system

Abstract

The system according to this embodiment aims to understand the user's nutritional status and provide appropriate meal suggestions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives a video from the user. The analysis unit analyzes the video received by the reception unit and determines the nutritional status. The suggestion unit suggests foods to eat and recipes based on the nutritional status determined by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to easily grasp the nutritional status of a user and make appropriate dietary suggestions.

[0005] The system according to the embodiment aims to grasp the nutritional status of a user and make appropriate dietary suggestions.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives a video of a user. The analysis unit analyzes the video received by the reception unit and determines the nutritional status. The proposal unit proposes foods and recipes to be eaten based on the nutritional status determined by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can understand the user's nutritional status and make appropriate meal suggestions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 3, 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The nutrition suggestion system according to an embodiment of the present invention is a system that uses a generating AI to determine the nutrients that a person is deficient in from a video taken with a smartphone or the like, and suggests foods and recipes that should be eaten to improve them. This nutrition suggestion system operates when a user inputs a video of their meals and lifestyle habits, taken with a smartphone or the like, into the generating AI. The generating AI analyzes the video and determines the user's nutritional status. For example, it analyzes the content of meals, frequency of eating, exercise level, etc., to identify deficient nutrients. Then, the generating AI suggests foods and recipes that should be eaten to supplement the deficient nutrients. As a result, the user can receive specific advice to improve their nutritional status. For example, if a user is deficient in a particular nutrient, they can improve their nutritional status by being suggested foods and recipes that contain a lot of that nutrient. In this way, the nutrition suggestion system can automatically analyze the user's nutritional status and suggest appropriate meals and recipes.

[0029] The nutrition suggestion system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives videos taken by the user with a smartphone or tablet. Videos taken by the user may include, for example, the contents of meals and lifestyle habits, but are not limited to such examples. The reception unit can, for example, take videos using a specific application and upload them to the generation AI. The reception unit can also accept input from various devices regardless of the format or type of video. The analysis unit uses the generation AI to analyze the videos received by the reception unit and determine the user's nutritional status. The analysis unit analyzes, for example, the contents of meals, the frequency of eating, and the amount of exercise, and identifies deficient nutrients. The generation AI uses algorithms and databases to analyze the contents of meals and lifestyle habits in the video. For example, the generation AI analyzes the contents of meals and evaluates the intake of calories, vitamins, and minerals. The generation AI can also analyze the amount of exercise and evaluate the calories burned and the frequency of exercise. Based on the nutritional status determined by the analysis unit, the suggestion unit suggests foods and recipes that should be eaten to supplement the deficient nutrients. The suggestion unit, for example, suggests foods and recipes rich in a particular nutrient if that nutrient is deficient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generation AI suggests foods and recipes rich in vitamin D. The generation AI can also suggest foods and recipes rich in iron if the user is deficient in iron. Thus, the nutrition suggestion system according to this embodiment can automatically analyze the user's nutritional status and suggest appropriate meals and recipes.

[0030] The reception unit can accept videos shot by users with their smartphones or tablets. For example, the reception unit accepts videos shot by users with their smartphones or tablets. Videos shot by users may include, but are not limited to, content of meals or lifestyle habits. The reception unit can shoot videos using specific applications and upload them to the generating AI. For example, users can shoot videos of content of meals, frequency of eating, exercise levels, etc., and input them into the generating AI. This allows the reception unit to accept videos shot by users with their smartphones, etc. Some or all of the above processing in the reception unit may be performed using the generating AI, for example, or without using the generating AI. For example, the reception unit can input a video shot by a user into the generating AI, and the generating AI can analyze the format and content of the video.

[0031] The analysis unit can analyze the content of meals and daily activity patterns in the video to determine the user's nutritional status. For example, the analysis unit analyzes the content of meals and daily activity patterns in the video to determine the user's nutritional status. The analysis unit uses a generation AI to analyze the content of meals, frequency of eating, exercise levels, etc., and identify deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in the video. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise levels and evaluate calorie expenditure and exercise frequency. This allows the analysis unit to analyze the content of meals and lifestyle habits in the video and determine the user's nutritional status. Some or all of the above processing in the analysis unit may be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input a video shot by the user into the generation AI, which can then analyze the content of the video.

[0032] The suggestion unit can suggest foods and recipes to eat to supplement any deficient nutrients. For example, the suggestion unit can suggest foods and recipes to eat to supplement any deficient nutrients. The suggestion unit uses a generative AI to suggest foods and recipes that are rich in a particular nutrient if that nutrient is deficient. The generative AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generative AI will suggest foods and recipes that are rich in vitamin D. The generative AI can also suggest foods and recipes that are rich in iron if the user is deficient in iron. This allows the suggestion unit to suggest foods and recipes that are good to eat to supplement any deficient nutrients. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can use an algorithm that allows the generative AI to suggest foods and recipes that are rich in a particular nutrient.

[0033] The analysis unit can analyze the content and frequency of meals and exercise levels to identify deficient nutrients. For example, the analysis unit analyzes the content and frequency of meals and exercise levels to identify deficient nutrients. The analysis unit uses a generation AI to analyze the content and frequency of meals, exercise levels, etc., to identify deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in videos. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise levels and evaluate calorie expenditure and exercise frequency. This allows the analysis of the content and frequency of meals and exercise levels to identify deficient nutrients. Some or all of the above processing in the analysis unit may be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input a video shot by the user into the generation AI, which can then analyze the video content.

[0034] The suggestion unit can suggest foods and recipes rich in specific nutrients if those nutrients are deficient. For example, if a specific nutrient is deficient, the suggestion unit will suggest foods and recipes rich in that nutrient. The suggestion unit uses a generation AI to suggest foods and recipes rich in specific nutrients if those nutrients are deficient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if a vitamin D deficiency occurs, the generation AI will suggest foods and recipes rich in vitamin D. The generation AI can also suggest foods and recipes rich in iron if an iron deficiency occurs. This allows the suggestion unit to suggest foods and recipes rich in specific nutrients if those nutrients are deficient. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can use an algorithm that allows the generation AI to suggest foods and recipes rich in specific nutrients.

[0035] The reception department can analyze a user's past video submission history and select an appropriate reception method. For example, the reception department can analyze a user's past video submission history and select an appropriate reception method. The reception department can use generative AI to analyze a user's past video submission history and select the optimal reception method. For example, it can analyze the time periods of videos previously submitted by the user and suggest the optimal reception time. It can also prioritize suggesting submission methods previously used by the user (upload, live streaming, etc.). Furthermore, it can consider the user's past submission frequency and suggest an appropriate submission interval. This allows the reception department to analyze a user's past video submission history and select the optimal reception method. Some or all of the above processing in the reception department may be performed using generative AI, for example, or without generative AI. For example, the reception department can input the user's past video submission history into the generative AI, which can then select the optimal reception method.

[0036] The reception unit can filter videos upon receiving them based on the user's current lifestyle and areas of interest. For example, the reception unit can filter videos upon receiving them based on the user's current lifestyle and areas of interest. The reception unit uses a generative AI to filter videos upon receiving them based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, health-related videos will be prioritized. Also, if the user leads a busy life, videos that can be filmed in a short time will be prioritized. Furthermore, if the user has specific dietary restrictions, videos that comply with those restrictions will be prioritized. This allows videos to be filtered based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's current lifestyle and areas of interest into a generative AI, which can then filter the videos.

[0037] The reception unit can prioritize receiving videos that are highly relevant based on the user's geographical location information when receiving videos. For example, when receiving videos, the reception unit prioritizes receiving videos that are highly relevant based on the user's geographical location information. The reception unit uses a generative AI to prioritize receiving videos that are highly relevant based on the user's geographical location information when receiving videos. For example, if the user lives in a specific region, videos using local ingredients will be prioritized. Also, if the user is traveling, videos related to the food culture of the travel destination will be prioritized. Furthermore, if the user has dietary restrictions in a specific region, videos that conform to those dietary restrictions will be prioritized. This allows for the priority reception of highly relevant videos while considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generative AI, for example, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select highly relevant videos.

[0038] The reception unit can analyze the user's social media activity when receiving a video and receive relevant videos. For example, the reception unit can analyze the user's social media activity when receiving a video and receive relevant videos. The reception unit uses generative AI to analyze the user's social media activity when receiving a video and receive relevant videos. For example, it can receive relevant videos based on the content of meals shared by the user on social media. It can also receive relevant videos based on health-related accounts that the user follows on social media. Furthermore, it can receive relevant videos based on dietary restriction groups that the user participates in on social media. This allows the reception unit to analyze the user's social media activity and receive relevant videos. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without generative AI. For example, the reception unit can input the user's social media activity into the generative AI, and the generative AI can select relevant videos.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the video during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the video during the analysis. The analysis unit uses a generation AI to adjust the level of detail of the analysis based on the importance of the video during the analysis. For example, if a video contains important meal information, a detailed analysis can be performed. If a video contains everyday meal information, a concise analysis can be performed. Furthermore, if a video concerns a specific nutrient, an analysis focusing on that nutrient can be performed. This allows the level of detail of the analysis to be adjusted based on the importance of the video. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the importance of the video into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the video category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the video category during analysis. The analysis unit uses a generation AI to apply different analysis algorithms depending on the video category during analysis. For example, for a video about food, an algorithm that analyzes the nutritional value of ingredients can be applied. For a video about exercise, an algorithm that analyzes calories burned can be applied. Furthermore, for a video about lifestyle habits, an algorithm that analyzes overall health status can be applied. This allows different analysis algorithms to be applied depending on the video category. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the video category into the generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the videos during analysis. For example, the analysis unit can determine the priority of analysis based on the submission date of the videos during analysis. The analysis unit uses a generative AI to determine the priority of analysis based on the submission date of the videos during analysis. For example, it can prioritize the analysis of the most recent videos. It can also prioritize the analysis of videos related to specific events. Furthermore, it can prioritize the analysis of important videos specified by the user. This allows the analysis priority to be determined based on the submission date of the videos. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the submission date of the videos into the generative AI, and the generative AI can determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the videos during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the videos during analysis. The analysis unit uses a generative AI to adjust the order of analysis based on the relevance of the videos during analysis. For example, it can analyze videos of the same category consecutively. It can also prioritize the analysis of videos related to the user's areas of interest. Furthermore, it can prioritize the analysis of videos related to specific nutrients. This allows the order of analysis to be adjusted based on the relevance of the videos. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the relevance of the videos into a generative AI, and the generative AI can adjust the order of analysis.

[0043] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. For example, the suggestion unit can adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. The suggestion unit uses a generation AI to adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. For example, if there is a deficiency in important nutrients, it can provide detailed suggestions. If there is a deficiency in everyday nutrients, it can provide concise suggestions. Furthermore, in the case of suggestions concerning a specific nutrient, it can provide suggestions that focus on that nutrient. This allows the level of detail of suggestions to be adjusted based on the importance of the nutrients. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the importance of nutrients into the generation AI, and the generation AI can adjust the level of detail of the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the nutrient category when making suggestions. For example, the suggestion unit can apply different suggestion algorithms depending on the nutrient category when making suggestions. The suggestion unit uses a generation AI to apply different suggestion algorithms depending on the nutrient category when making suggestions. For example, for suggestions about vitamins, an algorithm that suggests foods rich in vitamins can be applied. For suggestions about minerals, an algorithm that suggests foods rich in minerals can be applied. Furthermore, for suggestions about proteins, an algorithm that suggests foods rich in protein can be applied. This allows different suggestion algorithms to be applied depending on the nutrient category. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the nutrient category into the generation AI, and the generation AI can apply an appropriate suggestion algorithm.

[0045] The suggestion unit can determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. For example, the suggestion unit can determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. The suggestion unit uses a generation AI to determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. For example, if there is a significant deficiency in an important nutrient, that nutrient will be suggested preferentially. Also, if there is a deficiency in a daily nutrient, that nutrient can be suggested preferentially. Furthermore, if there is a deficiency in a specific nutrient, that nutrient can be suggested preferentially. In this way, the priority of suggestions can be determined based on the degree of nutrient deficiency. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the degree of nutrient deficiency into a generation AI, and the generation AI can determine the priority of suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relationships between nutrients when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relationships between nutrients when making suggestions. The suggestion unit uses a generation AI to adjust the order of suggestions based on the relationships between nutrients when making suggestions. For example, it may suggest nutrients from the same category consecutively. It can also prioritize suggesting nutrients related to the user's areas of interest. Furthermore, it can prioritize suggestions related to specific nutrients. This allows the order of suggestions to be adjusted based on the relationships between nutrients. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the relationships between nutrients into a generation AI, and the generation AI can adjust the order of suggestions.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analysis unit can analyze not only the user's diet but also their sleep patterns and stress levels. For example, if a user stays up late at night, the analysis unit can take this information into account, as sleep deprivation may affect nutrient absorption. Furthermore, if a user is experiencing high stress levels, the unit can assess the impact of stress on nutritional balance and suggest foods and recipes that can help reduce stress. It can also analyze the user's water intake and provide advice on appropriate hydration.

[0049] The recommendation system can also suggest specific supplements based on the user's nutritional status. For example, if a user is deficient in vitamin D, it can suggest taking a vitamin D supplement. Similarly, if a user is deficient in iron, it can suggest taking an iron supplement. Furthermore, if a user has a specific allergy, it can suggest supplements that address that allergy. This allows users to improve their nutritional status not only through diet but also through supplements.

[0050] The reception desk can automatically generate questions about the user's diet and lifestyle and ask the user to answer them. For example, it can generate questions about how often the user exercises and what dietary restrictions they follow. If the user has specific health goals, it can also generate questions related to those goals. Furthermore, based on the user's answers, it can generate more detailed questions to gain a more accurate understanding of the user's nutritional status.

[0051] The analysis unit can analyze the user's genetic information and make suggestions based on their genetic nutritional requirements. For example, if a user has a specific gene mutation, it can evaluate the impact of that mutation on nutrient absorption. Furthermore, if the intake of a specific nutrient is recommended based on the user's genetic information, it can suggest foods and recipes rich in that nutrient. It can also assess the risk of specific diseases based on genetic information and provide nutritional suggestions to mitigate that risk.

[0052] The suggestion function can analyze a user's eating history and provide future meal suggestions based on past eating patterns. For example, if a user has consumed a large amount of a particular nutrient in the past, it can suggest ways to consume that nutrient in a balanced way. It can also suggest new recipes that include a particular food if the user has enjoyed eating that food in the past. Furthermore, it can provide seasonal meal suggestions based on the user's eating history.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk accepts videos shot by users using their smartphones or tablets. These videos may include, but are not limited to, content such as meals or lifestyle habits. The reception desk can shoot videos using specific applications and upload them to the generating AI. Furthermore, the reception desk can accept input from various devices, regardless of video format or type. Step 2: The analysis unit uses a generation AI to analyze the video received by the reception unit and determine the user's nutritional status. The analysis unit analyzes the content of meals, frequency of eating, exercise level, etc., and identifies any deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in the video. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise level and evaluate calories burned and exercise frequency. Step 3: The suggestion unit, based on the nutritional status determined by the analysis unit, suggests foods and recipes that would be good to eat to supplement any deficient nutrients. If a specific nutrient is deficient, the suggestion unit will suggest foods and recipes that are rich in that nutrient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generation AI will suggest foods and recipes that are rich in vitamin D. Similarly, if the user is deficient in iron, the generation AI can suggest foods and recipes that are rich in iron.

[0055] (Example of form 2) The nutrition suggestion system according to an embodiment of the present invention is a system that uses a generating AI to determine the nutrients that a person is deficient in from a video taken with a smartphone or the like, and suggests foods and recipes that should be eaten to improve them. This nutrition suggestion system operates when a user inputs a video of their meals and lifestyle habits, taken with a smartphone or the like, into the generating AI. The generating AI analyzes the video and determines the user's nutritional status. For example, it analyzes the content of meals, frequency of eating, exercise level, etc., to identify deficient nutrients. Then, the generating AI suggests foods and recipes that should be eaten to supplement the deficient nutrients. As a result, the user can receive specific advice to improve their nutritional status. For example, if a user is deficient in a particular nutrient, they can improve their nutritional status by being suggested foods and recipes that contain a lot of that nutrient. In this way, the nutrition suggestion system can automatically analyze the user's nutritional status and suggest appropriate meals and recipes.

[0056] The nutrition suggestion system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives videos taken by the user with a smartphone or tablet. Videos taken by the user may include, for example, the contents of meals and lifestyle habits, but are not limited to such examples. The reception unit can, for example, take videos using a specific application and upload them to the generation AI. The reception unit can also accept input from various devices regardless of the format or type of video. The analysis unit uses the generation AI to analyze the videos received by the reception unit and determine the user's nutritional status. The analysis unit analyzes, for example, the contents of meals, the frequency of eating, and the amount of exercise, and identifies deficient nutrients. The generation AI uses algorithms and databases to analyze the contents of meals and lifestyle habits in the video. For example, the generation AI analyzes the contents of meals and evaluates the intake of calories, vitamins, and minerals. The generation AI can also analyze the amount of exercise and evaluate the calories burned and the frequency of exercise. Based on the nutritional status determined by the analysis unit, the suggestion unit suggests foods and recipes that should be eaten to supplement the deficient nutrients. The suggestion unit, for example, suggests foods and recipes rich in a particular nutrient if that nutrient is deficient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generation AI suggests foods and recipes rich in vitamin D. The generation AI can also suggest foods and recipes rich in iron if the user is deficient in iron. Thus, the nutrition suggestion system according to this embodiment can automatically analyze the user's nutritional status and suggest appropriate meals and recipes.

[0057] The reception unit can accept videos shot by users with their smartphones or tablets. For example, the reception unit accepts videos shot by users with their smartphones or tablets. Videos shot by users may include, but are not limited to, content of meals or lifestyle habits. The reception unit can shoot videos using specific applications and upload them to the generating AI. For example, users can shoot videos of content of meals, frequency of eating, exercise levels, etc., and input them into the generating AI. This allows the reception unit to accept videos shot by users with their smartphones, etc. Some or all of the above processing in the reception unit may be performed using the generating AI, for example, or without using the generating AI. For example, the reception unit can input a video shot by a user into the generating AI, and the generating AI can analyze the format and content of the video.

[0058] The analysis unit can analyze the content of meals and daily activity patterns in the video to determine the user's nutritional status. For example, the analysis unit analyzes the content of meals and daily activity patterns in the video to determine the user's nutritional status. The analysis unit uses a generation AI to analyze the content of meals, frequency of eating, exercise levels, etc., and identify deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in the video. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise levels and evaluate calorie expenditure and exercise frequency. This allows the analysis unit to analyze the content of meals and lifestyle habits in the video and determine the user's nutritional status. Some or all of the above processing in the analysis unit may be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input a video shot by the user into the generation AI, which can then analyze the content of the video.

[0059] The suggestion unit can suggest foods and recipes to eat to supplement any deficient nutrients. For example, the suggestion unit can suggest foods and recipes to eat to supplement any deficient nutrients. The suggestion unit uses a generative AI to suggest foods and recipes that are rich in a particular nutrient if that nutrient is deficient. The generative AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generative AI will suggest foods and recipes that are rich in vitamin D. The generative AI can also suggest foods and recipes that are rich in iron if the user is deficient in iron. This allows the suggestion unit to suggest foods and recipes that are good to eat to supplement any deficient nutrients. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can use an algorithm that allows the generative AI to suggest foods and recipes that are rich in a particular nutrient.

[0060] The analysis unit can analyze the content and frequency of meals and exercise levels to identify deficient nutrients. For example, the analysis unit analyzes the content and frequency of meals and exercise levels to identify deficient nutrients. The analysis unit uses a generation AI to analyze the content and frequency of meals, exercise levels, etc., to identify deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in videos. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise levels and evaluate calorie expenditure and exercise frequency. This allows the analysis of the content and frequency of meals and exercise levels to identify deficient nutrients. Some or all of the above processing in the analysis unit may be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input a video shot by the user into the generation AI, which can then analyze the video content.

[0061] The suggestion unit can suggest foods and recipes rich in specific nutrients if those nutrients are deficient. For example, if a specific nutrient is deficient, the suggestion unit will suggest foods and recipes rich in that nutrient. The suggestion unit uses a generation AI to suggest foods and recipes rich in specific nutrients if those nutrients are deficient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if a vitamin D deficiency occurs, the generation AI will suggest foods and recipes rich in vitamin D. The generation AI can also suggest foods and recipes rich in iron if an iron deficiency occurs. This allows the suggestion unit to suggest foods and recipes rich in specific nutrients if those nutrients are deficient. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can use an algorithm that allows the generation AI to suggest foods and recipes rich in specific nutrients.

[0062] The reception unit can estimate the user's emotions and adjust the timing of video submission based on the estimated emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of video submission based on the estimated emotions. The reception unit uses generative AI to estimate the user's emotions and adjusts the timing of video submission based on the estimated emotions. For example, if the user is stressed, it can prompt them to submit a video during a time when they can relax. If the user is relaxed, it can submit a video immediately and start analysis quickly. Furthermore, if the user is busy, it can submit a video at an appropriate time. This allows the timing of video submission to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without generative AI. For example, the reception unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0063] The reception department can analyze a user's past video submission history and select an appropriate reception method. For example, the reception department can analyze a user's past video submission history and select an appropriate reception method. The reception department can use generative AI to analyze a user's past video submission history and select the optimal reception method. For example, it can analyze the time periods of videos previously submitted by the user and suggest the optimal reception time. It can also prioritize suggesting submission methods previously used by the user (upload, live streaming, etc.). Furthermore, it can consider the user's past submission frequency and suggest an appropriate submission interval. This allows the reception department to analyze a user's past video submission history and select the optimal reception method. Some or all of the above processing in the reception department may be performed using generative AI, for example, or without generative AI. For example, the reception department can input the user's past video submission history into the generative AI, which can then select the optimal reception method.

[0064] The reception unit can filter videos upon receiving them based on the user's current lifestyle and areas of interest. For example, the reception unit can filter videos upon receiving them based on the user's current lifestyle and areas of interest. The reception unit uses a generative AI to filter videos upon receiving them based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, health-related videos will be prioritized. Also, if the user leads a busy life, videos that can be filmed in a short time will be prioritized. Furthermore, if the user has specific dietary restrictions, videos that comply with those restrictions will be prioritized. This allows videos to be filtered based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's current lifestyle and areas of interest into a generative AI, which can then filter the videos.

[0065] The reception unit can estimate the user's emotions and determine the priority of videos to receive based on the estimated emotions. For example, the reception unit can estimate the user's emotions and determine the priority of videos to receive based on the estimated emotions. The reception unit uses generative AI to estimate the user's emotions and determine the priority of videos to receive based on the estimated emotions. For example, if the user is stressed, videos with relaxing content will be given priority. Also, if the user is relaxed, videos related to health may be given priority. Furthermore, if the user is busy, videos that can be filmed in a short time may be given priority. In this way, the priority of videos to receive can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without using generative AI. For example, the reception desk can input user emotion data into a generating AI, which can then estimate the emotion.

[0066] The reception unit can prioritize receiving videos that are highly relevant based on the user's geographical location information when receiving videos. For example, when receiving videos, the reception unit prioritizes receiving videos that are highly relevant based on the user's geographical location information. The reception unit uses a generative AI to prioritize receiving videos that are highly relevant based on the user's geographical location information when receiving videos. For example, if the user lives in a specific region, videos using local ingredients will be prioritized. Also, if the user is traveling, videos related to the food culture of the travel destination will be prioritized. Furthermore, if the user has dietary restrictions in a specific region, videos that conform to those dietary restrictions will be prioritized. This allows for the priority reception of highly relevant videos while considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using a generative AI, for example, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then select highly relevant videos.

[0067] The reception unit can analyze the user's social media activity when receiving a video and receive relevant videos. For example, the reception unit can analyze the user's social media activity when receiving a video and receive relevant videos. The reception unit uses generative AI to analyze the user's social media activity when receiving a video and receive relevant videos. For example, it can receive relevant videos based on the content of meals shared by the user on social media. It can also receive relevant videos based on health-related accounts that the user follows on social media. Furthermore, it can receive relevant videos based on dietary restriction groups that the user participates in on social media. This allows the reception unit to analyze the user's social media activity and receive relevant videos. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without generative AI. For example, the reception unit can input the user's social media activity into the generative AI, and the generative AI can select relevant videos.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit uses a generative AI to estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is stressed, it can provide concise and to-the-point analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the video during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the video during the analysis. The analysis unit uses a generation AI to adjust the level of detail of the analysis based on the importance of the video during the analysis. For example, if a video contains important meal information, a detailed analysis can be performed. If a video contains everyday meal information, a concise analysis can be performed. Furthermore, if a video concerns a specific nutrient, an analysis focusing on that nutrient can be performed. This allows the level of detail of the analysis to be adjusted based on the importance of the video. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the importance of the video into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the video category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the video category during analysis. The analysis unit uses a generation AI to apply different analysis algorithms depending on the video category during analysis. For example, for a video about food, an algorithm that analyzes the nutritional value of ingredients can be applied. For a video about exercise, an algorithm that analyzes calories burned can be applied. Furthermore, for a video about lifestyle habits, an algorithm that analyzes overall health status can be applied. This allows different analysis algorithms to be applied depending on the video category. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the video category into the generation AI, and the generation AI can apply an appropriate analysis algorithm.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit uses generative AI to estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, a short, to-the-point analysis can be provided. If the user is relaxed, a detailed analysis can be provided. Furthermore, if the user is excited, a visually stimulating analysis can be provided. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0072] The analysis unit can determine the priority of analysis based on the submission date of the videos during analysis. For example, the analysis unit can determine the priority of analysis based on the submission date of the videos during analysis. The analysis unit uses a generative AI to determine the priority of analysis based on the submission date of the videos during analysis. For example, it can prioritize the analysis of the most recent videos. It can also prioritize the analysis of videos related to specific events. Furthermore, it can prioritize the analysis of important videos specified by the user. This allows the analysis priority to be determined based on the submission date of the videos. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the submission date of the videos into the generative AI, and the generative AI can determine the priority of analysis.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the videos during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the videos during analysis. The analysis unit uses a generative AI to adjust the order of analysis based on the relevance of the videos during analysis. For example, it can analyze videos of the same category consecutively. It can also prioritize the analysis of videos related to the user's areas of interest. Furthermore, it can prioritize the analysis of videos related to specific nutrients. This allows the order of analysis to be adjusted based on the relevance of the videos. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the relevance of the videos into a generative AI, and the generative AI can adjust the order of analysis.

[0074] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. The suggestion unit uses generative AI to estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise and to-the-point suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. This allows the way suggestions are presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using generative AI, or not using generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0075] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. For example, the suggestion unit can adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. The suggestion unit uses a generation AI to adjust the level of detail of its suggestions based on the importance of the nutrients when making suggestions. For example, if there is a deficiency in important nutrients, it can provide detailed suggestions. If there is a deficiency in everyday nutrients, it can provide concise suggestions. Furthermore, in the case of suggestions concerning a specific nutrient, it can provide suggestions that focus on that nutrient. This allows the level of detail of suggestions to be adjusted based on the importance of the nutrients. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the importance of nutrients into the generation AI, and the generation AI can adjust the level of detail of the suggestions.

[0076] The suggestion unit can apply different suggestion algorithms depending on the nutrient category when making suggestions. For example, the suggestion unit can apply different suggestion algorithms depending on the nutrient category when making suggestions. The suggestion unit uses a generation AI to apply different suggestion algorithms depending on the nutrient category when making suggestions. For example, for suggestions about vitamins, an algorithm that suggests foods rich in vitamins can be applied. For suggestions about minerals, an algorithm that suggests foods rich in minerals can be applied. Furthermore, for suggestions about proteins, an algorithm that suggests foods rich in protein can be applied. This allows different suggestion algorithms to be applied depending on the nutrient category. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the nutrient category into the generation AI, and the generation AI can apply an appropriate suggestion algorithm.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit uses generative AI to estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, to-the-point suggestion. If the user is relaxed, it can provide a detailed suggestion. Furthermore, if the user is excited, it can provide a visually stimulating suggestion. This allows the length of the suggestion to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using generative AI, for example, or without generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0078] The suggestion unit can determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. For example, the suggestion unit can determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. The suggestion unit uses a generation AI to determine the priority of suggestions based on the degree of nutrient deficiency at the time of suggestion. For example, if there is a significant deficiency in an important nutrient, that nutrient will be suggested preferentially. Also, if there is a deficiency in a daily nutrient, that nutrient can be suggested preferentially. Furthermore, if there is a deficiency in a specific nutrient, that nutrient can be suggested preferentially. In this way, the priority of suggestions can be determined based on the degree of nutrient deficiency. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the degree of nutrient deficiency into a generation AI, and the generation AI can determine the priority of suggestions.

[0079] The suggestion unit can adjust the order of suggestions based on the relationships between nutrients when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relationships between nutrients when making suggestions. The suggestion unit uses a generation AI to adjust the order of suggestions based on the relationships between nutrients when making suggestions. For example, it may suggest nutrients from the same category consecutively. It can also prioritize suggesting nutrients related to the user's areas of interest. Furthermore, it can prioritize suggestions related to specific nutrients. This allows the order of suggestions to be adjusted based on the relationships between nutrients. Some or all of the above processing in the suggestion unit may be performed using a generation AI, for example, or without a generation AI. For example, the suggestion unit can input the relationships between nutrients into a generation AI, and the generation AI can adjust the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, and suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives videos taken by the user with a smartphone or tablet. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI to determine the user's nutritional status. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests meals and recipes to supplement any deficient nutrients. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives videos taken by the user with a smartphone or tablet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI to determine the user's nutritional status. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests meals and recipes to supplement any deficient nutrients. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives videos taken by the user with a smartphone or tablet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI to determine the user's nutritional status. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests meals and recipes to supplement any deficient nutrients. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, and suggestion unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives videos taken by the user with a smartphone or tablet. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the video using generating AI to determine the user's nutritional status. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests meals and recipes to supplement any deficient nutrients.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The analysis unit can analyze not only the user's diet but also their sleep patterns and stress levels. For example, if a user stays up late at night, the analysis unit can take this information into account, as sleep deprivation may affect nutrient absorption. Furthermore, if a user is experiencing high stress levels, the unit can assess the impact of stress on nutritional balance and suggest foods and recipes that can help reduce stress. It can also analyze the user's water intake and provide advice on appropriate hydration.

[0082] The recommendation system can also suggest specific supplements based on the user's nutritional status. For example, if a user is deficient in vitamin D, it can suggest taking a vitamin D supplement. Similarly, if a user is deficient in iron, it can suggest taking an iron supplement. Furthermore, if a user has a specific allergy, it can suggest supplements that address that allergy. This allows users to improve their nutritional status not only through diet but also through supplements.

[0083] The reception desk can automatically generate questions about the user's diet and lifestyle and ask the user to answer them. For example, it can generate questions about how often the user exercises and what dietary restrictions they follow. If the user has specific health goals, it can also generate questions related to those goals. Furthermore, based on the user's answers, it can generate more detailed questions to gain a more accurate understanding of the user's nutritional status.

[0084] The analysis unit can analyze the user's genetic information and make suggestions based on their genetic nutritional requirements. For example, if a user has a specific gene mutation, it can evaluate the impact of that mutation on nutrient absorption. Furthermore, if the intake of a specific nutrient is recommended based on the user's genetic information, it can suggest foods and recipes rich in that nutrient. It can also assess the risk of specific diseases based on genetic information and provide nutritional suggestions to mitigate that risk.

[0085] The suggestion function can analyze a user's eating history and provide future meal suggestions based on past eating patterns. For example, if a user has consumed a large amount of a particular nutrient in the past, it can suggest ways to consume that nutrient in a balanced way. It can also suggest new recipes that include a particular food if the user has enjoyed eating that food in the past. Furthermore, it can provide seasonal meal suggestions based on the user's eating history.

[0086] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it will prioritize analyzing nutrients that help reduce stress. If the user is relaxed, it can analyze the overall nutritional balance. Furthermore, if the user is excited, it can analyze nutrients that help replenish energy. This allows the analysis priority to be adjusted according to the user's emotions.

[0087] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, suggestions can be made during a time when they can relax. If the user is relaxed, suggestions can be made immediately, providing advice to quickly improve their nutritional status. Furthermore, if the user is busy, suggestions can be made at an appropriate time. In this way, the timing of suggestions can be adjusted according to the user's emotions.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is stressed, it can provide concise and to-the-point analysis results. Furthermore, if the user is excited, it can provide visually stimulating analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions.

[0089] The suggestion function can estimate the user's emotions and adjust the content of suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest foods and recipes that help reduce stress. If the user is relaxed, it can suggest suggestions that take overall nutritional balance into consideration. Furthermore, if the user is excited, it can suggest foods and recipes that help replenish energy. In this way, the content of suggestions can be adjusted according to the user's emotions.

[0090] The suggestion function can estimate the user's emotions and adjust the format of the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise and to-the-point suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. In this way, the format of suggestions can be adjusted according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk accepts videos shot by users using their smartphones or tablets. These videos may include, but are not limited to, content such as meals or lifestyle habits. The reception desk can shoot videos using specific applications and upload them to the generating AI. Furthermore, the reception desk can accept input from various devices, regardless of video format or type. Step 2: The analysis unit uses a generation AI to analyze the video received by the reception unit and determine the user's nutritional status. The analysis unit analyzes the content of meals, frequency of eating, exercise level, etc., and identifies any deficient nutrients. The generation AI uses algorithms and databases to analyze the content of meals and lifestyle habits in the video. For example, the generation AI analyzes the content of meals and evaluates calorie intake and vitamin and mineral intake. The generation AI can also analyze exercise level and evaluate calories burned and exercise frequency. Step 3: The suggestion unit, based on the nutritional status determined by the analysis unit, suggests foods and recipes that would be good to eat to supplement any deficient nutrients. If a specific nutrient is deficient, the suggestion unit will suggest foods and recipes that are rich in that nutrient. The generation AI uses a database and algorithms for suggestions to provide the user with the most suitable meals and recipes. For example, if the user is deficient in vitamin D, the generation AI will suggest foods and recipes that are rich in vitamin D. Similarly, if the user is deficient in iron, the generation AI can suggest foods and recipes that are rich in iron.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] 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.

[0156] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user videos, An analysis unit analyzes the video received by the reception unit and determines the nutritional status, The system includes a suggestion unit that proposes foods to eat and recipes based on the nutritional status determined by the analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts videos shot by users on their smartphones or tablets. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the content of meals and daily behavior patterns shown in the video to determine the user's nutritional status. The system according to feature 1.

4. The aforementioned proposal section is, We suggest foods and recipes to help you supplement any missing nutrients. The system according to feature 1.

5. The aforementioned analysis unit, We analyze dietary content, frequency of eating, and exercise levels to identify any nutritional deficiencies. The system according to feature 1.

6. The aforementioned proposal section is, If you are deficient in a particular nutrient, we will suggest foods and recipes that are rich in that nutrient. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of video submissions based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past video submission history and select the appropriate submission method. The system according to feature 1.

9. The aforementioned reception unit is When a video is submitted, it is filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of videos to accept based on the estimated user emotions. The system according to feature 1.

Images