System

Abstract

An object of a system according to an embodiment is to propose an optimal mix juice recipe according to a user's preference or physical condition.SOLUTION: A system according to an embodiment includes a user information collection unit, a recipe generation unit, and a proposal unit. The user information collection unit collects information related to preferences and physical conditions of the user. The recipe generating unit generates an optimum recipe for mix juice based on the information collected by the user information collecting unit. The proposal unit proposes the recipe generated by the recipe generation unit to the user.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] With conventional technology, it was difficult to create original mixed juices that suited the user's preferences and physical condition.

[0005] The system according to the embodiment aims to propose optimal mixed juice recipes that suit the user's preferences and physical condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a recipe generation unit, and a suggestion unit. The user information collection unit collects information related to the user's preferences and physical condition. The recipe generation unit generates an optimal mixed juice recipe based on the information collected by the user information collection unit. The suggestion unit suggests the recipe generated by the recipe generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the optimum mixed juice recipe according to the user's preferences and physical condition. [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 mixed juice preparation system according to the embodiment of the present invention is a system that proposes an optimal mixed juice recipe taking into consideration the user's preferences, physical condition, seasonal fruits, etc. As a result, the mixed juice preparation system allows the user to enjoy their own original mixed juice.

[0029] A mixed juice generation system according to an embodiment includes a user information collection unit, a recipe generation unit, and a suggestion unit. The user information collection unit collects information about a user's preferences and physical condition. For example, when a user inputs information such as "I like sweet flavors" or "I want to consume a lot of vitamin C," the user information collection unit collects the information. The user information collection unit can also collect the user's allergy information and health condition. The recipe generation unit generates an optimal mixed juice recipe based on the information collected by the user information collection unit. For example, the generation AI can suggest an appropriate combination of fruits and vegetables based on the user's preferences and physical condition. The generation AI can also generate recipes taking nutritional balance and calorie restrictions into consideration. The suggestion unit suggests the recipe generated by the recipe generation unit to the user. For example, the suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This allows the mixed juice generation system according to an embodiment to suggest an optimal mixed juice recipe based on the user's preferences and physical condition.

[0030] The user information collection unit can analyze the user's past eating history, predict changes in preferences and physical condition, and suggest recipes. The user information collection unit, for example, collects data on meals the user has eaten in the past and analyzes that data to predict changes in the user's preferences and physical condition. For example, the user's current preferences can be inferred based on data on fruits and vegetables that the user liked to eat in the past. The user information collection unit also collects data on the user's weight fluctuations and whether or not they have had allergic reactions, and suggests recipes based on that data. This makes it possible to predict changes in preferences and physical condition based on the user's past eating history and suggest optimal recipes.

[0031] The user information collection unit can collect real-time physical condition data and adjust recipes based on that data. For example, the user information collection unit analyzes the user's heart rate and sleep data collected from a smartwatch and adjusts recipes based on that data. For example, if the user is under high stress, the unit can suggest recipes using fruits that have a relaxing effect. The user information collection unit can also collect data such as the user's blood pressure and body temperature and adjust recipes based on that data. This allows recipes to be adjusted based on the user's real-time physical condition data.

[0032] The recipe generation unit can also suggest recipes for health foods other than juices based on the user's preferences and physical condition. The recipe generation unit, for example, suggests smoothie recipes based on the user's preferences and physical condition. For example, if the user wants to consume a lot of vitamin C, it suggests smoothies using oranges or kiwis. The recipe generation unit can also suggest salad recipes. For example, it suggests a salad using spinach or lentils, which are rich in iron. This makes it possible to suggest recipes for health foods other than juices based on the user's preferences and physical condition.

[0033] The suggestion unit may have a function that allows recipes that match the user's preferences and physical condition to be shared with family and friends. The suggestion unit may, for example, have a function that allows the user to share recipes created by the user with family and friends. For example, the recipes may be shared via social networking sites or messaging apps. The suggestion unit may also send the created recipes by email. This allows recipes that match the user's preferences and physical condition to be shared with family and friends.

[0034] The recipe generation unit can analyze seasonal variations in the nutritional value of fruits and propose recipes suited to the time when they are most nutritious. For example, the recipe generation unit collects seasonal data on the nutritional value of fruits, analyzes the data, and proposes recipes suited to the time when they are most nutritious. For example, in summer, it proposes recipes using watermelon, which is rich in vitamin C. The recipe generation unit can also propose recipes using mandarin oranges, which are rich in vitamin A, in winter. This allows the unit to analyze seasonal variations in the nutritional value of fruits and propose recipes suited to the time when they are most nutritious.

[0035] The recipe generation unit can also suggest methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time. The recipe generation unit, for example, suggests methods for storing seasonal fruits. For example, it introduces methods for freezing and drying the fruits, allowing them to be enjoyed for a long period of time. The recipe generation unit also suggests methods for cooking seasonal fruits. For example, it introduces methods for steaming and baking the fruits, allowing them to be enjoyed for a long period of time. In this way, it suggests methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time.

[0036] The recipe generation unit can also suggest recipes for desserts and snacks using seasonal fruits. The recipe generation unit suggests, for example, recipes for desserts using seasonal fruits. For example, in the summer, it suggests recipes for watermelon sorbet and fruit punch. The recipe generation unit can also suggest recipes for snacks using seasonal fruits. For example, in the winter, it suggests a recipe for a fruit bar using mandarin oranges. In this way, it also suggests recipes for desserts and snacks using seasonal fruits.

[0037] The recipe generation unit can suggest pairings of juices using seasonal fruits. The recipe generation unit can suggest pairings of juices using seasonal fruits with meals, for example. For example, in summer, the recipe generation unit can suggest a combination of watermelon juice and salad. The recipe generation unit can also suggest pairings of juices using seasonal fruits with sweets. For example, in winter, the recipe generation unit can suggest a combination of mandarin orange juice and cookies. In this way, pairings of juices using seasonal fruits are suggested.

[0038] The recipe generation unit can collect the user's blood test results, analyze the data, and propose recipes that supplement necessary nutrients. The recipe generation unit, for example, collects the user's blood test results, analyzes the data, and proposes recipes that supplement necessary nutrients. For example, the recipe generation unit proposes recipes that supplement necessary nutrients based on data such as the user's blood sugar level, cholesterol level, and vitamin D concentration. In this way, recipes that supplement necessary nutrients are proposed based on the user's blood test results.

[0039] The recipe generation unit can also suggest supplements for the purpose of supplementing nutrients. For example, the recipe generation unit suggests supplements to make up for a nutrient deficiency in the user. For example, if there is an iron deficiency, an iron supplement is suggested. The recipe generation unit can also suggest vitamin C and protein supplements. This allows for suggesting supplements for the purpose of supplementing nutrients.

[0040] The recipe generation unit can propose an entire meal plan aimed at supplementing nutrients. For example, the recipe generation unit proposes an entire meal plan to supplement a user's nutrient deficiency. For example, if there is an iron deficiency, the recipe generation unit proposes a meal plan using ingredients that are high in iron. The recipe generation unit can also propose a meal plan that takes into account daily meal menus and nutritional balance. In this way, the recipe generation unit proposes an entire meal plan aimed at supplementing nutrients.

[0041] The recipe generation unit can analyze user feedback and automatically learn how to improve a recipe. For example, the recipe generation unit collects feedback provided by users, analyzes the data, and automatically learns how to improve a recipe. For example, if a user rates a recipe as "delicious," the next recipe suggestion is improved based on that information. The recipe generation unit can also learn how to improve a recipe based on user comments and survey results. This allows the recipe generation unit to analyze user feedback and automatically learn how to improve a recipe.

[0042] The recipe generation unit can track changes in a user's preferences over the long term and suggest recipes based on the data. For example, the recipe generation unit can track changes in a user's preferences over the long term and suggest recipes based on that data. For example, the recipe generation unit can infer current preferences based on data on fruits and vegetables that the user liked in the past. The recipe generation unit can also track changes in a user's taste preferences and ingredient preferences and suggest recipes based on those preferences. This allows the recipe generation unit to track changes in a user's preferences over the long term and suggest recipes based on those preferences.

[0043] The recipe generation unit can improve the recipe so that it can be applied to other users based on user feedback. For example, the recipe generation unit collects user feedback and improves the recipe so that it can be applied to other users based on that data. For example, if multiple users rate the recipe as "delicious," the recipe generation unit can suggest it to other users based on that information. The recipe generation unit can also improve the recipe so that it can be applied to other users based on user comments and survey results. In this way, the recipe generation unit improves the recipe so that it can be applied to other users based on user feedback.

[0044] The recipe generation unit can suggest new fruit and vegetable combinations based on user feedback. For example, the recipe generation unit collects user feedback and suggests new fruit and vegetable combinations based on that data. For example, if a user rates a recipe as "delicious," the recipe generation unit suggests new combinations based on that information. The recipe generation unit can also suggest new fruit and vegetable combinations based on user comments and survey results. This allows the recipe generation unit to suggest new fruit and vegetable combinations based on user feedback.

[0045] The recipe generation unit can analyze the user's past recipe history and suggest untried combinations. For example, the recipe generation unit collects the user's past recipe history and analyzes that data to suggest untried combinations. For example, it can suggest combinations of fruits and vegetables that the user has not tried before. The recipe generation unit can also suggest untried combinations based on the user's ratings and comments. This allows the recipe generation unit to analyze the user's past recipe history and suggest untried combinations.

[0046] The recipe generation unit can propose recipes using different cooking methods according to the user's preferences and physical condition. The recipe generation unit, for example, proposes recipes for frozen juice according to the user's preferences and physical condition. For example, juice using frozen fruit is proposed in the summer. The recipe generation unit can also propose hot juice. For example, hot fruit juice is proposed in the winter. In this way, recipes using different cooking methods according to the user's preferences and physical condition are proposed.

[0047] The recipe generation unit can propose a variety of juice recipes to suit different meal occasions. For example, the recipe generation unit proposes a variety of juice recipes to suit breakfast. For example, it proposes a breakfast juice that uses fruit that is rich in vitamin C. The recipe generation unit can also propose juice recipes to suit lunch and dinner. For example, it proposes a lunch juice that uses fruit that can replenish energy, or a dinner juice that uses fruit that has a relaxing effect. In this way, a variety of juice recipes can be proposed to suit different meal occasions.

[0048] The recipe generation unit can propose a variety of juice recipes using fruits from different cultures and regions. For example, the recipe generation unit proposes a variety of juice recipes using fruits from different cultures. For example, juices using Asian fruits are proposed. The recipe generation unit can also propose juice recipes using fruits that are local specialties of a region. For example, juices using locally grown fruits are proposed. In this way, a variety of juice recipes are proposed using fruits from different cultures and regions.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The user information collection unit can collect data on the user's exercise habits and suggest recipes based on that data. For example, when the user inputs the type and frequency of exercise they do daily, the user information collection unit collects that information. The user information collection unit can also collect the user's post-exercise fatigue level and energy expenditure. The recipe generation unit generates a mixed juice recipe suitable for post-exercise recovery based on the information on the exercise habits collected by the user information collection unit. For example, the recipe generation unit can suggest a combination of fruits and vegetables that are high in protein and amino acids to promote muscle recovery after exercise. It can also generate a recipe using fruits that are high in electrolytes, taking into account post-exercise hydration. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest optimal mixed juice recipes based on the user's exercise habits.

[0051] The user information collection unit can collect the user's ingredient allergy information and suggest recipes based on that information. For example, if the user is allergic to a specific fruit or vegetable, the user information collection unit collects that information by inputting that information. The user information collection unit can also collect the user's allergic reaction intensity and past allergy history. The recipe generation unit generates a safe mixed juice recipe based on the allergy information collected by the user information collection unit. For example, the recipe generation unit can suggest recipes that avoid ingredients that may cause allergies and use alternative fruits and vegetables. It can also generate recipes that combine ingredients that are effective in reducing allergic reactions. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest safe and optimal mixed juice recipes based on the user's allergy information.

[0052] The user information collection unit can collect data on the user's sleep patterns and suggest recipes based on the collected data. For example, when the user inputs how much time they spend sleeping on a daily basis and the quality of their sleep, the user information collection unit collects the information. The user information collection unit can also collect the user's heart rate and breathing patterns while sleeping. The recipe generation unit generates a mixed juice recipe that improves sleep quality based on the information on the sleep patterns collected by the user information collection unit. For example, the recipe generation unit can suggest a recipe using fruits and vegetables that have a relaxing effect. It can also generate a recipe that combines ingredients that are effective in regulating the body clock during sleep. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest an optimal mixed juice recipe based on the user's sleep patterns.

[0053] The recipe generation unit can also suggest recipes for health foods other than juices based on the user's preferences and physical condition. For example, a smoothie recipe can be suggested based on the user's preferences and physical condition. If the user wants to get a lot of vitamin C, a smoothie using orange or kiwi can be suggested. The recipe generation unit can also suggest salad recipes. A salad using spinach or lentils, which are rich in iron, can be suggested. This makes it possible to suggest recipes for health foods other than juices based on the user's preferences and physical condition.

[0054] The suggestion unit may have a function that allows recipes that match the user's preferences and physical condition to be shared with family and friends. For example, the function may allow the user to share recipes that the user has created with family and friends. Recipes can be shared via social networking sites or messaging apps. The suggestion unit can also send the created recipes by email. This allows recipes that match the user's preferences and physical condition to be shared with family and friends.

[0055] The recipe generation unit can analyze seasonal variations in the nutritional value of fruits and suggest recipes that are suited to the time when they are most nutritious. For example, it can collect nutritional value data for fruits by season, analyze that data, and suggest recipes that are suited to the time when they are most nutritious. In summer, it can suggest recipes that use watermelon, which is rich in vitamin C. In winter, it can also suggest recipes that use mandarin oranges, which are rich in vitamin A. This allows it to analyze seasonal variations in the nutritional value of fruits and suggest recipes that are suited to the time when they are most nutritious.

[0056] The recipe generation unit can also suggest methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time. For example, it suggests methods for storing seasonal fruits, introducing methods such as freezing and drying them, allowing them to be enjoyed for a long period of time. The recipe generation unit also suggests methods for cooking seasonal fruits, introducing methods such as steaming and baking, allowing them to be enjoyed for a long period of time. In this way, it suggests methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The user information collection unit collects information about the user's preferences and physical condition. For example, if the user inputs information such as "I like sweet tastes" or "I want to take in a lot of vitamin C," the user information collection unit collects that information. The user information collection unit can also collect information about the user's allergies and health condition. Step 2: The recipe generation unit generates an optimal mixed juice recipe based on the information collected by the user information collection unit. For example, the generation AI may suggest an appropriate combination of fruits and vegetables based on the user's preferences and physical condition. The generation AI may also generate recipes taking into account nutritional balance and calorie restrictions. Step 3: The suggestion unit suggests the recipe generated by the recipe generation unit to the user. For example, the suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe.

[0059] (Example 2) The mixed juice preparation system according to the embodiment of the present invention is a system that proposes an optimal mixed juice recipe taking into consideration the user's preferences, physical condition, seasonal fruits, etc. As a result, the mixed juice preparation system allows the user to enjoy their own original mixed juice.

[0060] A mixed juice generation system according to an embodiment includes a user information collection unit, a recipe generation unit, and a suggestion unit. The user information collection unit collects information about a user's preferences and physical condition. For example, when a user inputs information such as "I like sweet flavors" or "I want to consume a lot of vitamin C," the user information collection unit collects the information. The user information collection unit can also collect the user's allergy information and health condition. The recipe generation unit generates an optimal mixed juice recipe based on the information collected by the user information collection unit. For example, the generation AI can suggest an appropriate combination of fruits and vegetables based on the user's preferences and physical condition. The generation AI can also generate recipes taking nutritional balance and calorie restrictions into consideration. The suggestion unit suggests the recipe generated by the recipe generation unit to the user. For example, the suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This allows the mixed juice generation system according to an embodiment to suggest an optimal mixed juice recipe based on the user's preferences and physical condition.

[0061] The user information collection unit can analyze the user's past eating history, predict changes in preferences and physical condition, and suggest recipes. The user information collection unit, for example, collects data on meals the user has eaten in the past and analyzes that data to predict changes in the user's preferences and physical condition. For example, the user's current preferences can be inferred based on data on fruits and vegetables that the user liked to eat in the past. The user information collection unit also collects data on the user's weight fluctuations and whether or not they have had allergic reactions, and suggests recipes based on that data. This makes it possible to predict changes in preferences and physical condition based on the user's past eating history and suggest optimal recipes.

[0062] The user information collection unit can collect real-time physical condition data and adjust recipes based on that data. For example, the user information collection unit analyzes the user's heart rate and sleep data collected from a smartwatch and adjusts recipes based on that data. For example, if the user is under high stress, the unit can suggest recipes using fruits that have a relaxing effect. The user information collection unit can also collect data such as the user's blood pressure and body temperature and adjust recipes based on that data. This allows recipes to be adjusted based on the user's real-time physical condition data.

[0063] The user information collection unit can use the emotion estimation function to analyze the user's emotional state and suggest recipes that have stress-reducing or relaxing effects. The user information collection unit, for example, analyzes the user's facial expressions and voice to estimate the emotional state. For example, it can use a camera or microphone to analyze the user's facial expressions and tone of voice, and if the user is under high stress, suggest recipes that use fruits that have a relaxing effect. The user information collection unit can also analyze the user's brain wave data to estimate the emotional state. This makes it possible to suggest recipes that have stress-reducing or relaxing effects based on the user's emotional state.

[0064] The recipe generation unit can also suggest recipes for health foods other than juices based on the user's preferences and physical condition. The recipe generation unit, for example, suggests smoothie recipes based on the user's preferences and physical condition. For example, if the user wants to consume a lot of vitamin C, it suggests smoothies using oranges or kiwis. The recipe generation unit can also suggest salad recipes. For example, it suggests a salad using spinach or lentils, which are rich in iron. This makes it possible to suggest recipes for health foods other than juices based on the user's preferences and physical condition.

[0065] The suggestion unit may have a function that allows recipes that match the user's preferences and physical condition to be shared with family and friends. The suggestion unit may, for example, have a function that allows the user to share recipes created by the user with family and friends. For example, the recipes may be shared via social networking sites or messaging apps. The suggestion unit may also send the created recipes by email. This allows recipes that match the user's preferences and physical condition to be shared with family and friends.

[0066] The suggestion unit can also use the emotion estimation function to suggest music and aromas that correspond to the user's emotions. The suggestion unit, for example, analyzes the user's emotional state and suggests music with a relaxing effect based on the results. For example, if the user is feeling stressed, the suggestion unit suggests classical music or natural sounds. The suggestion unit also suggests aromas based on the user's emotional state. For example, it suggests lavender or chamomile aromas. In this way, music and aromas that correspond to the user's emotions can also be suggested.

[0067] The recipe generation unit can analyze seasonal variations in the nutritional value of fruits and propose recipes suited to the time when they are most nutritious. For example, the recipe generation unit collects seasonal data on the nutritional value of fruits, analyzes the data, and proposes recipes suited to the time when they are most nutritious. For example, in summer, it proposes recipes using watermelon, which is rich in vitamin C. The recipe generation unit can also propose recipes using mandarin oranges, which are rich in vitamin A, in winter. This allows the unit to analyze seasonal variations in the nutritional value of fruits and propose recipes suited to the time when they are most nutritious.

[0068] The recipe generation unit can also suggest methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time. The recipe generation unit, for example, suggests methods for storing seasonal fruits. For example, it introduces methods for freezing and drying the fruits, allowing them to be enjoyed for a long period of time. The recipe generation unit also suggests methods for cooking seasonal fruits. For example, it introduces methods for steaming and baking the fruits, allowing them to be enjoyed for a long period of time. In this way, it suggests methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time.

[0069] The recipe generation unit can use the emotion estimation function to select fruits that match the emotional fluctuations of each season. The recipe generation unit, for example, collects data on the emotional fluctuations of each season and selects fruits based on that data. For example, since people tend to feel depressed in winter, the recipe generation unit can suggest fruits that have a relaxing effect. The recipe generation unit can also suggest fruits that can replenish energy in summer. In this way, the emotion estimation function can be used to select fruits that match the emotional fluctuations of each season.

[0070] The recipe generation unit can also suggest recipes for desserts and snacks using seasonal fruits. The recipe generation unit suggests, for example, recipes for desserts using seasonal fruits. For example, in the summer, it suggests recipes for watermelon sorbet and fruit punch. The recipe generation unit can also suggest recipes for snacks using seasonal fruits. For example, in the winter, it suggests a recipe for a fruit bar using mandarin oranges. In this way, it also suggests recipes for desserts and snacks using seasonal fruits.

[0071] The recipe generation unit can suggest pairings of juices using seasonal fruits. The recipe generation unit can suggest pairings of juices using seasonal fruits with meals, for example. For example, in summer, the recipe generation unit can suggest a combination of watermelon juice and salad. The recipe generation unit can also suggest pairings of juices using seasonal fruits with sweets. For example, in winter, the recipe generation unit can suggest a combination of mandarin orange juice and cookies. In this way, pairings of juices using seasonal fruits are suggested.

[0072] The recipe generation unit can use the emotion estimation function to suggest recipes that match seasonal events and occasions. The recipe generation unit, for example, suggests recipes that match seasonal events and occasions. For example, it suggests recipes for watermelon juice and fruit punch for summer festivals. The recipe generation unit can also suggest recipes for desserts using mandarin oranges for Christmas. In this way, the emotion estimation function is used to suggest recipes that match seasonal events and occasions.

[0073] The recipe generation unit can collect the user's blood test results, analyze the data, and propose recipes that supplement necessary nutrients. The recipe generation unit, for example, collects the user's blood test results, analyzes the data, and proposes recipes that supplement necessary nutrients. For example, the recipe generation unit proposes recipes that supplement necessary nutrients based on data such as the user's blood sugar level, cholesterol level, and vitamin D concentration. In this way, recipes that supplement necessary nutrients are proposed based on the user's blood test results.

[0074] The recipe generation unit can use the emotion estimation function to supplement nutrients according to the emotional state. For example, the recipe generation unit uses the emotion estimation function to analyze the user's emotional state and propose a recipe that supplements nutrients based on the results. For example, if the user is under high stress, the recipe generation unit can propose a recipe that uses fruits that are high in magnesium. The recipe generation unit can also propose a recipe that includes nutrients that have a relaxing effect based on the user's emotional state. In this way, the emotion estimation function can be used to supplement nutrients according to the emotional state.

[0075] The recipe generation unit can also suggest supplements for the purpose of supplementing nutrients. For example, the recipe generation unit suggests supplements to make up for a nutrient deficiency in the user. For example, if there is an iron deficiency, an iron supplement is suggested. The recipe generation unit can also suggest vitamin C and protein supplements. This allows for suggesting supplements for the purpose of supplementing nutrients.

[0076] The recipe generation unit can propose an entire meal plan aimed at supplementing nutrients. For example, the recipe generation unit proposes an entire meal plan to supplement a user's nutrient deficiency. For example, if there is an iron deficiency, the recipe generation unit proposes a meal plan using ingredients that are high in iron. The recipe generation unit can also propose a meal plan that takes into account daily meal menus and nutritional balance. In this way, the recipe generation unit proposes an entire meal plan aimed at supplementing nutrients.

[0077] The recipe generation unit can use the emotion estimation function to supplement nutrients according to the user's emotions. For example, the recipe generation unit uses the emotion estimation function to analyze the user's emotional state and propose a recipe that supplements nutrients based on the results. For example, if the user is under high stress, the recipe generation unit can propose a recipe that uses fruits that are high in magnesium. The recipe generation unit can also propose a recipe that includes nutrients that have a relaxing effect based on the user's emotional state. In this way, the emotion estimation function can be used to supplement nutrients according to the user's emotions.

[0078] The recipe generation unit can analyze user feedback and automatically learn how to improve a recipe. For example, the recipe generation unit collects feedback provided by users, analyzes the data, and automatically learns how to improve a recipe. For example, if a user rates a recipe as "delicious," the next recipe suggestion is improved based on that information. The recipe generation unit can also learn how to improve a recipe based on user comments and survey results. This allows the recipe generation unit to analyze user feedback and automatically learn how to improve a recipe.

[0079] The recipe generation unit can track changes in a user's preferences over the long term and suggest recipes based on the data. For example, the recipe generation unit can track changes in a user's preferences over the long term and suggest recipes based on that data. For example, the recipe generation unit can infer current preferences based on data on fruits and vegetables that the user liked in the past. The recipe generation unit can also track changes in a user's taste preferences and ingredient preferences and suggest recipes based on those preferences. This allows the recipe generation unit to track changes in a user's preferences over the long term and suggest recipes based on those preferences.

[0080] The recipe generation unit can use the emotion estimation function to collect feedback based on the user's emotions and use it to improve the recipe. For example, the recipe generation unit uses the emotion estimation function to analyze the user's emotional state and collect feedback based on the results. For example, if the user feels that "this recipe was delicious," the recipe generation unit can improve the next recipe suggestion based on that information. The recipe generation unit can also learn how to improve the recipe based on the user's emotional state. In this way, the emotion estimation function can be used to collect feedback based on the user's emotions and use it to improve the recipe.

[0081] The recipe generation unit can improve the recipe so that it can be applied to other users based on user feedback. For example, the recipe generation unit collects user feedback and improves the recipe so that it can be applied to other users based on that data. For example, if multiple users rate the recipe as "delicious," the recipe generation unit can suggest it to other users based on that information. The recipe generation unit can also improve the recipe so that it can be applied to other users based on user comments and survey results. In this way, the recipe generation unit improves the recipe so that it can be applied to other users based on user feedback.

[0082] The recipe generation unit can suggest new fruit and vegetable combinations based on user feedback. For example, the recipe generation unit collects user feedback and suggests new fruit and vegetable combinations based on that data. For example, if a user rates a recipe as "delicious," the recipe generation unit suggests new combinations based on that information. The recipe generation unit can also suggest new fruit and vegetable combinations based on user comments and survey results. This allows the recipe generation unit to suggest new fruit and vegetable combinations based on user feedback.

[0083] The recipe generation unit can use the emotion estimation function to collect feedback based on the user's emotions and improve the recipe so that it can be applied to other users. The recipe generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and collect feedback based on the results. For example, if the user feels that "this recipe was delicious," the next recipe suggestion is improved based on that information. The recipe generation unit can also improve the recipe so that it can be applied to other users based on the user's emotional state. In this way, the emotion estimation function can be used to collect feedback based on the user's emotions and improve the recipe so that it can be applied to other users.

[0084] The recipe generation unit can analyze the user's past recipe history and suggest untried combinations. For example, the recipe generation unit collects the user's past recipe history and analyzes that data to suggest untried combinations. For example, it can suggest combinations of fruits and vegetables that the user has not tried before. The recipe generation unit can also suggest untried combinations based on the user's ratings and comments. This allows the recipe generation unit to analyze the user's past recipe history and suggest untried combinations.

[0085] The recipe generation unit can propose recipes using different cooking methods according to the user's preferences and physical condition. The recipe generation unit, for example, proposes recipes for frozen juice according to the user's preferences and physical condition. For example, juice using frozen fruit is proposed in the summer. The recipe generation unit can also propose hot juice. For example, hot fruit juice is proposed in the winter. In this way, recipes using different cooking methods according to the user's preferences and physical condition are proposed.

[0086] The recipe generation unit can use the emotion estimation function to propose a variety of recipes according to the user's emotions. For example, the recipe generation unit uses the emotion estimation function to analyze the user's emotional state and propose a variety of recipes based on the results. For example, if the user wants to relax, the recipe generation unit proposes a recipe using fruits that have a relaxing effect. The recipe generation unit can also propose a recipe that can replenish energy based on the user's emotional state. In this way, the emotion estimation function is used to propose a variety of recipes according to the user's emotions.

[0087] The recipe generation unit can propose a variety of juice recipes to suit different meal occasions. For example, the recipe generation unit proposes a variety of juice recipes to suit breakfast. For example, it proposes a breakfast juice that uses fruit that is rich in vitamin C. The recipe generation unit can also propose juice recipes to suit lunch and dinner. For example, it proposes a lunch juice that uses fruit that can replenish energy, or a dinner juice that uses fruit that has a relaxing effect. In this way, a variety of juice recipes can be proposed to suit different meal occasions.

[0088] The recipe generation unit can propose a variety of juice recipes using fruits from different cultures and regions. For example, the recipe generation unit proposes a variety of juice recipes using fruits from different cultures. For example, juices using Asian fruits are proposed. The recipe generation unit can also propose juice recipes using fruits that are local specialties of a region. For example, juices using locally grown fruits are proposed. In this way, a variety of juice recipes are proposed using fruits from different cultures and regions.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The user information collection unit can collect data on the user's exercise habits and suggest recipes based on that data. For example, when the user inputs the type and frequency of exercise they do daily, the user information collection unit collects that information. The user information collection unit can also collect the user's post-exercise fatigue level and energy expenditure. The recipe generation unit generates a mixed juice recipe suitable for post-exercise recovery based on the information on the exercise habits collected by the user information collection unit. For example, the recipe generation unit can suggest a combination of fruits and vegetables that are high in protein and amino acids to promote muscle recovery after exercise. It can also generate a recipe using fruits that are high in electrolytes, taking into account post-exercise hydration. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest optimal mixed juice recipes based on the user's exercise habits.

[0091] The user information collection unit can collect the user's ingredient allergy information and suggest recipes based on that information. For example, if the user is allergic to a specific fruit or vegetable, the user information collection unit collects that information by inputting that information. The user information collection unit can also collect the user's allergic reaction intensity and past allergy history. The recipe generation unit generates a safe mixed juice recipe based on the allergy information collected by the user information collection unit. For example, the recipe generation unit can suggest recipes that avoid ingredients that may cause allergies and use alternative fruits and vegetables. It can also generate recipes that combine ingredients that are effective in reducing allergic reactions. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest safe and optimal mixed juice recipes based on the user's allergy information.

[0092] The user information collection unit can collect data on the user's sleep patterns and suggest recipes based on the collected data. For example, when the user inputs how much time they spend sleeping on a daily basis and the quality of their sleep, the user information collection unit collects the information. The user information collection unit can also collect the user's heart rate and breathing patterns while sleeping. The recipe generation unit generates a mixed juice recipe that improves sleep quality based on the information on the sleep patterns collected by the user information collection unit. For example, the recipe generation unit can suggest a recipe using fruits and vegetables that have a relaxing effect. It can also generate a recipe that combines ingredients that are effective in regulating the body clock during sleep. The suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe. This makes it possible to suggest an optimal mixed juice recipe based on the user's sleep patterns.

[0093] The user information collection unit can analyze the user's emotional state and suggest recipes that have stress-reducing or relaxing effects. For example, the emotional state can be estimated by analyzing the user's facial expressions and voice. A camera or microphone can be used to analyze the user's facial expressions and tone of voice, and if stress is high, recipes using fruits that have a relaxing effect can be suggested. The user information collection unit can also analyze the user's brain wave data to estimate the emotional state. This makes it possible to suggest recipes that have stress-reducing or relaxing effects based on the user's emotional state.

[0094] The recipe generation unit can also suggest recipes for health foods other than juices based on the user's preferences and physical condition. For example, a smoothie recipe can be suggested based on the user's preferences and physical condition. If the user wants to get a lot of vitamin C, a smoothie using orange or kiwi can be suggested. The recipe generation unit can also suggest salad recipes. A salad using spinach or lentils, which are rich in iron, can be suggested. This makes it possible to suggest recipes for health foods other than juices based on the user's preferences and physical condition.

[0095] The suggestion unit may have a function that allows recipes that match the user's preferences and physical condition to be shared with family and friends. For example, the function may allow the user to share recipes that the user has created with family and friends. Recipes can be shared via social networking sites or messaging apps. The suggestion unit can also send the created recipes by email. This allows recipes that match the user's preferences and physical condition to be shared with family and friends.

[0096] The suggestion unit can also use the emotion estimation function to suggest music and aromas that correspond to the user's emotions. For example, it analyzes the user's emotional state and suggests music with a relaxing effect based on the results. If the user is feeling stressed, it suggests classical music or natural sounds. The suggestion unit also suggests aromas based on the user's emotional state. For example, it suggests lavender or chamomile aromas. This allows it to suggest music and aromas that correspond to the user's emotions.

[0097] The recipe generation unit can analyze seasonal variations in the nutritional value of fruits and suggest recipes that are suited to the time when they are most nutritious. For example, it can collect nutritional value data for fruits by season, analyze that data, and suggest recipes that are suited to the time when they are most nutritious. In summer, it can suggest recipes that use watermelon, which is rich in vitamin C. In winter, it can also suggest recipes that use mandarin oranges, which are rich in vitamin A. This allows it to analyze seasonal variations in the nutritional value of fruits and suggest recipes that are suited to the time when they are most nutritious.

[0098] The recipe generation unit can also suggest methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time. For example, it suggests methods for storing seasonal fruits, introducing methods such as freezing and drying them, allowing them to be enjoyed for a long period of time. The recipe generation unit also suggests methods for cooking seasonal fruits, introducing methods such as steaming and baking, allowing them to be enjoyed for a long period of time. In this way, it suggests methods for storing and cooking seasonal fruits, allowing them to be enjoyed for a long period of time.

[0099] The recipe generation unit can use the emotion estimation function to select fruits that match the emotional fluctuations of each season. For example, it can collect data on the emotional fluctuations of each season and select fruits based on that data. Since people tend to feel depressed in winter, it can suggest fruits that have a relaxing effect. The recipe generation unit can also suggest fruits that can replenish energy in summer. In this way, the emotion estimation function can be used to select fruits that match the emotional fluctuations of each season.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The user information collection unit collects information about the user's preferences and physical condition. For example, if the user inputs information such as "I like sweet tastes" or "I want to take in a lot of vitamin C," the user information collection unit collects that information. The user information collection unit can also collect information about the user's allergies and health condition. Step 2: The recipe generation unit generates an optimal mixed juice recipe based on the information collected by the user information collection unit. For example, the generation AI may suggest an appropriate combination of fruits and vegetables based on the user's preferences and physical condition. The generation AI may also generate recipes taking into account nutritional balance and calorie restrictions. Step 3: The suggestion unit suggests the recipe generated by the recipe generation unit to the user. For example, the suggestion unit displays the generated recipe on the user's smartphone or tablet. The suggestion unit can also print and provide the generated recipe.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 user information collection unit that collects information about the user's preferences and physical condition; a recipe generation unit that generates an optimal mixed juice recipe based on the information collected by the user information collection unit; a suggestion unit that suggests the recipe generated by the recipe generation unit to a user. A system characterized by:

2. The user information collection unit Collects real-time health data from users and adjusts recipes accordingly 2. The system of claim 1.

3. The recipe generation unit It also suggests recipes for healthy foods other than juice based on the user's preferences and physical condition.

2. The system of claim 1.

4. The proposal unit It has a function that allows users to share recipes based on their preferences and physical condition with family and friends.

2. The system of claim 1.

5. The recipe generation unit Using emotion estimation function, we select fruits that match seasonal emotional fluctuations.

2. The system of claim 1.

6. The recipe generation unit It collects the user's blood test results, analyzes the data, and suggests recipes that supplement the necessary nutrients.

2. The system of claim 1.

7. The recipe generation unit Analyzes user feedback and automatically learns how to improve recipes 2. The system of claim 1.

8. The user information collection unit Using emotion estimation function, the system analyzes the user's emotional state and suggests recipes that will reduce stress and have a relaxing effect.

2. The system of claim 1.

Images