KR-20260067931-A - ELECTRONIC DEVICE FOR PROVIDING RECOMMENDATION MESSAGE BY ANALYZING FOOD INTAKE, OPERATION METHOD THEREOF AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM

Abstract

The electronic device of the present disclosure comprises a communication interface, a memory that stores instructions and includes one or more storage media, and at least one processor that includes a processing circuit, wherein the instructions are executed individually or collectively by at least one processor, the electronic device is configured to receive Advanced Glycation End Products (AGEs) data of a user from a wearable device, obtain data related to food consumed by the user, input the Advanced Glycation End Products data and the food related data into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model is trained to identify the correlation between the food related data and the Advanced Glycation End Products data, identify the user's state information based on the Advanced Glycation End Products data, and generate a recommendation message that guides the user's meal based on the correlation and state information.

Inventors

• 슈키 아리아지나
• 박민호
• 안중우

Assignees

• 삼성전자주식회사

Dates

Publication Date

20260513

Application Date

20241127

Priority Date

20241105

Claims (20)

1. In electronic devices, Communication interface; Memory that stores instructions and includes one or more storage media; and at least one processor including a processing circuit; and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Receiving user's Advanced Glycation End Products (AGEs) data (C) from a wearable device, Obtain food-related data (B) consumed by the above user, and The above-mentioned final glycation product data (C) and the above-mentioned food-related data (B) are input into an artificial intelligence model (1000) to provide a recommendation message (D) generated by the artificial intelligence model (1000). The above artificial intelligence model (1000) is, An electronic device trained to identify the correlation between the above food-related data (B) and the above final glycation product data (C), identify the user's state information based on the above final glycation product data (C), and generate the above recommendation message (D) that guides the user's meal based on the correlation and the above state information.
2. In paragraph 1, Including a camera; further The above instructions cause the electronic device, The image received from the camera is input into the artificial intelligence model (1000) to obtain the food-related data (B), The above artificial intelligence model (1000) is, It is trained to recognize food included in the above image and acquire the above food-related data (B), and The above food-related data (B) is, An electronic device comprising at least one of the intake amount, calorie, carbohydrate, fat, protein, or mineral content, or the cooking method of the food.
3. In paragraph 1 or 2, Including a camera; further The above instructions cause the electronic device, If the image received from the camera includes barcode information, the food-related data (B) is obtained based on the barcode information, and An electronic device configured to obtain food-related data (B) based on the nutrition facts (A) when the image above includes a nutrition facts table (A).
4. In paragraph 2, The above instructions cause the electronic device, Based on the above food-related data (B) and the user's profile information, it is determined whether the food contains allergy-causing ingredients, and An electronic device configured to provide a notification when the allergenic component in the above food is identified.
5. In paragraph 1 or 2, The above artificial intelligence model (1000) is, An electronic device trained to identify the correlation by identifying the change in the user's end glycation product according to the food consumed by the user based on the above food-related data (B) and the above end glycation product data (C).
6. In paragraph 5, The above instructions cause the electronic device, When the user's biometric data is received from the wearable device, the food-related data (B), the final glycation product data (C), and the biometric data are configured to be input into the artificial intelligence model (1000). The above artificial intelligence model (1000) is, It is trained to acquire the state information based on the above biological data and the above final glycation product data (C), and The above biometric data is, An electronic device comprising at least one of heart rate, stress index, sleep information, or activity level.
7. In paragraph 1 or 2, The above artificial intelligence model (1000) is, Based on the above food-related data (B), a meal score (E) for the food consumed by the user is obtained, and An electronic device trained to generate the recommendation message (D) that guides the user's meal when the above meal score (E) is below a threshold score.
8. In paragraph 1 or 2, The above artificial intelligence model (1000) is, An electronic device trained to generate the recommendation message (D) that guides the user's meal when the above correlation is greater than or equal to a threshold value.
9. In paragraph 8, The above artificial intelligence model (1000) is, If the above correlation is less than the above threshold value, identify the range among the plurality of ranges that includes the final saccharification product data (C), and If the above final saccharification product data (C) is included in the first range among the plurality of ranges, a first recommendation message (D) is generated based on the first state information corresponding to the first range, and An electronic device trained to generate a second recommendation message (D) based on second state information corresponding to the second range when the final saccharification product data (C) is included in the second range among the plurality of ranges.
10. In paragraph 1 or 2, The above instructions cause the electronic device, An electronic device configured to provide the above recommendation message (D) in either a graphic form or a text form.
11. In the method of operating an electronic device, The above method is, The operation of receiving a user's Advanced Glycation End Products (AGEs) data from a wearable device; The operation of obtaining data related to food consumed by the above user; and The operation of inputting the above-mentioned final glycation product data and the above-mentioned food-related data into an artificial intelligence model and providing a recommendation message generated by the artificial intelligence model; The above artificial intelligence model is, A method of operation trained to identify the correlation between the above food-related data and the above final glycation product data, identify the user's state information based on the above final glycation product data, and generate the above recommendation message that guides the user's meal based on the correlation and the above state information.
12. In Paragraph 11, The operation of acquiring the above food-related data is, The operation of acquiring food-related data by inputting an image received from a camera into the artificial intelligence model; The above artificial intelligence model is, It is trained to recognize food included in the above image and acquire food-related data, The above food-related data is, A method of operation comprising at least one of the intake amount, the content of calories, carbohydrates, fats, proteins, or minerals, or the cooking method of the food.
13. In Article 11 or Article 12, The operation of acquiring the above food-related data is, If the image received from the camera includes barcode information, the operation of acquiring the food-related data based on the barcode information; and A method of operation comprising: acquiring food-related data based on nutrition facts when the above image includes nutrition facts.
14. In Paragraph 12, An operation to identify whether the food contains allergy-causing ingredients based on the above food-related data and the user's profile information; and A method of operation comprising: providing a notification when the allergenic component is identified in the above food.
15. In Article 11 or Article 12, The above artificial intelligence model is, A method of operation learned to identify the correlation by identifying the change in the user's end glycation product according to the food consumed by the user, based on the above food-related data and the above end glycation product data.
16. In paragraph 15, The operation of providing the above recommendation message is, When the user's biometric data is received from the wearable device, the operation of inputting the food-related data, the final glycation product data, and the biometric data into the artificial intelligence model is included; The above artificial intelligence model is, It is trained to acquire the state information based on the above biological data and the above final glycation product data, and The above biometric data is, A method of operation comprising at least one of heart rate, stress index, sleep information, or activity level.
17. In Article 11 or Article 12, The above artificial intelligence model is, Based on the above food-related data, a meal score for the food consumed by the user is obtained, and A method of operation trained to generate a recommendation message guiding the user's meal when the above meal score is below a threshold score.
18. In Article 11 or Article 12, The above artificial intelligence model is, A method of operation trained to generate the recommendation message that guides the user's meal when the above correlation is greater than or equal to a threshold value.
19. In Paragraph 18, The above artificial intelligence model is, If the above correlation is less than the above threshold value, identify the range among the plurality of ranges that includes the final saccharification product data, and If the above final saccharification product data is included in the first range among the plurality of ranges, a first recommendation message is generated based on the first state information corresponding to the first range, and A method of operation learned to generate a second recommendation message based on second state information corresponding to the second range when the above-mentioned final saccharification product data is included in the second range among the above-mentioned plurality of ranges.
20. In a storage medium storing computer-readable instructions, the instructions, when executed by at least one processor of an electronic device, cause the electronic device, Receives the user's Advanced Glycation End Products (AGEs) data from a wearable device, and Acquire data related to the food consumed by the above user, and It is configured to input the above-mentioned final glycation product data and the above-mentioned food-related data into an artificial intelligence model to provide recommendation messages generated by the artificial intelligence model, and The above artificial intelligence model is, A storage medium trained to identify the correlation between the above food-related data and the above final glycation product data, identify the user's state information based on the above final glycation product data, and generate the above recommendation message that guides the user's meal based on the correlation and the above state information.

Description

Electronic device for providing recommendation messages by analyzing ingested food, method of operation thereof, and storage medium { ELECTRONIC DEVICE FOR PROVIDING RECOMMENDATION MESSAGE BY ANALYZING FOOD INTAKE, OPERATION METHOD THEREOF AND NON-TRANSITORY COMPUTER-READABLE STORAGE MEDIUM } The present disclosure relates to an electronic device that analyzes food consumed by a user and provides a recommendation message, a method of operation thereof, and a storage medium. With the recent advancement of electronic technology, various types of electronic devices are being developed. In particular, wearable devices capable of contacting parts of the user's body are being developed and distributed. Wearable devices can acquire and provide biometric information of a user by coming into contact with a part of the user's body. Wearable devices can measure advanced glycation end products, and since advanced glycation end products are closely related to the user's dietary habits, this can be utilized in a method to guide the user's food intake. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. FIG. 1 illustrates an electronic device and a wearable device according to an embodiment of the present disclosure. FIG. 2 is a block diagram of a wearable device according to an embodiment of the present disclosure. FIG. 3 is a perspective view of the front of a wearable device according to an embodiment of the present disclosure. FIG. 4 is a perspective view of the rear side of a wearable device according to an embodiment of the present disclosure. FIG. 5 is an exploded perspective view of a wearable device according to an embodiment of the present disclosure. FIG. 6 illustrates a sensor according to an embodiment of the present disclosure. FIG. 7 illustrates an electronic device for identifying food consumed by a user according to an embodiment of the present disclosure. FIG. 8 illustrates an electronic device that acquires food-related data based on an artificial intelligence model according to an embodiment of the present disclosure. FIG. 9 illustrates food-related data according to food consumed on a daily basis according to an embodiment of the present disclosure. FIG. 10 illustrates an artificial intelligence model for obtaining a recommendation message according to an embodiment of the present disclosure. FIG. 11 illustrates an electronic device that provides a recommendation message according to an embodiment of the present disclosure. FIG. 12 illustrates an electronic device that provides a recommendation message guiding a meal according to an embodiment of the present disclosure. FIG. 13 illustrates an electronic device that provides notification of an allergenic component according to an embodiment of the present disclosure. FIG. 14 is a flowchart illustrating a method of operation of an electronic device according to an embodiment of the present disclosure. The present disclosure will be described in detail below with reference to the attached drawings. The terms used in the embodiments of this disclosure have been selected to be as widely used as possible, taking into account their functions within this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section of this disclosure. Therefore, terms used in this disclosure should be defined not merely by their names, but based on their meanings and the overall content of this disclosure. In this specification, expressions such as “have,” “may have,” “include,” or “may include” indicate the presence of such features (e.g., numerical values, functions, operations, or components such as parts) and do not exclude the presence of additional features. The expression "at least one of A and/or B" should be understood as representing either "A" and "B" or "A or B". Expressions such as "first," "second," "first," or "second" used in this specification may modify various components regardless of order and/or importance, and are used only to distinguish one component from another and do not limit said components. Where it is stated that a component (e.g., Component 1) is "(operatively or communicatively) coupled with/to" or "connected to" another component (e.g., Component 2), it should be understood that the component may be directly connected to the other component or connected through the other component (e.g., Component 3). Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "consisting of" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in t