System

Abstract

An object of a system according to an embodiment is to automatically calculate a nutrition value of a meal and propose a menu for maintaining an appropriate nutrition balance.SOLUTION: A system includes a photograph analysis part, a nutrition value calculation part, a nutrient determination part, a leaflet information collection part, and a menu suggestion part. The photograph analysis unit analyzes the photograph of the meal. The nutrition value calculation section calculates a nutrition value of the meal analyzed by the photograph analysis section. The nutrient determination part specifies the insufficient or excessive nutrient based on the nutrition value calculated by the nutrition value calculation part. The handbill information collection unit collects handbill information of nearby supermarkets based on the user's location information. The menu suggestion unit suggests a menu for supplementing the insufficient or excessive nutrient specified by the nutrient determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of being time-consuming and inefficient, as it requires manually calculating the nutritional value of meals and planning menus that maintain an appropriate nutritional balance.

[0005] The system according to the embodiment aims to automatically calculate the nutritional value of meals and propose menus that maintain an appropriate nutritional balance. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a nutritional value calculation unit, a nutrient determination unit, a flyer information collection unit, and a menu suggestion unit. The photo analysis unit analyzes photos of meals. The nutritional value calculation unit calculates the nutritional value of the meals analyzed by the photo analysis unit. The nutrient determination unit identifies nutrients that are deficient or in excess based on the nutritional value calculated by the nutritional value calculation unit. The flyer information collection unit collects flyer information from nearby supermarkets based on the user's location information. The menu suggestion unit suggests menus to supplement the deficient or excess nutrients identified by the nutrient determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically calculate the nutritional value of meals and suggest menus that maintain an appropriate nutritional balance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The nutritional value calculation system according to an embodiment of the present invention automatically analyzes photos of meals, calculates nutritional values, identifies nutrients that are deficient or in excess, and suggests recommended menu items by comparing them with flyers from nearby supermarkets. This allows users to easily manage the nutritional balance of their meals and purchase ingredients economically by utilizing sales information from nearby supermarkets.

[0029] The nutritional value calculation system according to the embodiment includes a photo analysis unit, a nutritional value calculation unit, a nutrient determination unit, a flyer information collection unit, and a menu suggestion unit. The photo analysis unit analyzes photos of meals. For example, the photo analysis unit uses image recognition technology to analyze photos of meals and identify the types of ingredients and dishes. The photo analysis unit can also analyze photos of meals at high resolution, accurately recognizing even the smallest details. The photo analysis unit can also integrate and analyze photos taken from multiple angles. The nutritional value calculation unit calculates the nutritional value of the meal analyzed by the photo analysis unit. For example, the nutritional value calculation unit calculates nutritional values ​​such as calories, vitamins, and minerals based on the types and amounts of ingredients. The nutritional value calculation unit can also adjust the nutritional value taking into account the freshness and cooking method of the ingredients. The nutritional value calculation unit can also identify the origin and production method of the ingredients and adjust the nutritional value based on this. The nutrient determination unit identifies nutrients that are deficient or in excess based on the nutritional values ​​calculated by the nutritional value calculation unit. For example, the nutrient determination unit compares the user's daily nutritional intakes to determine whether the user is lacking or consuming excessive amounts of nutrients. The nutrient determination unit can also refer to the user's past health checkup results and medical data to make a determination based on the user's individual health condition. Furthermore, the nutrient determination unit can also apply seasonal nutritional intake standards, taking seasonal and climate changes into account. The flyer information collection unit collects flyer information from nearby supermarkets based on the user's location information. For example, the flyer information collection unit obtains information about special sales and new products at supermarkets in the user's area. The flyer information collection unit can also integrate information from online shops to provide options that the user can purchase from home. Furthermore, when referring to supermarket flyer information, the flyer information collection unit can collect not only sale information but also information about the quality and producers of ingredients. The menu suggestion unit proposes menus to supplement the nutrient deficiencies or excesses identified by the nutrient determination unit. For example, if the user is deficient in vitamin C, the menu suggestion unit proposes menus using ingredients rich in vitamin C and also provides information about supermarkets where those ingredients are on sale.The menu suggestion unit can also learn the user's past eating history and preferences and suggest individually customized menus. Furthermore, the menu suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest menus that elicit positive emotions. This allows the nutritional value calculation system according to the embodiment to enable users to easily manage the nutritional balance of their meals and purchase ingredients economically by utilizing information on sales at nearby supermarkets. For example, users can easily prepare nutritionally balanced meals and maintain a healthy lifestyle. Furthermore, utilizing information on sales at supermarkets can also help save on food costs.

[0030] The photo analysis unit takes into account the freshness of ingredients and cooking method, and can reflect fluctuations in nutritional value in the calculation. For example, when the generation AI analyzes a photo of a meal, the photo analysis unit detects changes in color and texture to determine the freshness of ingredients. For example, if the color of vegetables is vivid, it will determine that they are fresh and set the nutritional value higher. The photo analysis unit also identifies cooking utensils and cooking processes from the photo of the meal to take cooking method into account. For example, in the case of fried foods, it will take into account the amount of oil used and set the nutritional value of fat higher. The photo analysis unit also combines the freshness of ingredients and cooking method to reflect fluctuations in nutritional value in the calculation. For example, if fresh vegetables are steamed, it will determine that there is less vitamin loss and set the nutritional value higher. This allows for more accurate nutritional value calculations by taking into account the freshness of ingredients and cooking method.

[0031] The photo analysis unit can identify the origin and production method of ingredients and adjust nutritional value based on that. For example, the photo analysis unit analyzes information from packaging and labels so that the generation AI can identify the origin of ingredients from a photo of a meal. For example, if an organically grown label is included, the nutritional value is set higher. The photo analysis unit also takes production method into account, so the generation AI analyzes the appearance and shape of ingredients to identify characteristics such as organic or pesticide-free. For example, irregularly shaped vegetables are determined to be pesticide-free and their nutritional value is set higher. The photo analysis unit also adjusts nutritional value by combining the origin and production method of ingredients. For example, if organically grown vegetables are used, the vitamin and mineral content is set higher. This allows for more accurate nutritional value calculations by taking into account the origin and production method of ingredients.

[0032] The photo analysis unit integrates photos taken from multiple angles to enable more accurate ingredient recognition. For example, the photo analysis unit uses a generation AI to integrate photos of a meal taken by the user from multiple angles and accurately recognize the shape and arrangement of ingredients. For example, it combines photos from above and the side for analysis. The photo analysis unit also analyzes photos taken from multiple angles and removes shadows and reflections from ingredients to enable accurate recognition. For example, it corrects the color of ingredients to take light reflections into account. The photo analysis unit also integrates photos taken from multiple angles to complement overlapping and hidden parts of ingredients and calculate nutritional values ​​more accurately. For example, it estimates hidden ingredients and calculates nutritional values. In this way, integrating photos from multiple angles enables more accurate ingredient recognition.

[0033] The nutritional value calculation unit can predict nutritional value by referencing the user's past meal photo data and learning their eating patterns. For example, the generation AI of the nutritional value calculation unit refers to the user's past meal photo data and learns their eating patterns. For example, it identifies trends in frequently eaten ingredients and dishes. The nutritional value calculation unit also analyzes the user's eating patterns based on the past meal photo data and predicts nutritional value. For example, it extracts trends in nutritional balance from past data. The nutritional value calculation unit also learns the past meal photo data and predicts nutritional value based on the user's eating patterns. For example, it predicts the nutritional value of dishes eaten on a specific day of the week. This makes it possible to make more accurate nutritional value predictions by referencing past meal data.

[0034] The nutrient determination unit can refer to the user's past health checkup results and medical data and make determinations based on the user's individual health condition. For example, the generation AI in the nutrient determination unit refers to the user's past health checkup results and determines whether there is a nutritional deficiency or excess. For example, it identifies vitamin and mineral deficiencies based on blood test results. The nutrient determination unit also analyzes the user's medical data and determines nutritional value based on the user's individual health condition. For example, it adjusts nutritional value taking into account the presence or absence of chronic diseases. The generation AI in the nutrient determination unit also learns from the user's past health checkup results and medical data and determines whether there is a nutritional deficiency or excess based on the user's individual health condition. For example, it provides appropriate nutritional values ​​for users with a specific medical history. This makes it possible to determine nutrients based on the user's individual health condition.

[0035] The nutrient determination unit can take seasonal and climate fluctuations into account and apply seasonal nutritional reference intakes. For example, the generation AI in the nutrient determination unit takes seasonal and climate fluctuations into account and determines whether there is a nutritional deficiency or excess. For example, it recommends taking vitamin D in winter. In addition, in order to apply seasonal nutritional reference intakes, the generation AI analyzes climate data and provides appropriate nutritional values. For example, it places emphasis on hydration in summer. In addition, the nutrient determination unit learns seasonal and climate fluctuations and applies seasonal nutritional reference intakes. For example, it recommends specific nutrients in spring as a measure against allergies. This makes it possible to determine more appropriate nutrients by taking seasonal and climate fluctuations into account.

[0036] The nutrient determination unit can monitor the user's exercise volume and activity level in real time and adjust the nutritional intake standards based on that. For example, the generation AI of the nutrient determination unit monitors the user's exercise volume in real time and adjusts the nutritional intake standards based on that. For example, if the amount of exercise is high, protein intake is increased. The nutrient determination unit also analyzes the activity level, and the generation AI provides appropriate nutritional intake standards. For example, if the user does a lot of desk work, calorie intake is reduced. The nutrient determination unit also learns the user's exercise volume and activity level, and adjusts the nutritional intake standards based on that. For example, if the user is not exercising enough, vitamin D intake is recommended. This allows for more appropriate nutritional management by adjusting the nutritional intake standards based on the amount of exercise and activity level.

[0037] The nutrient determination unit can analyze a user's dietary history over the long term, grasp trends in nutritional intake, and reflect this in the determination. For example, the generation AI in the nutrient determination unit analyzes a user's dietary history over the long term to grasp trends in nutritional intake. For example, it evaluates nutritional balance based on data from the past few months. The nutrient determination unit also analyzes long-term dietary history, and the generation AI reflects trends in nutritional intake in the determination. For example, it issues a warning if the intake of a specific nutrient is continuously insufficient. The nutrient determination unit also learns the user's dietary history, grasps trends in long-term nutritional intake, and reflects this in the determination. For example, it analyzes seasonal nutritional intake patterns. This allows for more accurate nutrient determination by analyzing long-term dietary history.

[0038] When referencing supermarket flyer information, the flyer information collection unit can collect not only sale information but also information on the quality and producers of ingredients, and reflect this in suggestions. For example, the generation AI in the flyer information collection unit analyzes supermarket flyer information and collects information on the quality of ingredients in addition to sale information. For example, it identifies organically grown and pesticide-free ingredients and reflects this in suggestions. In addition, to collect producer information, the generation AI identifies the producer's name and region from the flyer information. For example, it prioritizes suggestions of fresh ingredients provided by local producers. In addition, the flyer information collection unit integrates the sale information with the quality and producer information of ingredients and provides it to the user. For example, it suggests a menu using organic vegetables on sale. This allows for more reliable suggestions by taking into account the quality and producer information of ingredients.

[0039] When referring to supermarket flyer information, the flyer information collection unit can analyze the user's purchasing history and make suggestions taking into account trends in ingredients purchased in the past. In the flyer information collection unit, for example, the generation AI analyzes the user's purchasing history and understands trends in ingredients purchased in the past. For example, it identifies ingredients that are frequently purchased and makes suggestions based on that. In addition, the flyer information collection unit makes suggestions based on the purchasing history, with the generation AI taking into account the user's preferences and frequency of ingredient use. For example, it suggests recipes using ingredients purchased in the past. In addition, the flyer information collection unit makes appropriate suggestions by learning the user's purchasing history and referring to supermarket flyer information based on past purchasing trends. For example, it suggests menus using ingredients that are on sale. This makes it possible to make more appropriate suggestions by taking into account the user's purchasing history.

[0040] When referring to supermarket flyer information, the flyer information collection unit also integrates information from online shops, and can provide options that allow the user to purchase from home. For example, the flyer information collection unit uses a generation AI to integrate supermarket flyer information with information from online shops, and provide options that allow the user to purchase from home. For example, it allows the user to order sale items online. The flyer information collection unit also analyzes information from online shops, and the generation AI suggests the optimal purchasing option to the user. For example, it offers the option of using a delivery service to have the item delivered to the user's home. The flyer information collection unit also uses a generation AI to integrate supermarket flyer information with information from online shops in real time, and provide options that allow the user to purchase from home. For example, it checks online inventory and makes suggestions. In this way, by integrating information from online shops, it is possible to provide options that allow the user to purchase from home.

[0041] The menu suggestion unit can learn the user's past eating history and preferences and suggest individually customized menus. For example, the generation AI of the menu suggestion unit analyzes the user's past eating history, learns their preferences and frequency of ingredient use, and suggests individually customized menus. For example, it suggests recipes using ingredients that are frequently eaten. The menu suggestion unit also learns the user's preferences, and the generation AI suggests individually customized menus. For example, if a user likes a particular dish, it suggests a menu that includes that dish. The menu suggestion unit also learns the user's past eating history, and the generation AI suggests individually customized menus. For example, it suggests menus using ingredients that are high in specific nutrients. In this way, it is possible to suggest individually customized menus by learning the user's past eating history and preferences.

[0042] The menu suggestion unit can make suggestions to reduce waste by taking into account the shelf life and stock status of ingredients. For example, the generation AI of the menu suggestion unit considers the shelf life of ingredients and suggests menus to reduce waste. For example, it suggests recipes that prioritize ingredients that are close to their expiration date. The menu suggestion unit also analyzes the stock status and the generation AI suggests menus to reduce waste. For example, it suggests recipes to use up ingredients in the refrigerator. The menu suggestion unit also learns the shelf life and stock status of ingredients and suggests menus to reduce waste. For example, it suggests remake recipes using leftover ingredients. This makes it possible to make suggestions to reduce waste by taking into account the shelf life and stock status of ingredients.

[0043] The menu suggestion unit can incorporate cuisine from different cultures and regions to provide a new food experience. For example, the generation AI in the menu suggestion unit learns about cuisine from different cultures and regions and proposes menus that offer a new food experience. For example, it can propose ethnic dishes that the user does not normally eat. The menu suggestion unit also has the generation AI propose menus that incorporate ingredients from different regions. For example, it can propose recipes that combine local ingredients with exotic spices. The menu suggestion unit also has the generation AI learn about cuisine from different cultures and regions and propose menus that offer a new food experience. For example, it can propose recipes that recreate dishes that the user ate while traveling. This makes it possible to provide a new food experience by incorporating cuisine from different cultures and regions.

[0044] The menu suggestion unit also takes into consideration the preferences and nutritional intake standards of the user's family and housemates, and can propose menus that will satisfy everyone. For example, the generation AI of the menu suggestion unit learns the preferences of the user's family and housemates and proposes menus that will satisfy everyone. For example, it proposes recipes that use ingredients that the whole family likes. The menu suggestion unit also takes into consideration the nutritional intake standards of the family and housemates, and proposes menus that will satisfy everyone. For example, it proposes recipes that take nutritional balance for children into consideration. The menu suggestion unit also learns the preferences and nutritional intake standards of the user's family and housemates, and proposes menus that will satisfy everyone. For example, it proposes recipes that take into consideration family members with specific allergies. In this way, by taking into consideration the preferences and nutritional intake standards of the family and housemates, it is possible to propose menus that will satisfy everyone.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The nutritional value calculation system can further include an exercise amount monitoring unit that monitors the user's exercise amount in real time. The exercise amount monitoring unit, for example, obtains data from the user's smartwatch or fitness tracker and records the user's daily exercise amount. This allows the nutritional value calculation unit to adjust the nutritional intake standard based on the user's exercise amount. For example, it can suggest increasing protein and calorie intake on days when the user exercises a lot and reducing calorie intake on days when the user exercises less. The exercise amount monitoring unit can also learn the user's exercise patterns and adjust the nutritional intake standard based on long-term fluctuations in the user's exercise amount. This enables more appropriate nutritional management according to the user's exercise amount.

[0047] The nutritional value calculation system may further include a sleep data acquisition unit that acquires the user's sleep data. The sleep data acquisition unit acquires data, for example, from the user's smartwatch or sleep tracker, and records the user's sleep patterns. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's sleep state. For example, on days when the user has insufficient sleep, the system may increase nutrients to replenish energy, and on days when the user has had sufficient sleep, the system may apply the normal nutritional intake standards. The sleep data acquisition unit may also learn the user's sleep patterns and adjust the nutritional intake standards based on the user's long-term sleep state. This enables more appropriate nutritional management according to the user's sleep state.

[0048] The nutritional value calculation system may further include a stress monitoring unit that monitors the user's stress level. The stress monitoring unit may, for example, measure the user's heart rate or electrodermal activity to assess the stress level. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's stress level. For example, on days with high stress, the intake of vitamin C and magnesium may be increased, and on days with low stress, the normal nutritional intake standards may be applied. The stress monitoring unit may also learn the user's stress patterns and adjust the nutritional intake standards based on the user's long-term stress state. This allows for more appropriate nutritional management according to the user's stress level.

[0049] The nutritional value calculation system may further include a water intake monitoring unit that monitors the user's water intake. The water intake monitoring unit, for example, records the amount of water the user drinks and manages the user's daily water intake. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's water intake. For example, on days when the user's water intake is insufficient, the unit may make suggestions to encourage hydration, and on days when the user is consuming sufficient water, the unit may apply the normal nutritional intake standards. The water intake monitoring unit may also learn the user's water intake pattern and adjust the nutritional intake standards based on the user's long-term water intake status. This allows for more appropriate nutritional management according to the user's water intake.

[0050] The nutritional value calculation system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, collects questionnaires and feedback entered by the user after a meal and evaluates the user's satisfaction with the meal. This allows the nutritional value calculation unit to adjust menu suggestions based on the user's satisfaction. For example, it can prioritize suggestions of menus with high satisfaction and suggest improvements to menus with low satisfaction. The satisfaction evaluation unit can also learn the user's satisfaction patterns and adjust menu suggestions based on long-term satisfaction. This makes it possible to suggest more appropriate menus according to the user's satisfaction with the meal.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The photo analysis unit analyzes the photo of the meal. For example, the photo analysis unit uses image recognition technology to analyze the photo of the meal and identify the ingredients and type of dish. The photo analysis unit can also analyze the photo of the meal at high resolution and accurately recognize even the smallest details. Furthermore, the photo analysis unit can also integrate and analyze photos taken from multiple angles. Step 2: The nutritional value calculation unit calculates the nutritional value of the meal analyzed by the photo analysis unit. For example, the nutritional value calculation unit calculates nutritional values ​​such as calories, vitamins, and minerals based on the type and amount of ingredients. The nutritional value calculation unit can also adjust the nutritional value taking into account the freshness of the ingredients and the cooking method. Furthermore, the nutritional value calculation unit can identify the origin and production method of the ingredients and adjust the nutritional value based on that. Step 3: The nutrient determination unit identifies nutrients that are deficient or in excess based on the nutritional values ​​calculated by the nutritional value calculation unit. For example, the nutrient determination unit determines which nutrients are deficient or excessively consumed by comparing them with the user's daily nutritional intake standards. The nutrient determination unit can also make determinations based on the user's individual health condition by referring to the user's past health checkup results and medical data. Furthermore, the nutrient determination unit can also apply seasonal nutritional intake standards, taking into account seasonal and climate changes. Step 4: The flyer information collection unit collects flyer information from nearby supermarkets based on the user's location information. For example, the flyer information collection unit obtains sale information and new product information from supermarkets in the user's area. The flyer information collection unit can also integrate information from online shops to provide users with options for purchasing from home. Furthermore, when referring to supermarket flyer information, the flyer information collection unit can collect not only sale information but also information on the quality and producers of ingredients. Step 5: The menu suggestion unit suggests a menu to supplement the deficiency or excess of nutrients identified by the nutrient determination unit. For example, if the user is deficient in vitamin C, the menu suggestion unit suggests a menu using ingredients that are high in vitamin C and also provides information on supermarkets where those ingredients are on sale. The menu suggestion unit can also learn the user's past eating history and preferences and suggest individually customized menus. Furthermore, the menu suggestion unit can use an emotion estimation function to analyze the user's emotional state and suggest menus that elicit positive emotions.

[0053] (Example 2) The nutritional value calculation system according to an embodiment of the present invention automatically analyzes photos of meals, calculates nutritional values, identifies nutrients that are deficient or in excess, and suggests recommended menu items by comparing them with flyers from nearby supermarkets. This allows users to easily manage the nutritional balance of their meals and purchase ingredients economically by utilizing sales information from nearby supermarkets.

[0054] The nutritional value calculation system according to the embodiment includes a photo analysis unit, a nutritional value calculation unit, a nutrient determination unit, a flyer information collection unit, and a menu suggestion unit. The photo analysis unit analyzes photos of meals. For example, the photo analysis unit uses image recognition technology to analyze photos of meals and identify the types of ingredients and dishes. The photo analysis unit can also analyze photos of meals at high resolution, accurately recognizing even the smallest details. The photo analysis unit can also integrate and analyze photos taken from multiple angles. The nutritional value calculation unit calculates the nutritional value of the meal analyzed by the photo analysis unit. For example, the nutritional value calculation unit calculates nutritional values ​​such as calories, vitamins, and minerals based on the types and amounts of ingredients. The nutritional value calculation unit can also adjust the nutritional value taking into account the freshness and cooking method of the ingredients. The nutritional value calculation unit can also identify the origin and production method of the ingredients and adjust the nutritional value based on this. The nutrient determination unit identifies nutrients that are deficient or in excess based on the nutritional values ​​calculated by the nutritional value calculation unit. For example, the nutrient determination unit compares the user's daily nutritional intakes to determine whether the user is lacking or consuming excessive amounts of nutrients. The nutrient determination unit can also refer to the user's past health checkup results and medical data to make a determination based on the user's individual health condition. Furthermore, the nutrient determination unit can also apply seasonal nutritional intake standards, taking seasonal and climate changes into account. The flyer information collection unit collects flyer information from nearby supermarkets based on the user's location information. For example, the flyer information collection unit obtains information about special sales and new products at supermarkets in the user's area. The flyer information collection unit can also integrate information from online shops to provide options that the user can purchase from home. Furthermore, when referring to supermarket flyer information, the flyer information collection unit can collect not only sale information but also information about the quality and producers of ingredients. The menu suggestion unit proposes menus to supplement the nutrient deficiencies or excesses identified by the nutrient determination unit. For example, if the user is deficient in vitamin C, the menu suggestion unit proposes menus using ingredients rich in vitamin C and also provides information about supermarkets where those ingredients are on sale.The menu suggestion unit can also learn the user's past eating history and preferences and suggest individually customized menus. Furthermore, the menu suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest menus that elicit positive emotions. This allows the nutritional value calculation system according to the embodiment to enable users to easily manage the nutritional balance of their meals and purchase ingredients economically by utilizing information on sales at nearby supermarkets. For example, users can easily prepare nutritionally balanced meals and maintain a healthy lifestyle. Furthermore, utilizing information on sales at supermarkets can also help save on food costs.

[0055] The photo analysis unit takes into account the freshness of ingredients and cooking method, and can reflect fluctuations in nutritional value in the calculation. For example, when the generation AI analyzes a photo of a meal, the photo analysis unit detects changes in color and texture to determine the freshness of ingredients. For example, if the color of vegetables is vivid, it will determine that they are fresh and set the nutritional value higher. The photo analysis unit also identifies cooking utensils and cooking processes from the photo of the meal to take cooking method into account. For example, in the case of fried foods, it will take into account the amount of oil used and set the nutritional value of fat higher. The photo analysis unit also combines the freshness of ingredients and cooking method to reflect fluctuations in nutritional value in the calculation. For example, if fresh vegetables are steamed, it will determine that there is less vitamin loss and set the nutritional value higher. This allows for more accurate nutritional value calculations by taking into account the freshness of ingredients and cooking method.

[0056] The photo analysis unit can identify the origin and production method of ingredients and adjust nutritional value based on that. For example, the photo analysis unit analyzes information from packaging and labels so that the generation AI can identify the origin of ingredients from a photo of a meal. For example, if an organically grown label is included, the nutritional value is set higher. The photo analysis unit also takes production method into account, so the generation AI analyzes the appearance and shape of ingredients to identify characteristics such as organic or pesticide-free. For example, irregularly shaped vegetables are determined to be pesticide-free and their nutritional value is set higher. The photo analysis unit also adjusts nutritional value by combining the origin and production method of ingredients. For example, if organically grown vegetables are used, the vitamin and mineral content is set higher. This allows for more accurate nutritional value calculations by taking into account the origin and production method of ingredients.

[0057] The photo analysis unit integrates photos taken from multiple angles to enable more accurate ingredient recognition. For example, the photo analysis unit uses a generation AI to integrate photos of a meal taken by the user from multiple angles and accurately recognize the shape and arrangement of ingredients. For example, it combines photos from above and the side for analysis. The photo analysis unit also analyzes photos taken from multiple angles and removes shadows and reflections from ingredients to enable accurate recognition. For example, it corrects the color of ingredients to take light reflections into account. The photo analysis unit also integrates photos taken from multiple angles to complement overlapping and hidden parts of ingredients and calculate nutritional values ​​more accurately. For example, it estimates hidden ingredients and calculates nutritional values. In this way, integrating photos from multiple angles enables more accurate ingredient recognition.

[0058] The nutritional value calculation unit can predict nutritional value by referencing the user's past meal photo data and learning their eating patterns. For example, the generation AI of the nutritional value calculation unit refers to the user's past meal photo data and learns their eating patterns. For example, it identifies trends in frequently eaten ingredients and dishes. The nutritional value calculation unit also analyzes the user's eating patterns based on the past meal photo data and predicts nutritional value. For example, it extracts trends in nutritional balance from past data. The nutritional value calculation unit also learns the past meal photo data and predicts nutritional value based on the user's eating patterns. For example, it predicts the nutritional value of dishes eaten on a specific day of the week. This makes it possible to make more accurate nutritional value predictions by referencing past meal data.

[0059] The nutrient determination unit can refer to the user's past health checkup results and medical data and make determinations based on the user's individual health condition. For example, the generation AI in the nutrient determination unit refers to the user's past health checkup results and determines whether there is a nutritional deficiency or excess. For example, it identifies vitamin and mineral deficiencies based on blood test results. The nutrient determination unit also analyzes the user's medical data and determines nutritional value based on the user's individual health condition. For example, it adjusts nutritional value taking into account the presence or absence of chronic diseases. The generation AI in the nutrient determination unit also learns from the user's past health checkup results and medical data and determines whether there is a nutritional deficiency or excess based on the user's individual health condition. For example, it provides appropriate nutritional values ​​for users with a specific medical history. This makes it possible to determine nutrients based on the user's individual health condition.

[0060] The nutrient determination unit can take seasonal and climate fluctuations into account and apply seasonal nutritional reference intakes. For example, the generation AI in the nutrient determination unit takes seasonal and climate fluctuations into account and determines whether there is a nutritional deficiency or excess. For example, it recommends taking vitamin D in winter. In addition, in order to apply seasonal nutritional reference intakes, the generation AI analyzes climate data and provides appropriate nutritional values. For example, it places emphasis on hydration in summer. In addition, the nutrient determination unit learns seasonal and climate fluctuations and applies seasonal nutritional reference intakes. For example, it recommends specific nutrients in spring as a measure against allergies. This makes it possible to determine more appropriate nutrients by taking seasonal and climate fluctuations into account.

[0061] The nutrient determination unit can monitor the user's exercise volume and activity level in real time and adjust the nutritional intake standards based on that. For example, the generation AI of the nutrient determination unit monitors the user's exercise volume in real time and adjusts the nutritional intake standards based on that. For example, if the amount of exercise is high, protein intake is increased. The nutrient determination unit also analyzes the activity level, and the generation AI provides appropriate nutritional intake standards. For example, if the user does a lot of desk work, calorie intake is reduced. The nutrient determination unit also learns the user's exercise volume and activity level, and adjusts the nutritional intake standards based on that. For example, if the user is not exercising enough, vitamin D intake is recommended. This allows for more appropriate nutritional management by adjusting the nutritional intake standards based on the amount of exercise and activity level.

[0062] The nutrient determination unit can analyze a user's dietary history over the long term, grasp trends in nutritional intake, and reflect this in the determination. For example, the generation AI in the nutrient determination unit analyzes a user's dietary history over the long term to grasp trends in nutritional intake. For example, it evaluates nutritional balance based on data from the past few months. The nutrient determination unit also analyzes long-term dietary history, and the generation AI reflects trends in nutritional intake in the determination. For example, it issues a warning if the intake of a specific nutrient is continuously insufficient. The nutrient determination unit also learns the user's dietary history, grasps trends in long-term nutritional intake, and reflects this in the determination. For example, it analyzes seasonal nutritional intake patterns. This allows for more accurate nutrient determination by analyzing long-term dietary history.

[0063] When referencing supermarket flyer information, the flyer information collection unit can collect not only sale information but also information on the quality and producers of ingredients, and reflect this in suggestions. For example, the generation AI in the flyer information collection unit analyzes supermarket flyer information and collects information on the quality of ingredients in addition to sale information. For example, it identifies organically grown and pesticide-free ingredients and reflects this in suggestions. In addition, to collect producer information, the generation AI identifies the producer's name and region from the flyer information. For example, it prioritizes suggestions of fresh ingredients provided by local producers. In addition, the flyer information collection unit integrates the sale information with the quality and producer information of ingredients and provides it to the user. For example, it suggests a menu using organic vegetables on sale. This allows for more reliable suggestions by taking into account the quality and producer information of ingredients.

[0064] When referring to supermarket flyer information, the flyer information collection unit can analyze the user's purchasing history and make suggestions taking into account trends in ingredients purchased in the past. In the flyer information collection unit, for example, the generation AI analyzes the user's purchasing history and understands trends in ingredients purchased in the past. For example, it identifies ingredients that are frequently purchased and makes suggestions based on that. In addition, the flyer information collection unit makes suggestions based on the purchasing history, with the generation AI taking into account the user's preferences and frequency of ingredient use. For example, it suggests recipes using ingredients purchased in the past. In addition, the flyer information collection unit makes appropriate suggestions by learning the user's purchasing history and referring to supermarket flyer information based on past purchasing trends. For example, it suggests menus using ingredients that are on sale. This makes it possible to make more appropriate suggestions by taking into account the user's purchasing history.

[0065] When referring to supermarket flyer information, the flyer information collection unit also integrates information from online shops, and can provide options that allow the user to purchase from home. For example, the flyer information collection unit uses a generation AI to integrate supermarket flyer information with information from online shops, and provide options that allow the user to purchase from home. For example, it allows the user to order sale items online. The flyer information collection unit also analyzes information from online shops, and the generation AI suggests the optimal purchasing option to the user. For example, it offers the option of using a delivery service to have the item delivered to the user's home. The flyer information collection unit also uses a generation AI to integrate supermarket flyer information with information from online shops in real time, and provide options that allow the user to purchase from home. For example, it checks online inventory and makes suggestions. In this way, by integrating information from online shops, it is possible to provide options that allow the user to purchase from home.

[0066] The menu suggestion unit can learn the user's past eating history and preferences and suggest individually customized menus. For example, the generation AI of the menu suggestion unit analyzes the user's past eating history, learns their preferences and frequency of ingredient use, and suggests individually customized menus. For example, it suggests recipes using ingredients that are frequently eaten. The menu suggestion unit also learns the user's preferences, and the generation AI suggests individually customized menus. For example, if a user likes a particular dish, it suggests a menu that includes that dish. The menu suggestion unit also learns the user's past eating history, and the generation AI suggests individually customized menus. For example, it suggests menus using ingredients that are high in specific nutrients. In this way, it is possible to suggest individually customized menus by learning the user's past eating history and preferences.

[0067] The menu suggestion unit can make suggestions to reduce waste by taking into account the shelf life and stock status of ingredients. For example, the generation AI of the menu suggestion unit considers the shelf life of ingredients and suggests menus to reduce waste. For example, it suggests recipes that prioritize ingredients that are close to their expiration date. The menu suggestion unit also analyzes the stock status and the generation AI suggests menus to reduce waste. For example, it suggests recipes to use up ingredients in the refrigerator. The menu suggestion unit also learns the shelf life and stock status of ingredients and suggests menus to reduce waste. For example, it suggests remake recipes using leftover ingredients. This makes it possible to make suggestions to reduce waste by taking into account the shelf life and stock status of ingredients.

[0068] The menu suggestion unit can incorporate cuisine from different cultures and regions to provide a new food experience. For example, the generation AI in the menu suggestion unit learns about cuisine from different cultures and regions and proposes menus that offer a new food experience. For example, it can propose ethnic dishes that the user does not normally eat. The menu suggestion unit also has the generation AI propose menus that incorporate ingredients from different regions. For example, it can propose recipes that combine local ingredients with exotic spices. The menu suggestion unit also has the generation AI learn about cuisine from different cultures and regions and propose menus that offer a new food experience. For example, it can propose recipes that recreate dishes that the user ate while traveling. This makes it possible to provide a new food experience by incorporating cuisine from different cultures and regions.

[0069] The menu suggestion unit also takes into consideration the preferences and nutritional intake standards of the user's family and housemates, and can propose menus that will satisfy everyone. For example, the generation AI of the menu suggestion unit learns the preferences of the user's family and housemates and proposes menus that will satisfy everyone. For example, it proposes recipes that use ingredients that the whole family likes. The menu suggestion unit also takes into consideration the nutritional intake standards of the family and housemates, and proposes menus that will satisfy everyone. For example, it proposes recipes that take nutritional balance for children into consideration. The menu suggestion unit also learns the preferences and nutritional intake standards of the user's family and housemates, and proposes menus that will satisfy everyone. For example, it proposes recipes that take into consideration family members with specific allergies. In this way, by taking into consideration the preferences and nutritional intake standards of the family and housemates, it is possible to propose menus that will satisfy everyone.

[0070] The menu suggestion unit can use the emotion estimation function to analyze the emotional state of the user and suggest a menu that elicits positive emotions. The menu suggestion unit, for example, uses the emotion estimation function to analyze the emotional state of the user and suggest a menu that elicits positive emotions. For example, if the user is smiling, a menu for maintaining that emotion is suggested. The menu suggestion unit also analyzes the user's tone of voice and suggests a menu that elicits positive emotions. For example, if the user is speaking in a bright tone, a menu for maintaining that emotion is suggested. The menu suggestion unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest a menu that elicits positive emotions. For example, if the positive emotion score is high, a menu for maintaining that emotion is suggested. In this way, a menu that elicits positive emotions can be suggested by taking the user's emotional state into consideration.

[0071] The menu suggestion unit can use the emotion estimation function to analyze the user's emotional state and suggest a menu for improving negative emotions. For example, the menu suggestion unit uses the emotion estimation function to analyze the user's emotional state and suggest a menu for improving negative emotions. For example, if the user is feeling down, the menu suggestion unit suggests a menu for lifting their spirits. The menu suggestion unit also analyzes the user's tone of voice and suggests a menu for improving negative emotions. For example, if the user speaks in a tired tone, the menu suggestion unit suggests a menu for refreshing them. The menu suggestion unit also uses the emotion estimation function to analyze the user's emotional state in real time and suggest a menu for improving negative emotions. For example, if the negative emotion score is high, the menu suggestion unit suggests a menu for improving their mood. In this way, a menu for improving negative emotions can be suggested by taking the user's emotional state into consideration.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The nutritional value calculation system can further include an exercise amount monitoring unit that monitors the user's exercise amount in real time. The exercise amount monitoring unit, for example, obtains data from the user's smartwatch or fitness tracker and records the user's daily exercise amount. This allows the nutritional value calculation unit to adjust the nutritional intake standard based on the user's exercise amount. For example, it can suggest increasing protein and calorie intake on days when the user exercises a lot and reducing calorie intake on days when the user exercises less. The exercise amount monitoring unit can also learn the user's exercise patterns and adjust the nutritional intake standard based on long-term fluctuations in the user's exercise amount. This enables more appropriate nutritional management according to the user's exercise amount.

[0074] The nutritional value calculation system may further include a sleep data acquisition unit that acquires the user's sleep data. The sleep data acquisition unit acquires data, for example, from the user's smartwatch or sleep tracker, and records the user's sleep patterns. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's sleep state. For example, on days when the user has insufficient sleep, the system may increase nutrients to replenish energy, and on days when the user has had sufficient sleep, the system may apply the normal nutritional intake standards. The sleep data acquisition unit may also learn the user's sleep patterns and adjust the nutritional intake standards based on the user's long-term sleep state. This enables more appropriate nutritional management according to the user's sleep state.

[0075] The nutritional value calculation system may further include a stress monitoring unit that monitors the user's stress level. The stress monitoring unit may, for example, measure the user's heart rate or electrodermal activity to assess the stress level. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's stress level. For example, on days with high stress, the intake of vitamin C and magnesium may be increased, and on days with low stress, the normal nutritional intake standards may be applied. The stress monitoring unit may also learn the user's stress patterns and adjust the nutritional intake standards based on the user's long-term stress state. This allows for more appropriate nutritional management according to the user's stress level.

[0076] The nutritional value calculation system may further include a water intake monitoring unit that monitors the user's water intake. The water intake monitoring unit, for example, records the amount of water the user drinks and manages the user's daily water intake. This allows the nutritional value calculation unit to adjust the nutritional intake standards based on the user's water intake. For example, on days when the user's water intake is insufficient, the unit may make suggestions to encourage hydration, and on days when the user is consuming sufficient water, the unit may apply the normal nutritional intake standards. The water intake monitoring unit may also learn the user's water intake pattern and adjust the nutritional intake standards based on the user's long-term water intake status. This allows for more appropriate nutritional management according to the user's water intake.

[0077] The nutritional value calculation system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit, for example, collects questionnaires and feedback entered by the user after a meal and evaluates the user's satisfaction with the meal. This allows the nutritional value calculation unit to adjust menu suggestions based on the user's satisfaction. For example, it can prioritize suggestions of menus with high satisfaction and suggest improvements to menus with low satisfaction. The satisfaction evaluation unit can also learn the user's satisfaction patterns and adjust menu suggestions based on long-term satisfaction. This makes it possible to suggest more appropriate menus according to the user's satisfaction with the meal.

[0078] The nutritional value calculation system can further analyze the user's emotional state and suggest menus to elicit positive emotions. For example, using the emotion estimation function, if the user is smiling, a menu to maintain that emotion can be suggested. It can also analyze the user's tone of voice and suggest menus to maintain that emotion if the user is speaking in a bright tone. Furthermore, using the emotion estimation function, it can analyze the user's emotional state in real time and suggest menus to maintain that emotion if the user has a high positive emotion score. In this way, it is possible to suggest menus to elicit positive emotions by taking the user's emotional state into consideration.

[0079] The nutritional value calculation system can further analyze the user's emotional state and suggest menus to improve negative emotions. For example, if the user is feeling down, the emotion estimation function can be used to suggest menus to lift their spirits. The system can also analyze the user's tone of voice and suggest menus to refresh them if they speak in a tired tone. Furthermore, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest menus to improve their mood if the negative emotion score is high. In this way, it is possible to suggest menus to improve negative emotions by taking the user's emotional state into consideration.

[0080] The nutritional value calculation system can further analyze the user's emotional state and suggest a menu to reduce stress. For example, using the emotion estimation function, if the user is feeling stressed, a menu to help them relax can be suggested. It can also analyze the user's tone of voice and suggest a menu to reduce stress if the user is speaking in a tense tone. Furthermore, using the emotion estimation function, it can analyze the user's emotional state in real time and suggest a menu to reduce stress if the stress score is high. In this way, it is possible to suggest a menu to reduce stress by taking the user's emotional state into consideration.

[0081] The nutritional value calculation system can further analyze the user's emotional state and suggest menus to improve concentration. For example, using the emotion estimation function, if the user needs to concentrate, a menu to improve concentration can be suggested. The system can also analyze the user's tone of voice and suggest menus to maintain concentration if the user is speaking in a tone that indicates concentration. Furthermore, using the emotion estimation function, the system can analyze the user's emotional state in real time and suggest menus to maintain concentration if the user's concentration score is high. In this way, it is possible to suggest menus to improve concentration by taking the user's emotional state into consideration.

[0082] The nutritional value calculation system can further analyze the user's emotional state and suggest menus with a relaxing effect. For example, using the emotion estimation function, if the user is nervous, a menu with a relaxing effect can be suggested. The system can also analyze the user's tone of voice and suggest a menu to maintain that relaxation effect if the user speaks in a calm tone. Furthermore, using the emotion estimation function, the system can analyze the user's emotional state in real time and suggest a menu to maintain that relaxation effect if the user has a high relaxation score. In this way, it is possible to suggest a menu with a relaxing effect by taking the user's emotional state into consideration.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The photo analysis unit analyzes the photo of the meal. For example, the photo analysis unit uses image recognition technology to analyze the photo of the meal and identify the ingredients and type of dish. The photo analysis unit can also analyze the photo of the meal at high resolution and accurately recognize even the smallest details. Furthermore, the photo analysis unit can also integrate and analyze photos taken from multiple angles. Step 2: The nutritional value calculation unit calculates the nutritional value of the meal analyzed by the photo analysis unit. For example, the nutritional value calculation unit calculates nutritional values ​​such as calories, vitamins, and minerals based on the type and amount of ingredients. The nutritional value calculation unit can also adjust the nutritional value taking into account the freshness of the ingredients and the cooking method. Furthermore, the nutritional value calculation unit can identify the origin and production method of the ingredients and adjust the nutritional value based on that. Step 3: The nutrient determination unit identifies nutrients that are deficient or in excess based on the nutritional values ​​calculated by the nutritional value calculation unit. For example, the nutrient determination unit determines which nutrients are deficient or excessively consumed by comparing them with the user's daily nutritional intake standards. The nutrient determination unit can also make determinations based on the user's individual health condition by referring to the user's past health checkup results and medical data. Furthermore, the nutrient determination unit can also apply seasonal nutritional intake standards, taking into account seasonal and climate changes. Step 4: The flyer information collection unit collects flyer information from nearby supermarkets based on the user's location information. For example, the flyer information collection unit obtains sale information and new product information from supermarkets in the user's area. The flyer information collection unit can also integrate information from online shops to provide users with options for purchasing from home. Furthermore, when referring to supermarket flyer information, the flyer information collection unit can collect not only sale information but also information on the quality and producers of ingredients. Step 5: The menu suggestion unit suggests a menu to supplement the deficiency or excess of nutrients identified by the nutrient determination unit. For example, if the user is deficient in vitamin C, the menu suggestion unit suggests a menu using ingredients that are high in vitamin C and also provides information on supermarkets where those ingredients are on sale. The menu suggestion unit can also learn the user's past eating history and preferences and suggest individually customized menus. Furthermore, the menu suggestion unit can use an emotion estimation function to analyze the user's emotional state and suggest menus that elicit positive emotions.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] 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.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 photo analysis unit that analyzes photos of meals; a nutritional value calculation unit that calculates the nutritional value of the meal analyzed by the photo analysis unit; a nutrient determination unit that identifies nutrients that are deficient or in excess based on the nutritional values ​​calculated by the nutritional value calculation unit; A flyer information collection unit that collects flyer information from nearby supermarkets based on the user's location information; a menu suggestion unit that suggests a menu for supplementing the nutrient that is insufficient or in excess and that is identified by the nutrient determination unit. A system characterized by:

2. The photo analysis unit The calculation takes into account the freshness of ingredients and cooking methods, and reflects fluctuations in nutritional value.

2. The system of claim 1.

3. The photo analysis unit Identify the origin and production method of ingredients and adjust nutritional values ​​accordingly 2. The system of claim 1.

4. The photo analysis unit Integrates photos taken from multiple angles for more accurate ingredient recognition 2. The system of claim 1.

5. The nutritional value calculation unit It references the user's past meal photo data, learns their eating patterns, and predicts nutritional value.

2. The system of claim 1.

Images