system

Abstract

The system according to this embodiment aims to control calorie intake while maintaining satisfaction in the user's meal management. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and an interpolation unit. The collection unit collects the user's dietary history or health status. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests alternative meals based on the analysis results obtained by the analysis unit. The interpolation unit interpolates visual information or aroma information.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to control calorie intake while maintaining satisfaction in the user's diet management.

[0005] The system according to the embodiment aims to control calorie intake while maintaining satisfaction in the user's diet management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and an interpolation unit. The data collection unit collects the user's dietary history or health status. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests alternative meals based on the analysis results obtained by the analysis unit. The interpolation unit interpolates visual or aromatic information. [Effects of the Invention]

[0007] The system according to this embodiment can control calorie intake while maintaining satisfaction in the user's meal management. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The meal management system according to an embodiment of the present invention is a system that adjusts the user's satiety and supports a balanced diet. This system utilizes a brain illusion called the cross-modal phenomenon to transform the eating experience without changing the actual amount or flavor of food consumed by the user. For example, if a user feels like eating rich ramen, the system will suggest low-calorie somen noodles instead. This suggestion is made by an AI that analyzes the user's eating history and health condition to select the optimal alternative meal. Next, visual and aromatic information is interpolated via a dedicated AR terminal. When the user wears the AR terminal, rich ramen is displayed visually, and the aroma of ramen is also perceived. In this way, the user can obtain the satisfaction of eating rich ramen even though they are actually eating low-calorie somen noodles. Furthermore, the system records the user's eating experience and reflects it in future suggestions. This allows the user to enjoy a satisfying meal while controlling calorie intake and reducing diet stress. This mechanism enables the user to continue healthy eating and contributes to long-term health maintenance. For example, even if a user prefers high-calorie meals, the system will suggest an appropriate alternative meal and provide satisfaction, allowing them to maintain a healthy diet without difficulty. This allows the meal management system to adjust the user's satiety level and support a balanced diet.

[0029] The meal management system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and an interpolation unit. The collection unit collects the user's meal history or health status. Meal history includes, but is not limited to, the type of meal, amount consumed, and time of consumption. Health status includes, but is not limited to, blood pressure, blood glucose level, and weight. The collection unit collects, for example, meal details and health data entered by the user into the app. The collection unit can also automatically collect data using wearable devices or smartphone sensors. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the user's meal patterns and health status. The analysis unit uses machine learning algorithms to build a model for making future meal suggestions based on the user's meal history and health data. The suggestion unit suggests alternative meals based on the analysis results obtained by the analysis unit. For example, if the user wants to eat rich ramen, the suggestion unit suggests low-calorie somen noodles. The suggestion unit selects the optimal alternative meal considering the user's eating history and health condition. The interpolation unit interpolates visual or aromatic information. For example, the interpolation unit uses a dedicated AR terminal to provide visual and aromatic information that makes the user feel as if they are eating rich ramen. The interpolation unit ensures that the user can feel satisfied as if they were eating rich ramen, even though they are actually eating low-calorie somen noodles. As a result, the meal management system according to this embodiment can adjust the user's satiety and support a balanced diet.

[0030] The adjustment unit can execute algorithms to adjust the user's satiety. For example, the adjustment unit can execute algorithms to adjust the user's satiety. Criteria for evaluating satiety include, but are not limited to, subjective evaluation, stomach fullness, and hormone levels. The adjustment unit can adjust the user's satiety using machine learning algorithms. For example, the adjustment unit can select the optimal algorithm for adjusting satiety based on the user's meal history and health data. The adjustment unit can also monitor the user's satiety in real time and make adjustments as needed. By adjusting the user's satiety, satisfaction with meals can be increased. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal history and health data into AI and have the AI ​​perform the satiety adjustment.

[0031] The recording unit can record the user's dining experience. For example, the recording unit records the user's dining experience. This dining experience includes, but is not limited to, comments on the meal, actions during the meal, and the dining environment. The recording unit records the details of the meal and comments entered by the user into the app. The recording unit can also automatically record data using wearable devices or smartphone sensors. The recording unit can record the user's dining experience in detail and reflect it in future suggestions. This allows for the recording of the user's dining experience to be reflected in future suggestions. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's dining experience into AI and have the AI ​​perform the recording.

[0032] The reflection unit can incorporate the user's dining experience into future suggestions. For example, the reflection unit can incorporate the user's dining experience into future suggestions. Specific methods of reflection include, but are not limited to, the use of past data and the timing of the reflection. The reflection unit optimizes future suggestions based on the user's dining history and health data. The reflection unit can use machine learning algorithms to incorporate the user's dining experience into future suggestions. This allows for more appropriate suggestions by incorporating the user's dining experience into future suggestions. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the user's dining experience into AI and incorporate it into future suggestions.

[0033] The sensor unit can use sensor technology or display technology. The sensor unit may, for example, use specific sensor technology. Sensor technology includes, but is not limited to, camera sensors, temperature sensors, and humidity sensors. The sensor unit can use sensor technology to record the user's dining experience in detail. The sensor unit may also use specific display technology. Display technology includes, but is not limited to, LCDs, OLEDs, and projection displays. The sensor unit can use display technology to visually display the user's dining experience. This makes it possible to reproduce the user's dining experience more realistically by using sensors and display technology. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input sensor technology and display technology into AI and have the AI ​​perform data acquisition and display.

[0034] The display unit can utilize display technology. For example, the display unit may use specific display technologies. These display technologies include, but are not limited to, LCDs, OLEDs, and projection displays. The display unit can use display technology to visually represent the user's dining experience. This allows for the provision of visual satisfaction through the use of display technology. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input display technology into AI and have the AI ​​perform the display of visual information.

[0035] The data collection unit can analyze the user's past meal history and select an appropriate collection method. For example, the data collection unit can analyze the user's past meal history and select the optimal collection method. Collection methods include, but are not limited to, manual input, automated sensors, and applications. The data collection unit can customize the collection method based on the meals the user has frequently eaten in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past meal history. Furthermore, the data collection unit can analyze the user's past meal history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past meal history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past meal history into AI and have the AI ​​select the optimal collection method.

[0036] The data collection unit can filter the collected meal history based on the user's current health status and lifestyle. For example, the data collection unit can filter the collected meal history based on the user's current health status and lifestyle. Specific filtering methods include, but are not limited to, health status evaluation criteria and lifestyle evaluation criteria. The data collection unit can prioritize the collection of meal history containing specific ingredients based on the user's health check results. The data collection unit can also filter the data to be collected considering the user's lifestyle (e.g., workload). Furthermore, the data collection unit can adjust the collected meal history based on the user's current health status (e.g., poor health). This allows for more appropriate data collection by filtering the data based on the current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health status and lifestyle data into AI and have the AI ​​perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data when collecting meal history, taking into account the user's geographical location information. For example, when collecting meal history, the data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information. Specific examples of geographical location information include, but are not limited to, GPS data, address information, and location sensors. If the user is in a specific region, the data collection unit can prioritize the collection of data related to ingredients and cuisine in that region. Also, if the user is traveling, the data collection unit can prioritize the collection of meal data from their travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data related to home cooking. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data when collecting meal history. For example, when collecting meal history, the data collection unit can analyze the user's social media activity and collect relevant data. Specific examples of social media activity include, but are not limited to, posts, the number of likes, and the number of followers. The data collection unit can collect meal history based on meal photos shared by the user on social media. The data collection unit can also collect data on ingredients and dishes mentioned by the user on social media. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect relevant meal data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0039] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. Specific examples of past health data include, but are not limited to, medical history, test results, and health checkup data. The analysis unit can improve the accuracy of its analysis based on the user's past health checkup results. The analysis unit can also improve the accuracy of its analysis by comparing the user's past dietary history with health data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's past exercise data. In this way, the accuracy of the analysis can be improved by referring to past health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's past health data into AI and have AI perform the analysis accuracy improvement.

[0040] The analysis unit can apply different analysis methods depending on the category of the user's meal history during analysis. For example, the analysis unit can apply different analysis methods depending on the category of the user's meal history during analysis. Specific classification methods for meal history categories include, but are not limited to, the type of meal, nutrient content, and timing of meals. If the user prefers Japanese food, the analysis unit can apply an analysis method specialized for Japanese food. If the user prefers Western food, the analysis unit can apply an analysis method specialized for Western food. Furthermore, if the user is vegetarian, the analysis unit can apply an analysis method specialized for vegetarian meals. This allows for more appropriate analysis by applying an analysis method according to the category of the meal history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the user's meal history into the AI ​​and have the AI ​​perform the application of the analysis method.

[0041] The analysis unit can improve the accuracy of its analysis by considering the user's lifestyle data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by considering the user's lifestyle data during the analysis. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The analysis unit can improve the accuracy of its analysis by referring to the user's sleep data. The analysis unit can also improve the accuracy of its analysis by referring to the user's exercise data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's stress level. In this way, the accuracy of the analysis can be improved by considering lifestyle data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input the user's lifestyle data into AI and have AI perform the analysis accuracy improvement.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. Specific examples of relevant literature include, but are not limited to, academic papers, technical reports, and patent documents. The analysis unit can improve the accuracy of its analysis by referring to health-related literature that the user has read in the past. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to materials from seminars the user has attended in the past. In addition, the analysis unit can improve the accuracy of its analysis by referring to health guidance the user has received in the past. Thus, by referring to relevant literature, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's relevant literature into AI and have AI perform the analysis accuracy improvement.

[0043] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the alternative food when making suggestions. For example, the suggestion unit can adjust the level of detail of its suggestions based on the importance of the alternative food when making suggestions. Specific evaluation criteria for the importance of an alternative food include, but are not limited to, nutritional value, calories, and allergy information. If the alternative food is important, the suggestion unit can make suggestions that include detailed nutritional information. If the alternative food is not very important, the suggestion unit can make simplified suggestions. Furthermore, the suggestion unit can adjust the level of detail of its suggestions in stages according to the importance of the alternative food. This allows for more appropriate suggestions by adjusting the level of detail of suggestions according to the importance of the alternative food. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the importance data of the alternative food into AI and have the AI ​​perform the adjustment of the level of detail of the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the alternative food when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the alternative food when making a suggestion. Specific classification methods for alternative food categories include, but are not limited to, staple foods, side dishes, and desserts. If the alternative food is Japanese food, the suggestion unit can apply a suggestion algorithm specialized for Japanese food. If the alternative food is Western food, the suggestion unit can apply a suggestion algorithm specialized for Western food. Furthermore, if the alternative food is vegetarian food, the suggestion unit can apply a suggestion algorithm specialized for vegetarian food. This allows for more appropriate suggestions by applying a suggestion algorithm according to the category of the alternative food. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input alternative food category data into AI and have the AI ​​perform the application of the suggestion algorithm.

[0045] The proposal department can determine the priority of proposals based on the timing of the alternative meal submission. For example, the proposal department can determine the priority of proposals based on the timing of the alternative meal submission. Specific criteria for submission timing include, but are not limited to, meal timing, season, and events. The proposal department can prioritize proposals when the alternative meal is needed immediately. Conversely, the proposal department can lower the priority of proposals when the alternative meal is needed later. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of the alternative meal submission. This allows for more appropriate proposals by determining the priority of proposals according to the timing of the alternative meal submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input alternative meal submission timing data into AI and have the AI ​​perform the determination of proposal priority.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the alternative foods when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the alternative foods when making suggestions. Specific evaluation criteria for relevance include, but are not limited to, similarity in nutritional value and commonality of ingredients. The suggestion unit can prioritize suggesting alternative foods if they suit the user's preferences. Conversely, the suggestion unit can lower the order of suggestions if they do not suit the user's preferences. Furthermore, the suggestion unit can adjust the order of suggestions in stages according to the relevance of the alternative foods. This allows for more appropriate suggestions by adjusting the order of suggestions according to the relevance of the alternative foods. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input alternative food relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0047] The interpolation unit can improve the accuracy of interpolation by referring to the user's past dining experiences during interpolation. For example, the interpolation unit can improve the accuracy of interpolation by referring to the user's past dining experiences during interpolation. Specific examples of past dining experiences include, but are not limited to, impressions of the food, behavior during meals, and the dining environment. The interpolation unit can improve the accuracy of interpolation based on visual information of meals the user has liked in the past. Furthermore, the interpolation unit can improve the accuracy of interpolation based on aroma information the user has liked in the past. In addition, the interpolation unit can analyze the user's past dining experiences and select the optimal interpolation method. This allows for improved interpolation accuracy by referring to past dining experiences. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's past dining experience data into AI and have the AI ​​perform the interpolation accuracy improvement.

[0048] The interpolation unit can apply different interpolation methods depending on the category of the user's meal history during interpolation. For example, the interpolation unit can apply different interpolation methods depending on the category of the user's meal history during interpolation. Specific classification methods for meal history categories include, but are not limited to, the type of meal, nutrient content, and timing of meals. If the user prefers Japanese food, the interpolation unit can apply visual and aromatic interpolation methods specialized for Japanese food. If the user prefers Western food, the interpolation unit can apply visual and aromatic interpolation methods specialized for Western food. Furthermore, if the user prefers vegetarian food, the interpolation unit can apply visual and aromatic interpolation methods specialized for vegetarian food. This allows for more appropriate interpolation by applying interpolation methods according to the category of the meal history. Some or all of the above processing in the interpolation unit may be performed using AI, for example, or without AI. For example, the interpolation unit can input the user's meal history category data into AI and have the AI ​​perform the application of interpolation methods.

[0049] The interpolation unit can improve the accuracy of interpolation by considering the user's lifestyle data during interpolation. For example, the interpolation unit can improve the accuracy of interpolation by considering the user's lifestyle data during interpolation. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The interpolation unit can improve the accuracy of interpolation by referring to the user's sleep data. The interpolation unit can also improve the accuracy of interpolation by referring to the user's exercise data. Furthermore, the interpolation unit can improve the accuracy of interpolation by referring to the user's stress level. In this way, the accuracy of interpolation can be improved by considering lifestyle data. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's lifestyle data into AI and have AI perform the interpolation accuracy improvement.

[0050] The interpolation unit can improve the accuracy of interpolation by referring to the user's relevant literature during the interpolation process. For example, the interpolation unit can improve the accuracy of interpolation by referring to the user's relevant literature during the interpolation process. Specific examples of relevant literature include, but are not limited to, academic papers, technical reports, and patent documents. The interpolation unit can improve the accuracy of interpolation by referring to health-related literature that the user has read in the past. Furthermore, the interpolation unit can improve the accuracy of interpolation by referring to materials from seminars the user has attended in the past. In addition, the interpolation unit can improve the accuracy of interpolation by referring to health guidance the user has received in the past. Thus, by referring to relevant literature, the accuracy of interpolation can be improved. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's relevant literature into AI and have the AI ​​perform the interpolation accuracy improvement.

[0051] The adjustment unit can select an appropriate adjustment method by referring to the user's past meal history during the adjustment process. For example, the adjustment unit can select an appropriate adjustment method by referring to the user's past meal history during the adjustment process. The specific contents of the past meal history include, but are not limited to, the type of meal, the amount consumed, and the time of consumption. The adjustment unit can select an adjustment method based on the level of satiety the user has preferred from meals in the past. The adjustment unit can also analyze the user's past meal history and select the optimal satiety adjustment method. Furthermore, the adjustment unit can refer to the user's past meal experiences and optimize the satiety adjustment method. In this way, the optimal satiety adjustment method can be selected by referring to the past meal history. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's past meal history data into AI and have the AI ​​perform the selection of the adjustment method.

[0052] The adjustment unit can optimize the adjustment method by considering the user's lifestyle data during the adjustment process. For example, the adjustment unit can optimize the adjustment method by considering the user's lifestyle data during the adjustment process. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The adjustment unit can refer to the user's sleep data to optimize the satiety adjustment method. The adjustment unit can also refer to the user's exercise data to optimize the satiety adjustment method. Furthermore, the adjustment unit can refer to the user's stress level to optimize the satiety adjustment method. In this way, the satiety adjustment method can be optimized by considering lifestyle data. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's lifestyle data into AI and have AI perform the optimization of the adjustment method.

[0053] The recording unit can improve the accuracy of recordings by referring to the user's past meal history during recording. For example, the recording unit can improve the accuracy of recordings by referring to the user's past meal history during recording. The specific contents of past meal history include, but are not limited to, the type of meal, amount consumed, and time of consumption. The recording unit can improve the accuracy of recordings based on the user's past meal history. The recording unit can also improve the accuracy of recordings by comparing the user's past meal history with health data. Furthermore, the recording unit can improve the accuracy of recordings by referring to the user's past eating experiences. In this way, the accuracy of recordings can be improved by referring to past meal history. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past meal history data into AI and have the AI ​​perform the improvement of recording accuracy.

[0054] The recording unit can optimize the recording method by considering the user's lifestyle data during recording. For example, the recording unit can optimize the recording method by considering the user's lifestyle data during recording. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The recording unit can optimize the recording method by referring to the user's sleep data. The recording unit can also optimize the recording method by referring to the user's exercise data. Furthermore, the recording unit can optimize the recording method by referring to the user's stress level. In this way, the recording method can be optimized by considering lifestyle data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's lifestyle data into AI and have AI perform the optimization of the recording method.

[0055] The reflection unit can improve the accuracy of the reflection by referring to the user's past meal history during the reflection process. For example, the reflection unit can improve the accuracy of the reflection by referring to the user's past meal history during the reflection process. The specific contents of past meal history include, but are not limited to, the type of meal, the amount consumed, and the time of consumption. The reflection unit can improve the accuracy of the reflection based on the user's past meal history. The reflection unit can also improve the accuracy of the reflection by comparing the user's past meal history with health data. Furthermore, the reflection unit can improve the accuracy of the reflection by referring to the user's past eating experiences. In this way, the accuracy of the reflection can be improved by referring to past meal history. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input the user's past meal history data into AI and have the AI ​​perform the improvement of the reflection accuracy.

[0056] The reflection unit can optimize the reflection method by considering the user's lifestyle data during the reflection process. For example, the reflection unit can optimize the reflection method by considering the user's lifestyle data during the reflection process. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The reflection unit can optimize the reflection method by referring to the user's sleep data. The reflection unit can also optimize the reflection method by referring to the user's exercise data. Furthermore, the reflection unit can optimize the reflection method by referring to the user's stress level. In this way, the reflection method can be optimized by considering lifestyle data. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without using AI. For example, the reflection unit can input the user's lifestyle data into AI and have AI perform the optimization of the reflection method.

[0057] The sensor unit can optimize the sensor settings by referring to the user's past data when the sensor is in use. For example, the sensor unit can optimize the sensor settings by referring to the user's past data when the sensor is in use. The specific contents of the past data include, but are not limited to, past measurement results and past setting values. The sensor unit can optimize the sensor settings based on the user's past health data. The sensor unit can also optimize the sensor settings by referring to the user's past dietary history. Furthermore, the sensor unit can optimize the sensor settings by referring to the user's past exercise data. In this way, the sensor settings can be optimized by referring to past data. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input the user's past data into AI and have AI perform the optimization of the sensor settings.

[0058] The sensor unit can optimize its settings when the sensor is in use, taking into account the user's lifestyle data. For example, the sensor unit can optimize its settings when the sensor is in use, taking into account the user's lifestyle data. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The sensor unit can refer to the user's sleep data and optimize the sensor settings. The sensor unit can also refer to the user's exercise data and optimize the sensor settings. Furthermore, the sensor unit can refer to the user's stress level and optimize the sensor settings. In this way, the sensor settings can be optimized by taking lifestyle data into account. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input the user's lifestyle data into AI and have AI perform the optimization of the sensor settings.

[0059] The display unit can select the optimal display method by referring to the user's past operation history when the display is in use. For example, the display unit selects the optimal display method by referring to the user's past operation history when the display is in use. The specific contents of the past operation history include, but are not limited to, the type of operation, the frequency of the operation, and the result of the operation. The display unit can select the optimal display method based on the user's past operation history. The display unit can also select the optimal display method by comparing the user's past operation history with health data. Furthermore, the display unit can select the optimal display method by analyzing the user's past operation history. In this way, the optimal display method can be selected by referring to the past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's past operation history data into AI and have the AI ​​perform the selection of the display method.

[0060] The display unit can select the optimal display method when the display is in use, taking into account the user's device information. For example, the display unit can select the optimal display method when the display is in use, taking into account the user's device information. Specific examples of device information include, but are not limited to, the type of device, device settings, and device performance. If the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking device information into account. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the user's device information into AI and have AI select the display method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The analytics unit can predict a user's eating patterns based on their meal history and health data, and provide suggestions for their next meal. For example, if a user tends to prefer certain foods on specific days of the week, the system can provide suggestions tailored to those days. Furthermore, the analytics unit can analyze users' eating patterns over the long term to understand seasonal eating trends. It can also compare a user's eating patterns with those of other users to identify common trends. This allows for more personalized suggestions by predicting the user's eating patterns.

[0063] The adjustment unit can change the temperature and texture of the food to adjust the user's feeling of fullness. For example, serving warm soup can increase satiety. The adjustment unit can also increase chewing by changing the texture of the food, thereby promoting satiety. Furthermore, the adjustment unit can customize the temperature and texture of the food to the user's preferences. This allows for effective control of satiety by adjusting the temperature and texture of the food.

[0064] The recording unit can record the user's dining experience, including the content of conversations and the type of music played during the meal. For example, it can record the music the user was listening to and the content of their conversations, and use this information to improve future dining recommendations. The recording unit can also record ambient sounds during the meal, recreating a relaxing environment for the user. Furthermore, it can record the user's behavior during the meal (e.g., smartphone usage) and provide suggestions to improve the quality of the meal. By recording the environment and behavior during the meal, a more fulfilling dining experience can be provided.

[0065] The sensor unit can detect the user's posture and movements while eating and provide feedback to improve the dining experience. For example, it can detect whether the user is eating with the correct posture and issue an alert if their posture is poor. The sensor unit can also detect the user's movements while eating (e.g., chewing frequency and eating speed) and provide feedback to promote healthy eating habits. Furthermore, the sensor unit can compare the user's eating movements with those of other users to identify common trends. This allows for a healthier dining experience by detecting posture and movements while eating.

[0066] The data collection unit can analyze a user's past meal history and select the appropriate collection method. For example, it can customize the collection method based on the meals a user has frequently eaten in the past. Furthermore, the data collection unit can concentrate data collection during specific time periods based on the user's past meal history. In addition, the data collection unit can analyze the user's past meal history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past meal history.

[0067] The data collection unit can filter the collected meal history based on the user's current health status and lifestyle. For example, it can prioritize the collection of meal history containing specific ingredients based on the user's health checkup results. The data collection unit can also filter the data collected considering the user's lifestyle (e.g., work workload). Furthermore, the data collection unit can adjust the collected meal history based on the user's current health status (e.g., poor health). This allows for more appropriate data collection by filtering data based on the user's current health status and lifestyle.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the user's meal history or health status. Meal history includes, but is not limited to, the type of meal, amount consumed, and time of consumption. Health status includes, but is not limited to, the type of meal, blood sugar level, and weight. The data collection unit collects, for example, meal details and health data entered by the user into the app. The data collection unit can also automatically collect data using sensors in wearable devices or smartphones. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the user's eating patterns and health status. The analysis unit uses machine learning algorithms to build a model for making future meal suggestions based on the user's eating history and health data. Step 3: The suggestion unit proposes alternative meals based on the analysis results obtained by the analysis unit. For example, if the user wants to eat rich ramen, the suggestion unit will propose low-calorie somen noodles. The suggestion unit selects the most suitable alternative meal considering the user's dietary history and health condition. Step 4: The interpolation unit interpolates visual or aromatic information. For example, using a dedicated AR terminal, the interpolation unit provides visual and aromatic information that makes the user feel as if they are eating rich ramen. The interpolation unit allows the user to experience the satisfaction of eating rich ramen, even though they are actually eating low-calorie somen noodles.

[0070] (Example of form 2) The meal management system according to an embodiment of the present invention is a system that adjusts the user's satiety and supports a balanced diet. This system utilizes a brain illusion called the cross-modal phenomenon to transform the eating experience without changing the actual amount or flavor of food consumed by the user. For example, if a user feels like eating rich ramen, the system will suggest low-calorie somen noodles instead. This suggestion is made by an AI that analyzes the user's eating history and health condition to select the optimal alternative meal. Next, visual and aromatic information is interpolated via a dedicated AR terminal. When the user wears the AR terminal, rich ramen is displayed visually, and the aroma of ramen is also perceived. In this way, the user can obtain the satisfaction of eating rich ramen even though they are actually eating low-calorie somen noodles. Furthermore, the system records the user's eating experience and reflects it in future suggestions. This allows the user to enjoy a satisfying meal while controlling calorie intake and reducing diet stress. This mechanism enables the user to continue healthy eating and contributes to long-term health maintenance. For example, even if a user prefers high-calorie meals, the system will suggest an appropriate alternative meal and provide satisfaction, allowing them to maintain a healthy diet without difficulty. This allows the meal management system to adjust the user's satiety level and support a balanced diet.

[0071] The meal management system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and an interpolation unit. The collection unit collects the user's meal history or health status. Meal history includes, but is not limited to, the type of meal, amount consumed, and time of consumption. Health status includes, but is not limited to, blood pressure, blood glucose level, and weight. The collection unit collects, for example, meal details and health data entered by the user into the app. The collection unit can also automatically collect data using wearable devices or smartphone sensors. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the user's meal patterns and health status. The analysis unit uses machine learning algorithms to build a model for making future meal suggestions based on the user's meal history and health data. The suggestion unit suggests alternative meals based on the analysis results obtained by the analysis unit. For example, if the user wants to eat rich ramen, the suggestion unit suggests low-calorie somen noodles. The suggestion unit selects the optimal alternative meal considering the user's eating history and health condition. The interpolation unit interpolates visual or aromatic information. For example, the interpolation unit uses a dedicated AR terminal to provide visual and aromatic information that makes the user feel as if they are eating rich ramen. The interpolation unit ensures that the user can feel satisfied as if they were eating rich ramen, even though they are actually eating low-calorie somen noodles. As a result, the meal management system according to this embodiment can adjust the user's satiety and support a balanced diet.

[0072] The adjustment unit can execute algorithms to adjust the user's satiety. For example, the adjustment unit can execute algorithms to adjust the user's satiety. Criteria for evaluating satiety include, but are not limited to, subjective evaluation, stomach fullness, and hormone levels. The adjustment unit can adjust the user's satiety using machine learning algorithms. For example, the adjustment unit can select the optimal algorithm for adjusting satiety based on the user's meal history and health data. The adjustment unit can also monitor the user's satiety in real time and make adjustments as needed. By adjusting the user's satiety, satisfaction with meals can be increased. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal history and health data into AI and have the AI ​​perform the satiety adjustment.

[0073] The recording unit can record the user's dining experience. For example, the recording unit records the user's dining experience. This dining experience includes, but is not limited to, comments on the meal, actions during the meal, and the dining environment. The recording unit records the details of the meal and comments entered by the user into the app. The recording unit can also automatically record data using wearable devices or smartphone sensors. The recording unit can record the user's dining experience in detail and reflect it in future suggestions. This allows for the recording of the user's dining experience to be reflected in future suggestions. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's dining experience into AI and have the AI ​​perform the recording.

[0074] The reflection unit can incorporate the user's dining experience into future suggestions. For example, the reflection unit can incorporate the user's dining experience into future suggestions. Specific methods of reflection include, but are not limited to, the use of past data and the timing of the reflection. The reflection unit optimizes future suggestions based on the user's dining history and health data. The reflection unit can use machine learning algorithms to incorporate the user's dining experience into future suggestions. This allows for more appropriate suggestions by incorporating the user's dining experience into future suggestions. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without AI. For example, the reflection unit can input the user's dining experience into AI and incorporate it into future suggestions.

[0075] The sensor unit can use sensor technology or display technology. The sensor unit may, for example, use specific sensor technology. Sensor technology includes, but is not limited to, camera sensors, temperature sensors, and humidity sensors. The sensor unit can use sensor technology to record the user's dining experience in detail. The sensor unit may also use specific display technology. Display technology includes, but is not limited to, LCDs, OLEDs, and projection displays. The sensor unit can use display technology to visually display the user's dining experience. This makes it possible to reproduce the user's dining experience more realistically by using sensors and display technology. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input sensor technology and display technology into AI and have the AI ​​perform data acquisition and display.

[0076] The display unit can utilize display technology. For example, the display unit may use specific display technologies. These display technologies include, but are not limited to, LCDs, OLEDs, and projection displays. The display unit can use display technology to visually represent the user's dining experience. This allows for the provision of visual satisfaction through the use of display technology. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input display technology into AI and have the AI ​​perform the display of visual information.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of collecting meal history based on the estimated emotions. For example, the data collection unit can estimate the user's emotions and adjust the timing of collecting meal history based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition, voice analysis, and self-reporting. If the user is stressed, the data collection unit can refrain from collecting meal history and collect it when the user is relaxed. If the user is relaxed, the data collection unit can actively collect meal history and obtain detailed data. Furthermore, if the user is in a hurry, the data collection unit can collect meal history in a simplified question format. By adjusting the collection timing according to the user's emotions, more appropriate data collection becomes possible. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the collection timing.

[0078] The data collection unit can analyze the user's past meal history and select an appropriate collection method. For example, the data collection unit can analyze the user's past meal history and select the optimal collection method. Collection methods include, but are not limited to, manual input, automated sensors, and applications. The data collection unit can customize the collection method based on the meals the user has frequently eaten in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past meal history. Furthermore, the data collection unit can analyze the user's past meal history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past meal history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past meal history into AI and have the AI ​​select the optimal collection method.

[0079] The data collection unit can filter the collected meal history based on the user's current health status and lifestyle. For example, the data collection unit can filter the collected meal history based on the user's current health status and lifestyle. Specific filtering methods include, but are not limited to, health status evaluation criteria and lifestyle evaluation criteria. The data collection unit can prioritize the collection of meal history containing specific ingredients based on the user's health check results. The data collection unit can also filter the data to be collected considering the user's lifestyle (e.g., workload). Furthermore, the data collection unit can adjust the collected meal history based on the user's current health status (e.g., poor health). This allows for more appropriate data collection by filtering the data based on the current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health status and lifestyle data into AI and have the AI ​​perform the filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. Specific criteria for determining data priority include, but are not limited to, importance, urgency, and relevance. If the user is stressed, the data collection unit can prioritize collecting dietary data that helps reduce stress. If the user is relaxed, the data collection unit can prioritize collecting data containing detailed nutritional information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting simplified data. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input user emotion data into AI and have the AI ​​perform the data priority determination.

[0081] The data collection unit can prioritize the collection of highly relevant data when collecting meal history, taking into account the user's geographical location information. For example, when collecting meal history, the data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information. Specific examples of geographical location information include, but are not limited to, GPS data, address information, and location sensors. If the user is in a specific region, the data collection unit can prioritize the collection of data related to ingredients and cuisine in that region. Also, if the user is traveling, the data collection unit can prioritize the collection of meal data from their travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data related to home cooking. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0082] The data collection unit can analyze the user's social media activity and collect relevant data when collecting meal history. For example, when collecting meal history, the data collection unit can analyze the user's social media activity and collect relevant data. Specific examples of social media activity include, but are not limited to, posts, the number of likes, and the number of followers. The data collection unit can collect meal history based on meal photos shared by the user on social media. The data collection unit can also collect data on ingredients and dishes mentioned by the user on social media. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect relevant meal data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​collect the relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. Specific examples of the analysis algorithm include, but are not limited to, machine learning algorithms and statistical analysis algorithms. If the user is stressed, the analysis unit can prioritize analyzing dietary data that helps reduce stress. If the user is relaxed, the analysis unit can prioritize analyzing data containing detailed nutritional information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing simplified data. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the analysis algorithm.

[0084] The analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's past health data during the analysis process. Specific examples of past health data include, but are not limited to, medical history, test results, and health checkup data. The analysis unit can improve the accuracy of its analysis based on the user's past health checkup results. The analysis unit can also improve the accuracy of its analysis by comparing the user's past dietary history with health data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's past exercise data. In this way, the accuracy of the analysis can be improved by referring to past health data. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's past health data into AI and have AI perform the analysis accuracy improvement.

[0085] The analysis unit can apply different analysis methods depending on the category of the user's meal history during analysis. For example, the analysis unit can apply different analysis methods depending on the category of the user's meal history during analysis. Specific classification methods for meal history categories include, but are not limited to, the type of meal, nutrient content, and timing of meals. If the user prefers Japanese food, the analysis unit can apply an analysis method specialized for Japanese food. If the user prefers Western food, the analysis unit can apply an analysis method specialized for Western food. Furthermore, if the user is vegetarian, the analysis unit can apply an analysis method specialized for vegetarian meals. This allows for more appropriate analysis by applying an analysis method according to the category of the meal history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the user's meal history into the AI ​​and have the AI ​​perform the application of the analysis method.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. Specific display methods include, but are not limited to, graph displays, text displays, and interactive displays. If the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method according to the user's emotions, it becomes possible to display more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0087] The analysis unit can improve the accuracy of its analysis by considering the user's lifestyle data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by considering the user's lifestyle data during the analysis. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The analysis unit can improve the accuracy of its analysis by referring to the user's sleep data. The analysis unit can also improve the accuracy of its analysis by referring to the user's exercise data. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to the user's stress level. In this way, the accuracy of the analysis can be improved by considering lifestyle data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without using AI. For example, the analysis unit can input the user's lifestyle data into AI and have AI perform the analysis accuracy improvement.

[0088] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. Specific examples of relevant literature include, but are not limited to, academic papers, technical reports, and patent documents. The analysis unit can improve the accuracy of its analysis by referring to health-related literature that the user has read in the past. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to materials from seminars the user has attended in the past. In addition, the analysis unit can improve the accuracy of its analysis by referring to health guidance the user has received in the past. Thus, by referring to relevant literature, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's relevant literature into AI and have AI perform the analysis accuracy improvement.

[0089] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. The specific content of the suggestion presentation may include, but is not limited to, text, images, and audio. If the user is stressed, the suggestion unit can provide a simple and highly visible suggestion presentation. If the user is relaxed, the suggestion unit can provide a suggestion presentation that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise suggestion presentation. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the way suggestions are presented.

[0090] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the alternative food when making suggestions. For example, the suggestion unit can adjust the level of detail of its suggestions based on the importance of the alternative food when making suggestions. Specific evaluation criteria for the importance of an alternative food include, but are not limited to, nutritional value, calories, and allergy information. If the alternative food is important, the suggestion unit can make suggestions that include detailed nutritional information. If the alternative food is not very important, the suggestion unit can make simplified suggestions. Furthermore, the suggestion unit can adjust the level of detail of its suggestions in stages according to the importance of the alternative food. This allows for more appropriate suggestions by adjusting the level of detail of suggestions according to the importance of the alternative food. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the importance data of the alternative food into AI and have the AI ​​perform the adjustment of the level of detail of the suggestions.

[0091] The suggestion unit can apply different suggestion algorithms depending on the category of the alternative food when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the alternative food when making a suggestion. Specific classification methods for alternative food categories include, but are not limited to, staple foods, side dishes, and desserts. If the alternative food is Japanese food, the suggestion unit can apply a suggestion algorithm specialized for Japanese food. If the alternative food is Western food, the suggestion unit can apply a suggestion algorithm specialized for Western food. Furthermore, if the alternative food is vegetarian food, the suggestion unit can apply a suggestion algorithm specialized for vegetarian food. This allows for more appropriate suggestions by applying a suggestion algorithm according to the category of the alternative food. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input alternative food category data into AI and have the AI ​​perform the application of the suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on those emotions. Specific criteria for suggestion length include, but are not limited to, the number of characters, the amount of information, and the display time. If the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the length of the suggestion.

[0093] The proposal department can determine the priority of proposals based on the timing of the alternative meal submission. For example, the proposal department can determine the priority of proposals based on the timing of the alternative meal submission. Specific criteria for submission timing include, but are not limited to, meal timing, season, and events. The proposal department can prioritize proposals when the alternative meal is needed immediately. Conversely, the proposal department can lower the priority of proposals when the alternative meal is needed later. Furthermore, the proposal department can adjust the priority of proposals in stages according to the timing of the alternative meal submission. This allows for more appropriate proposals by determining the priority of proposals according to the timing of the alternative meal submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input alternative meal submission timing data into AI and have the AI ​​perform the determination of proposal priority.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the alternative foods when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the alternative foods when making suggestions. Specific evaluation criteria for relevance include, but are not limited to, similarity in nutritional value and commonality of ingredients. The suggestion unit can prioritize suggesting alternative foods if they suit the user's preferences. Conversely, the suggestion unit can lower the order of suggestions if they do not suit the user's preferences. Furthermore, the suggestion unit can adjust the order of suggestions in stages according to the relevance of the alternative foods. This allows for more appropriate suggestions by adjusting the order of suggestions according to the relevance of the alternative foods. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input alternative food relevance data into AI and have the AI ​​perform the adjustment of the suggestion order.

[0095] The interpolation unit can estimate the user's emotions and adjust the interpolation method for visual and scent information based on the estimated emotions. For example, the interpolation unit can estimate the user's emotions and adjust the interpolation method for visual and scent information based on the estimated emotions. Specific details of the interpolation method for visual and scent information include, but are not limited to, image resolution and scent intensity. If the user is relaxed, the interpolation unit can interpolate with visually calming colors. If the user is excited, the interpolation unit can interpolate with visually stimulating effects. Furthermore, if the user is stressed, the interpolation unit can interpolate with relaxing scents. This allows for more appropriate interpolation by adjusting the interpolation method for visual and scent information according to the user's emotions. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or without AI. For example, the interpolation unit can input user emotion data into AI and have the AI ​​adjust the interpolation method.

[0096] The interpolation unit can improve the accuracy of interpolation by referring to the user's past dining experiences during interpolation. For example, the interpolation unit can improve the accuracy of interpolation by referring to the user's past dining experiences during interpolation. Specific examples of past dining experiences include, but are not limited to, impressions of the food, behavior during meals, and the dining environment. The interpolation unit can improve the accuracy of interpolation based on visual information of meals the user has liked in the past. Furthermore, the interpolation unit can improve the accuracy of interpolation based on aroma information the user has liked in the past. In addition, the interpolation unit can analyze the user's past dining experiences and select the optimal interpolation method. This allows for improved interpolation accuracy by referring to past dining experiences. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's past dining experience data into AI and have the AI ​​perform the interpolation accuracy improvement.

[0097] The interpolation unit can apply different interpolation methods depending on the category of the user's meal history during interpolation. For example, the interpolation unit can apply different interpolation methods depending on the category of the user's meal history during interpolation. Specific classification methods for meal history categories include, but are not limited to, the type of meal, nutrient content, and timing of meals. If the user prefers Japanese food, the interpolation unit can apply visual and aromatic interpolation methods specialized for Japanese food. If the user prefers Western food, the interpolation unit can apply visual and aromatic interpolation methods specialized for Western food. Furthermore, if the user prefers vegetarian food, the interpolation unit can apply visual and aromatic interpolation methods specialized for vegetarian food. This allows for more appropriate interpolation by applying interpolation methods according to the category of the meal history. Some or all of the above processing in the interpolation unit may be performed using AI, for example, or without AI. For example, the interpolation unit can input the user's meal history category data into AI and have the AI ​​perform the application of interpolation methods.

[0098] The interpolation unit can estimate the user's emotions and adjust the display method of the interpolation results based on the estimated emotions. For example, the interpolation unit estimates the user's emotions and adjusts the display method of the interpolation results based on the estimated emotions. Specific examples of how the interpolation results are displayed include, but are not limited to, graph displays, text displays, and interactive displays. The interpolation unit can provide a simple and highly visible display method when the user is tense. It can also provide a display method that includes detailed information when the user is relaxed. Furthermore, it can provide a concise display method when the user is in a hurry. By adjusting the display method of the interpolation results according to the user's emotions, a more appropriate display becomes possible. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not. For example, the interpolation unit can input user emotion data into AI and have the AI ​​adjust the display method.

[0099] The interpolation unit can improve the accuracy of interpolation by considering the user's lifestyle data during interpolation. For example, the interpolation unit can improve the accuracy of interpolation by considering the user's lifestyle data during interpolation. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The interpolation unit can improve the accuracy of interpolation by referring to the user's sleep data. The interpolation unit can also improve the accuracy of interpolation by referring to the user's exercise data. Furthermore, the interpolation unit can improve the accuracy of interpolation by referring to the user's stress level. In this way, the accuracy of interpolation can be improved by considering lifestyle data. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's lifestyle data into AI and have AI perform the interpolation accuracy improvement.

[0100] The interpolation unit can improve the accuracy of interpolation by referring to the user's relevant literature during the interpolation process. For example, the interpolation unit can improve the accuracy of interpolation by referring to the user's relevant literature during the interpolation process. Specific examples of relevant literature include, but are not limited to, academic papers, technical reports, and patent documents. The interpolation unit can improve the accuracy of interpolation by referring to health-related literature that the user has read in the past. Furthermore, the interpolation unit can improve the accuracy of interpolation by referring to materials from seminars the user has attended in the past. In addition, the interpolation unit can improve the accuracy of interpolation by referring to health guidance the user has received in the past. Thus, by referring to relevant literature, the accuracy of interpolation can be improved. Some or all of the above processing in the interpolation unit may be performed using, for example, AI, or not using AI. For example, the interpolation unit can input the user's relevant literature into AI and have the AI ​​perform the interpolation accuracy improvement.

[0101] The adjustment unit can estimate the user's emotions and change the satiety adjustment method based on the estimated user emotions. For example, the adjustment unit can estimate the user's emotions and change the satiety adjustment method based on the estimated user emotions. Specific details of the satiety adjustment method include, but are not limited to, the amount of food, the timing of meals, and the type of food. If the user is stressed, the adjustment unit can apply an adjustment method that emphasizes satiety. If the user is relaxed, the adjustment unit can apply an adjustment method that provides a natural feeling of fullness. Furthermore, if the user is in a hurry, the adjustment unit can apply an adjustment method that provides a quick feeling of fullness. By changing the satiety adjustment method according to the user's emotions, more appropriate satiety adjustment becomes possible. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform the changes to the satiety adjustment method.

[0102] The adjustment unit can select an appropriate adjustment method by referring to the user's past meal history during the adjustment process. For example, the adjustment unit can select an appropriate adjustment method by referring to the user's past meal history during the adjustment process. The specific contents of the past meal history include, but are not limited to, the type of meal, the amount consumed, and the time of consumption. The adjustment unit can select an adjustment method based on the level of satiety the user has preferred from meals in the past. The adjustment unit can also analyze the user's past meal history and select the optimal satiety adjustment method. Furthermore, the adjustment unit can refer to the user's past meal experiences and optimize the satiety adjustment method. In this way, the optimal satiety adjustment method can be selected by referring to the past meal history. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's past meal history data into AI and have the AI ​​perform the selection of the adjustment method.

[0103] The adjustment unit can estimate the user's emotions and change the satiety adjustment frequency based on the estimated user emotions. For example, the adjustment unit estimates the user's emotions and changes the satiety adjustment frequency based on the estimated user emotions. Specific criteria for satiety adjustment frequency include, but are not limited to, the number of meals and the interval between meals. The adjustment unit can increase the satiety adjustment frequency when the user is stressed. Conversely, the adjustment unit can decrease the adjustment frequency to provide a natural feeling of fullness when the user is relaxed. Furthermore, the adjustment unit can increase the adjustment frequency to provide a quick feeling of fullness when the user is in a hurry. This allows for more appropriate satiety adjustment by changing the satiety adjustment frequency according to the user's emotions. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform the changes to the satiety adjustment frequency.

[0104] The adjustment unit can optimize the adjustment method by considering the user's lifestyle data during the adjustment process. For example, the adjustment unit can optimize the adjustment method by considering the user's lifestyle data during the adjustment process. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The adjustment unit can refer to the user's sleep data to optimize the satiety adjustment method. The adjustment unit can also refer to the user's exercise data to optimize the satiety adjustment method. Furthermore, the adjustment unit can refer to the user's stress level to optimize the satiety adjustment method. In this way, the satiety adjustment method can be optimized by considering lifestyle data. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the user's lifestyle data into AI and have AI perform the optimization of the adjustment method.

[0105] The recording unit can estimate the user's emotions and adjust the recording method of the dining experience based on the estimated emotions. For example, the recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. Specific recording methods include, but are not limited to, text recordings, audio recordings, and video recordings. If the user is nervous, the recording unit can provide a simple and visually clear recording method. If the user is relaxed, the recording unit can provide a recording method that includes detailed information. Furthermore, if the user is in a hurry, the recording unit can provide a concise recording method. This allows for more appropriate recording by adjusting the recording method according to the user's emotions. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can input user emotion data into AI and have the AI ​​adjust the recording method.

[0106] The recording unit can improve the accuracy of recordings by referring to the user's past meal history during recording. For example, the recording unit can improve the accuracy of recordings by referring to the user's past meal history during recording. The specific contents of past meal history include, but are not limited to, the type of meal, amount consumed, and time of consumption. The recording unit can improve the accuracy of recordings based on the user's past meal history. The recording unit can also improve the accuracy of recordings by comparing the user's past meal history with health data. Furthermore, the recording unit can improve the accuracy of recordings by referring to the user's past eating experiences. In this way, the accuracy of recordings can be improved by referring to past meal history. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past meal history data into AI and have the AI ​​perform the improvement of recording accuracy.

[0107] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. For example, the recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. Specific criteria for determining the priority of recordings include, but are not limited to, importance, urgency, and relevance. If the user is feeling stressed, the recording unit can prioritize recording meal data that helps reduce stress. If the user is relaxed, the recording unit can prioritize recording data that includes detailed nutritional information. Furthermore, if the user is in a hurry, the recording unit can prioritize recording simplified data. This allows for more appropriate recordings by determining the priority of recordings according to the user's emotions. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can input user emotion data into AI and have the AI ​​perform the determination of the priority of recordings.

[0108] The recording unit can optimize the recording method by considering the user's lifestyle data during recording. For example, the recording unit can optimize the recording method by considering the user's lifestyle data during recording. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The recording unit can optimize the recording method by referring to the user's sleep data. The recording unit can also optimize the recording method by referring to the user's exercise data. Furthermore, the recording unit can optimize the recording method by referring to the user's stress level. In this way, the recording method can be optimized by considering lifestyle data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's lifestyle data into AI and have AI perform the optimization of the recording method.

[0109] The reflection unit can estimate the user's emotions and adjust the method of reflecting the next suggestion based on the estimated user emotions. For example, the reflection unit estimates the user's emotions and adjusts the method of reflecting the next suggestion based on the estimated user emotions. The specific details of the reflection method include, but are not limited to, how the data is used and the timing of the reflection. If the user is stressed, the reflection unit can provide a simple and highly visible suggestion method. If the user is relaxed, the reflection unit can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the reflection unit can provide a suggestion method that gets straight to the point. By adjusting the method of reflecting the next suggestion according to the user's emotions, more appropriate suggestions become possible. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input user emotion data into AI and have the AI ​​perform the adjustment of the reflection method.

[0110] The reflection unit can improve the accuracy of the reflection by referring to the user's past meal history during the reflection process. For example, the reflection unit can improve the accuracy of the reflection by referring to the user's past meal history during the reflection process. The specific contents of past meal history include, but are not limited to, the type of meal, the amount consumed, and the time of consumption. The reflection unit can improve the accuracy of the reflection based on the user's past meal history. The reflection unit can also improve the accuracy of the reflection by comparing the user's past meal history with health data. Furthermore, the reflection unit can improve the accuracy of the reflection by referring to the user's past eating experiences. In this way, the accuracy of the reflection can be improved by referring to past meal history. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input the user's past meal history data into AI and have the AI ​​perform the improvement of the reflection accuracy.

[0111] The reflection unit can estimate the user's emotions and determine the priority of reflections based on the estimated emotions. For example, the reflection unit can estimate the user's emotions and determine the priority of reflections based on the estimated emotions. Specific criteria for determining the priority of reflections include, but are not limited to, importance, urgency, and relevance. If the user is feeling stressed, the reflection unit can prioritize reflecting dietary data that helps reduce stress. If the user is relaxed, the reflection unit can prioritize reflecting data that includes detailed nutritional information. Furthermore, if the user is in a hurry, the reflection unit can prioritize reflecting simplified data. This allows for more appropriate reflections by determining the priority of reflections according to the user's emotions. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or not using AI. For example, the reflection unit can input user emotion data into AI and have the AI ​​determine the priority of reflections.

[0112] The reflection unit can optimize the reflection method by considering the user's lifestyle data during the reflection process. For example, the reflection unit can optimize the reflection method by considering the user's lifestyle data during the reflection process. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The reflection unit can optimize the reflection method by referring to the user's sleep data. The reflection unit can also optimize the reflection method by referring to the user's exercise data. Furthermore, the reflection unit can optimize the reflection method by referring to the user's stress level. In this way, the reflection method can be optimized by considering lifestyle data. Some or all of the above processing in the reflection unit may be performed using, for example, AI, or without using AI. For example, the reflection unit can input the user's lifestyle data into AI and have AI perform the optimization of the reflection method.

[0113] The sensor unit can estimate the user's emotions and adjust the sensor's sensitivity based on the estimated emotions. For example, the sensor unit estimates the user's emotions and adjusts the sensor's sensitivity based on the estimated emotions. Specific methods for adjusting the sensor's sensitivity include, but are not limited to, the sensitivity range and the timing of the adjustment. If the user is tense, the sensor unit can increase the sensor's sensitivity to acquire detailed data. If the user is relaxed, the sensor unit can decrease the sensor's sensitivity to acquire natural data. Furthermore, if the user is in a hurry, the sensor unit can adjust the sensor's sensitivity to acquire data quickly. This allows for more appropriate data acquisition by adjusting the sensor's sensitivity according to the user's emotions. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input user emotion data into AI and have the AI ​​adjust the sensor's sensitivity.

[0114] The sensor unit can optimize the sensor settings by referring to the user's past data when the sensor is in use. For example, the sensor unit can optimize the sensor settings by referring to the user's past data when the sensor is in use. The specific contents of the past data include, but are not limited to, past measurement results and past setting values. The sensor unit can optimize the sensor settings based on the user's past health data. The sensor unit can also optimize the sensor settings by referring to the user's past dietary history. Furthermore, the sensor unit can optimize the sensor settings by referring to the user's past exercise data. In this way, the sensor settings can be optimized by referring to past data. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input the user's past data into AI and have AI perform the optimization of the sensor settings.

[0115] The sensor unit can estimate the user's emotions and adjust the frequency of sensor usage based on the estimated emotions. For example, the sensor unit can estimate the user's emotions and adjust the frequency of sensor usage based on the estimated emotions. Specific methods for adjusting the frequency of sensor usage include, but are not limited to, the number of uses and the interval between uses. If the user is tense, the sensor unit can increase the frequency of sensor usage to acquire more detailed data. If the user is relaxed, the sensor unit can decrease the frequency of sensor usage to acquire more natural data. Furthermore, if the user is in a hurry, the sensor unit can adjust the frequency of sensor usage to acquire data quickly. By adjusting the frequency of sensor usage according to the user's emotions, more appropriate data acquisition becomes possible. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input user emotion data into AI and have the AI ​​adjust the frequency of sensor usage.

[0116] The sensor unit can optimize its settings when the sensor is in use, taking into account the user's lifestyle data. For example, the sensor unit can optimize its settings when the sensor is in use, taking into account the user's lifestyle data. Specific examples of lifestyle data include, but are not limited to, exercise habits, sleep patterns, and eating habits. The sensor unit can refer to the user's sleep data and optimize the sensor settings. The sensor unit can also refer to the user's exercise data and optimize the sensor settings. Furthermore, the sensor unit can refer to the user's stress level and optimize the sensor settings. In this way, the sensor settings can be optimized by taking lifestyle data into account. Some or all of the above processing in the sensor unit may be performed using, for example, AI, or not using AI. For example, the sensor unit can input the user's lifestyle data into AI and have AI perform the optimization of the sensor settings.

[0117] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, the display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. The specific details of the display method include, but are not limited to, the display format, display content, and display timing. When the user is tense, the display unit can provide a simple and highly visible display method. When the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display becomes possible. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0118] The display unit can select the optimal display method by referring to the user's past operation history when the display is in use. For example, the display unit selects the optimal display method by referring to the user's past operation history when the display is in use. The specific contents of the past operation history include, but are not limited to, the type of operation, the frequency of the operation, and the result of the operation. The display unit can select the optimal display method based on the user's past operation history. The display unit can also select the optimal display method by comparing the user's past operation history with health data. Furthermore, the display unit can select the optimal display method by analyzing the user's past operation history. In this way, the optimal display method can be selected by referring to the past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can input the user's past operation history data into AI and have the AI ​​perform the selection of the display method.

[0119] The display unit can estimate the user's emotions and adjust the display's operating procedures based on the estimated emotions. For example, the display unit can estimate the user's emotions and adjust the display's operating procedures based on the estimated emotions. The specific content of the display's operating procedures includes, but is not limited to, the steps of operation, the order of operation, and the method of operation. If the user is tense, the display unit can provide simple and highly visible operating procedures. If the user is relaxed, the display unit can provide operating procedures that include detailed information. Furthermore, if the user is in a hurry, the display unit can provide concise operating procedures. This allows for more appropriate operation by adjusting the operating procedures according to the user's emotions. Some or all of the above processing in the display unit may be performed using, for example, AI, or not. For example, the display unit can input user emotion data into AI and have the AI ​​adjust the operating procedures.

[0120] The display unit can select the optimal display method when the display is in use, taking into account the user's device information. For example, the display unit can select the optimal display method when the display is in use, taking into account the user's device information. Specific examples of device information include, but are not limited to, the type of device, device settings, and device performance. If the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking device information into account. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the user's device information into AI and have AI select the display method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and interpolation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's dietary history and health status using sensors and applications of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's dietary patterns and health status. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal alternative meal based on the analysis results. The interpolation unit is implemented in the control unit 46A of the smart device 14, for example, and interpolates visual information and aroma information using an AR terminal. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and interpolation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's eating history and health status using the sensors and applications of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and evaluates the user's eating patterns and health status. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests the optimal alternative food based on the analysis results. The interpolation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which interpolates visual information and aroma information using an AR terminal. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and interpolation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's eating history and health status using the sensors and applications of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and evaluates the user's eating patterns and health status. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests the optimal alternative meal based on the analysis results. The interpolation unit is implemented, for example, by the control unit 46A of the headset terminal 314, which interpolates visual information and aroma information using an AR terminal. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and interpolation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's eating history and health status using the sensors and applications of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and evaluates the user's eating patterns and health status. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal alternative meal based on the analysis results. The interpolation unit is implemented, for example, by the control unit 46A of the robot 414, which interpolates visual information and aroma information using an AR terminal.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The analytics unit can predict a user's eating patterns based on their meal history and health data, and provide suggestions for their next meal. For example, if a user tends to prefer certain foods on specific days of the week, the system can provide suggestions tailored to those days. Furthermore, the analytics unit can analyze users' eating patterns over the long term to understand seasonal eating trends. It can also compare a user's eating patterns with those of other users to identify common trends. This allows for more personalized suggestions by predicting the user's eating patterns.

[0123] The adjustment unit can change the temperature and texture of the food to adjust the user's feeling of fullness. For example, serving warm soup can increase satiety. The adjustment unit can also increase chewing by changing the texture of the food, thereby promoting satiety. Furthermore, the adjustment unit can customize the temperature and texture of the food to the user's preferences. This allows for effective control of satiety by adjusting the temperature and texture of the food.

[0124] The recording unit can record the user's dining experience, including the content of conversations and the type of music played during the meal. For example, it can record the music the user was listening to and the content of their conversations, and use this information to improve future dining recommendations. The recording unit can also record ambient sounds during the meal, recreating a relaxing environment for the user. Furthermore, it can record the user's behavior during the meal (e.g., smartphone usage) and provide suggestions to improve the quality of the meal. By recording the environment and behavior during the meal, a more fulfilling dining experience can be provided.

[0125] The feedback unit can consider the user's emotions during a meal when incorporating their dining experience into future recommendations. For example, it can record the user's satisfaction or dissatisfaction during the meal and reflect this in future recommendations. Furthermore, the feedback unit can analyze the user's emotions during the meal and provide meal suggestions to reduce stress. It can also compare the user's emotions during a meal with those of other users to identify common trends. This allows for more appropriate meal suggestions by considering the user's emotions.

[0126] The sensor unit can detect the user's posture and movements while eating and provide feedback to improve the dining experience. For example, it can detect whether the user is eating with the correct posture and issue an alert if their posture is poor. The sensor unit can also detect the user's movements while eating (e.g., chewing frequency and eating speed) and provide feedback to promote healthy eating habits. Furthermore, the sensor unit can compare the user's eating movements with those of other users to identify common trends. This allows for a healthier dining experience by detecting posture and movements while eating.

[0127] The display unit can estimate the user's emotions and customize the visual information about the meal based on those emotions. For example, if the user is relaxed, it can provide visual information in calming colors. If the user is excited, the display unit can provide information with visually stimulating effects. Furthermore, if the user is stressed, the display unit can provide visual information with a relaxing effect. In this way, by customizing the visual information according to the user's emotions, a more appropriate dining experience can be provided.

[0128] The data collection unit can estimate the user's emotions and adjust the timing of meal history collection based on those emotions. For example, if the user is stressed, the data collection can be postponed and collected when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can actively collect meal history and obtain detailed data. Furthermore, if the user is in a hurry, the data collection unit can collect meal history using a simplified question format. By adjusting the collection timing according to the user's emotions, more appropriate data collection becomes possible.

[0129] The data collection unit can analyze a user's past meal history and select the appropriate collection method. For example, it can customize the collection method based on the meals a user has frequently eaten in the past. Furthermore, the data collection unit can concentrate data collection during specific time periods based on the user's past meal history. In addition, the data collection unit can analyze the user's past meal history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past meal history.

[0130] The data collection unit can filter the collected meal history based on the user's current health status and lifestyle. For example, it can prioritize the collection of meal history containing specific ingredients based on the user's health checkup results. The data collection unit can also filter the data collected considering the user's lifestyle (e.g., work workload). Furthermore, the data collection unit can adjust the collected meal history based on the user's current health status (e.g., poor health). This allows for more appropriate data collection by filtering data based on the user's current health status and lifestyle.

[0131] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting dietary data that helps reduce stress. If the user is relaxed, it can prioritize collecting data that includes detailed nutritional information. Furthermore, if the user is in a hurry, it can prioritize collecting simplified data. By prioritizing data according to the user's emotions, more appropriate data collection becomes possible.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The data collection unit collects the user's meal history or health status. Meal history includes, but is not limited to, the type of meal, amount consumed, and time of consumption. Health status includes, but is not limited to, the type of meal, blood sugar level, and weight. The data collection unit collects, for example, meal details and health data entered by the user into the app. The data collection unit can also automatically collect data using sensors in wearable devices or smartphones. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI to evaluate the user's eating patterns and health status. The analysis unit uses machine learning algorithms to build a model for making future meal suggestions based on the user's eating history and health data. Step 3: The suggestion unit proposes alternative meals based on the analysis results obtained by the analysis unit. For example, if the user wants to eat rich ramen, the suggestion unit will propose low-calorie somen noodles. The suggestion unit selects the most suitable alternative meal considering the user's dietary history and health condition. Step 4: The interpolation unit interpolates visual or aromatic information. For example, using a dedicated AR terminal, the interpolation unit provides visual and aromatic information that makes the user feel as if they are eating rich ramen. The interpolation unit allows the user to experience the satisfaction of eating rich ramen, even though they are actually eating low-calorie somen noodles.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0187] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0197] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's dietary history or health status, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes alternative foods, It comprises an interpolation unit that interpolates visual information or scent information. A system characterized by the following features.

2. It includes an adjustment unit that executes an algorithm to adjust the user's feeling of fullness. The system according to feature 1.

3. It includes a recording unit that records the user's dining experience. The system according to feature 1.

4. It includes a section for incorporating the findings into future proposals. The system according to feature 1.

5. It includes a sensor unit that uses sensor technology or display technology. The system according to feature 1.

6. It features a display unit that utilizes display technology. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of meal history collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past meal history and select the appropriate data collection method. The system according to feature 1.

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