System

Abstract

An object of a system according to an embodiment is to easily set a diet goal and generate a meal menu.SOLUTION: A system according to an embodiment includes a goal setting unit, a menu generation unit, and a recording unit. The goal setting unit sets a goal of the diet using the generated AI. The menu generation unit generates a menu based on the objective set by the objective setting unit. The recording unit records the menu generated by the menu generation unit and the calorie intake.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that setting diet goals and creating meal menus is time-consuming and difficult to continue.

[0005] The system according to the embodiment aims to easily set diet goals and generate meal menus. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a menu generation unit, and a recording unit. The goal setting unit sets diet goals using a generation AI. The menu generation unit generates a menu based on the goals set by the goal setting unit. The recording unit records the menu generated by the menu generation unit and the calorie intake. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy setting of diet goals and creation of meal menus. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The meal app service according to an embodiment of the present invention is a meal app service for people who want to diet but don't want to go too seriously, helping them achieve their target weight. This service uses a generation AI to set diet goals and suggest meal menus, supporting users to continue their diet without straining themselves. This allows the meal app service to support users to continue their diet without straining themselves.

[0029] A meal app service according to an embodiment includes a goal setting unit, a menu generation unit, and a recording unit. The goal setting unit uses a generation AI to set diet goals. For example, the generation AI creates a daily calorie intake goal based on information input by the user, such as the period, target weight, available time, level of commitment, and previous dieting experience. For example, if a user inputs, "I want to lose 5 kg in 3 months," the generation AI calculates the daily calorie intake based on that goal. The generation AI can also set a reasonable goal taking into account the user's dieting experience. The menu generation unit generates menus based on the goals set by the goal setting unit. For example, when a user uploads a photo of their refrigerator to the app, the generation AI analyzes the photo and generates menus and recipes based on the user's calorie intake goal. For example, the generation AI can suggest, based on the ingredients in the refrigerator, such as, "Let's make a healthy salad using these ingredients today." The generation AI can also generate menus taking into account the user's preferences and allergy information. The recording unit records the menus and calorie intake generated by the menu generation unit. For example, the generation AI can record the recipes and calorie intake created by the user and use them as reference for the next meal. For example, the generation AI adjusts the calorie intake for the next meal based on the calories ingested in the previous meal. The generation AI also suggests recommended ingredients. For example, it makes suggestions such as, "You should use this ingredient for your next meal." This allows the meal app service according to the embodiment to support users in continuing their diet without any stress. For example, the generation AI suggests, "Let's make a healthy salad using these ingredients today," and the user follows the recipe, naturally leading to a healthy diet. Furthermore, recording calorie intake and using it as a reference for the next meal allows users to maintain a balanced diet.

[0030] The goal setting unit can analyze the user's past diet history or exercise history and set more personalized diet goals. For example, the generation AI in the goal setting unit analyzes the user's past diet history and sets personalized diet goals taking into account calorie intake and nutritional balance. For example, if the user has eaten many high-calorie meals in the past, the generation AI in the goal setting unit can analyze the user's exercise history and set diet goals according to the amount of exercise. For example, for a user who exercises little, the generation AI can suggest goals that incorporate light exercise. The goal setting unit can also comprehensively analyze the user's diet history and exercise history and set optimal diet goals. For example, the generation AI can suggest goals that take into account the balance between diet and exercise. In this way, by setting personalized diet goals, the user's diet effects can be improved.

[0031] The goal setting unit can propose reasonable goals by taking into account the user's lifestyle rhythm or stress level. In the goal setting unit, for example, the generation AI analyzes the user's lifestyle rhythm and sets reasonable diet goals. For example, for a night owl user, it would suggest low-calorie meals that can be consumed late at night. In addition, the goal setting unit analyzes the user's stress level and sets diet goals to reduce stress. For example, during times of high stress, it would suggest foods that have a relaxing effect. In addition, the goal setting unit analyzes the user's lifestyle rhythm and stress level in an integrated manner and sets optimal diet goals. For example, it would suggest goals that take into account meal times that match the user's lifestyle rhythm and stress reduction. In this way, by suggesting reasonable goals, it becomes easier for the user to continue.

[0032] The menu generation unit can analyze the freshness or expiration date of ingredients from photos of the refrigerator and suggest the optimal order of use. For example, the menu generation unit uses a generation AI to analyze photos of the refrigerator and identify the freshness and expiration date of ingredients. For example, it can suggest a menu that prioritizes the use of ingredients that are close to their expiration date. The menu generation unit also uses a generation AI to analyze the freshness of ingredients and suggest the optimal order of use. For example, it can suggest a menu that uses ingredients that tend to lose their freshness quickly. The menu generation unit also uses a generation AI to suggest menus that take the expiration date of ingredients into consideration based on photos of the refrigerator. For example, it can prioritize recipes that use ingredients that are close to their expiration date. This reduces waste of ingredients and allows them to be used more efficiently.

[0033] The menu generation unit can diversify the menu based on the user's past eating history to prevent boredom. For example, the menu generation unit uses a generation AI to analyze the user's past eating history and diversify the menu to prevent boredom. For example, different recipes using the same ingredients are proposed. The menu generation unit also uses the generation AI to propose a wide variety of menus based on the user's eating history. For example, different cooking methods and seasonings are proposed. The menu generation unit also uses the generation AI to consider the user's past eating history and diversify the menu to prevent boredom. For example, recipes using cuisines from different countries or seasonal ingredients are proposed. This allows the user to continue their diet without getting bored.

[0034] The recording unit can analyze the user's calorie intake and exercise amount in real time and optimize the calories for the next meal. In the recording unit, for example, the generation AI analyzes the user's calorie intake and exercise amount in real time and optimizes the calories for the next meal. For example, on days when the amount of exercise is heavy, the calories are set higher. In addition, the recording unit uses the generation AI to analyze the user's calorie intake and exercise amount and suggest a balanced meal. For example, after exercise, it suggests a meal high in protein. In addition, the recording unit uses the generation AI to analyze the user's calorie intake and exercise amount in real time and adjust the calories for the next meal. For example, if the user took in too many calories in the previous meal, the calories for the next meal can be reduced. In this way, a balanced meal can be achieved by optimizing the calories for the next meal.

[0035] The recording unit can predict future health risks and make dietary suggestions based on the user's long-term health data. In the recording unit, for example, the generation AI analyzes the user's long-term health data and predicts future health risks. For example, the risk of diabetes and high blood pressure is predicted based on past dietary and exercise history. The recording unit also allows the generation AI to predict health risks and make dietary suggestions based on them. For example, a low-carbohydrate diet is suggested for a user at high risk of diabetes. The recording unit also allows the generation AI to predict health risks based on the user's long-term health data and make preventative dietary suggestions. For example, a low-fat diet is suggested for a user at high risk of heart disease. In this way, the user's health can be maintained by predicting future health risks and making preventative dietary suggestions.

[0036] The recording unit can also consider the eating history of the user's family or friends and suggest menus that can be enjoyed together. For example, the generation AI in the recording unit analyzes the eating history of the user's family and friends and suggests menus that can be enjoyed together. For example, it suggests recipes using ingredients that everyone in the family likes. The generation AI in the recording unit also considers the eating history of the user's family and friends and suggests balanced menus. For example, it suggests nutritionally balanced meals that will satisfy everyone. The generation AI in the recording unit also comprehensively analyzes the eating history of the user's family and friends and suggests the optimal shared menu. For example, it suggests a variety of dishes that everyone can enjoy. This makes mealtimes more enjoyable by suggesting menus that can be enjoyed together with family and friends.

[0037] The recording unit can make suggestions to compensate for deficiencies or excesses of specific nutrients based on the user's dietary history. For example, the generation AI analyzes the user's dietary history and, if a specific nutrient is lacking, suggests ingredients to compensate for that nutrient. For example, if there is a vitamin C deficiency, the recording unit suggests ingredients that are high in vitamin C. The generation AI also makes suggestions to reduce excess intake of nutrients based on the user's dietary history. For example, if a user has been eating a high-fat diet, the recording unit suggests low-fat ingredients. The generation AI also analyzes the user's dietary history and makes suggestions to help the user consume balanced nutrients. For example, the recording unit suggests ingredients that take into account the balance of protein, carbohydrates, and lipids. This makes it possible to achieve a balanced diet by making suggestions to compensate for deficiencies or excesses of specific nutrients.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The goal setting unit can also analyze the user's sleep patterns and set optimal diet goals. For example, the generation AI can analyze the user's sleep data and set goals taking into account the impact of lack of sleep on dieting. The goal setting unit can also provide advice to improve the user's sleep quality. For example, it can suggest ingredients that should be eaten before bed or herbal teas that have a relaxing effect. Furthermore, the goal setting unit can adjust meal timing based on the user's sleep patterns. For example, it can advise the user to avoid eating late at night. This can improve the user's sleep quality and enhance the effectiveness of the diet.

[0040] The goal setting unit can also set diet goals that can be continued while having fun, taking into account the user's hobbies and interests. For example, the generation AI suggests goals that incorporate activities related to the user's hobbies. For example, for a user who likes dancing, an exercise goal that incorporates dance can be set. The goal setting unit can also suggest ingredients and recipes that will interest the user. For example, for a user who likes cooking, the goal can be set to try a new dish. Furthermore, the goal setting unit can introduce events and communities that make dieting fun, based on the user's hobbies and interests. This can support the user so that they can continue their diet while having fun.

[0041] The goal setting unit can also set realistic diet goals by taking into account the user's social environment. For example, the generation AI can analyze the user's work and home situation and set reasonable goals. For example, for a busy businessman, it can suggest short exercises and easy meal plans. The goal setting unit can also utilize the user's social support system. For example, it can suggest a diet plan to work on with family and friends. Furthermore, the goal setting unit can provide advice on reducing stress based on the user's social environment. This can support the user in continuing their diet without straining themselves.

[0042] The menu generation unit can also propose personalized menus taking into account the user's food preferences and allergy information. For example, the generation AI can analyze the user's past eating history and propose menus that include many of the user's favorite ingredients. The menu generation unit can also propose menus that use safe ingredients based on the user's allergy information. For example, it can propose recipes that do not contain nuts to a user with a nut allergy. Furthermore, the menu generation unit can introduce new ingredients and recipes based on the user's food preferences and allergy information. This allows users to enjoy their meals with peace of mind.

[0043] The recording unit can also analyze the user's meal history and make suggestions to reduce food waste. For example, the generation AI can analyze the user's past meal history and suggest recipes using leftover ingredients. For example, it can suggest a soup using leftover vegetables from the previous night's dinner. The recording unit can also suggest efficient ways to use ingredients based on the user's food purchasing history. For example, it can suggest a menu that uses up ingredients purchased in large quantities at once. Furthermore, the recording unit can provide advice on how to store and use ingredients based on the user's meal history. This allows for less food waste and more efficient meal enjoyment.

[0044] The recording unit can also analyze the user's dietary history and suggest cooking methods to maximize the nutritional value of ingredients. For example, the generative AI can analyze the user's past dietary history and suggest cooking methods to enhance nutritional value. For example, it can advise cooking ingredients that are high in vitamin C for a short period of time. The recording unit can also suggest storage methods that do not lose nutritional value based on the user's dietary history. For example, it can suggest ingredients that are suitable for freezing. Furthermore, the recording unit can suggest combinations of ingredients to maximize nutritional value based on the user's dietary history. This allows the user to eat nutritious meals and maintain their health.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The goal setting unit uses the generation AI to set diet goals. For example, the generation AI creates a daily calorie intake goal based on information entered by the user, such as the period, target weight, available time, level of seriousness, and previous dieting experience. If the user enters "I want to lose 5 kg in 3 months," the generation AI will calculate the daily calorie intake based on that goal. The generation AI can also set reasonable goals by taking into account the user's dieting experience. Step 2: The menu generation unit generates a menu based on the goals set by the goal setting unit. For example, if a user uploads a photo of their refrigerator to the app, the generation AI analyzes the photo and generates menus and recipes based on the user's target calorie intake. Based on the ingredients in the refrigerator, the generation AI makes suggestions such as, "Let's make a healthy salad using these ingredients today." The generation AI can also generate menus taking into account the user's preferences and allergy information. Step 3: The recording unit records the menu generated by the menu generation unit and the calorie intake. For example, the generation AI records the recipe created by the user and the calorie intake, and uses this as a reference for the next meal. The generation AI adjusts the calorie intake for the next meal based on the calories consumed in the previous meal. The generation AI also suggests recommended ingredients. For example, it makes suggestions such as, "It would be good to use this ingredient for your next meal."

[0047] (Example 2) The meal app service according to an embodiment of the present invention is a meal app service for people who want to diet but don't want to go too seriously, helping them achieve their target weight. This service uses a generation AI to set diet goals and suggest meal menus, supporting users to continue their diet without straining themselves. This allows the meal app service to support users to continue their diet without straining themselves.

[0048] A meal app service according to an embodiment includes a goal setting unit, a menu generation unit, and a recording unit. The goal setting unit uses a generation AI to set diet goals. For example, the generation AI creates a daily calorie intake goal based on information input by the user, such as the period, target weight, available time, level of commitment, and previous dieting experience. For example, if a user inputs, "I want to lose 5 kg in 3 months," the generation AI calculates the daily calorie intake based on that goal. The generation AI can also set a reasonable goal taking into account the user's dieting experience. The menu generation unit generates menus based on the goals set by the goal setting unit. For example, when a user uploads a photo of their refrigerator to the app, the generation AI analyzes the photo and generates menus and recipes based on the user's calorie intake goal. For example, the generation AI can suggest, based on the ingredients in the refrigerator, such as, "Let's make a healthy salad using these ingredients today." The generation AI can also generate menus taking into account the user's preferences and allergy information. The recording unit records the menus and calorie intake generated by the menu generation unit. For example, the generation AI can record the recipes and calorie intake created by the user and use them as reference for the next meal. For example, the generation AI adjusts the calorie intake for the next meal based on the calories ingested in the previous meal. The generation AI also suggests recommended ingredients. For example, it makes suggestions such as, "You should use this ingredient for your next meal." This allows the meal app service according to the embodiment to support users in continuing their diet without any stress. For example, the generation AI suggests, "Let's make a healthy salad using these ingredients today," and the user follows the recipe, naturally leading to a healthy diet. Furthermore, recording calorie intake and using it as a reference for the next meal allows users to maintain a balanced diet.

[0049] The goal setting unit can analyze the user's past diet history or exercise history and set more personalized diet goals. For example, the generation AI in the goal setting unit analyzes the user's past diet history and sets personalized diet goals taking into account calorie intake and nutritional balance. For example, if the user has eaten many high-calorie meals in the past, the generation AI in the goal setting unit can analyze the user's exercise history and set diet goals according to the amount of exercise. For example, for a user who exercises little, the generation AI can suggest goals that incorporate light exercise. The goal setting unit can also comprehensively analyze the user's diet history and exercise history and set optimal diet goals. For example, the generation AI can suggest goals that take into account the balance between diet and exercise. In this way, by setting personalized diet goals, the user's diet effects can be improved.

[0050] The goal setting unit can propose reasonable goals by taking into account the user's lifestyle rhythm or stress level. In the goal setting unit, for example, the generation AI analyzes the user's lifestyle rhythm and sets reasonable diet goals. For example, for a night owl user, it would suggest low-calorie meals that can be consumed late at night. In addition, the goal setting unit analyzes the user's stress level and sets diet goals to reduce stress. For example, during times of high stress, it would suggest foods that have a relaxing effect. In addition, the goal setting unit analyzes the user's lifestyle rhythm and stress level in an integrated manner and sets optimal diet goals. For example, it would suggest goals that take into account meal times that match the user's lifestyle rhythm and stress reduction. In this way, by suggesting reasonable goals, it becomes easier for the user to continue.

[0051] The goal setting unit can use the emotion estimation function to grasp the user's emotional state and set goals that elicit positive emotions. For example, the goal setting unit uses a generation AI to analyze the user's emotional state in real time and set diet goals that elicit positive emotions. For example, it suggests goals that will make the user feel joyful. The goal setting unit also uses the emotion estimation function to provide advice to reduce the anxiety and stress the user feels when setting goals. For example, it suggests foods that have a relaxing effect. The goal setting unit also uses a generation AI to analyze the user's emotional state and set goals that will elicit positive emotions. For example, it sets small goals that will make the user feel a sense of accomplishment. This can elicit positive emotions and increase the user's motivation.

[0052] The menu generation unit can analyze the freshness or expiration date of ingredients from photos of the refrigerator and suggest the optimal order of use. For example, the menu generation unit uses a generation AI to analyze photos of the refrigerator and identify the freshness and expiration date of ingredients. For example, it can suggest a menu that prioritizes the use of ingredients that are close to their expiration date. The menu generation unit also uses a generation AI to analyze the freshness of ingredients and suggest the optimal order of use. For example, it can suggest a menu that uses ingredients that tend to lose their freshness quickly. The menu generation unit also uses a generation AI to suggest menus that take the expiration date of ingredients into consideration based on photos of the refrigerator. For example, it can prioritize recipes that use ingredients that are close to their expiration date. This reduces waste of ingredients and allows them to be used more efficiently.

[0053] The menu generation unit can diversify the menu based on the user's past eating history to prevent boredom. For example, the menu generation unit uses a generation AI to analyze the user's past eating history and diversify the menu to prevent boredom. For example, different recipes using the same ingredients are proposed. The menu generation unit also uses the generation AI to propose a wide variety of menus based on the user's eating history. For example, different cooking methods and seasonings are proposed. The menu generation unit also uses the generation AI to consider the user's past eating history and diversify the menu to prevent boredom. For example, recipes using cuisines from different countries or seasonal ingredients are proposed. This allows the user to continue their diet without getting bored.

[0054] The menu generation unit can use the emotion estimation function to suggest a menu that matches the user's preferences or mood. For example, the menu generation unit uses the emotion estimation function to analyze the user's preferences and mood and suggest a menu based on that. For example, when the user wants to relax, it suggests a light meal. The menu generation unit also uses the generation AI to analyze the user's emotional state and suggest a menu that matches the mood. For example, when the user wants to cheer up, it suggests an energetic meal. The menu generation unit also uses the emotion estimation function to suggest a menu that matches the user's preferences and mood. For example, when the user is feeling stressed, it suggests a recipe using ingredients that have a relaxing effect. In this way, by suggesting a menu that matches the user's preferences and mood, it is possible to increase meal satisfaction.

[0055] The recording unit can analyze the user's calorie intake and exercise amount in real time and optimize the calories for the next meal. In the recording unit, for example, the generation AI analyzes the user's calorie intake and exercise amount in real time and optimizes the calories for the next meal. For example, on days when the amount of exercise is heavy, the calories are set higher. In addition, the recording unit uses the generation AI to analyze the user's calorie intake and exercise amount and suggest a balanced meal. For example, after exercise, it suggests a meal high in protein. In addition, the recording unit uses the generation AI to analyze the user's calorie intake and exercise amount in real time and adjust the calories for the next meal. For example, if the user took in too many calories in the previous meal, the calories for the next meal can be reduced. In this way, a balanced meal can be achieved by optimizing the calories for the next meal.

[0056] The recording unit can predict future health risks and make dietary suggestions based on the user's long-term health data. In the recording unit, for example, the generation AI analyzes the user's long-term health data and predicts future health risks. For example, the risk of diabetes and high blood pressure is predicted based on past dietary and exercise history. The recording unit also allows the generation AI to predict health risks and make dietary suggestions based on them. For example, a low-carbohydrate diet is suggested for a user at high risk of diabetes. The recording unit also allows the generation AI to predict health risks based on the user's long-term health data and make preventative dietary suggestions. For example, a low-fat diet is suggested for a user at high risk of heart disease. In this way, the user's health can be maintained by predicting future health risks and making preventative dietary suggestions.

[0057] The recording unit can use the emotion estimation function to make meal suggestions based on the user's emotional state. For example, the recording unit uses the emotion estimation function to analyze the user's emotional state in real time and make meal suggestions based on that. For example, when the user is feeling stressed, the recording unit suggests ingredients that have a relaxing effect. The generation AI in the recording unit also analyzes the user's emotional state and makes meal suggestions that match the user's mood. For example, when the user wants to cheer up, the recording unit suggests an energetic meal. The recording unit also uses the emotion estimation function to make meal suggestions based on the user's emotional state. For example, when the user wants to relax, the recording unit suggests a light meal. In this way, meal suggestions based on the user's emotional state can be made, thereby increasing meal satisfaction.

[0058] The recording unit can also consider the eating history of the user's family or friends and suggest menus that can be enjoyed together. For example, the generation AI in the recording unit analyzes the eating history of the user's family and friends and suggests menus that can be enjoyed together. For example, it suggests recipes using ingredients that everyone in the family likes. The generation AI in the recording unit also considers the eating history of the user's family and friends and suggests balanced menus. For example, it suggests nutritionally balanced meals that will satisfy everyone. The generation AI in the recording unit also comprehensively analyzes the eating history of the user's family and friends and suggests the optimal shared menu. For example, it suggests a variety of dishes that everyone can enjoy. This makes mealtimes more enjoyable by suggesting menus that can be enjoyed together with family and friends.

[0059] The recording unit can make suggestions to compensate for deficiencies or excesses of specific nutrients based on the user's dietary history. For example, the generation AI analyzes the user's dietary history and, if a specific nutrient is lacking, suggests ingredients to compensate for that nutrient. For example, if there is a vitamin C deficiency, the recording unit suggests ingredients that are high in vitamin C. The generation AI also makes suggestions to reduce excess intake of nutrients based on the user's dietary history. For example, if a user has been eating a high-fat diet, the recording unit suggests low-fat ingredients. The generation AI also analyzes the user's dietary history and makes suggestions to help the user consume balanced nutrients. For example, the recording unit suggests ingredients that take into account the balance of protein, carbohydrates, and lipids. This makes it possible to achieve a balanced diet by making suggestions to compensate for deficiencies or excesses of specific nutrients.

[0060] The recording unit can use the emotion estimation function to suggest new recipes for the user to enjoy a meal. For example, the recording unit uses the emotion estimation function to suggest new recipes for the user to enjoy a meal. For example, when the user wants to relax, the recording unit suggests recipes using ingredients that have a relaxing effect. In addition, the recording unit uses the generation AI to analyze the user's emotional state and suggest new recipes that match the user's mood. For example, when the user wants to cheer up, the recording unit suggests recipes using ingredients that have an energy effect. In addition, the recording unit uses the emotion estimation function to suggest new recipes for the user to enjoy a meal. For example, when the user is feeling stressed, the recording unit suggests recipes using ingredients that have a relaxing effect. In this way, by suggesting new recipes for the user to enjoy a meal, it is possible to increase meal satisfaction.

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

[0062] The goal setting unit can also analyze the user's sleep patterns and set optimal diet goals. For example, the generation AI can analyze the user's sleep data and set goals taking into account the impact of lack of sleep on dieting. The goal setting unit can also provide advice to improve the user's sleep quality. For example, it can suggest ingredients that should be eaten before bed or herbal teas that have a relaxing effect. Furthermore, the goal setting unit can adjust meal timing based on the user's sleep patterns. For example, it can advise the user to avoid eating late at night. This can improve the user's sleep quality and enhance the effectiveness of the diet.

[0063] The goal setting unit can also set diet goals that can be continued while having fun, taking into account the user's hobbies and interests. For example, the generation AI suggests goals that incorporate activities related to the user's hobbies. For example, for a user who likes dancing, an exercise goal that incorporates dance can be set. The goal setting unit can also suggest ingredients and recipes that will interest the user. For example, for a user who likes cooking, the goal can be set to try a new dish. Furthermore, the goal setting unit can introduce events and communities that make dieting fun, based on the user's hobbies and interests. This can support the user so that they can continue their diet while having fun.

[0064] The goal setting unit can also set realistic diet goals by taking into account the user's social environment. For example, the generation AI can analyze the user's work and home situation and set reasonable goals. For example, for a busy businessman, it can suggest short exercises and easy meal plans. The goal setting unit can also utilize the user's social support system. For example, it can suggest a diet plan to work on with family and friends. Furthermore, the goal setting unit can provide advice on reducing stress based on the user's social environment. This can support the user in continuing their diet without straining themselves.

[0065] The goal setting unit can also analyze the user's emotional state and provide positive feedback. For example, the generation AI can analyze the user's emotional state in real time and provide feedback that makes them feel a sense of accomplishment. For example, when a goal is achieved, it can display praise or encouraging messages. The goal setting unit can also provide advice to increase motivation when the user is about to give up. For example, it can encourage the user to reflect on past successes. Furthermore, the goal setting unit can introduce a reward system based on the user's emotional state. For example, each time a goal is achieved, points can be accumulated and rewards can be received. This can increase the user's motivation and make it easier for them to continue their diet.

[0066] The menu generation unit can also propose personalized menus taking into account the user's food preferences and allergy information. For example, the generation AI can analyze the user's past eating history and propose menus that include many of the user's favorite ingredients. The menu generation unit can also propose menus that use safe ingredients based on the user's allergy information. For example, it can propose recipes that do not contain nuts to a user with a nut allergy. Furthermore, the menu generation unit can introduce new ingredients and recipes based on the user's food preferences and allergy information. This allows users to enjoy their meals with peace of mind.

[0067] The menu generation unit can also analyze the user's emotional state and suggest ingredients and recipes that match their mood. For example, the generation AI can analyze the user's emotional state in real time and suggest ingredients that have a relaxing effect. For example, if the user is feeling stressed, it can suggest chamomile tea or dark chocolate. The menu generation unit can also suggest ingredients to replenish energy based on the user's emotional state. For example, if the user is tired, it can suggest bananas or nuts. Furthermore, the menu generation unit can suggest recipes to lift the user's mood based on the user's emotional state. This allows the system to provide meals that match the user's emotional state and increase meal satisfaction.

[0068] The recording unit can also analyze the user's meal history and make suggestions to reduce food waste. For example, the generation AI can analyze the user's past meal history and suggest recipes using leftover ingredients. For example, it can suggest a soup using leftover vegetables from the previous night's dinner. The recording unit can also suggest efficient ways to use ingredients based on the user's food purchasing history. For example, it can suggest a menu that uses up ingredients purchased in large quantities at once. Furthermore, the recording unit can provide advice on how to store and use ingredients based on the user's meal history. This allows for less food waste and more efficient meal enjoyment.

[0069] The recording unit can also analyze the user's emotional state and optimize meal timing. For example, the generative AI analyzes the user's emotional state in real time and adjusts meal timing when stress levels are high. For example, it can suggest light snacks when stress levels are high. The recording unit can also provide advice for adjusting meal timing based on the user's emotional state. For example, it can encourage the user to eat meals in a relaxed state. Furthermore, the recording unit can set reminders to optimize meal timing based on the user's emotional state. This allows the user to eat meals at optimal times and increase meal satisfaction.

[0070] The recording unit can also analyze the user's dietary history and suggest cooking methods to maximize the nutritional value of ingredients. For example, the generative AI can analyze the user's past dietary history and suggest cooking methods to enhance nutritional value. For example, it can advise cooking ingredients that are high in vitamin C for a short period of time. The recording unit can also suggest storage methods that do not lose nutritional value based on the user's dietary history. For example, it can suggest ingredients that are suitable for freezing. Furthermore, the recording unit can suggest combinations of ingredients to maximize nutritional value based on the user's dietary history. This allows the user to eat nutritious meals and maintain their health.

[0071] The recording unit can also analyze the user's emotional state and suggest activities to increase meal satisfaction. For example, the generation AI can analyze the user's emotional state in real time and suggest activities to do before and after a meal. For example, it can suggest yoga or deep breathing, which have a relaxing effect. The recording unit can also suggest environmental settings to increase meal satisfaction based on the user's emotional state. For example, it can suggest music that is good to listen to while eating or lighting that is relaxing. Furthermore, the recording unit can suggest communication methods to increase meal satisfaction based on the user's emotional state. For example, it can encourage the user to enjoy a meal with family or friends. This allows the user to enjoy their meal more and increase their satisfaction.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The goal setting unit uses the generation AI to set diet goals. For example, the generation AI creates a daily calorie intake goal based on information entered by the user, such as the period, target weight, available time, level of seriousness, and previous dieting experience. If the user enters "I want to lose 5 kg in 3 months," the generation AI will calculate the daily calorie intake based on that goal. The generation AI can also set reasonable goals by taking into account the user's dieting experience. Step 2: The menu generation unit generates a menu based on the goals set by the goal setting unit. For example, if a user uploads a photo of their refrigerator to the app, the generation AI analyzes the photo and generates menus and recipes based on the user's target calorie intake. Based on the ingredients in the refrigerator, the generation AI makes suggestions such as, "Let's make a healthy salad using these ingredients today." The generation AI can also generate menus taking into account the user's preferences and allergy information. Step 3: The recording unit records the menu generated by the menu generation unit and the calorie intake. For example, the generation AI records the recipe created by the user and the calorie intake, and uses this as a reference for the next meal. The generation AI adjusts the calorie intake for the next meal based on the calories consumed in the previous meal. The generation AI also suggests recommended ingredients. For example, it makes suggestions such as, "It would be good to use this ingredient for your next meal."

[0074] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, a 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] 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.

[0133] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A goal setting unit that sets diet goals using a generation AI; a menu generation unit that generates a menu based on the goal set by the goal setting unit; a recording unit that records the menu generated by the menu generating unit and the calorie intake. A system characterized by:

2. The goal setting unit Understand the user's emotional state and set goals that elicit positive emotions 2. The system of claim 1.

3. The menu generation unit Analyzes the freshness or expiration date of ingredients from photos of the refrigerator and suggests the optimal order of use 2. The system of claim 1.

4. The recording unit Analyzes the user's calorie intake and exercise volume in real time to optimize the calories in the next meal 2. The system of claim 1.

5. The goal setting unit Analyzing a user's past diet or exercise history to set more personalized diet goals 2. The system of claim 1.

6. The menu generation unit Diversify the menu based on the user's past eating history to prevent boredom 2. The system of claim 1.

7. The recording unit Predicts future health risks and makes dietary recommendations based on the user's long-term health data 2. The system of claim 1.

8. The recording unit Providing meal suggestions based on the user's emotional state 2. The system of claim 1.

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