System

Abstract

To provide a system for proposing nutrients, food materials, and intake timing optimal for an individual user.SOLUTION: A system comprises a receiving part, a specifying part, a proposal part, a generation part, and a timing part. The receiving part inputs user information. The specifying part specifies necessary nutrients on the basis of information input by the receiving part. The proposal part proposes food materials and their amount on the basis of the nutrients specified by the specifying part. The generation part generates a recipe by using the food materials by the proposal part proposed. The timing part proposes intake timing of the food materials on the basis of the recipe generated by the generation part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to suggest optimal nutrients, ingredients, and intake timings for each individual user.

[0005] The system according to the embodiment aims to suggest optimal nutrients, ingredients, and intake timings for each user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an identifying unit, a suggestion unit, a generation unit, and a timing unit. The receiving unit inputs user information. The identifying unit identifies necessary nutrients based on the information input by the receiving unit. The suggestion unit suggests ingredients and their amounts based on the nutrients identified by the identifying unit. The generation unit generates a recipe using the ingredients suggested by the suggestion unit. The timing unit suggests the timing for consuming ingredients based on the recipe generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can suggest optimal nutrients, ingredients, and intake timings for each individual user. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The nutrition suggestion system according to the embodiment of the present invention is a system that automatically generates recipes for easy cooking by suggesting which ingredients should be taken, how much, and at what timing according to the physique and nutrient needs that differ for each sport. The nutrition suggestion system is not only for athletes, but also for sick people and small children. First, the user inputs information such as his / her physique, sport, health condition, and age. Next, the generation AI analyzes this information and identifies the necessary nutrients. For example, marathon runners need a lot of carbohydrates, and athletes who do strength training need a lot of protein. In addition, sick people may need certain vitamins and minerals. The generation AI suggests appropriate ingredients and their amounts based on the identified nutrients. For example, pasta and bananas are suggested for marathon runners, chicken and tofu are suggested for athletes who do strength training, and spinach and oranges are suggested for sick people. Furthermore, the generation AI automatically generates recipes that can be easily cooked using the suggested ingredients. For example, a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. This allows the user to easily take in the necessary nutrients. The generation AI also suggests the timing of taking ingredients. For example, before training, one would take in carbohydrates to replenish energy, and after training, one would take in protein to help repair muscles. This allows the nutrition suggestion system to provide optimal nutritional intake according to the user's physique, sport, and health condition, which is expected to improve performance and maintain health. This allows the nutrition suggestion system to provide optimal nutritional intake according to the user's physique, sport, and health condition.

[0029] The nutrition suggestion system according to the embodiment includes a reception unit, an identification unit, a proposal unit, a generation unit, and a timing unit. The reception unit inputs user information. The user information includes, for example, age, sex, weight, height, health condition, and the like, but is not limited to such examples. The reception unit, for example, stores the information input by the user in a database and provides it to the identification unit. The identification unit uses a generation AI to identify necessary nutrients based on the information input by the reception unit. The identification unit analyzes information such as the user's physique, sport, health condition, and age, and identifies necessary nutrients. The generation AI calculates the required amounts of carbohydrates, proteins, vitamins, minerals, and the like, depending on the user's physique and sport, for example. The proposal unit proposes appropriate ingredients and their amounts based on the nutrients identified by the specification unit. The proposal unit uses, for example, the generation AI to propose appropriate ingredients and their amounts based on the identified nutrients. The proposal unit proposes, for example, pasta and bananas for marathon runners, chicken and tofu for athletes who do strength training, and spinach and oranges for sick people. The generation unit generates recipes that can be easily cooked using the proposed ingredients. The generating unit generates a recipe that can be easily cooked using the proposed ingredients, for example, using a generating AI. The generating unit generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The timing unit suggests the timing of taking in ingredients based on the recipe generated by the generating unit. The timing unit suggests the timing of taking in ingredients, for example, using a generating AI. The timing unit, for example, takes carbohydrates to replenish energy before training, and takes protein to help repair muscles after training. This allows the nutrition suggestion system according to the embodiment to take in optimal nutrition according to the user's physique and health condition. A part or all of the above-mentioned processing in the identifying unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the identifying unit can input information such as the user's physique, sport, health condition, and age into the generating AI, and cause the generating AI to identify the necessary nutrients. A part or all of the above-mentioned processing in the suggesting unit may be performed, for example, using an AI, or may be performed without using an AI.For example, the suggestion unit can input appropriate ingredients and their amounts to the generation AI based on the identified nutrients, and cause the generation AI to suggest ingredients and their amounts. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input suggested ingredients to the generation AI, and cause the generation AI to generate a recipe. A part or all of the above-mentioned processing in the timing unit can be performed, for example, using AI, or can be performed without using AI. For example, the timing unit can input the generated recipe to the generation AI, and cause the generation AI to suggest the timing for ingesting ingredients.

[0030] The reception unit inputs user information. The user information includes, but is not limited to, for example, age, sex, weight, height, health condition, etc. The reception unit, for example, stores the information input by the user in a database and provides it to the identification unit. Specifically, the reception unit provides an interface for the user to access a dedicated application or website using a smartphone or a personal computer and input the necessary information. The user can input age, sex, weight, height, health condition, exercise habits, dietary preferences, allergy information, etc. This information is stored in a secure database and is designed to protect the user's privacy. Furthermore, the reception unit also has a function of checking the information input by the user in real time and notifying the user of any missing or inaccurate information and urging the user to correct it. For example, if the user forgets to input weight or makes a mistake in the unit of height, the system automatically displays an error message and urges the user to input the correct information. The reception unit also provides a function of recording information previously input by the user and eliminating the need to re-input the information the next time the user uses the system. This allows the user to input information easily and quickly, making it easier to use the system.

[0031] The identification unit uses the generation AI to identify the necessary nutrients based on the information input by the reception unit. The identification unit analyzes information such as the user's physique, sport, health condition, and age to identify the necessary nutrients. The generation AI calculates the required amounts of carbohydrates, proteins, vitamins, minerals, etc., depending on the user's physique and sport. Specifically, the generation AI executes an algorithm to calculate the optimal nutritional balance for each individual user based on the user's input information. For example, if the user is a marathon runner, the generation AI calculates the amount of carbohydrates required to maintain endurance for a long time and identifies the amount of protein to promote muscle repair and growth. In addition, if the user has a specific health condition, such as diabetes or high blood pressure, the generation AI takes into account the balance of nutrients appropriate for that condition. The generation AI refers to the latest nutritional research and databases to provide the optimal combination of nutrients according to the user's health condition and goals. In addition, the identification unit can analyze the user's past data and trends and provide advice for long-term nutritional management. As a result, the identification unit accurately identifies nutrients according to the user's individual needs, supporting health maintenance and performance improvement.

[0032] The suggestion unit suggests appropriate ingredients and their amounts based on the nutrients identified by the identification unit. The suggestion unit suggests appropriate ingredients and their amounts based on the identified nutrients, for example, using a generation AI. Specifically, the generation AI generates a list of optimal ingredients based on the user's nutrient needs. For example, it suggests pasta and bananas to replenish energy for marathon runners, and chicken and tofu, which are high in protein, to athletes who do strength training. It also suggests spinach and oranges, which are rich in vitamin C, to sick people to boost their immunity. The generation AI takes into account the nutritional value, calories, allergy information, etc. of the ingredients and calculates the optimal ingredients and their amounts for the user. In addition, the suggestion unit takes into account the user's dietary preferences, budget, and cooking effort, and suggests ingredients that are easy to actually incorporate. For example, it also provides options such as ingredients that are easy to cook and frozen foods for busy users. In addition, the suggestion unit also suggests ingredients that utilize seasonal and local specialties, allowing the user to enjoy fresh and delicious meals. In this way, the suggestion unit optimizes the user's nutritional balance and supports a healthy diet.

[0033] The generation unit generates recipes that can be easily cooked using the proposed ingredients. The generation unit generates recipes that can be easily cooked using the proposed ingredients, for example, using a generation AI. Specifically, the generation AI creates easy and delicious recipes based on the user's ingredient list. For example, it generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The generation AI takes into account the user's cooking skills and time constraints and provides recipes that even beginners can easily make and recipes that can be cooked in a short time. In addition, the generation unit takes into account the user's dietary preferences and allergy information and provides individually customized recipes. For example, it proposes recipes that do not use meat for vegetarian users and provides recipes that do not use wheat for gluten-free users. In addition, the generation unit also provides nutritional value and calorie information of the recipe, allowing the user to check the nutritional balance of their meal. As a result, the generation unit allows the user to effectively use the suggested ingredients and enjoy healthy and delicious meals.

[0034] The timing unit suggests the timing of food intake based on the recipe generated by the generation unit. The timing unit suggests the timing of food intake using, for example, a generation AI. Specifically, the generation AI calculates the optimal intake timing by considering the user's life rhythm and exercise schedule. For example, before training, carbohydrates are taken to replenish energy, and after training, protein is taken to help repair muscles. The generation AI suggests a specific intake timing by considering the user's exercise intensity, time of day, meal intervals, etc. Furthermore, the timing unit adjusts the timing of food intake according to the user's goals and health condition. For example, for a user who is aiming to lose weight, advice is provided to set longer meal intervals to prevent a sudden rise in blood sugar levels. In addition, the timing unit provides a reminder function so that the user can take meals according to the suggested timing. For example, a reminder that notifies the user of meal times can be set using the notification function of a smartphone. As a result, the timing unit supports the user in taking nutrients at the optimal timing and maintaining health and improving performance.

[0035] The identification unit can analyze information on the user's physique, sport, health condition, and age, and identify the necessary nutrients. The identification unit, for example, analyzes the user's physique. The physique includes, for example, BMI (Body Mass Index), body fat percentage, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's sport. The sport includes, for example, track and field, swimming, soccer, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's health condition. The health condition includes, for example, blood pressure, blood glucose level, cholesterol level, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's age. The age includes, for example, child, adult, elderly, etc., but is not limited to such examples. This allows the identification unit to identify the necessary nutrients based on the user's individual information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input information such as the user's physique, sport, health condition, age, etc. into the generation AI and cause the generation AI to identify the necessary nutrients.

[0036] The suggestion unit can suggest appropriate ingredients and their amounts based on the identified nutrients. The suggestion unit, for example, suggests appropriate ingredients based on the identified nutrients. Suitable ingredients include, for example, vegetables, fruits, meats, and fish, but are not limited to such examples. The suggestion unit, for example, suggests appropriate amounts of ingredients based on the identified nutrients. The amounts include, for example, grams, cups, and numbers, but are not limited to such examples. In this way, the suggestion unit suggests appropriate ingredients and their amounts, allowing the user to ingest the necessary nutrients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input appropriate ingredients and their amounts to the generation AI based on the identified nutrients, and cause the generation AI to execute the suggestion of ingredients and their amounts.

[0037] The generation unit can generate a recipe that can be easily cooked using the proposed ingredients. The generation unit generates, for example, a recipe that can be easily cooked using the proposed ingredients. Easy cooking includes, for example, cooking time and simplicity of cooking procedures, but is not limited to such examples. The generation unit generates, for example, a recipe for a simple stir-fry using chicken and vegetables. The generation unit generates, for example, a recipe for a smoothie using banana and yogurt. In this way, the generation unit generates a recipe that can be easily cooked, allowing the user to easily ingest nutrients. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the proposed ingredients into the generation AI and cause the generation AI to generate a recipe.

[0038] The timing unit can suggest the timing of ingestion of ingredients. The timing unit, for example, suggests the timing of ingestion of ingredients. Ingestion timing includes, for example, a time period during which meals are taken, timing after exercise, and the like, but is not limited to such examples. The timing unit, for example, suggests the timing of ingestion of carbohydrates to replenish energy before training. The timing unit, for example, suggests the timing of ingestion of protein to help repair muscles after training. In this way, the timing unit suggests the timing of ingestion of ingredients, allowing the user to ingest nutrients at the optimal timing. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the timing unit can input the generated recipe into the generation AI and cause the generation AI to execute the suggestion of the timing of ingestion of ingredients.

[0039] The generation unit can generate a recipe for a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The generation unit generates, for example, a recipe for a simple stir-fry using chicken and vegetables. The generation unit generates, for example, a recipe for a smoothie using bananas and yogurt. In this way, the generation unit can generate a specific recipe, allowing the user to easily cook a dish. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input suggested ingredients into the generation AI and cause the generation AI to generate a recipe.

[0040] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit, for example, analyzes the user's past input history and proposes the optimal input method. The past input history includes, for example, input frequency, tendencies of input contents, and the like, but is not limited to such examples. The reception unit, for example, automatically displays as candidates the physique and athletic information frequently input by the user in the past. The reception unit, for example, preferentially proposes the input method (voice, text, etc.) used by the user in the past. The reception unit, for example, predicts and proposes information to be used in a specific time period from the user's past input history. Thereby, the reception unit can improve the efficiency of input by proposing the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0041] The reception unit can dynamically change the input items based on the user's current health condition and activity level. The reception unit dynamically changes the input items based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The activity level includes, but is not limited to, the amount of exercise, the number of steps, and calories burned, and the like. For example, when the user is tired, the reception unit simplifies the input items and requests only the minimum necessary information. For example, when the user is in good health, the reception unit provides detailed input items and collects more precise information. For example, when the user shows a high activity level, the reception unit provides additional input items and collects more specific information. In this way, the reception unit can collect more accurate information by providing input items according to the user's health condition and activity level. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the input items.

[0042] The reception unit can add input items related to ingredients and nutrients specific to a region, taking into account the geographical location information of the user. The reception unit adds input items related to ingredients and nutrients specific to a region, taking into account the geographical location information of the user, for example. The geographical location information includes, for example, GPS data, ingredient information specific to a region, and the like, but is not limited to such examples. The ingredients and nutrients specific to a region include, for example, local specialty products, seasonal ingredients, and the like, but are not limited to such examples. For example, when the user is in a specific region, the reception unit adds ingredients available in the region as input items. For example, when the user is traveling, the reception unit adds nutrients specific to the region of the travel destination as input items. For example, the reception unit adds ingredients according to the climate and season of the region in which the user lives as input items. As a result, the reception unit can make more appropriate suggestions by adding input items related to ingredients and nutrients specific to a region. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to add input items related to ingredients and nutrients specific to the region.

[0043] The reception unit can analyze the social media activity of the user and automatically suggest related input items. The reception unit, for example, analyzes the social media activity of the user and automatically suggests related input items. The social media activity includes, for example, but is not limited to, the content of the post, the number of likes, the number of followers, etc. The related input items include, for example, but are not limited to, the food ingredient preferences, the past meal history, etc. The reception unit, for example, suggests related input items based on the meal content shared by the user on social media. The reception unit, for example, analyzes the posts of nutrition experts and athletes followed by the user and suggests related input items. The reception unit, for example, analyzes the trends of online communities in which the user participates and suggests related input items. In this way, the reception unit can improve the efficiency of input by suggesting related input items based on the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the suggestion of related input items.

[0044] The identification unit can analyze the user's past nutritional intake history and identify optimal nutrients. The identification unit, for example, analyzes the user's past nutritional intake history and identifies optimal nutrients. The past nutritional intake history includes, for example, a record of meals, and the type and amount of nutrients taken, but is not limited to such examples. The identification unit, for example, identifies nutrients currently required by the user based on data of nutrients taken in the past. For example, when a specific nutrient is lacking from the user's past nutritional intake history, the identification unit preferentially identifies the nutrient. For example, the identification unit analyzes the user's past nutritional intake history and identifies balanced nutrients. As a result, the identification unit can improve the accuracy of nutritional intake by identifying optimal nutrients based on the past nutritional intake history. Part or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the user's past nutritional intake history to the generation AI and cause the generation AI to identify optimal nutrients.

[0045] The identification unit can dynamically change the nutrient identification algorithm based on the user's current health condition and activity level. The identification unit dynamically changes the nutrient identification algorithm based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, etc. The activity level includes, but is not limited to, the amount of exercise, number of steps, calories burned, etc. For example, when the user is tired, the identification unit identifies nutrients suitable for energy replenishment. For example, when the user shows a high activity level, the identification unit identifies nutrients necessary for muscle repair and growth. For example, when the user is in good health, the identification unit identifies nutrients suitable for maintaining overall health. This enables the identification unit to identify nutrients according to the health condition and activity level. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the nutrient identification algorithm.

[0046] The identification unit can identify nutrients specific to a region by considering the geographical location information of the user. The identification unit identifies nutrients specific to a region by considering, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, nutrient information specific to a region, and the like, but is not limited to such examples. The nutrients specific to a region include, for example, local specialty products, seasonal ingredients, and the like, but is not limited to such examples. For example, when the user is in a specific region, the identification unit identifies nutrients available in the region. For example, when the user is traveling, the identification unit identifies nutrients specific to the region of the travel destination. For example, the identification unit identifies nutrients according to the climate and season of the region in which the user lives. As a result, the identification unit identifies nutrients specific to the region, thereby enabling more appropriate nutritional intake. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit may input the geographical location information of the user to the generation AI and cause the generation AI to execute identification of nutrients specific to the region.

[0047] The identification unit can analyze the social media activity of the user and identify the relevant nutrients. The identification unit, for example, analyzes the social media activity of the user and identifies the relevant nutrients. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The relevant nutrients include, for example, the preference of ingredients, the past meal history, etc., but is not limited to such examples. The identification unit, for example, identifies the relevant nutrients based on the meal content shared by the user on social media. The identification unit, for example, analyzes the posts of nutrition experts and athletes followed by the user and identifies the relevant nutrients. The identification unit, for example, analyzes the trends of the online community in which the user participates and identifies the relevant nutrients. As a result, the identification unit identifies the relevant nutrients based on the social media activity, thereby enabling more appropriate nutritional intake. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the identification of the relevant nutrients.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the ingredients. The suggestion unit applies different suggestion algorithms depending on the category of the ingredients, for example. The categories of ingredients include, but are not limited to, vegetables, fruits, meats, and fishes. For example, the suggestion unit makes suggestions according to the season for vegetables and fruits. For example, the suggestion unit makes suggestions according to the cooking method for meat and fish. For example, the suggestion unit makes suggestions according to the nutritional value for grains and beans. In this way, the suggestion unit provides suggestions according to the category of the ingredients, allowing the user to select appropriate ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input ingredient category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0049] The suggestion unit can determine the priority of the suggestions based on the time when the ingredients were obtained. The suggestion unit determines the priority of the suggestions based on, for example, the time when the ingredients were obtained. The time when the ingredients were obtained includes, for example, a season, a harvest season, and the like, but is not limited to such examples. For example, the suggestion unit preferentially suggests seasonal ingredients. For example, the suggestion unit preferentially suggests in-season ingredients. For example, the suggestion unit preferentially suggests ingredients that are easy for the user to obtain at a specific time. In this way, the suggestion unit provides suggestions according to the time when the ingredients were obtained, allowing the user to select appropriate ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data on the time when the ingredients were obtained to the generation AI, and cause the generation AI to execute the determination of the priority of the suggestions.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of ingredients. The suggestion unit adjusts the order of suggestions based on the relevance of ingredients, for example. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the balance of nutrients. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the ease of cooking methods. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the preferences of the user. In this way, the suggestion unit provides suggestions according to the relevance of ingredients, allowing the user to select appropriate ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input the relevance data of ingredients to the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients. The generation unit adjusts the level of detail of the recipe based on the importance of the ingredients, for example. The importance of the ingredients includes, but is not limited to, nutritional value, allergy information, and the like. The generation unit provides, for example, a detailed cooking method for important ingredients. The generation unit provides, for example, a simple cooking method for ingredients that are not so important. The generation unit preferentially includes ingredients with high importance in the recipe, for example, according to the health condition of the user. In this way, the generation unit provides a recipe according to the importance of the ingredients, allowing the user to select an appropriate cooking method. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the importance data of ingredients into the generation AI and causes the generation AI to adjust the level of detail of the recipe.

[0052] The generation unit can apply different recipe generation algorithms depending on the category of ingredients. The generation unit applies different recipe generation algorithms depending on the category of ingredients, for example. The categories of ingredients include, but are not limited to, vegetables, fruits, meats, and fishes. For example, for vegetables and fruits, the generation unit generates recipes according to the season. For example, for meat and fish, the generation unit generates recipes according to the cooking method. For example, for grains and beans, the generation unit generates recipes according to the nutritional value. In this way, the generation unit provides recipes according to the category of ingredients, allowing the user to select an appropriate cooking method. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs ingredient category data to the generation AI and causes the generation AI to apply the recipe generation algorithm.

[0053] The generation unit can determine the priority of the recipe based on the time when the ingredients were obtained. The generation unit determines the priority of the recipe based on, for example, the time when the ingredients were obtained. The time when the ingredients were obtained includes, for example, the season, the harvest season, and the like, but is not limited to such examples. The generation unit, for example, preferentially includes seasonal ingredients in the recipe. The generation unit, for example, preferentially includes in-season ingredients in the recipe. The generation unit, for example, preferentially includes ingredients that are easy for the user to obtain at a specific time in the recipe. In this way, the generation unit provides a recipe according to the time when the ingredients were obtained, allowing the user to select appropriate ingredients. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the ingredients were obtained to the generation AI and cause the generation AI to execute the determination of the priority of the recipe.

[0054] The generation unit can adjust the order of the recipe based on the relevance of ingredients. The generation unit adjusts the order of the recipe based on the relevance of ingredients, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the balance of nutrients. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the ease of cooking methods. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the preferences of a user. In this way, the generation unit provides a recipe according to the relevance of ingredients, allowing the user to select an appropriate cooking method. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of ingredients to the generation AI and cause the generation AI to adjust the order of the recipe.

[0055] The timing unit can analyze the user's past intake history and propose the optimal timing. The timing unit, for example, analyzes the user's past intake history and proposes the optimal timing. The past intake history includes, for example, the time period of intake, the type and amount of nutrients taken, and the like, but is not limited to such examples. The timing unit, for example, proposes the current optimal intake timing based on data on the timing of the user's past intake. For example, if the user's past intake history shows that the user often takes in a specific time period, the timing unit preferentially proposes that time period. The timing unit, for example, analyzes the user's past intake history and proposes a balanced intake timing. As a result, the timing unit can improve the efficiency of intake by proposing the optimal intake timing based on the past intake history. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the user's past intake history data to the generation AI and cause the generation AI to execute the proposal of the optimal intake timing.

[0056] The timing unit can dynamically change the proposal based on the user's current health condition and activity level. The timing unit dynamically changes the proposal based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, etc. The activity level includes, but is not limited to, the amount of exercise, number of steps, calories burned, etc. The timing unit, for example, when the user is tired, suggests a timing suitable for energy replenishment. The timing unit, for example, when the user shows a high activity level, suggests a timing suitable for muscle repair and growth. The timing unit, for example, when the user is in good health, suggests a timing suitable for maintaining overall health. This enables the timing unit to suggest an intake timing according to the health condition and activity level. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the proposal.

[0057] The timing unit can propose the optimal timing in consideration of the geographical location information of the user. The timing unit proposes the optimal timing in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, climate information specific to a region, and the like, but is not limited to such examples. For example, when the user is in a specific region, the timing unit proposes an intake timing according to the climate and time zone of the region. For example, when the user is traveling, the timing unit proposes an intake timing according to the time zone of the travel destination. For example, the timing unit proposes an intake timing according to the climate and season of the region in which the user lives. As a result, the timing unit proposes the optimal intake timing based on the geographical location information, so that the user can take in nutrients at an appropriate timing. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute a proposal of the optimal intake timing.

[0058] The timing unit can analyze the social media activity of the user to suggest relevant timing. The timing unit, for example, analyzes the social media activity of the user to suggest relevant timing. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The timing unit, for example, proposes relevant intake timing based on the activity content shared by the user on social media. The timing unit, for example, analyzes posts of nutrition experts and athletes followed by the user, and proposes relevant intake timing. The timing unit, for example, analyzes trends of online communities in which the user participates, and proposes relevant intake timing. In this way, the timing unit can provide more appropriate intake timing by proposing relevant intake timing based on social media activity. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the proposal of relevant intake timing.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past dietary history and provide it to the identification unit. For example, by collecting data on ingredients and nutrients that the user has taken in the past and providing it to the identification unit, it becomes possible to identify nutrients with higher accuracy. Furthermore, the reception unit can extract preferences and allergy information for specific ingredients from the user's dietary history and provide it to the identification unit. This enables the identification unit to identify nutrients taking into account the user's past dietary history, making it possible to realize more personalized suggestions.

[0061] The suggestion unit can suggest ingredients and nutrients specific to a region, taking into account the geographical location information of the user. For example, when the user is in a specific region, ingredients available in that region can be suggested. Also, when the user is traveling, nutrients specific to the region of the travel destination can be suggested. Furthermore, it is also possible to suggest ingredients according to the climate and season of the region in which the user lives. This enables the suggestion unit to make suggestions taking into account ingredients and nutrients specific to a region, thereby realizing more appropriate nutrition suggestions.

[0062] The timing unit can analyze the user's past intake history and suggest the optimal intake timing. For example, the current optimal intake timing can be suggested based on the time period and nutrient data of the user's past intakes. In addition, if the user's past intake history shows that they often take in nutrients during a specific time period, it is possible to suggest that time period with priority. Furthermore, it is also possible to analyze the user's past intake history and suggest a balanced intake timing. In this way, the timing unit can improve intake efficiency by suggesting the optimal intake timing based on the past intake history.

[0063] The reception unit can analyze the user's social media activity and automatically suggest related input items. For example, related input items can be suggested based on the meal contents shared by the user on social media. Also, related input items can be suggested by analyzing posts of nutrition experts and athletes followed by the user. Furthermore, it is also possible to analyze trends in online communities in which the user participates and suggest related input items. In this way, the reception unit can improve the efficiency of input by suggesting related input items based on social media activity.

[0064] The suggestion unit can determine the priority of suggestions based on the time of acquisition of ingredients. For example, seasonal ingredients can be preferentially suggested. Also, in-season ingredients can be preferentially suggested. Furthermore, it is also possible to preferentially suggest ingredients that are easy for the user to obtain at a particular time. In this way, the suggestion unit can provide suggestions according to the time of acquisition of ingredients, thereby enabling the user to select appropriate ingredients.

[0065] The timing unit can suggest the optimal intake timing in consideration of the geographical location information of the user. For example, if the user is in a specific area, the timing unit can suggest the intake timing according to the climate and time of the area. Also, if the user is traveling, the timing unit can suggest the intake timing according to the time of the travel destination. Furthermore, it is also possible to suggest the intake timing according to the climate and season of the area where the user lives. In this way, the timing unit can suggest the optimal intake timing based on the geographical location information, thereby enabling the user to take in nutrients at an appropriate time.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit inputs user information. The user information includes, for example, age, sex, weight, height, health condition, etc. The reception unit stores the information input by the user in a database and provides it to the identification unit. Step 2: The identification unit identifies the necessary nutrients based on the information entered by the reception unit. The identification unit uses the generation AI to analyze information such as the user's physique, sport, health condition, and age, and identifies the necessary nutrients. For example, it calculates the required amounts of carbohydrates, proteins, vitamins, minerals, etc. Step 3: The suggestion unit suggests appropriate ingredients and their amounts based on the nutrients identified by the identification unit. The suggestion unit uses generative AI to suggest appropriate ingredients and their amounts based on the nutrients identified. For example, it suggests pasta and bananas for marathon runners, chicken and tofu for strength-training athletes, and spinach and oranges for sick people. Step 4: The generator generates recipes that can be easily cooked using the suggested ingredients. The generator uses the generative AI to generate recipes that can be easily cooked using the suggested ingredients. For example, it generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. Step 5: The timing unit suggests the timing of food intake based on the recipe generated by the generation unit. The timing unit uses the generation AI to suggest the timing of food intake. For example, before training, consume carbohydrates to replenish energy, and after training, consume protein to help repair muscles.

[0068] (Example 2) The nutrition suggestion system according to the embodiment of the present invention is a system that automatically generates recipes for easy cooking by suggesting which ingredients should be taken, how much, and at what timing according to the physique and nutrient needs that differ for each sport. The nutrition suggestion system is not only for athletes, but also for sick people and small children. First, the user inputs information such as his / her physique, sport, health condition, and age. Next, the generation AI analyzes this information and identifies the necessary nutrients. For example, marathon runners need a lot of carbohydrates, and athletes who do strength training need a lot of protein. In addition, sick people may need certain vitamins and minerals. The generation AI suggests appropriate ingredients and their amounts based on the identified nutrients. For example, pasta and bananas are suggested for marathon runners, chicken and tofu are suggested for athletes who do strength training, and spinach and oranges are suggested for sick people. Furthermore, the generation AI automatically generates recipes that can be easily cooked using the suggested ingredients. For example, a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. This allows the user to easily take in the necessary nutrients. The generation AI also suggests the timing of taking ingredients. For example, before training, one would take in carbohydrates to replenish energy, and after training, one would take in protein to help repair muscles. This allows the nutrition suggestion system to provide optimal nutritional intake according to the user's physique, sport, and health condition, which is expected to improve performance and maintain health. This allows the nutrition suggestion system to provide optimal nutritional intake according to the user's physique, sport, and health condition.

[0069] The nutrition suggestion system according to the embodiment includes a reception unit, an identification unit, a proposal unit, a generation unit, and a timing unit. The reception unit inputs user information. The user information includes, for example, age, sex, weight, height, health condition, and the like, but is not limited to such examples. The reception unit, for example, stores the information input by the user in a database and provides it to the identification unit. The identification unit uses a generation AI to identify necessary nutrients based on the information input by the reception unit. The identification unit analyzes information such as the user's physique, sport, health condition, and age, and identifies necessary nutrients. The generation AI calculates the required amounts of carbohydrates, proteins, vitamins, minerals, and the like, depending on the user's physique and sport, for example. The proposal unit proposes appropriate ingredients and their amounts based on the nutrients identified by the specification unit. The proposal unit uses, for example, the generation AI to propose appropriate ingredients and their amounts based on the identified nutrients. The proposal unit proposes, for example, pasta and bananas for marathon runners, chicken and tofu for athletes who do strength training, and spinach and oranges for sick people. The generation unit generates recipes that can be easily cooked using the proposed ingredients. The generating unit generates a recipe that can be easily cooked using the proposed ingredients, for example, using a generating AI. The generating unit generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The timing unit suggests the timing of taking in ingredients based on the recipe generated by the generating unit. The timing unit suggests the timing of taking in ingredients, for example, using a generating AI. The timing unit, for example, takes carbohydrates to replenish energy before training, and takes protein to help repair muscles after training. This allows the nutrition suggestion system according to the embodiment to take in optimal nutrition according to the user's physique and health condition. A part or all of the above-mentioned processing in the identifying unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the identifying unit can input information such as the user's physique, sport, health condition, and age into the generating AI, and cause the generating AI to identify the necessary nutrients. A part or all of the above-mentioned processing in the suggesting unit may be performed, for example, using an AI, or may be performed without using an AI.For example, the suggestion unit can input appropriate ingredients and their amounts to the generation AI based on the identified nutrients, and cause the generation AI to suggest ingredients and their amounts. A part or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input suggested ingredients to the generation AI, and cause the generation AI to generate a recipe. A part or all of the above-mentioned processing in the timing unit can be performed, for example, using AI, or can be performed without using AI. For example, the timing unit can input the generated recipe to the generation AI, and cause the generation AI to suggest the timing for ingesting ingredients.

[0070] The reception unit inputs user information. The user information includes, but is not limited to, for example, age, sex, weight, height, health condition, etc. The reception unit, for example, stores the information input by the user in a database and provides it to the identification unit. Specifically, the reception unit provides an interface for the user to access a dedicated application or website using a smartphone or a personal computer and input the necessary information. The user can input age, sex, weight, height, health condition, exercise habits, dietary preferences, allergy information, etc. This information is stored in a secure database and is designed to protect the user's privacy. Furthermore, the reception unit also has a function of checking the information input by the user in real time and notifying the user of any missing or inaccurate information and urging the user to correct it. For example, if the user forgets to input weight or makes a mistake in the unit of height, the system automatically displays an error message and urges the user to input the correct information. The reception unit also provides a function of recording information previously input by the user and eliminating the need to re-input the information the next time the user uses the system. This allows the user to input information easily and quickly, making it easier to use the system.

[0071] The identification unit uses the generation AI to identify the necessary nutrients based on the information input by the reception unit. The identification unit analyzes information such as the user's physique, sport, health condition, and age to identify the necessary nutrients. The generation AI calculates the required amounts of carbohydrates, proteins, vitamins, minerals, etc., depending on the user's physique and sport. Specifically, the generation AI executes an algorithm to calculate the optimal nutritional balance for each individual user based on the user's input information. For example, if the user is a marathon runner, the generation AI calculates the amount of carbohydrates required to maintain endurance for a long time and identifies the amount of protein to promote muscle repair and growth. In addition, if the user has a specific health condition, such as diabetes or high blood pressure, the generation AI takes into account the balance of nutrients appropriate for that condition. The generation AI refers to the latest nutritional research and databases to provide the optimal combination of nutrients according to the user's health condition and goals. In addition, the identification unit can analyze the user's past data and trends and provide advice for long-term nutritional management. As a result, the identification unit accurately identifies nutrients according to the user's individual needs, supporting health maintenance and performance improvement.

[0072] The suggestion unit suggests appropriate ingredients and their amounts based on the nutrients identified by the identification unit. The suggestion unit suggests appropriate ingredients and their amounts based on the identified nutrients, for example, using a generation AI. Specifically, the generation AI generates a list of optimal ingredients based on the user's nutrient needs. For example, it suggests pasta and bananas to replenish energy for marathon runners, and chicken and tofu, which are high in protein, to athletes who do strength training. It also suggests spinach and oranges, which are rich in vitamin C, to sick people to boost their immunity. The generation AI takes into account the nutritional value, calories, allergy information, etc. of the ingredients and calculates the optimal ingredients and their amounts for the user. In addition, the suggestion unit takes into account the user's dietary preferences, budget, and cooking effort, and suggests ingredients that are easy to actually incorporate. For example, it also provides options such as ingredients that are easy to cook and frozen foods for busy users. In addition, the suggestion unit also suggests ingredients that utilize seasonal and local specialties, allowing the user to enjoy fresh and delicious meals. In this way, the suggestion unit optimizes the user's nutritional balance and supports a healthy diet.

[0073] The generation unit generates recipes that can be easily cooked using the proposed ingredients. The generation unit generates recipes that can be easily cooked using the proposed ingredients, for example, using a generation AI. Specifically, the generation AI creates easy and delicious recipes based on the user's ingredient list. For example, it generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The generation AI takes into account the user's cooking skills and time constraints and provides recipes that even beginners can easily make and recipes that can be cooked in a short time. In addition, the generation unit takes into account the user's dietary preferences and allergy information and provides individually customized recipes. For example, it proposes recipes that do not use meat for vegetarian users and provides recipes that do not use wheat for gluten-free users. In addition, the generation unit also provides nutritional value and calorie information of the recipe, allowing the user to check the nutritional balance of their meal. As a result, the generation unit allows the user to effectively use the suggested ingredients and enjoy healthy and delicious meals.

[0074] The timing unit suggests the timing of food intake based on the recipe generated by the generation unit. The timing unit suggests the timing of food intake using, for example, a generation AI. Specifically, the generation AI calculates the optimal intake timing by considering the user's life rhythm and exercise schedule. For example, before training, carbohydrates are taken to replenish energy, and after training, protein is taken to help repair muscles. The generation AI suggests a specific intake timing by considering the user's exercise intensity, time of day, meal intervals, etc. Furthermore, the timing unit adjusts the timing of food intake according to the user's goals and health condition. For example, for a user who is aiming to lose weight, advice is provided to set longer meal intervals to prevent a sudden rise in blood sugar levels. In addition, the timing unit provides a reminder function so that the user can take meals according to the suggested timing. For example, a reminder that notifies the user of meal times can be set using the notification function of a smartphone. As a result, the timing unit supports the user in taking nutrients at the optimal timing and maintaining health and improving performance.

[0075] The identification unit can analyze information on the user's physique, sport, health condition, and age, and identify the necessary nutrients. The identification unit, for example, analyzes the user's physique. The physique includes, for example, BMI (Body Mass Index), body fat percentage, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's sport. The sport includes, for example, track and field, swimming, soccer, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's health condition. The health condition includes, for example, blood pressure, blood glucose level, cholesterol level, etc., but is not limited to such examples. The identification unit, for example, analyzes the user's age. The age includes, for example, child, adult, elderly, etc., but is not limited to such examples. This allows the identification unit to identify the necessary nutrients based on the user's individual information. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input information such as the user's physique, sport, health condition, age, etc. into the generation AI and cause the generation AI to identify the necessary nutrients.

[0076] The suggestion unit can suggest appropriate ingredients and their amounts based on the identified nutrients. The suggestion unit, for example, suggests appropriate ingredients based on the identified nutrients. Suitable ingredients include, for example, vegetables, fruits, meats, and fish, but are not limited to such examples. The suggestion unit, for example, suggests appropriate amounts of ingredients based on the identified nutrients. The amounts include, for example, grams, cups, and numbers, but are not limited to such examples. In this way, the suggestion unit suggests appropriate ingredients and their amounts, allowing the user to ingest the necessary nutrients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input appropriate ingredients and their amounts to the generation AI based on the identified nutrients, and cause the generation AI to execute the suggestion of ingredients and their amounts.

[0077] The generation unit can generate a recipe that can be easily cooked using the proposed ingredients. The generation unit generates, for example, a recipe that can be easily cooked using the proposed ingredients. Easy cooking includes, for example, cooking time and simplicity of cooking procedures, but is not limited to such examples. The generation unit generates, for example, a recipe for a simple stir-fry using chicken and vegetables. The generation unit generates, for example, a recipe for a smoothie using banana and yogurt. In this way, the generation unit generates a recipe that can be easily cooked, allowing the user to easily ingest nutrients. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the proposed ingredients into the generation AI and cause the generation AI to generate a recipe.

[0078] The timing unit can suggest the timing of ingestion of ingredients. The timing unit, for example, suggests the timing of ingestion of ingredients. Ingestion timing includes, for example, a time period during which meals are taken, timing after exercise, and the like, but is not limited to such examples. The timing unit, for example, suggests the timing of ingestion of carbohydrates to replenish energy before training. The timing unit, for example, suggests the timing of ingestion of protein to help repair muscles after training. In this way, the timing unit suggests the timing of ingestion of ingredients, allowing the user to ingest nutrients at the optimal timing. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the timing unit can input the generated recipe into the generation AI and cause the generation AI to execute the suggestion of the timing of ingestion of ingredients.

[0079] The generation unit can generate a recipe for a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. The generation unit generates, for example, a recipe for a simple stir-fry using chicken and vegetables. The generation unit generates, for example, a recipe for a smoothie using bananas and yogurt. In this way, the generation unit can generate a specific recipe, allowing the user to easily cook a dish. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using the generation AI. For example, the generation unit can input suggested ingredients into the generation AI and cause the generation AI to generate a recipe.

[0080] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the display method of the input interface based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. For example, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to input information quickly. In this way, the reception unit can reduce input stress by providing an interface according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0081] The reception unit can analyze the user's past input history and propose the optimal input method. The reception unit, for example, analyzes the user's past input history and proposes the optimal input method. The past input history includes, for example, input frequency, tendencies of input contents, and the like, but is not limited to such examples. The reception unit, for example, automatically displays as candidates the physique and athletic information frequently input by the user in the past. The reception unit, for example, preferentially proposes the input method (voice, text, etc.) used by the user in the past. The reception unit, for example, predicts and proposes information to be used in a specific time period from the user's past input history. Thereby, the reception unit can improve the efficiency of input by proposing the optimal input method based on the past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0082] The reception unit can dynamically change the input items based on the user's current health condition and activity level. The reception unit dynamically changes the input items based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, and the like. The activity level includes, but is not limited to, the amount of exercise, the number of steps, and calories burned, and the like. For example, when the user is tired, the reception unit simplifies the input items and requests only the minimum necessary information. For example, when the user is in good health, the reception unit provides detailed input items and collects more precise information. For example, when the user shows a high activity level, the reception unit provides additional input items and collects more specific information. In this way, the reception unit can collect more accurate information by providing input items according to the user's health condition and activity level. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the input items.

[0083] The reception unit can estimate the user's emotion and determine the priority of input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of input based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is nervous, the reception unit displays the most important input item with priority and postpones other items. For example, when the user is relaxed, the reception unit displays all input items evenly so that the user can freely select. For example, when the user is in a hurry, the reception unit displays the item that can be input most quickly with priority. Thereby, the reception unit can improve the efficiency of input by determining the priority of input according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0084] The reception unit can add input items related to ingredients and nutrients specific to a region, taking into account the geographical location information of the user. The reception unit adds input items related to ingredients and nutrients specific to a region, taking into account the geographical location information of the user, for example. The geographical location information includes, for example, GPS data, ingredient information specific to a region, and the like, but is not limited to such examples. The ingredients and nutrients specific to a region include, for example, local specialty products, seasonal ingredients, and the like, but are not limited to such examples. For example, when the user is in a specific region, the reception unit adds ingredients available in the region as input items. For example, when the user is traveling, the reception unit adds nutrients specific to the region of the travel destination as input items. For example, the reception unit adds ingredients according to the climate and season of the region in which the user lives as input items. As a result, the reception unit can make more appropriate suggestions by adding input items related to ingredients and nutrients specific to a region. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to add input items related to ingredients and nutrients specific to the region.

[0085] The reception unit can analyze the social media activity of the user and automatically suggest related input items. The reception unit, for example, analyzes the social media activity of the user and automatically suggests related input items. The social media activity includes, for example, but is not limited to, the content of the post, the number of likes, the number of followers, etc. The related input items include, for example, but are not limited to, the food ingredient preferences, the past meal history, etc. The reception unit, for example, suggests related input items based on the meal content shared by the user on social media. The reception unit, for example, analyzes the posts of nutrition experts and athletes followed by the user and suggests related input items. The reception unit, for example, analyzes the trends of online communities in which the user participates and suggests related input items. In this way, the reception unit can improve the efficiency of input by suggesting related input items based on the social media activity. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the suggestion of related input items.

[0086] The identification unit can estimate the user's emotion and adjust the method of identifying nutrients based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and adjusts the method of identifying nutrients based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the identification unit preferentially identifies nutrients that are effective for stress reduction. For example, when the user is relaxed, the identification unit identifies nutrients that emphasize overall balance. For example, when the user is in a hurry, the identification unit identifies nutrients that can quickly replenish energy. In this way, the identification unit can identify more appropriate nutrients by providing a method of identifying nutrients according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input user emotion data to the generation AI and cause the generation AI to adjust the nutrient identification method.

[0087] The identification unit can analyze the user's past nutritional intake history and identify optimal nutrients. The identification unit, for example, analyzes the user's past nutritional intake history and identifies optimal nutrients. The past nutritional intake history includes, for example, a record of meals, and the type and amount of nutrients taken, but is not limited to such examples. The identification unit, for example, identifies nutrients currently required by the user based on data of nutrients taken in the past. For example, when a specific nutrient is lacking from the user's past nutritional intake history, the identification unit preferentially identifies the nutrient. For example, the identification unit analyzes the user's past nutritional intake history and identifies balanced nutrients. As a result, the identification unit can improve the accuracy of nutritional intake by identifying optimal nutrients based on the past nutritional intake history. Part or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the user's past nutritional intake history to the generation AI and cause the generation AI to identify optimal nutrients.

[0088] The identification unit can dynamically change the nutrient identification algorithm based on the user's current health condition and activity level. The identification unit dynamically changes the nutrient identification algorithm based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, etc. The activity level includes, but is not limited to, the amount of exercise, number of steps, calories burned, etc. For example, when the user is tired, the identification unit identifies nutrients suitable for energy replenishment. For example, when the user shows a high activity level, the identification unit identifies nutrients necessary for muscle repair and growth. For example, when the user is in good health, the identification unit identifies nutrients suitable for maintaining overall health. This enables the identification unit to identify nutrients according to the health condition and activity level. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the nutrient identification algorithm.

[0089] The identification unit can estimate the user's emotion and determine the priority order of the identified nutrients based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and determines the priority order of the identified nutrients based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the identification unit preferentially suggests nutrients that are effective for stress reduction. For example, when the user is relaxed, the identification unit preferentially suggests nutrients that emphasize overall balance. For example, when the user is in a hurry, the identification unit preferentially suggests nutrients that can quickly replenish energy. Thereby, the identification unit can suggest more appropriate nutrients by determining the priority order of nutrients according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of nutrients.

[0090] The identification unit can identify nutrients specific to a region by considering the geographical location information of the user. The identification unit identifies nutrients specific to a region by considering, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, nutrient information specific to a region, and the like, but is not limited to such examples. The nutrients specific to a region include, for example, local specialty products, seasonal ingredients, and the like, but is not limited to such examples. For example, when the user is in a specific region, the identification unit identifies nutrients available in the region. For example, when the user is traveling, the identification unit identifies nutrients specific to the region of the travel destination. For example, the identification unit identifies nutrients according to the climate and season of the region in which the user lives. As a result, the identification unit identifies nutrients specific to the region, thereby enabling more appropriate nutritional intake. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit may input the geographical location information of the user to the generation AI and cause the generation AI to execute identification of nutrients specific to the region.

[0091] The identification unit can analyze the social media activity of the user and identify the relevant nutrients. The identification unit, for example, analyzes the social media activity of the user and identifies the relevant nutrients. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, etc., but is not limited to such examples. The relevant nutrients include, for example, the preference of ingredients, the past meal history, etc., but is not limited to such examples. The identification unit, for example, identifies the relevant nutrients based on the meal content shared by the user on social media. The identification unit, for example, analyzes the posts of nutrition experts and athletes followed by the user and identifies the relevant nutrients. The identification unit, for example, analyzes the trends of the online community in which the user participates and identifies the relevant nutrients. As a result, the identification unit identifies the relevant nutrients based on the social media activity, thereby enabling more appropriate nutritional intake. Part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the identification of the relevant nutrients.

[0092] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the proposal based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the suggestion unit makes a simple and highly visible proposal. For example, when the user is relaxed, the suggestion unit makes a proposal including detailed information. For example, when the user is in a hurry, the suggestion unit makes a proposal that covers the main points. Thereby, the suggestion unit can improve the acceptability of the proposal by providing a way of expressing the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the way the suggestion is expressed.

[0093] The suggestion unit can apply different suggestion algorithms depending on the category of the ingredients. The suggestion unit applies different suggestion algorithms depending on the category of the ingredients, for example. The categories of ingredients include, but are not limited to, vegetables, fruits, meats, and fishes. For example, the suggestion unit makes suggestions according to the season for vegetables and fruits. For example, the suggestion unit makes suggestions according to the cooking method for meat and fish. For example, the suggestion unit makes suggestions according to the nutritional value for grains and beans. In this way, the suggestion unit provides suggestions according to the category of the ingredients, allowing the user to select appropriate ingredients. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input ingredient category data to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0094] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the proposal based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the suggestion unit makes a short and to the point proposal. For example, when the user is relaxed, the suggestion unit makes a longer proposal including detailed explanations. For example, when the user is in a hurry, the suggestion unit makes a quick and concise proposal. Thereby, the suggestion unit can improve the acceptability of the proposal by providing a length of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input user emotion data to the generation AI and cause the generation AI to adjust the length of the suggestions.

[0095] The suggestion unit can determine the priority of the suggestions based on the time when the ingredients were obtained. The suggestion unit determines the priority of the suggestions based on, for example, the time when the ingredients were obtained. The time when the ingredients were obtained includes, for example, a season, a harvest season, and the like, but is not limited to such examples. For example, the suggestion unit preferentially suggests seasonal ingredients. For example, the suggestion unit preferentially suggests in-season ingredients. For example, the suggestion unit preferentially suggests ingredients that are easy for the user to obtain at a specific time. In this way, the suggestion unit provides suggestions according to the time when the ingredients were obtained, allowing the user to select appropriate ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input data on the time when the ingredients were obtained to the generation AI, and cause the generation AI to execute the determination of the priority of the suggestions.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of ingredients. The suggestion unit adjusts the order of suggestions based on the relevance of ingredients, for example. The relevance of ingredients includes, for example, nutrient combinations, commonalities in cooking methods, and the like, but is not limited to such examples. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the balance of nutrients. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the ease of cooking methods. The suggestion unit proposes related ingredients in an orderly manner, for example, taking into consideration the preferences of the user. In this way, the suggestion unit provides suggestions according to the relevance of ingredients, allowing the user to select appropriate ingredients. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input the relevance data of ingredients to the generation AI and cause the generation AI to adjust the order of suggestions.

[0097] The generation unit can estimate the user's emotion and adjust the expression method of the recipe based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the recipe based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the generation unit generates a simple and highly visible recipe. For example, when the user is relaxed, the generation unit generates a recipe including detailed explanations. For example, when the user is in a hurry, the generation unit generates a recipe that can be cooked quickly. Thereby, the generation unit can improve the acceptability of the recipe by providing a recipe expression method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the recipe is expressed.

[0098] The generation unit can adjust the level of detail of the recipe based on the importance of the ingredients. The generation unit adjusts the level of detail of the recipe based on the importance of the ingredients, for example. The importance of the ingredients includes, but is not limited to, nutritional value, allergy information, and the like. The generation unit provides, for example, a detailed cooking method for important ingredients. The generation unit provides, for example, a simple cooking method for ingredients that are not so important. The generation unit preferentially includes ingredients with high importance in the recipe, for example, according to the health condition of the user. In this way, the generation unit provides a recipe according to the importance of the ingredients, allowing the user to select an appropriate cooking method. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the importance data of ingredients into the generation AI and causes the generation AI to adjust the level of detail of the recipe.

[0099] The generation unit can apply different recipe generation algorithms depending on the category of ingredients. The generation unit applies different recipe generation algorithms depending on the category of ingredients, for example. The categories of ingredients include, but are not limited to, vegetables, fruits, meats, and fishes. For example, for vegetables and fruits, the generation unit generates recipes according to the season. For example, for meat and fish, the generation unit generates recipes according to the cooking method. For example, for grains and beans, the generation unit generates recipes according to the nutritional value. In this way, the generation unit provides recipes according to the category of ingredients, allowing the user to select an appropriate cooking method. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs ingredient category data to the generation AI and causes the generation AI to apply the recipe generation algorithm.

[0100] The generation unit can estimate the user's emotion and adjust the length of the recipe based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the recipe based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the generation unit generates a short and to the point recipe. For example, when the user is relaxed, the generation unit generates a longer recipe with detailed explanations. For example, when the user is in a hurry, the generation unit generates a quick and concise recipe. Thereby, the generation unit can improve the acceptability of the recipe by providing the length of the recipe according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input a user's emotional data into the generation AI and have the generation AI adjust the length of the recipe.

[0101] The generation unit can determine the priority of the recipe based on the time when the ingredients were obtained. The generation unit determines the priority of the recipe based on, for example, the time when the ingredients were obtained. The time when the ingredients were obtained includes, for example, the season, the harvest season, and the like, but is not limited to such examples. The generation unit, for example, preferentially includes seasonal ingredients in the recipe. The generation unit, for example, preferentially includes in-season ingredients in the recipe. The generation unit, for example, preferentially includes ingredients that are easy for the user to obtain at a specific time in the recipe. In this way, the generation unit provides a recipe according to the time when the ingredients were obtained, allowing the user to select appropriate ingredients. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the ingredients were obtained to the generation AI and cause the generation AI to execute the determination of the priority of the recipe.

[0102] The generation unit can adjust the order of the recipe based on the relevance of ingredients. The generation unit adjusts the order of the recipe based on the relevance of ingredients, for example. The relevance of ingredients includes, but is not limited to, for example, nutrient combinations and commonalities in cooking methods. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the balance of nutrients. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the ease of cooking methods. The generation unit includes related ingredients in the recipe in an orderly manner, for example, taking into account the preferences of a user. In this way, the generation unit provides a recipe according to the relevance of ingredients, allowing the user to select an appropriate cooking method. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of ingredients to the generation AI and cause the generation AI to adjust the order of the recipe.

[0103] The timing unit can estimate the user's emotion and adjust the method of proposing the intake timing based on the estimated user's emotion. The timing unit, for example, estimates the user's emotion and adjusts the method of proposing the intake timing based on the estimated user's emotion. The emotion includes, for example, joy, sadness, stress, etc., but is not limited to such examples. For example, when the user is stressed, the timing unit proposes intake at a timing that allows the user to relax. For example, when the user is relaxed, the timing unit proposes an intake timing that takes into account the overall balance. For example, when the user is in a hurry, the timing unit proposes a timing that allows the user to take in quickly. Thereby, the timing unit can improve the acceptability of intake by providing a proposal of an intake timing according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the timing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the timing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the intake timing suggestion method.

[0104] The timing unit can analyze the user's past intake history and propose the optimal timing. The timing unit, for example, analyzes the user's past intake history and proposes the optimal timing. The past intake history includes, for example, the time period of intake, the type and amount of nutrients taken, and the like, but is not limited to such examples. The timing unit, for example, proposes the current optimal intake timing based on data on the timing of the user's past intake. For example, if the user's past intake history shows that the user often takes in a specific time period, the timing unit preferentially proposes that time period. The timing unit, for example, analyzes the user's past intake history and proposes a balanced intake timing. As a result, the timing unit can improve the efficiency of intake by proposing the optimal intake timing based on the past intake history. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the user's past intake history data to the generation AI and cause the generation AI to execute the proposal of the optimal intake timing.

[0105] The timing unit can dynamically change the proposal based on the user's current health condition and activity level. The timing unit dynamically changes the proposal based on the user's current health condition and activity level, for example. The current health condition includes, but is not limited to, blood pressure, blood glucose level, cholesterol level, etc. The activity level includes, but is not limited to, the amount of exercise, number of steps, calories burned, etc. The timing unit, for example, when the user is tired, suggests a timing suitable for energy replenishment. The timing unit, for example, when the user shows a high activity level, suggests a timing suitable for muscle repair and growth. The timing unit, for example, when the user is in good health, suggests a timing suitable for maintaining overall health. This enables the timing unit to suggest an intake timing according to the health condition and activity level. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input data on the user's health condition and activity level to the generation AI and cause the generation AI to dynamically change the proposal.

[0106] The timing unit can estimate the user's emotion and determine the priority of intake timing based on the estimated user's emotion. The timing unit, for example, estimates the user's emotion and determines the priority of intake timing based on the estimated user's emotion. Emotions include, for example, joy, sadness, stress, etc., but are not limited to such examples. For example, when the user is stressed, the timing unit preferentially suggests a timing at which the user can relax. For example, when the user is relaxed, the timing unit preferentially suggests an intake timing that takes into account overall balance. For example, when the user is in a hurry, the timing unit preferentially suggests a timing at which the user can quickly take in the food. Thereby, the timing unit can propose a more appropriate intake timing by determining the priority of intake timing according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the timing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the timing unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of intake timing.

[0107] The timing unit can propose the optimal timing in consideration of the geographical location information of the user. The timing unit proposes the optimal timing in consideration of, for example, the geographical location information of the user. The geographical location information includes, for example, GPS data, climate information specific to a region, and the like, but is not limited to such examples. For example, when the user is in a specific region, the timing unit proposes an intake timing according to the climate and time zone of the region. For example, when the user is traveling, the timing unit proposes an intake timing according to the time zone of the travel destination. For example, the timing unit proposes an intake timing according to the climate and season of the region in which the user lives. As a result, the timing unit proposes the optimal intake timing based on the geographical location information, so that the user can take in nutrients at an appropriate timing. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute a proposal of the optimal intake timing.

[0108] The timing unit can analyze the social media activity of the user to suggest relevant timing. The timing unit, for example, analyzes the social media activity of the user to suggest relevant timing. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The timing unit, for example, proposes relevant intake timing based on the activity content shared by the user on social media. The timing unit, for example, analyzes posts of nutrition experts and athletes followed by the user, and proposes relevant intake timing. The timing unit, for example, analyzes trends of online communities in which the user participates, and proposes relevant intake timing. In this way, the timing unit can provide more appropriate intake timing by proposing relevant intake timing based on social media activity. Part or all of the above-mentioned processing in the timing unit may be performed, for example, using AI, or may be performed without using AI. For example, the timing unit can input the social media activity data of the user to the generation AI and cause the generation AI to execute the proposal of relevant intake timing.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The reception unit can analyze the user's past dietary history and provide it to the identification unit. For example, by collecting data on ingredients and nutrients that the user has taken in the past and providing it to the identification unit, it becomes possible to identify nutrients with higher accuracy. Furthermore, the reception unit can extract preferences and allergy information for specific ingredients from the user's dietary history and provide it to the identification unit. This enables the identification unit to identify nutrients taking into account the user's past dietary history, making it possible to realize more personalized suggestions.

[0111] The identification unit can estimate the user's emotions and adjust the method of identifying nutrients based on the estimated emotions. For example, when the user is stressed, nutrients that are effective in reducing stress can be preferentially identified. When the user is relaxed, nutrients that emphasize overall balance can also be identified. Furthermore, when the user is in a hurry, nutrients that can quickly replenish energy can also be identified. This enables the identification unit to identify nutrients according to the user's emotions, and more appropriate nutritional suggestions can be realized.

[0112] The suggestion unit can suggest ingredients and nutrients specific to a region, taking into account the geographical location information of the user. For example, when the user is in a specific region, ingredients available in that region can be suggested. Also, when the user is traveling, nutrients specific to the region of the travel destination can be suggested. Furthermore, it is also possible to suggest ingredients according to the climate and season of the region in which the user lives. This enables the suggestion unit to make suggestions taking into account ingredients and nutrients specific to a region, thereby realizing more appropriate nutrition suggestions.

[0113] The generation unit can estimate the user's emotions and adjust the way in which the recipe is expressed based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible recipe can be generated. If the user is relaxed, a recipe including detailed instructions can be generated. Furthermore, if the user is in a hurry, a recipe that can be cooked quickly can be generated. In this way, the generation unit can improve the acceptability of the recipe by providing a recipe expression method according to the user's emotions.

[0114] The timing unit can analyze the user's past intake history and suggest the optimal intake timing. For example, the current optimal intake timing can be suggested based on the time period and nutrient data of the user's past intakes. In addition, if the user's past intake history shows that they often take in nutrients during a specific time period, it is possible to suggest that time period with priority. Furthermore, it is also possible to analyze the user's past intake history and suggest a balanced intake timing. In this way, the timing unit can improve intake efficiency by suggesting the optimal intake timing based on the past intake history.

[0115] The reception unit can analyze the user's social media activity and automatically suggest related input items. For example, related input items can be suggested based on the meal contents shared by the user on social media. Also, related input items can be suggested by analyzing posts of nutrition experts and athletes followed by the user. Furthermore, it is also possible to analyze trends in online communities in which the user participates and suggest related input items. In this way, the reception unit can improve the efficiency of input by suggesting related input items based on social media activity.

[0116] The identification unit can estimate the user's emotions and determine the priority order of nutrients based on the estimated emotions. For example, when the user is stressed, nutrients that are effective in reducing stress can be preferentially suggested. When the user is relaxed, nutrients that emphasize overall balance can be preferentially suggested. When the user is in a hurry, nutrients that can quickly replenish energy can also be preferentially suggested. In this way, the identification unit can realize more appropriate nutritional suggestions by determining the priority order of nutrients according to the user's emotions.

[0117] The suggestion unit can determine the priority of suggestions based on the time of acquisition of ingredients. For example, seasonal ingredients can be preferentially suggested. Also, in-season ingredients can be preferentially suggested. Furthermore, it is also possible to preferentially suggest ingredients that are easy for the user to obtain at a particular time. In this way, the suggestion unit can provide suggestions according to the time of acquisition of ingredients, thereby enabling the user to select appropriate ingredients.

[0118] The generator can estimate the user's emotions and adjust the length of the recipe based on the estimated emotions. For example, if the user is stressed, a short and to the point recipe can be generated. If the user is relaxed, a longer recipe with detailed instructions can be generated. If the user is in a hurry, a quick and concise recipe can be generated. In this way, the generator can improve the acceptability of the recipe by providing a recipe length according to the user's emotions.

[0119] The timing unit can suggest the optimal intake timing in consideration of the geographical location information of the user. For example, if the user is in a specific area, the timing unit can suggest the intake timing according to the climate and time of the area. Also, if the user is traveling, the timing unit can suggest the intake timing according to the time of the travel destination. Furthermore, it is also possible to suggest the intake timing according to the climate and season of the area where the user lives. In this way, the timing unit can suggest the optimal intake timing based on the geographical location information, thereby enabling the user to take in nutrients at an appropriate time.

[0120] The process flow of the second embodiment will be briefly described below.

[0121] Step 1: The reception unit inputs user information. The user information includes, for example, age, sex, weight, height, health condition, etc. The reception unit stores the information input by the user in a database and provides it to the identification unit. Step 2: The identification unit identifies the necessary nutrients based on the information entered by the reception unit. The identification unit uses the generation AI to analyze information such as the user's physique, sport, health condition, and age, and identifies the necessary nutrients. For example, it calculates the required amounts of carbohydrates, proteins, vitamins, minerals, etc. Step 3: The suggestion unit suggests appropriate ingredients and their amounts based on the nutrients identified by the identification unit. The suggestion unit uses generative AI to suggest appropriate ingredients and their amounts based on the nutrients identified. For example, it suggests pasta and bananas for marathon runners, chicken and tofu for strength-training athletes, and spinach and oranges for sick people. Step 4: The generator generates recipes that can be easily cooked using the suggested ingredients. The generator uses the generative AI to generate recipes that can be easily cooked using the suggested ingredients. For example, it generates recipes such as a simple stir-fry using chicken and vegetables, or a smoothie using bananas and yogurt. Step 5: The timing unit suggests the timing of food intake based on the recipe generated by the generation unit. The timing unit uses the generation AI to suggest the timing of food intake. For example, before training, consume carbohydrates to replenish energy, and after training, consume protein to help repair muscles.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0124] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, generation unit, and timing unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit inputs user information by the control unit 46A of the smart device 14 and provides it to the identification processing unit 290 of the data processing device 12. The identification unit identifies necessary nutrients using a generation AI by the identification processing unit 290 of the data processing device 12, for example. The suggestion unit suggests appropriate ingredients and their amounts by the identification processing unit 290 of the data processing device 12, for example. The generation unit generates a recipe using ingredients suggested by the identification processing unit 290 of the data processing device 12, for example. The timing unit suggests the timing of intake of ingredients by the identification processing unit 290 of the data processing device 12, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0129] 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.

[0130] 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 the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] 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 performed in a secure state.

[0133] Fig. 4 shows an example of 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.

[0134] 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.

[0135] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0139] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, generation unit, and timing unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit inputs user information by the control unit 46A of the smart glasses 214 and provides it to the identification processing unit 290 of the data processing device 12. The identification unit identifies necessary nutrients using a generation AI by the identification processing unit 290 of the data processing device 12, for example. The suggestion unit suggests appropriate ingredients and their amounts by the identification processing unit 290 of the data processing device 12, for example. The generation unit generates a recipe using ingredients suggested by the identification processing unit 290 of the data processing device 12, for example. The timing unit suggests the timing of intake of ingredients by the identification processing unit 290 of the data processing device 12, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0145] 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.

[0146] 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 the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0148] 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 performed in a secure state.

[0149] 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.

[0150] 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.

[0151] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0152] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0154] 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 voice indicating a user input for 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.

[0155] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0156] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, generation unit, and timing unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit inputs user information by the control unit 46A of the headset type terminal 314 and provides it to the identification processing unit 290 of the data processing device 12. The identification unit identifies necessary nutrients using a generation AI by the identification processing unit 290 of the data processing device 12, for example. The suggestion unit suggests appropriate ingredients and their amounts by the identification processing unit 290 of the data processing device 12, for example. The generation unit generates a recipe using ingredients suggested by the identification processing unit 290 of the data processing device 12, for example. The timing unit suggests the timing of intake of ingredients by the identification processing unit 290 of the data processing device 12, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0161] 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.

[0162] 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 the voice according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0164] 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 performed in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0166] 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.

[0167] 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.

[0168] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0169] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0170] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0171] 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 a voice indicating a user input for 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.

[0172] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0173] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0174] Each of the multiple elements including the above-mentioned reception unit, identification unit, suggestion unit, generation unit, and timing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs user information by the control unit 46A of the robot 414 and provides it to the identification processing unit 290 of the data processing device 12. The identification unit identifies necessary nutrients using a generation AI by the specific processing unit 290 of the data processing device 12, for example. The suggestion unit suggests appropriate ingredients and their amounts by the specific processing unit 290 of the data processing device 12, for example. The generation unit generates a recipe using ingredients suggested by the specific processing unit 290 of the data processing device 12, for example. The timing unit suggests the timing of intake of ingredients by the specific processing unit 290 of the data processing device 12, for example. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0176] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0178] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0179] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0180] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0182] 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.

[0183] 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 Universal Serial Bus (USB) 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.

[0184] In addition, 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 upon request from the data processing device 12.

[0185] 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.

[0186] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0187] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0188] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0189] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0191] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0193] (Appendix 1) A reception unit for inputting user information; An identification unit that identifies necessary nutrients based on the information input by the reception unit; a suggestion unit that suggests ingredients and their amounts based on the nutrients identified by the identification unit; A generation unit that generates a recipe using ingredients suggested by the suggestion unit; A timing unit that suggests the timing of intake of ingredients based on the recipe generated by the generation unit. A system characterized by: (Appendix 2) The identification unit is Analyzes the user's physique, sport, health condition, and age information to identify the nutrients they need 2. The system of claim 1, (Appendix 3) The suggestion unit, Suggest appropriate ingredients and portions based on identified nutrients 2. The system of claim 1, (Appendix 4) The generation unit is Generate easy-to-cook recipes using suggested ingredients 2. The system of claim 1, (Appendix 5) The timing section Suggesting the timing of food intake 2. The system of claim 1, (Appendix 6) The generation unit is Generate recipes for a simple chicken and vegetable stir fry or a banana and yogurt smoothie 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Dynamically change input fields based on the user's current health and activity level 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The reception unit is Consider the user's geographic location and add input fields for local ingredients and nutrients. 2. The system of claim 1, (Appendix 12) The reception unit is Analyze users' social media activity and automatically suggest relevant entries 2. The system of claim 1, (Appendix 13) The identification unit is Inferring user emotions and adjusting the nutrient identification method based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The identification unit is Analyze the user's nutritional intake history to identify optimal nutrients 2. The system of claim 1, (Appendix 15) The identification unit is Dynamically change nutrient identification algorithms based on the user's current health and activity level 2. The system of claim 1, (Appendix 16) The identification unit is Inferring a user's emotion and prioritizing the identified nutrients based on the estimated user's emotion 2. The system of claim 1, (Appendix 17) The identification unit is Identify region-specific nutrients by taking into account the user's geographic location 2. The system of claim 1, (Appendix 18) The identification unit is Analyze users' social media activity to identify relevant nutrients 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, Apply different suggestion algorithms depending on the ingredient category 2. The system of claim 1, (Appendix 21) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The suggestion unit, Prioritize suggestions based on ingredient availability 2. The system of claim 1, (Appendix 23) The suggestion unit, Adjust the order of suggestions based on ingredient relevance 2. The system of claim 1, (Appendix 24) The generation unit is Infer user emotions and adjust recipe presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The generation unit is Adjust the recipe detail based on the importance of ingredients 2. The system of claim 1, (Appendix 26) The generation unit is Apply different recipe generation algorithms depending on ingredient categories 2. The system of claim 1, (Appendix 27) The generation unit is Infer user sentiment and adjust recipe length based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The generation unit is Prioritize recipes based on ingredient availability 2. The system of claim 1, (Appendix 29) The generation unit is Order recipes based on ingredient relationships 2. The system of claim 1, (Appendix 30) The timing section Estimate the user's emotions and adjust the intake timing suggestion method based on the estimated user emotions. 2. The system of claim 1, (Appendix 31) The timing section Analyze the user's past intake history to suggest optimal timing 2. The system of claim 1, (Appendix 32) The timing section Dynamically change recommendations based on the user's current health and activity level 2. The system of claim 1, (Appendix 33) The timing section Estimating a user's emotions and determining the priority of intake timing based on the estimated user's emotions 2. The system of claim 1, (Appendix 34) The timing section Suggest optimal timing based on user's geographic location 2. The system of claim 1, (Appendix 35) The timing section Analyze users' social media activity to suggest relevant moments 2. The system of claim 1, [Explanation of symbols]

[0194] 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 reception unit for inputting user information; An identification unit that identifies necessary nutrients based on the information input by the reception unit; a suggestion unit that suggests ingredients and their amounts based on the nutrients identified by the identification unit; A generation unit that generates a recipe using ingredients suggested by the suggestion unit; A timing unit that suggests the timing of intake of ingredients based on the recipe generated by the generation unit. A system characterized by:

2. The identification unit is Analyzes the user's physique, sport, health condition, and age information to identify the nutrients they need 2. The system of claim 1 .

3. The suggestion unit, Suggest appropriate ingredients and portions based on identified nutrients 2. The system of claim 1 .

4. The generation unit is Generate easy-to-cook recipes using suggested ingredients 2. The system of claim 1 .

5. The timing section Suggesting the timing of food intake 2. The system of claim 1 .

6. The generation unit is Generate recipes for a simple chicken and vegetable stir fry or a banana and yogurt smoothie 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest the optimal input method 2. The system of claim 1 .

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