system

Abstract

The system according to this embodiment aims to allow users to easily set diet goals and continuously manage their meals. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a suggestion unit, a recording unit, and another suggestion unit. The reception unit receives information for setting the user's diet goal. The generation unit creates a daily calorie intake goal based on the information received by the reception unit. The suggestion unit analyzes the contents of the refrigerator and suggests a menu that matches the target calorie intake. The recording unit records the selected recipe and calorie intake. The other suggestion unit suggests ingredients that the user should try next.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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 prior art, there was a problem that diet management for a user to achieve a diet goal was complicated and difficult to continue.

[0005] The system according to the embodiment aims to enable a user to easily set a diet goal and continuously perform diet management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, a recording unit, and another suggestion unit. The reception unit receives information for setting the user's diet goal. The generation unit creates a daily calorie intake goal based on the information received by the reception unit. The suggestion unit analyzes the contents of the refrigerator and suggests a menu that matches the target calorie intake. The recording unit records the selected recipe and calorie intake. The other suggestion unit suggests ingredients that the user should try next. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily set diet goals and continuously manage their meals. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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 diet support system according to an embodiment of the present invention is an app for people aiming for a healthy weight. This diet support system can make eating and lifestyle healthier. First, the system sets the diet goals desired by the user through conversation with AI (generative AI). Specifically, it creates a daily calorie intake target based on information such as the period, target weight, available time, level of motivation for dieting, and past dieting experience. Next, when the user prepares meals each day, the system suggests menus and recipes that match the target calorie intake simply by uploading a photo of the contents of the refrigerator to the app. This provides healthy meals that suit the user's preferences, making dieting enjoyable and stress-free. Furthermore, the system records the selected recipes and calorie intake, which are used as a reference for future meal preparation. It also suggests recommended ingredients to try next. By using this app, dieting becomes a part of life, and the user's daily life becomes a little healthier. For example, when the user sets a diet goal, the system receives information such as the period, target weight, available time, level of motivation for dieting, and past dieting experience through conversation with AI. Next, the system generates a daily calorie intake target based on the received information. Furthermore, the system suggests menus and recipes that match the user's target calorie intake simply by uploading a photo of the contents of their refrigerator. For example, the system analyzes the contents of the refrigerator and suggests healthy menus that suit the user's preferences. The system also records the selected recipes and calorie intake to use as a reference for future meal preparation. In addition, the system suggests ingredients that the user should try next. This allows users to continue dieting in an enjoyable way and lead a healthy lifestyle. In this way, the diet support system can efficiently set the user's diet goals, suggest appropriate menus and recipes, and record them.

[0029] The diet support system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, a recording unit, and another suggestion unit. The reception unit receives information for setting the user's diet goals. The user's diet goals include, but are not limited to, a period, target weight, available time, level of motivation for dieting, and previous dieting experience. For example, the reception unit prompts the user to input the desired diet period in order to set the period. The reception unit can also prompt the user to input the desired weight in order to set the target weight. Furthermore, the reception unit can also prompt the user to input how much time they can dedicate to dieting each day in order to set the available time. The generation unit creates a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight. The generation unit can analyze the user's information using generation AI and generate an optimal calorie intake target. The suggestion unit analyzes the contents of the refrigerator and proposes menus and recipes that match the target calorie intake. The suggestion unit, for example, analyzes a photo of the contents of the refrigerator and suggests healthy menus that suit the user's preferences. The suggestion unit can analyze the contents of the refrigerator using image recognition technology and suggest appropriate menus. The recording unit records the selected recipes and calorie intake. The recording unit, for example, automatically records the recipes and calorie intake selected by the user and uses this as a reference for future meal preparation. The recording unit can also use AI to analyze the user's meal history and suggest ingredients that the user should try next. Another suggestion unit suggests ingredients that the user should try next based on the recorded data. Another suggestion unit, for example, analyzes the user's meal history and suggests ingredients that the user should try next. As a result, the diet support system according to this embodiment can efficiently set the user's diet goals, suggest appropriate menus and recipes, and record them.

[0030] The reception desk can receive information such as the duration, target weight, available time, level of commitment to dieting, past dieting experience, and other relevant information. For example, the reception desk can ask the user to input their desired diet duration. For example, it can provide options such as short-term, medium-term, and long-term, allowing the user to select their preferred duration. The reception desk can also ask the user to input their desired target weight. For example, they can input their current weight and ideal weight to set a target weight. Furthermore, the reception desk can ask the user to input how much time they can dedicate to dieting each day. For example, they can input their daily exercise time and meal preparation time. The reception desk can also provide questionnaires or self-assessment scales to evaluate the user's level of commitment to dieting. For example, it can provide questions to assess how seriously the user is committed to dieting and evaluate their level of commitment based on their answers. In addition, the reception desk can collect information about the user's past dieting experience, such as past successes and failures and the dieting methods used. For example, it can ask the user to input the dieting methods they have tried in the past and their results. By receiving detailed information from the user, the reception desk can set more appropriate dieting goals.

[0031] The generation unit can create a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight. For example, basal metabolic rate is calculated based on information such as the user's age, gender, weight, and height. Activity level is assessed based on the user's daily life and exercise habits. Based on this information, the generation unit can calculate the amount of calories the user should consume in a day. The generation unit can use a generation AI to analyze the user's information and generate an optimal calorie intake target. For example, the generation AI takes user information as input and outputs a calorie intake target. Because the generation AI has learned from a large amount of data and has advanced analytical capabilities, it can provide the user with an optimal calorie intake target. As a result, the generation unit can create an appropriate calorie intake target based on the user's information.

[0032] The suggestion unit can analyze photos of the contents of a refrigerator and suggest menus and recipes that match the target calorie intake. For example, the suggestion unit can analyze photos of the refrigerator contents and suggest healthy menus that suit the user's preferences. The suggestion unit can analyze the contents of a refrigerator using image recognition technology and suggest appropriate menus. For example, the suggestion unit can recognize the ingredients in the refrigerator and suggest menus using those ingredients. The suggestion unit can consider calorie calculations and nutritional balance in order to suggest menus that match the user's target calorie intake. For example, the suggestion unit can suggest appropriate combinations of ingredients based on the amount of calories the user should consume. The suggestion unit can also customize menus considering the user's preferences and allergy information. For example, if the user wants to avoid a particular ingredient, it can suggest menus that do not include that ingredient. In this way, the suggestion unit can suggest appropriate menus and recipes based on the contents of the refrigerator.

[0033] The recording unit can record selected recipes and calorie intake. For example, the recording unit can automatically record the recipes and calorie intake selected by the user, to serve as a reference for future meal preparation. The recording unit can also use AI to analyze the user's meal history and suggest ingredients to try next. For example, the recording unit can save the recipes and calorie intake selected by the user to a database, to refer to when preparing future meals. The recording unit can also analyze the user's meal history and suggest ingredients to try next. For example, the recording unit can suggest ingredients to try next based on the recipes and calorie intake selected by the user in the past. In this way, the recording unit can record selected recipes and calorie intake, which can serve as a reference for future meal preparation.

[0034] The suggestion unit can suggest ingredients to try next based on recorded data. For example, the suggestion unit can analyze the user's eating history and suggest ingredients to try next. The suggestion unit can use AI to analyze the user's eating history and suggest ingredients to try next. For example, the suggestion unit can suggest ingredients to try next based on the recipes and calorie intake the user has previously chosen. The suggestion unit can also suggest ingredients to try next while considering the user's preferences and allergy information. For example, if the user wants to avoid a particular ingredient, the suggestion unit can make suggestions that do not include that ingredient. In this way, the suggestion unit can suggest ingredients to try next based on recorded data.

[0035] The reception desk can analyze a user's past dieting history and select an appropriate goal-setting method. For example, the reception desk can suggest similar goal settings based on dieting methods the user has successfully used in the past. For example, the reception desk can suggest a different approach to help the user avoid dieting methods that have failed in the past. The reception desk can also select the most effective goal-setting method from the user's past dieting history. For example, the reception desk can analyze dieting methods the user has tried in the past and their results to suggest the optimal goal-setting method. In this way, the reception desk can select the optimal goal-setting method based on the user's past dieting history.

[0036] The reception desk can filter users based on their current health status and lifestyle when setting diet goals. For example, the reception desk can set realistic goals considering the user's current health status. For example, the reception desk can suggest achievable goals based on the user's lifestyle. The reception desk can also set appropriate goals by referring to the user's health check results. For example, the reception desk can assess health risks based on the user's health check results and set realistic goals. In this way, the reception desk can set realistic goals based on the user's health status and lifestyle.

[0037] The reception desk can prioritize setting highly relevant goals based on the user's geographical location when setting diet goals. For example, the reception desk can set realistic goals by considering the food culture of the area where the user lives. For example, the reception desk can also set appropriate exercise goals by considering the climate of the area where the user lives. Furthermore, the reception desk can set exercise goals by considering accessible gyms and parks in the area where the user lives. In this way, the reception desk can set realistic goals based on the user's geographical location.

[0038] The reception desk can analyze a user's social media activity when setting diet goals and set relevant goals. For example, the reception desk can analyze posts about meals and exercise that the user has shared on social media and set relevant goals. For example, the reception desk can also set goals by referring to health-related accounts that the user follows. Furthermore, the reception desk can identify diet methods that the user is interested in from their social media activity and set goals based on that. In this way, the reception desk can set relevant goals based on the user's social media activity.

[0039] The generation unit can adjust the level of detail of the calorie intake target based on the user's level of importance when generating the target. For example, if the user has a high level of interest in calorie management, the generation unit will provide a detailed calorie target. For example, if the user has a low level of interest in calorie management, the generation unit can provide a simple calorie target. Alternatively, if the user has a moderate level of interest, the generation unit can provide a balanced calorie target. In this way, the generation unit can adjust the level of detail of the calorie intake target according to the user's level of interest.

[0040] The generation unit can apply different generation algorithms depending on the user's category when generating calorie intake targets. For example, if the user is a beginner, the generation unit can apply a simple algorithm to generate the calorie target. For example, if the user is an intermediate user, the generation unit can apply a more detailed algorithm to generate the calorie target. Furthermore, if the user is an advanced user, the generation unit can apply a more complex algorithm to generate the calorie target. In this way, the generation unit can apply an appropriate generation algorithm according to the user's category.

[0041] The generation unit can prioritize calorie intake targets based on when the user submits them. For example, if a user sets a target early in the morning, the generation unit will prioritize providing the calorie intake target for that day. For example, if a user sets a target at night, the generation unit can prioritize providing the calorie intake target for the following day. Furthermore, if a user sets a target on the weekend, the generation unit can provide the calorie intake target for the entire week. This allows the generation unit to set appropriate priorities based on when the user submits their targets.

[0042] The generation unit can adjust the order of calorie intake targets based on user relevance when generating them. For example, if a user is interested in a particular food, the generation unit will prioritize providing calorie targets that include that food. For example, if a user places emphasis on a specific mealtime, the generation unit can also prioritize providing calorie targets for that time slot. Furthermore, if a user is interested in a particular diet, the generation unit can provide calorie targets based on that diet. This allows the generation unit to provide targets in an appropriate order based on user relevance.

[0043] The suggestion department can adjust the level of detail in menu and recipe suggestions based on the importance of the contents of the refrigerator. For example, if the refrigerator is full of ingredients, the suggestion department will provide a detailed recipe. For example, if the refrigerator is sparse, the suggestion department may provide a simple recipe. Also, if the refrigerator is heavily reliant on certain ingredients, the suggestion department may provide recipes that utilize those ingredients. This allows the suggestion department to provide appropriate suggestions according to the importance of the contents of the refrigerator.

[0044] The suggestion function can apply different suggestion algorithms depending on the categories of items in the refrigerator when suggesting menus and recipes. For example, if the refrigerator is mainly vegetables, the suggestion function will provide recipes that utilize vegetables. For example, if the refrigerator is mainly meat, the suggestion function can also provide recipes that utilize meat. Furthermore, if the refrigerator is well-balanced, the suggestion function can provide balanced recipes. In this way, the suggestion function can make appropriate suggestions according to the categories of items in the refrigerator.

[0045] The suggestion department can prioritize menu and recipe suggestions based on when the contents of the refrigerator are submitted. For example, if the contents of the refrigerator are fresh, the suggestion department can provide recipes that prioritize the use of those ingredients. For example, if the contents of the refrigerator are getting old, the suggestion department can also provide recipes that encourage their use sooner. Furthermore, if the contents of the refrigerator are heavily skewed towards a particular ingredient, the suggestion department can provide recipes that prioritize the use of that ingredient. This allows the suggestion department to make suggestions with appropriate priorities based on when the contents of the refrigerator are submitted.

[0046] The suggestion function can adjust the order of menu and recipe suggestions based on the relevance of the contents of the refrigerator. For example, if the refrigerator is heavily skewed towards a particular ingredient, the suggestion function will provide recipes that prioritize the use of that ingredient. For example, if the refrigerator is well-balanced, the suggestion function can provide balanced recipes. Furthermore, if the refrigerator is sparsely stocked, the suggestion function can provide simple recipes. In this way, the suggestion function can make suggestions in the appropriate order based on the relevance of the contents of the refrigerator.

[0047] The recording unit can analyze the user's past records to select the optimal recording method when recording recipes or calorie intake. For example, if the user previously preferred detailed records, the recording unit can provide a detailed recording method. For example, if the user previously preferred simple records, the recording unit can provide a simple recording method. The recording unit can also analyze the user's past recording patterns to select the optimal recording method. In this way, the recording unit can select the optimal recording method based on the user's past records.

[0048] The recording unit can customize the recording method based on the user's current lifestyle when recording recipes or calorie intake. For example, if the user is busy, the recording unit can provide a quick recording method. For example, if the user is relaxed, the recording unit can also provide a detailed recording method. Furthermore, the recording unit can customize the optimal recording method according to the user's lifestyle. In this way, the recording unit can provide the optimal recording method according to the user's lifestyle.

[0049] The recording unit can select the optimal recording method when recording recipes or calorie intake, taking into account the user's geographical location. For example, if the user is at home, the recording unit can provide a detailed recording method. For example, if the user is out, the recording unit can also provide a method that allows for quick recording. Furthermore, the recording unit can select the optimal recording method based on the user's geographical location. This allows the recording unit to select the optimal recording method based on the user's geographical location.

[0050] The recording unit can analyze the user's social media activity and suggest recording methods when recording recipes or calorie intake. For example, the recording unit can analyze meal posts shared by the user on social media and suggest recording methods. For example, the recording unit can also suggest recording methods by referring to health-related accounts that the user follows. Furthermore, the recording unit can suggest the most suitable recording method based on the user's social media activity. In this way, the recording unit can suggest the most suitable recording method based on the user's social media activity.

[0051] The suggestion function can provide optimal suggestions for ingredients to try next by referring to the user's past food history. For example, the suggestion function can suggest ingredients to try next based on ingredients the user has previously enjoyed using. For example, the suggestion function can also suggest different ingredients based on ingredients the user has previously avoided. Furthermore, the suggestion function can analyze the user's past food history and suggest the most effective ingredients. In this way, the suggestion function can suggest the most suitable ingredients based on the user's past food history.

[0052] The suggestion function can customize its recommendations for ingredients to try next based on the user's current lifestyle. For example, if the user is busy, the suggestion function can suggest ingredients that can be cooked quickly. For example, if the user is relaxed, the suggestion function can suggest ingredients with detailed cooking instructions. The suggestion function can also suggest the most suitable ingredients according to the user's lifestyle. In this way, the suggestion function can suggest the most suitable ingredients according to the user's lifestyle.

[0053] The suggestion function can provide optimal suggestions for ingredients to try next, taking into account the user's geographical location. For example, the suggestion function can suggest practical ingredients considering the food culture of the area where the user lives. For example, the suggestion function can also suggest appropriate ingredients considering the climate of the area where the user lives. Furthermore, the suggestion function can make suggestions considering the ingredients available in the user's area. In this way, the suggestion function can suggest the most suitable ingredients based on the user's geographical location.

[0054] The suggestion department can analyze a user's social media activity to suggest ingredients they should try next. For example, it can analyze food posts a user has shared on social media and suggest ingredients they should try next. For example, it can also refer to health-related accounts a user follows to suggest ingredients they should try next. Furthermore, the suggestion department can suggest the most suitable ingredients based on the user's social media activity. In this way, the suggestion department can suggest the most suitable ingredients based on the user's social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past dieting history and select an appropriate goal-setting method. For example, it can suggest similar goal settings based on the user's past successful dieting methods. It can also suggest different approaches to help the user avoid past unsuccessful dieting methods. Furthermore, it can select the most effective goal-setting method based on the user's past dieting history. In this way, the reception desk can select the optimal goal-setting method based on the user's past dieting history.

[0057] The generation unit can apply different generation algorithms depending on the user's category when generating calorie intake targets. For example, if the user is a beginner, a simple algorithm is applied to generate the calorie target. If the user is an intermediate user, a more detailed algorithm can be applied to generate the calorie target. Furthermore, if the user is an advanced user, a complex algorithm can be applied to generate the calorie target. In this way, the generation unit can apply the appropriate generation algorithm according to the user's category.

[0058] The suggestion department can adjust the level of detail in menu and recipe suggestions based on the importance of the contents of the refrigerator. For example, if the refrigerator is full of ingredients, a detailed recipe can be provided. If the refrigerator is sparse, a simple recipe can be offered. Also, if the refrigerator is heavily reliant on certain ingredients, recipes utilizing those ingredients can be provided. This allows the suggestion department to provide appropriate suggestions according to the importance of the contents of the refrigerator.

[0059] The recording unit can analyze the user's past records to select the optimal recording method when recording recipes or calorie intake. For example, if the user previously preferred detailed records, it can provide a detailed recording method. If the user previously preferred simple records, it can also provide a simple recording method. Furthermore, it can analyze the user's past recording patterns to select the optimal recording method. In this way, the recording unit can select the optimal recording method based on the user's past records.

[0060] The suggestion function can provide optimal suggestions for ingredients to try next by referring to the user's past food history. For example, it can suggest ingredients that the user has liked to try in the past. It can also suggest different ingredients based on ingredients the user has avoided in the past. Furthermore, it can analyze the user's past food history and suggest the most effective ingredients. In this way, the suggestion function can suggest the most suitable ingredients based on the user's past food history.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives information to set the user's diet goals. The user's diet goals include the duration, target weight, available time, level of motivation for dieting, and previous dieting experience. For example, the user is asked to input their desired diet duration, target weight, and how much time they can dedicate to dieting each day. Step 2: The generation unit creates a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight, and generates the optimal calorie intake target using the generation AI. Step 3: The suggestion unit analyzes the contents of the refrigerator and proposes menus and recipes that match the target calorie intake. The suggestion unit uses image recognition technology to analyze photos of the refrigerator contents and propose healthy menus that suit the user's preferences. Step 4: The recording unit records the selected recipe and calorie intake. The recording unit automatically records the recipe and calorie intake selected by the user, which can be used as a reference for future meal preparation. It can also use AI to analyze the user's meal history and suggest ingredients that they should try next. Step 5: Another suggestion unit suggests ingredients to try next based on the recorded data. Another suggestion unit analyzes the user's eating history and suggests ingredients to try next.

[0063] (Example of form 2) The diet support system according to an embodiment of the present invention is an app for people aiming for a healthy weight. This diet support system can make eating and lifestyle healthier. First, the system sets the diet goals desired by the user through conversation with AI (generative AI). Specifically, it creates a daily calorie intake target based on information such as the period, target weight, available time, level of motivation for dieting, and past dieting experience. Next, when the user prepares meals each day, the system suggests menus and recipes that match the target calorie intake simply by uploading a photo of the contents of the refrigerator to the app. This provides healthy meals that suit the user's preferences, making dieting enjoyable and stress-free. Furthermore, the system records the selected recipes and calorie intake, which are used as a reference for future meal preparation. It also suggests recommended ingredients to try next. By using this app, dieting becomes a part of life, and the user's daily life becomes a little healthier. For example, when the user sets a diet goal, the system receives information such as the period, target weight, available time, level of motivation for dieting, and past dieting experience through conversation with AI. Next, the system generates a daily calorie intake target based on the received information. Furthermore, the system suggests menus and recipes that match the user's target calorie intake simply by uploading a photo of the contents of their refrigerator. For example, the system analyzes the contents of the refrigerator and suggests healthy menus that suit the user's preferences. The system also records the selected recipes and calorie intake to use as a reference for future meal preparation. In addition, the system suggests ingredients that the user should try next. This allows users to continue dieting in an enjoyable way and lead a healthy lifestyle. In this way, the diet support system can efficiently set the user's diet goals, suggest appropriate menus and recipes, and record them.

[0064] The diet support system according to this embodiment comprises a reception unit, a generation unit, a suggestion unit, a recording unit, and another suggestion unit. The reception unit receives information for setting the user's diet goals. The user's diet goals include, but are not limited to, a period, target weight, available time, level of motivation for dieting, and previous dieting experience. For example, the reception unit prompts the user to input the desired diet period in order to set the period. The reception unit can also prompt the user to input the desired weight in order to set the target weight. Furthermore, the reception unit can also prompt the user to input how much time they can dedicate to dieting each day in order to set the available time. The generation unit creates a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight. The generation unit can analyze the user's information using generation AI and generate an optimal calorie intake target. The suggestion unit analyzes the contents of the refrigerator and proposes menus and recipes that match the target calorie intake. The suggestion unit, for example, analyzes a photo of the contents of the refrigerator and suggests healthy menus that suit the user's preferences. The suggestion unit can analyze the contents of the refrigerator using image recognition technology and suggest appropriate menus. The recording unit records the selected recipes and calorie intake. The recording unit, for example, automatically records the recipes and calorie intake selected by the user and uses this as a reference for future meal preparation. The recording unit can also use AI to analyze the user's meal history and suggest ingredients that the user should try next. Another suggestion unit suggests ingredients that the user should try next based on the recorded data. Another suggestion unit, for example, analyzes the user's meal history and suggests ingredients that the user should try next. As a result, the diet support system according to this embodiment can efficiently set the user's diet goals, suggest appropriate menus and recipes, and record them.

[0065] The reception desk can receive information such as the duration, target weight, available time, level of commitment to dieting, past dieting experience, and other relevant information. For example, the reception desk can ask the user to input their desired diet duration. For example, it can provide options such as short-term, medium-term, and long-term, allowing the user to select their preferred duration. The reception desk can also ask the user to input their desired target weight. For example, they can input their current weight and ideal weight to set a target weight. Furthermore, the reception desk can ask the user to input how much time they can dedicate to dieting each day. For example, they can input their daily exercise time and meal preparation time. The reception desk can also provide questionnaires or self-assessment scales to evaluate the user's level of commitment to dieting. For example, it can provide questions to assess how seriously the user is committed to dieting and evaluate their level of commitment based on their answers. In addition, the reception desk can collect information about the user's past dieting experience, such as past successes and failures and the dieting methods used. For example, it can ask the user to input the dieting methods they have tried in the past and their results. By receiving detailed information from the user, the reception desk can set more appropriate dieting goals.

[0066] The generation unit can create a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight. For example, basal metabolic rate is calculated based on information such as the user's age, gender, weight, and height. Activity level is assessed based on the user's daily life and exercise habits. Based on this information, the generation unit can calculate the amount of calories the user should consume in a day. The generation unit can use a generation AI to analyze the user's information and generate an optimal calorie intake target. For example, the generation AI takes user information as input and outputs a calorie intake target. Because the generation AI has learned from a large amount of data and has advanced analytical capabilities, it can provide the user with an optimal calorie intake target. As a result, the generation unit can create an appropriate calorie intake target based on the user's information.

[0067] The suggestion unit can analyze photos of the contents of a refrigerator and suggest menus and recipes that match the target calorie intake. For example, the suggestion unit can analyze photos of the refrigerator contents and suggest healthy menus that suit the user's preferences. The suggestion unit can analyze the contents of a refrigerator using image recognition technology and suggest appropriate menus. For example, the suggestion unit can recognize the ingredients in the refrigerator and suggest menus using those ingredients. The suggestion unit can consider calorie calculations and nutritional balance in order to suggest menus that match the user's target calorie intake. For example, the suggestion unit can suggest appropriate combinations of ingredients based on the amount of calories the user should consume. The suggestion unit can also customize menus considering the user's preferences and allergy information. For example, if the user wants to avoid a particular ingredient, it can suggest menus that do not include that ingredient. In this way, the suggestion unit can suggest appropriate menus and recipes based on the contents of the refrigerator.

[0068] The recording unit can record selected recipes and calorie intake. For example, the recording unit can automatically record the recipes and calorie intake selected by the user, to serve as a reference for future meal preparation. The recording unit can also use AI to analyze the user's meal history and suggest ingredients to try next. For example, the recording unit can save the recipes and calorie intake selected by the user to a database, to refer to when preparing future meals. The recording unit can also analyze the user's meal history and suggest ingredients to try next. For example, the recording unit can suggest ingredients to try next based on the recipes and calorie intake selected by the user in the past. In this way, the recording unit can record selected recipes and calorie intake, which can serve as a reference for future meal preparation.

[0069] The suggestion unit can suggest ingredients to try next based on recorded data. For example, the suggestion unit can analyze the user's eating history and suggest ingredients to try next. The suggestion unit can use AI to analyze the user's eating history and suggest ingredients to try next. For example, the suggestion unit can suggest ingredients to try next based on the recipes and calorie intake the user has previously chosen. The suggestion unit can also suggest ingredients to try next while considering the user's preferences and allergy information. For example, if the user wants to avoid a particular ingredient, the suggestion unit can make suggestions that do not include that ingredient. In this way, the suggestion unit can suggest ingredients to try next based on recorded data.

[0070] The reception desk can estimate the user's emotions and adjust how diet goals are set based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick diet goal setting. This allows the reception desk to set more appropriate goals by adjusting how diet goals are set according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The reception desk can analyze a user's past dieting history and select an appropriate goal-setting method. For example, the reception desk can suggest similar goal settings based on dieting methods the user has successfully used in the past. For example, the reception desk can suggest a different approach to help the user avoid dieting methods that have failed in the past. The reception desk can also select the most effective goal-setting method from the user's past dieting history. For example, the reception desk can analyze dieting methods the user has tried in the past and their results to suggest the optimal goal-setting method. In this way, the reception desk can select the optimal goal-setting method based on the user's past dieting history.

[0072] The reception desk can filter users based on their current health status and lifestyle when setting diet goals. For example, the reception desk can set realistic goals considering the user's current health status. For example, the reception desk can suggest achievable goals based on the user's lifestyle. The reception desk can also set appropriate goals by referring to the user's health check results. For example, the reception desk can assess health risks based on the user's health check results and set realistic goals. In this way, the reception desk can set realistic goals based on the user's health status and lifestyle.

[0073] The reception desk can estimate the user's emotions and determine the priority of goal setting based on the estimated emotions. For example, if the user is highly motivated, the reception desk may prioritize challenging goals. For example, if the user is feeling anxious, the reception desk may prioritize easily achievable goals. Also, if the user has neutral emotions, the reception desk may set balanced goals. In this way, the reception desk can set more appropriate goals by determining the priority of goal setting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The reception desk can prioritize setting highly relevant goals based on the user's geographical location when setting diet goals. For example, the reception desk can set realistic goals by considering the food culture of the area where the user lives. For example, the reception desk can also set appropriate exercise goals by considering the climate of the area where the user lives. Furthermore, the reception desk can set exercise goals by considering accessible gyms and parks in the area where the user lives. In this way, the reception desk can set realistic goals based on the user's geographical location.

[0075] The reception desk can analyze a user's social media activity when setting diet goals and set relevant goals. For example, the reception desk can analyze posts about meals and exercise that the user has shared on social media and set relevant goals. For example, the reception desk can also set goals by referring to health-related accounts that the user follows. Furthermore, the reception desk can identify diet methods that the user is interested in from their social media activity and set goals based on that. In this way, the reception desk can set relevant goals based on the user's social media activity.

[0076] The generation unit can estimate the user's emotions and adjust how the calorie intake target is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed calorie target. For example, if the user is stressed, the generation unit can provide a simple calorie target. Also, if the user is in a hurry, the generation unit can provide a calorie target that can be quickly understood. In this way, the generation unit can provide a more understandable target by adjusting how the calorie intake target is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The generation unit can adjust the level of detail of the calorie intake target based on the user's level of importance when generating the target. For example, if the user has a high level of interest in calorie management, the generation unit will provide a detailed calorie target. For example, if the user has a low level of interest in calorie management, the generation unit can provide a simple calorie target. Alternatively, if the user has a moderate level of interest, the generation unit can provide a balanced calorie target. In this way, the generation unit can adjust the level of detail of the calorie intake target according to the user's level of interest.

[0078] The generation unit can apply different generation algorithms depending on the user's category when generating calorie intake targets. For example, if the user is a beginner, the generation unit can apply a simple algorithm to generate the calorie target. For example, if the user is an intermediate user, the generation unit can apply a more detailed algorithm to generate the calorie target. Furthermore, if the user is an advanced user, the generation unit can apply a more complex algorithm to generate the calorie target. In this way, the generation unit can apply an appropriate generation algorithm according to the user's category.

[0079] The generation unit can estimate the user's emotions and adjust the length of the calorie intake target based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed calorie target. For example, if the user is stressed, the generation unit can provide a simple calorie target. Also, if the user is in a hurry, the generation unit can provide a calorie target that can be quickly understood. In this way, the generation unit can provide more easily understandable targets by adjusting the length of the calorie intake target according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The generation unit can prioritize calorie intake targets based on when the user submits them. For example, if a user sets a target early in the morning, the generation unit will prioritize providing the calorie intake target for that day. For example, if a user sets a target at night, the generation unit can prioritize providing the calorie intake target for the following day. Furthermore, if a user sets a target on the weekend, the generation unit can provide the calorie intake target for the entire week. This allows the generation unit to set appropriate priorities based on when the user submits their targets.

[0081] The generation unit can adjust the order of calorie intake targets based on user relevance when generating them. For example, if a user is interested in a particular food, the generation unit will prioritize providing calorie targets that include that food. For example, if a user places emphasis on a specific mealtime, the generation unit can also prioritize providing calorie targets for that time slot. Furthermore, if a user is interested in a particular diet, the generation unit can provide calorie targets based on that diet. This allows the generation unit to provide targets in an appropriate order based on user relevance.

[0082] The suggestion unit can estimate the user's emotions and adjust how it suggests menus and recipes based on those emotions. For example, if the user is relaxed, the suggestion unit can provide a detailed recipe. If the user is stressed, it can provide a simple recipe. If the user is in a hurry, it can provide a recipe that can be prepared quickly. In this way, the suggestion unit can provide more appropriate suggestions by adjusting how it suggests menus and recipes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The suggestion department can adjust the level of detail in menu and recipe suggestions based on the importance of the contents of the refrigerator. For example, if the refrigerator is full of ingredients, the suggestion department will provide a detailed recipe. For example, if the refrigerator is sparse, the suggestion department may provide a simple recipe. Also, if the refrigerator is heavily reliant on certain ingredients, the suggestion department may provide recipes that utilize those ingredients. This allows the suggestion department to provide appropriate suggestions according to the importance of the contents of the refrigerator.

[0084] The suggestion function can apply different suggestion algorithms depending on the categories of items in the refrigerator when suggesting menus and recipes. For example, if the refrigerator is mainly vegetables, the suggestion function will provide recipes that utilize vegetables. For example, if the refrigerator is mainly meat, the suggestion function can also provide recipes that utilize meat. Furthermore, if the refrigerator is well-balanced, the suggestion function can provide balanced recipes. In this way, the suggestion function can make appropriate suggestions according to the categories of items in the refrigerator.

[0085] The suggestion function can estimate the user's emotions and adjust the length of menu and recipe suggestions based on those emotions. For example, if the user is relaxed, the suggestion function may offer a detailed recipe. For example, if the user is stressed, the suggestion function may offer a simple recipe. Also, if the user is in a hurry, the suggestion function may offer a recipe that can be prepared quickly. In this way, the suggestion function can make more appropriate suggestions by adjusting the length of suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The suggestion department can prioritize menu and recipe suggestions based on when the contents of the refrigerator are submitted. For example, if the contents of the refrigerator are fresh, the suggestion department can provide recipes that prioritize the use of those ingredients. For example, if the contents of the refrigerator are getting old, the suggestion department can also provide recipes that encourage their use sooner. Furthermore, if the contents of the refrigerator are heavily skewed towards a particular ingredient, the suggestion department can provide recipes that prioritize the use of that ingredient. This allows the suggestion department to make suggestions with appropriate priorities based on when the contents of the refrigerator are submitted.

[0087] The suggestion function can adjust the order of menu and recipe suggestions based on the relevance of the contents of the refrigerator. For example, if the refrigerator is heavily skewed towards a particular ingredient, the suggestion function will provide recipes that prioritize the use of that ingredient. For example, if the refrigerator is well-balanced, the suggestion function can provide balanced recipes. Furthermore, if the refrigerator is sparsely stocked, the suggestion function can provide simple recipes. In this way, the suggestion function can make suggestions in the appropriate order based on the relevance of the contents of the refrigerator.

[0088] The recording unit can estimate the user's emotions and adjust the method of recording recipes and calorie intake based on the estimated emotions. For example, if the user is relaxed, the recording unit can provide a detailed recording method. For example, if the user is stressed, the recording unit can provide a simple recording method. Also, if the user is in a hurry, the recording unit can provide a method for quick recording. In this way, the recording unit can make more appropriate records by adjusting the recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The recording unit can analyze the user's past records to select the optimal recording method when recording recipes or calorie intake. For example, if the user previously preferred detailed records, the recording unit can provide a detailed recording method. For example, if the user previously preferred simple records, the recording unit can provide a simple recording method. The recording unit can also analyze the user's past recording patterns to select the optimal recording method. In this way, the recording unit can select the optimal recording method based on the user's past records.

[0090] The recording unit can customize the recording method based on the user's current lifestyle when recording recipes or calorie intake. For example, if the user is busy, the recording unit can provide a quick recording method. For example, if the user is relaxed, the recording unit can also provide a detailed recording method. Furthermore, the recording unit can customize the optimal recording method according to the user's lifestyle. In this way, the recording unit can provide the optimal recording method according to the user's lifestyle.

[0091] The recording unit can estimate the user's emotions and prioritize recording recipes and calorie intake based on those estimated emotions. For example, if the user is relaxed, the recording unit may prioritize detailed recording. For example, if the user is stressed, the recording unit may prioritize simpler recording. Also, if the user is in a hurry, the recording unit may prioritize methods that allow for quick recording. In this way, the recording unit can make more appropriate records by prioritizing recordings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The recording unit can select the optimal recording method when recording recipes or calorie intake, taking into account the user's geographical location. For example, if the user is at home, the recording unit can provide a detailed recording method. For example, if the user is out, the recording unit can also provide a method that allows for quick recording. Furthermore, the recording unit can select the optimal recording method based on the user's geographical location. This allows the recording unit to select the optimal recording method based on the user's geographical location.

[0093] The recording unit can analyze the user's social media activity and suggest recording methods when recording recipes or calorie intake. For example, the recording unit can analyze meal posts shared by the user on social media and suggest recording methods. For example, the recording unit can also suggest recording methods by referring to health-related accounts that the user follows. Furthermore, the recording unit can suggest the most suitable recording method based on the user's social media activity. In this way, the recording unit can suggest the most suitable recording method based on the user's social media activity.

[0094] The suggestion function can estimate the user's emotions and adjust how it suggests ingredients to try next based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. For example, if the user is stressed, the suggestion function may provide simple suggestions. Also, if the user is in a hurry, the suggestion function may provide suggestions that can be quickly understood. In this way, the suggestion function can make more appropriate suggestions by adjusting how it suggests ingredients to try next according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The suggestion function can provide optimal suggestions for ingredients to try next by referring to the user's past food history. For example, the suggestion function can suggest ingredients to try next based on ingredients the user has previously enjoyed using. For example, the suggestion function can also suggest different ingredients based on ingredients the user has previously avoided. Furthermore, the suggestion function can analyze the user's past food history and suggest the most effective ingredients. In this way, the suggestion function can suggest the most suitable ingredients based on the user's past food history.

[0096] The suggestion function can customize its recommendations for ingredients to try next based on the user's current lifestyle. For example, if the user is busy, the suggestion function can suggest ingredients that can be cooked quickly. For example, if the user is relaxed, the suggestion function can suggest ingredients with detailed cooking instructions. The suggestion function can also suggest the most suitable ingredients according to the user's lifestyle. In this way, the suggestion function can suggest the most suitable ingredients according to the user's lifestyle.

[0097] The suggestion function can estimate the user's emotions and, based on those emotions, prioritize the ingredients the user should try next. For example, if the user is relaxed, the suggestion function will prioritize detailed suggestions. For example, if the user is stressed, the suggestion function may prioritize simple suggestions. Also, if the user is in a hurry, the suggestion function may prioritize suggestions that can be quickly understood. In this way, the suggestion function can make more appropriate suggestions by prioritizing the ingredients the user should try next according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The suggestion function can provide optimal suggestions for ingredients to try next, taking into account the user's geographical location. For example, the suggestion function can suggest practical ingredients considering the food culture of the area where the user lives. For example, the suggestion function can also suggest appropriate ingredients considering the climate of the area where the user lives. Furthermore, the suggestion function can make suggestions considering the ingredients available in the user's area. In this way, the suggestion function can suggest the most suitable ingredients based on the user's geographical location.

[0099] The suggestion department can analyze a user's social media activity to suggest ingredients they should try next. For example, it can analyze food posts a user has shared on social media and suggest ingredients they should try next. For example, it can also refer to health-related accounts a user follows to suggest ingredients they should try next. Furthermore, the suggestion department can suggest the most suitable ingredients based on the user's social media activity. In this way, the suggestion department can suggest the most suitable ingredients based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, suggestion unit, recording unit, and another suggestion unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information about the user's diet goal. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates a daily calorie intake target based on the received information. The suggestion unit is implemented, for example, by the control unit 46A of the smart device 14 and analyzes the contents of the refrigerator to suggest menus and recipes that match the target calorie intake. The recording unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and records the selected recipe and calorie intake. Another suggestion unit is implemented, for example, by the control unit 46A of the smart device 14 and suggests ingredients that the user should try next based on the recorded data. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, suggestion unit, recording unit, and another suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information about the user's diet goal. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a daily calorie intake target based on the received information. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the contents of the refrigerator to suggest menus and recipes that match the target calorie intake. The recording unit is implemented by the identification processing unit 290 of the data processing unit 12 and records the selected recipe and calorie intake. Another suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests ingredients that the user should try next based on the recorded data. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, suggestion unit, recording unit, and another suggestion unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information about the user's diet goal. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates a daily calorie intake target based on the received information. The suggestion unit is implemented, for example, by the control unit 46A of the headset terminal 314 and analyzes the contents of the refrigerator to suggest menus and recipes that match the target calorie intake. The recording unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and records the selected recipe and calorie intake. Another suggestion unit is implemented, for example, by the control unit 46A of the headset terminal 314 and suggests ingredients that the user should try next based on the recorded data. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, suggestion unit, recording unit, and another suggestion unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information about the user's diet goal. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and creates a daily calorie intake target based on the received information. The suggestion unit is implemented, for example, by the control unit 46A of the robot 414 and analyzes the contents of the refrigerator to suggest menus and recipes that match the target calorie intake. The recording unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and records the selected recipe and calorie intake. Another suggestion unit is implemented, for example, by the control unit 46A of the robot 414 and suggests ingredients that the user should try next based on the recorded data.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and adjust how diet goals are set based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick diet goal setting. In this way, the reception desk can set more appropriate goals by adjusting how diet goals are set according to the user's emotions.

[0102] The generation unit can estimate the user's emotions and adjust how the calorie intake target is presented based on those emotions. For example, if the user is relaxed, it can provide a detailed calorie target. If the user is stressed, it can provide a simpler calorie target. If the user is in a hurry, it can provide a calorie target that can be quickly understood. In this way, the generation unit can provide a more easily understandable target by adjusting how the calorie intake target is presented according to the user's emotions.

[0103] The suggestion function can estimate the user's emotions and adjust how menus and recipes are suggested based on those emotions. For example, if the user is relaxed, it can provide detailed recipes. If the user is stressed, it can provide simple recipes. If the user is in a hurry, it can provide recipes that can be prepared quickly. In this way, the suggestion function can provide more appropriate suggestions by adjusting how menus and recipes are suggested according to the user's emotions.

[0104] The recording unit can estimate the user's emotions and adjust the method of recording recipes and calorie intake based on those emotions. For example, if the user is relaxed, it can provide a detailed recording method. If the user is stressed, it can provide a simpler recording method. Furthermore, if the user is in a hurry, it can provide a method that allows for quick recording. This allows the recording unit to provide more appropriate records by adjusting the recording method according to the user's emotions.

[0105] The suggestion function can estimate the user's emotions and adjust how it suggests ingredients to try next based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide simpler suggestions. If the user is in a hurry, it can provide suggestions that can be quickly understood. In this way, the suggestion function can provide more appropriate suggestions by adjusting how it suggests ingredients to try next according to the user's emotions.

[0106] The reception desk can analyze a user's past dieting history and select an appropriate goal-setting method. For example, it can suggest similar goal settings based on the user's past successful dieting methods. It can also suggest different approaches to help the user avoid past unsuccessful dieting methods. Furthermore, it can select the most effective goal-setting method based on the user's past dieting history. In this way, the reception desk can select the optimal goal-setting method based on the user's past dieting history.

[0107] The generation unit can apply different generation algorithms depending on the user's category when generating calorie intake targets. For example, if the user is a beginner, a simple algorithm is applied to generate the calorie target. If the user is an intermediate user, a more detailed algorithm can be applied to generate the calorie target. Furthermore, if the user is an advanced user, a complex algorithm can be applied to generate the calorie target. In this way, the generation unit can apply the appropriate generation algorithm according to the user's category.

[0108] The suggestion department can adjust the level of detail in menu and recipe suggestions based on the importance of the contents of the refrigerator. For example, if the refrigerator is full of ingredients, a detailed recipe can be provided. If the refrigerator is sparse, a simple recipe can be offered. Also, if the refrigerator is heavily reliant on certain ingredients, recipes utilizing those ingredients can be provided. This allows the suggestion department to provide appropriate suggestions according to the importance of the contents of the refrigerator.

[0109] The recording unit can analyze the user's past records to select the optimal recording method when recording recipes or calorie intake. For example, if the user previously preferred detailed records, it can provide a detailed recording method. If the user previously preferred simple records, it can also provide a simple recording method. Furthermore, it can analyze the user's past recording patterns to select the optimal recording method. In this way, the recording unit can select the optimal recording method based on the user's past records.

[0110] The suggestion function can provide optimal suggestions for ingredients to try next by referring to the user's past food history. For example, it can suggest ingredients that the user has liked to try in the past. It can also suggest different ingredients based on ingredients the user has avoided in the past. Furthermore, it can analyze the user's past food history and suggest the most effective ingredients. In this way, the suggestion function can suggest the most suitable ingredients based on the user's past food history.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives information to set the user's diet goals. The user's diet goals include the duration, target weight, available time, level of motivation for dieting, and previous dieting experience. For example, the user is asked to input their desired diet duration, target weight, and how much time they can dedicate to dieting each day. Step 2: The generation unit creates a daily calorie intake target based on the information received by the reception unit. The generation unit calculates calorie intake based on information such as basal metabolic rate, activity level, and target weight, and generates the optimal calorie intake target using the generation AI. Step 3: The suggestion unit analyzes the contents of the refrigerator and proposes menus and recipes that match the target calorie intake. The suggestion unit uses image recognition technology to analyze photos of the refrigerator contents and propose healthy menus that suit the user's preferences. Step 4: The recording unit records the selected recipe and calorie intake. The recording unit automatically records the recipe and calorie intake selected by the user, which can be used as a reference for future meal preparation. It can also use AI to analyze the user's meal history and suggest ingredients that they should try next. Step 5: Another suggestion unit suggests ingredients to try next based on the recorded data. Another suggestion unit analyzes the user's eating history and suggests ingredients to try next.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 reception desk that receives information for setting the user's diet goal, A generating unit that creates a daily calorie intake target based on the information received by the aforementioned receiving unit, The proposal department analyzes the contents of the refrigerator and suggests menus that match the target calorie intake, A recording section for recording selected recipes and calorie intake, It also includes a separate suggestion section that proposes ingredients you should try next. A system characterized by the following features.

2. The aforementioned reception unit is We will accept information regarding the duration, target weight, available time, level of motivation for dieting, previous dieting experience, and other relevant details. The system according to feature 1.

3. The generating unit is Based on the information received by the aforementioned reception desk, a daily calorie intake target is created. The system according to feature 1.

4. The aforementioned proposal section is, It analyzes photos of the contents of your refrigerator and suggests menus and recipes that match your target calorie intake. The system according to feature 1.

5. The aforementioned recording unit is Record the recipes you choose and the calories you consume. The system according to feature 1.

6. The aforementioned proposal section is, Based on the recorded data, we suggest ingredients you should try next. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how diet goals are set based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past dieting history and select the appropriate goal-setting method. The system according to feature 1.

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