a bar code, a QR code or a visually recognizable identifier associated with a tray used by the user.

11 . The system of claim 1 , wherein the identifier comprises an identifier, a radio frequency identifier, a near filed communication identifier, a smart wearable identifier, a smart ring identifier, a fingerprint, a biometric identifier and a visually recognizable identifier associated with the user.

12 . The system of claim 1 , further comprising a waste collecting point comprising:
a weighing device configured to weigh the amount of biowaste left by the user to provide a waste weighing result; a reader configured to read the food collecting session identifier associated with the food collecting session of the user; and at least one sensor configured to provide sensor data about the food left by the user, wherein the training system is configured to: obtain the waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user; and provide based on the obtained waste weighing result, the food collecting session identifier associated with the food collecting session of the user and the sensor data about the food left by the user additional information about the food left by the user.

13 . The system of claim 1 , wherein the at least one sensor comprise at least one of a camera, a stereo camera, a depth camera, a multispectral camera, a hyperspectral camera, an infrared camera, a RGB camera, an ultraviolet camera a spectroscopy sensor, a near infrared sensor, a spectroscopy sensor, a photogrammetry sensor, a lidar sensor, a three-dimensional scanner and a photodetector.

14 . The system of claim 1 , wherein the training system is configured to:
obtain additional sensor data and manually labeled data associated with the additional sensor data; and use the obtained additional sensor data and manually labeled data as training data for the machine learning algorithm to complement the model.

15 . A computer-implemented method comprising:
obtaining sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier; obtaining weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtaining data relating to food served in the plurality of food serving points; and using at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food.

16 .- 20 . (canceled)

21 . The computer program comprising instructions for causing an apparatus to perform the method of claim 15 .

22 . An apparatus comprising:
at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: obtain sensor data about food collected by a user from a plurality of food serving points, the sensor data being associated with a food collecting session identifier; obtain weighing results associated with the food collecting session identifier, each weighing result providing a weight of a food collected from a food serving point; obtain data relating to food served in the plurality of food serving points; and use at least the sensor data, the weighing results and the data relating to food served in the plurality of food serving points as training data for a machine learning algorithm to build a model enabling a subsequent classification of food based at least on sensor data associated with the food.

Assignees

• Turun Yliopisto

Inventors

• Ojansivu Pauliina
• Koivunen Lauri
• Norrdal Mari

Classifications

• G01G19/4146 Primary

  for controlling caloric intake, e.g. diet control · CPC title